Synthetic Aperture Ladar Imaging and Atmospheric Turbulence

Science.gov (United States)

2016-06-09

Cyanide (HCN). This keeps the absolute center frequency of the chirp from drifting which leads to undesirable phase drifts on the FMCW ladar...is known as the modulus of the complex coherence factor (, ′⃗⃗⃗⃗ ) = |Γ(, ′⃗⃗⃗⃗ )| |Γ(, )Γ (′⃗⃗⃗, ′⃗⃗⃗⃗ )| 1/2 Which is related...to the wavestructure function as D(, ′⃗⃗⃗⃗ ) = −2 ln μ(, ′⃗⃗⃗⃗ ). What is nice about using the modulus of the complex coherence factor is

Ghost image in enhanced self-heterodyne synthetic aperture imaging ladar

Science.gov (United States)

Zhang, Guo; Sun, Jianfeng; Zhou, Yu; Lu, Zhiyong; Li, Guangyuan; Xu, Mengmeng; Zhang, Bo; Lao, Chenzhe; He, Hongyu

2018-03-01

The enhanced self-heterodyne synthetic aperture imaging ladar (SAIL) self-heterodynes two polarization-orthogonal echo signals to eliminate the phase disturbance caused by atmospheric turbulence and mechanical trembling, uses heterodyne receiver instead of self-heterodyne receiver to improve signal-to-noise ratio. The principle and structure of the enhanced self-heterodyne SAIL are presented. The imaging process of enhanced self-heterodyne SAIL for distributed target is also analyzed. In enhanced self-heterodyne SAIL, the phases of two orthogonal-polarization beams are modulated by four cylindrical lenses in transmitter to improve resolutions in orthogonal direction and travel direction, which will generate ghost image. The generation process of ghost image in enhanced self-heterodyne SAIL is mathematically detailed, and a method of eliminating ghost image is also presented, which is significant for far-distance imaging. A number of experiments of enhanced self-heterodyne SAIL for distributed target are presented, these experimental results verify the theoretical analysis of enhanced self-heterodyne SAIL. The enhanced self-heterodyne SAIL has the capability to eliminate the influence from the atmospheric turbulence and mechanical trembling, has high advantage in detecting weak signals, and has promising application for far-distance ladar imaging.

Unambiguous range-Doppler LADAR processing using 2 giga-sample-per-second noise waveforms

International Nuclear Information System (INIS)

Cole, Z.; Roos, P.A.; Berg, T.; Kaylor, B.; Merkel, K.D.; Babbitt, W.R.; Reibel, R.R.

2007-01-01

We demonstrate sub-nanosecond range and unambiguous sub-50-Hz Doppler resolved laser radar (LADAR) measurements using spectral holographic processing in rare-earth ion doped crystals. The demonstration utilizes pseudo-random-noise 2 giga-sample-per-second baseband waveforms modulated onto an optical carrier

Unambiguous range-Doppler LADAR processing using 2 giga-sample-per-second noise waveforms

Energy Technology Data Exchange (ETDEWEB)

Cole, Z. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States)]. E-mail: cole@s2corporation.com; Roos, P.A. [Spectrum Lab, Montana State University, P.O. Box 173510, Bozeman, MT 59717 (United States); Berg, T. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States); Kaylor, B. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States); Merkel, K.D. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States); Babbitt, W.R. [Spectrum Lab, Montana State University, P.O. Box 173510, Bozeman, MT 59717 (United States); Reibel, R.R. [S2 Corporation, 2310 University Way 4-1, Bozeman, MT 59715 (United States)

2007-11-15

We demonstrate sub-nanosecond range and unambiguous sub-50-Hz Doppler resolved laser radar (LADAR) measurements using spectral holographic processing in rare-earth ion doped crystals. The demonstration utilizes pseudo-random-noise 2 giga-sample-per-second baseband waveforms modulated onto an optical carrier.

Geiger-mode APD camera system for single-photon 3D LADAR imaging

Science.gov (United States)

Entwistle, Mark; Itzler, Mark A.; Chen, Jim; Owens, Mark; Patel, Ketan; Jiang, Xudong; Slomkowski, Krystyna; Rangwala, Sabbir

2012-06-01

The unparalleled sensitivity of 3D LADAR imaging sensors based on single photon detection provides substantial benefits for imaging at long stand-off distances and minimizing laser pulse energy requirements. To obtain 3D LADAR images with single photon sensitivity, we have demonstrated focal plane arrays (FPAs) based on InGaAsP Geiger-mode avalanche photodiodes (GmAPDs) optimized for use at either 1.06 μm or 1.55 μm. These state-of-the-art FPAs exhibit excellent pixel-level performance and the capability for 100% pixel yield on a 32 x 32 format. To realize the full potential of these FPAs, we have recently developed an integrated camera system providing turnkey operation based on FPGA control. This system implementation enables the extremely high frame-rate capability of the GmAPD FPA, and frame rates in excess of 250 kHz (for 0.4 μs range gates) can be accommodated using an industry-standard CameraLink interface in full configuration. Real-time data streaming for continuous acquisition of 2 μs range gate point cloud data with 13-bit time-stamp resolution at 186 kHz frame rates has been established using multiple solid-state storage drives. Range gate durations spanning 4 ns to 10 μs provide broad operational flexibility. The camera also provides real-time signal processing in the form of multi-frame gray-scale contrast images and single-frame time-stamp histograms, and automated bias control has been implemented to maintain a constant photon detection efficiency in the presence of ambient temperature changes. A comprehensive graphical user interface has been developed to provide complete camera control using a simple serial command set, and this command set supports highly flexible end-user customization.

Target recognition of ladar range images using even-order Zernike moments.

Science.gov (United States)

Liu, Zheng-Jun; Li, Qi; Xia, Zhi-Wei; Wang, Qi

2012-11-01

Ladar range images have attracted considerable attention in automatic target recognition fields. In this paper, Zernike moments (ZMs) are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of target recognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks (BPNNs) are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis. The experimental results demonstrate that combining the even-order ZMs with serial-parallel BPNNs can significantly improve the recognition rate for small samples.

Target recognition of ladar range images using slice image: comparison of four improved algorithms

Science.gov (United States)

Xia, Wenze; Han, Shaokun; Cao, Jingya; Wang, Liang; Zhai, Yu; Cheng, Yang

2017-07-01

Compared with traditional 3-D shape data, ladar range images possess properties of strong noise, shape degeneracy, and sparsity, which make feature extraction and representation difficult. The slice image is an effective feature descriptor to resolve this problem. We propose four improved algorithms on target recognition of ladar range images using slice image. In order to improve resolution invariance of the slice image, mean value detection instead of maximum value detection is applied in these four improved algorithms. In order to improve rotation invariance of the slice image, three new improved feature descriptors-which are feature slice image, slice-Zernike moments, and slice-Fourier moments-are applied to the last three improved algorithms, respectively. Backpropagation neural networks are used as feature classifiers in the last two improved algorithms. The performance of these four improved recognition systems is analyzed comprehensively in the aspects of the three invariances, recognition rate, and execution time. The final experiment results show that the improvements for these four algorithms reach the desired effect, the three invariances of feature descriptors are not directly related to the final recognition performance of recognition systems, and these four improved recognition systems have different performances under different conditions.

Doublet Pulse Coherent Laser Radar for Tracking of Resident Space Objects

Science.gov (United States)

2014-09-01

any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a...tracking 10 cm2 cross section targets in LEO as well as tracking near Earth objects (NEOs) such as meteoroids, and asteroids may well be possible...using short pulsewidth doublet pulse coherent ladar technique offers a means for precision tracking. The technique offers best of both worlds ; precise

Design and performance of single photon APD focal plane arrays for 3-D LADAR imaging

Science.gov (United States)

Itzler, Mark A.; Entwistle, Mark; Owens, Mark; Patel, Ketan; Jiang, Xudong; Slomkowski, Krystyna; Rangwala, Sabbir; Zalud, Peter F.; Senko, Tom; Tower, John; Ferraro, Joseph

2010-08-01

×We describe the design, fabrication, and performance of focal plane arrays (FPAs) for use in 3-D LADAR imaging applications requiring single photon sensitivity. These 32 × 32 FPAs provide high-efficiency single photon sensitivity for three-dimensional LADAR imaging applications at 1064 nm. Our GmAPD arrays are designed using a planarpassivated avalanche photodiode device platform with buried p-n junctions that has demonstrated excellent performance uniformity, operational stability, and long-term reliability. The core of the FPA is a chip stack formed by hybridizing the GmAPD photodiode array to a custom CMOS read-out integrated circuit (ROIC) and attaching a precision-aligned GaP microlens array (MLA) to the back-illuminated detector array. Each ROIC pixel includes an active quenching circuit governing Geiger-mode operation of the corresponding avalanche photodiode pixel as well as a pseudo-random counter to capture per-pixel time-of-flight timestamps in each frame. The FPA has been designed to operate at frame rates as high as 186 kHz for 2 μs range gates. Effective single photon detection efficiencies as high as 40% (including all optical transmission and MLA losses) are achieved for dark count rates below 20 kHz. For these planar-geometry diffused-junction GmAPDs, isolation trenches are used to reduce crosstalk due to hot carrier luminescence effects during avalanche events, and we present details of the crosstalk performance for different operating conditions. Direct measurement of temporal probability distribution functions due to cumulative timing uncertainties of the GmAPDs and ROIC circuitry has demonstrated a FWHM timing jitter as low as 265 ps (standard deviation is ~100 ps).

Study of Wide Swath Synthetic Aperture Ladar Imaging Techology

Directory of Open Access Journals (Sweden)

Zhang Keshu

2017-02-01

Full Text Available Combining synthetic-aperture imaging and coherent-light detection technology, the weak signal identification capacity of Synthetic Aperture Ladar (SAL reaches the photo level, and the image resolution exceeds the diffraction limit of the telescope to obtain high-resolution images irrespective to ranges. This paper introduces SAL, including the development path, technology characteristics, and the restriction of imaging swath. On the basis of this, we propose to integrate the SAL technology for extending its swath. By analyzing the scanning-operation mode and the signal model, the paper explicitly proposes that the former mode will be the developmental trend of the SAL technology. This paper also introduces the flight demonstrations of the SAL and the imaging results of remote targets, showing the potential of the SAL in long-range, high-resolution, and scanning-imaging applications. The technology and the theory of the scanning mode of SAL compensates for the defects related to the swath and operation efficiency of the current SAL. It provides scientific foundation for the SAL system applied in wide swath, high resolution earth observation, and the ISAL system applied in space-targets imaging.

Object-Based Benefits without Object-Based Representations

OpenAIRE

Alvarez, George Angelo; Fougnie, Daryl; Cormiea, Sarah M

2012-01-01

The organization of visual information into objects strongly influences visual memory: Displays with objects defined by two features (e.g. color, orientation) are easier to remember than displays with twice as many objects defined by one feature (Olson & Jiang, 2002). Existing theories suggest that this ‘object-benefit’ is based on object-based limitations in working memory: because a limited number of objects can be stored, packaging features together so that fewer objects have to be remembe...

Image Science Research for Speckle-based LADAR (Speckle Research for 3D Imaging LADAR)

Science.gov (United States)

2008-04-03

INVARIANT + FERGUS, TORRALBA, AND FREEMAN. MIT-CSAIL-TR-2006-058 MAP DETECTOR PATTERN FOR EACH POINT IN OBJECT SPACE DEBLURRING PROBLEM IMPULSE RESPONSE...GENERALIZED THEORY FOR THE LOGARITHMIC ASPHERE ( )( ) it e φ ρρ −= IMPULSE RESPONSE (PSF) 2 2 2 2 0 0 22 2( ) 2 2 0 2 2 2 2 2 2 0 0 2 ( ; ) 2 ( ) i s i i t R...ascent; γ=1, Burch, Skilling, Gull; Loops needed Noise deviation Area of PSF New parameter L σ A γ COMPARISON OF MAXIMUM ENTROPY METHODS † † W. CHI

Object width modulates object-based attentional selection.

Science.gov (United States)

Nah, Joseph C; Neppi-Modona, Marco; Strother, Lars; Behrmann, Marlene; Shomstein, Sarah

2018-04-24

Visual input typically includes a myriad of objects, some of which are selected for further processing. While these objects vary in shape and size, most evidence supporting object-based guidance of attention is drawn from paradigms employing two identical objects. Importantly, object size is a readily perceived stimulus dimension, and whether it modulates the distribution of attention remains an open question. Across four experiments, the size of the objects in the display was manipulated in a modified version of the two-rectangle paradigm. In Experiment 1, two identical parallel rectangles of two sizes (thin or thick) were presented. Experiments 2-4 employed identical trapezoids (each having a thin and thick end), inverted in orientation. In the experiments, one end of an object was cued and participants performed either a T/L discrimination or a simple target-detection task. Combined results show that, in addition to the standard object-based attentional advantage, there was a further attentional benefit for processing information contained in the thick versus thin end of objects. Additionally, eye-tracking measures demonstrated increased saccade precision towards thick object ends, suggesting that Fitts's Law may play a role in object-based attentional shifts. Taken together, these results suggest that object-based attentional selection is modulated by object width.

Object-based attention: strength of object representation and attentional guidance.

Science.gov (United States)

Shomstein, Sarah; Behrmann, Marlene

2008-01-01

Two or more features belonging to a single object are identified more quickly and more accurately than are features belonging to different objects--a finding attributed to sensory enhancement of all features belonging to an attended or selected object. However, several recent studies have suggested that this "single-object advantage" may be a product of probabilistic and configural strategic prioritizations rather than of object-based perceptual enhancement per se, challenging the underlying mechanism that is thought to give rise to object-based attention. In the present article, we further explore constraints on the mechanisms of object-based selection by examining the contribution of the strength of object representations to the single-object advantage. We manipulated factors such as exposure duration (i.e., preview time) and salience of configuration (i.e., objects). Varying preview time changes the magnitude of the object-based effect, so that if there is ample time to establish an object representation (i.e., preview time of 1,000 msec), then both probability and configuration (i.e., objects) guide attentional selection. If, however, insufficient time is provided to establish a robust object-based representation, then only probabilities guide attentional selection. Interestingly, at a short preview time of 200 msec, when the two objects were sufficiently different from each other (i.e., different colors), both configuration and probability guided attention selection. These results suggest that object-based effects can be explained both in terms of strength of object representations (established at longer exposure durations and by pictorial cues) and probabilistic contingencies in the visual environment.

Connection-based and object-based grouping in multiple-object tracking: A developmental study.

Science.gov (United States)

Van der Hallen, Ruth; Reusens, Julie; Evers, Kris; de-Wit, Lee; Wagemans, Johan

2018-03-30

Developmental research on Gestalt laws has previously revealed that, even as young as infancy, we are bound to group visual elements into unitary structures in accordance with a variety of organizational principles. Here, we focus on the developmental trajectory of both connection-based and object-based grouping, and investigate their impact on object formation in participants, aged 9-21 years old (N = 113), using a multiple-object tracking paradigm. Results reveal a main effect of both age and grouping type, indicating that 9- to 21-year-olds are sensitive to both connection-based and object-based grouping interference, and tracking ability increases with age. In addition to its importance for typical development, these results provide an informative baseline to understand clinical aberrations in this regard. Statement of contribution What is already known on this subject? The origin of the Gestalt principles is still an ongoing debate: Are they innate, learned over time, or both? Developmental research has revealed how each Gestalt principle has its own trajectory and unique relationship to visual experience. Both connectedness and object-based grouping play an important role in object formation during childhood. What does this study add? The study identifies how sensitivity to connectedness and object-based grouping evolves in individuals, aged 9-21 years old. Using multiple-object tracking, results reveal that the ability to track multiple objects increases with age. These results provide an informative baseline to understand clinical aberrations in different types of grouping. © 2018 The Authors. British Journal of Developmental Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

Spin-image surface matching based target recognition in laser radar range imagery

International Nuclear Information System (INIS)

Li, Wang; Jian-Feng, Sun; Qi, Wang

2010-01-01

We explore the problem of in-plane rotation-invariance existing in the vertical detection of laser radar (Ladar) using the algorithm of spin-image surface matching. The method used to recognize the target in the range imagery of Ladar is time-consuming, owing to its complicated procedure, which violates the requirement of real-time target recognition in practical applications. To simplify the troublesome procedures, we improve the spin-image algorithm by introducing a statistical correlated coefficient into target recognition in range imagery of Ladar. The system performance is demonstrated on sixteen simulated noise range images with targets rotated through an arbitrary angle in plane. A high efficiency and an acceptable recognition rate obtained herein testify the validity of the improved algorithm for practical applications. The proposed algorithm not only solves the problem of in-plane rotation-invariance rationally, but also meets the real-time requirement. This paper ends with a comparison of the proposed method and the previous one. (classical areas of phenomenology)

Object-based connectedness facilitates matching

NARCIS (Netherlands)

Koning, A.R.; Lier, R.J. van

2003-01-01

In two matching tasks, participants had to match two images of object pairs. Image-based (113) connectedness refers to connectedness between the objects in an image. Object-based (OB) connectedness refers to connectedness between the interpreted objects. In Experiment 1, a monocular depth cue

Exploring the relationship between object realism and object-based attention effects.

Science.gov (United States)

Roque, Nelson; Boot, Walter R

2015-09-01

Visual attention prioritizes processing of locations in space, and evidence also suggests that the benefits of attention can be shaped by the presence of objects (object-based attention). However, the prevalence of object-based attention effects has been called into question recently by evidence from a large-sampled study employing classic attention paradigms (Pilz et al., 2012). We conducted two experiments to explore factors that might determine when and if object-based attention effects are observed, focusing on the degree to which the concreteness and realism of objects might contribute to these effects. We adapted the classic attention paradigm first reported by Egly, Driver, and Rafal (1994) by replacing abstract bar stimuli in some conditions with objects that were more concrete and familiar to participants: items of silverware. Furthermore, we varied the realism of these items of silverware, presenting either cartoon versions or photo-realistic versions. Contrary to predictions, increased realism did not increase the size of object-based effects. In fact, no clear object-based effects were observed in either experiment, consistent with previous failures to replicate these effects in similar paradigms. While object-based attention may exist, and may have important influences on how we parse the visual world, these and other findings suggest that the two-object paradigm typically relied upon to study object-based effects may not be the best paradigm to investigate these issues. Copyright © 2015 Elsevier B.V. All rights reserved.

Object-based warping: an illusory distortion of space within objects.

Science.gov (United States)

Vickery, Timothy J; Chun, Marvin M

2010-12-01

Visual objects are high-level primitives that are fundamental to numerous perceptual functions, such as guidance of attention. We report that objects warp visual perception of space in such a way that spatial distances within objects appear to be larger than spatial distances in ground regions. When two dots were placed inside a rectangular object, they appeared farther apart from one another than two dots with identical spacing outside of the object. To investigate whether this effect was object based, we measured the distortion while manipulating the structure surrounding the dots. Object displays were constructed with a single object, multiple objects, a partially occluded object, and an illusory object. Nonobject displays were constructed to be comparable to object displays in low-level visual attributes. In all cases, the object displays resulted in a more powerful distortion of spatial perception than comparable non-object-based displays. These results suggest that perception of space within objects is warped.

Connection-based and object-based grouping in multiple-object tracking: A developmental study

OpenAIRE

Hallen, Ruth; Reusens, J. (Julie); Evers, K. (Kris); de-Wit, Lee; Wagemans, Johan

2018-01-01

textabstractDevelopmental research on Gestalt laws has previously revealed that, even as young as infancy, we are bound to group visual elements into unitary structures in accordance with a variety of organizational principles. Here, we focus on the developmental trajectory of both connection-based and object-based grouping, and investigate their impact on object formation in participants, aged 9-21 years old (N = 113), using a multiple-object tracking paradigm. Results reveal a main effect o...

Manifold-Based Visual Object Counting.

Science.gov (United States)

Wang, Yi; Zou, Yuexian; Wang, Wenwu

2018-07-01

Visual object counting (VOC) is an emerging area in computer vision which aims to estimate the number of objects of interest in a given image or video. Recently, object density based estimation method is shown to be promising for object counting as well as rough instance localization. However, the performance of this method tends to degrade when dealing with new objects and scenes. To address this limitation, we propose a manifold-based method for visual object counting (M-VOC), based on the manifold assumption that similar image patches share similar object densities. Firstly, the local geometry of a given image patch is represented linearly by its neighbors using a predefined patch training set, and the object density of this given image patch is reconstructed by preserving the local geometry using locally linear embedding. To improve the characterization of local geometry, additional constraints such as sparsity and non-negativity are also considered via regularization, nonlinear mapping, and kernel trick. Compared with the state-of-the-art VOC methods, our proposed M-VOC methods achieve competitive performance on seven benchmark datasets. Experiments verify that the proposed M-VOC methods have several favorable properties, such as robustness to the variation in the size of training dataset and image resolution, as often encountered in real-world VOC applications.

View-based 3-D object retrieval

CERN Document Server

Gao, Yue

2014-01-01

Content-based 3-D object retrieval has attracted extensive attention recently and has applications in a variety of fields, such as, computer-aided design, tele-medicine,mobile multimedia, virtual reality, and entertainment. The development of efficient and effective content-based 3-D object retrieval techniques has enabled the use of fast 3-D reconstruction and model design. Recent technical progress, such as the development of camera technologies, has made it possible to capture the views of 3-D objects. As a result, view-based 3-D object retrieval has become an essential but challenging res

R-FCN Object Detection Ensemble based on Object Resolution and Image Quality

DEFF Research Database (Denmark)

Rasmussen, Christoffer Bøgelund; Nasrollahi, Kamal; Moeslund, Thomas B.

2017-01-01

Object detection can be difficult due to challenges such as variations in objects both inter- and intra-class. Additionally, variations can also be present between images. Based on this, research was conducted into creating an ensemble of Region-based Fully Convolutional Networks (R-FCN) object d...

Object-Based Attention and Cognitive Tunneling

Science.gov (United States)

Jarmasz, Jerzy; Herdman, Chris M.; Johannsdottir, Kamilla Run

2005-01-01

Simulator-based research has shown that pilots cognitively tunnel their attention on head-up displays (HUDs). Cognitive tunneling has been linked to object-based visual attention on the assumption that HUD symbology is perceptually grouped into an object that is perceived and attended separately from the external scene. The present research…

Object formation in visual working memory: Evidence from object-based attention.

Science.gov (United States)

Zhou, Jifan; Zhang, Haihang; Ding, Xiaowei; Shui, Rende; Shen, Mowei

2016-09-01

We report on how visual working memory (VWM) forms intact perceptual representations of visual objects using sub-object elements. Specifically, when objects were divided into fragments and sequentially encoded into VWM, the fragments were involuntarily integrated into objects in VWM, as evidenced by the occurrence of both positive and negative object-based attention effects: In Experiment 1, when subjects' attention was cued to a location occupied by the VWM object, the target presented at the location of that object was perceived as occurring earlier than that presented at the location of a different object. In Experiment 2, responses to a target were significantly slower when a distractor was presented at the same location as the cued object (Experiment 2). These results suggest that object fragments can be integrated into objects within VWM in a manner similar to that of visual perception. Copyright © 2016 Elsevier B.V. All rights reserved.

HgCdTe APD-based linear-mode photon counting components and ladar receivers

Science.gov (United States)

Jack, Michael; Wehner, Justin; Edwards, John; Chapman, George; Hall, Donald N. B.; Jacobson, Shane M.

2011-05-01

Linear mode photon counting (LMPC) provides significant advantages in comparison with Geiger Mode (GM) Photon Counting including absence of after-pulsing, nanosecond pulse to pulse temporal resolution and robust operation in the present of high density obscurants or variable reflectivity objects. For this reason Raytheon has developed and previously reported on unique linear mode photon counting components and modules based on combining advanced APDs and advanced high gain circuits. By using HgCdTe APDs we enable Poisson number preserving photon counting. A metric of photon counting technology is dark count rate and detection probability. In this paper we report on a performance breakthrough resulting from improvement in design, process and readout operation enabling >10x reduction in dark counts rate to ~10,000 cps and >104x reduction in surface dark current enabling long 10 ms integration times. Our analysis of key dark current contributors suggest that substantial further reduction in DCR to ~ 1/sec or less can be achieved by optimizing wavelength, operating voltage and temperature.

Object-based attention in chimpanzees (Pan troglodytes).

Science.gov (United States)

Ushitani, Tomokazu; Imura, Tomoko; Tomonaga, Masaki

2010-03-17

We conducted three experiments to investigate how object-based components contribute to the attentional processes of chimpanzees and to examine how such processes operate with regard to perceptually structured objects. In Experiment 1, chimpanzees responded to a spatial cueing task that required them to touch a target appearing at either end of two parallel rectangles. We compared the time involved in shifting attention (cost of attentional shift) when the locations of targets were cued and non cued. Results showed that the cost of the attentional shift within one rectangle was smaller than that beyond the object's boundary, demonstrating object-based attention in chimpanzees. The results of Experiment 2, conducted with different stimulus configurations, replicated the results of Experiment 1, supporting that object-based attention operates in chimpanzees. In Experiment 3, the cost of attentional shift within a cued but partly occluded rectangle was shorter than that within a rectangle that was cued but divided in the middle. The results suggest that the attention of chimpanzees is activated not only by an explicit object but also by fragmented patches represented as an object at a higher-order perceptual level. Chimpanzees' object-based attention may be similar to that of humans. Copyright 2010 Elsevier Ltd. All rights reserved.

A Psychoacoustic-Based Multiple Audio Object Coding Approach via Intra-Object Sparsity

Directory of Open Access Journals (Sweden)

Maoshen Jia

2017-12-01

Full Text Available Rendering spatial sound scenes via audio objects has become popular in recent years, since it can provide more flexibility for different auditory scenarios, such as 3D movies, spatial audio communication and virtual classrooms. To facilitate high-quality bitrate-efficient distribution for spatial audio objects, an encoding scheme based on intra-object sparsity (approximate k-sparsity of the audio object itself is proposed in this paper. The statistical analysis is presented to validate the notion that the audio object has a stronger sparseness in the Modified Discrete Cosine Transform (MDCT domain than in the Short Time Fourier Transform (STFT domain. By exploiting intra-object sparsity in the MDCT domain, multiple simultaneously occurring audio objects are compressed into a mono downmix signal with side information. To ensure a balanced perception quality of audio objects, a Psychoacoustic-based time-frequency instants sorting algorithm and an energy equalized Number of Preserved Time-Frequency Bins (NPTF allocation strategy are proposed, which are employed in the underlying compression framework. The downmix signal can be further encoded via Scalar Quantized Vector Huffman Coding (SQVH technique at a desirable bitrate, and the side information is transmitted in a lossless manner. Both objective and subjective evaluations show that the proposed encoding scheme outperforms the Sparsity Analysis (SPA approach and Spatial Audio Object Coding (SAOC in cases where eight objects were jointly encoded.

Voting based object boundary reconstruction

Science.gov (United States)

Tian, Qi; Zhang, Like; Ma, Jingsheng

2005-07-01

A voting-based object boundary reconstruction approach is proposed in this paper. Morphological technique was adopted in many applications for video object extraction to reconstruct the missing pixels. However, when the missing areas become large, the morphological processing cannot bring us good results. Recently, Tensor voting has attracted people"s attention, and it can be used for boundary estimation on curves or irregular trajectories. However, the complexity of saliency tensor creation limits its applications in real-time systems. An alternative approach based on tensor voting is introduced in this paper. Rather than creating saliency tensors, we use a "2-pass" method for orientation estimation. For the first pass, Sobel d*etector is applied on a coarse boundary image to get the gradient map. In the second pass, each pixel puts decreasing weights based on its gradient information, and the direction with maximum weights sum is selected as the correct orientation of the pixel. After the orientation map is obtained, pixels begin linking edges or intersections along their direction. The approach is applied to various video surveillance clips under different conditions, and the experimental results demonstrate significant improvement on the final extracted objects accuracy.

Object-based spatial attention when objects have sufficient depth cues.

Science.gov (United States)

Takeya, Ryuji; Kasai, Tetsuko

2015-01-01

Attention directed to a part of an object tends to obligatorily spread over all of the spatial regions that belong to the object, which may be critical for rapid object-recognition in cluttered visual scenes. Previous studies have generally used simple rectangles as objects and have shown that attention spreading is reflected by amplitude modulation in the posterior N1 component (150-200 ms poststimulus) of event-related potentials, while other interpretations (i.e., rectangular holes) may arise implicitly in early visual processing stages. By using modified Kanizsa-type stimuli that provided less ambiguity of depth ordering, the present study examined early event-related potential spatial-attention effects for connected and separated objects, both of which were perceived in front of (Experiment 1) and in back of (Experiment 2) the surroundings. Typical P1 (100-140 ms) and N1 (150-220 ms) attention effects of ERP in response to unilateral probes were observed in both experiments. Importantly, the P1 attention effect was decreased for connected objects compared to separated objects only in Experiment 1, and the typical object-based modulations of N1 were not observed in either experiment. These results suggest that spatial attention spreads over a figural object at earlier stages of processing than previously indicated, in three-dimensional visual scenes with multiple depth cues.

Introducing a performance-based objective clinical examination into ...

African Journals Online (AJOL)

Purpose: To describe how a formative Objective Structured Clinical Examination was applied to fourth year pharmacy students at a university in Northern Cyprus. Methods: A blueprint-guided performance-based objective clinical examination was implemented. Group-prepared case scenarios based on course objectives ...

Object-based target templates guide attention during visual search.

Science.gov (United States)

Berggren, Nick; Eimer, Martin

2018-05-03

During visual search, attention is believed to be controlled in a strictly feature-based fashion, without any guidance by object-based target representations. To challenge this received view, we measured electrophysiological markers of attentional selection (N2pc component) and working memory (sustained posterior contralateral negativity; SPCN) in search tasks where two possible targets were defined by feature conjunctions (e.g., blue circles and green squares). Critically, some search displays also contained nontargets with two target features (incorrect conjunction objects, e.g., blue squares). Because feature-based guidance cannot distinguish these objects from targets, any selective bias for targets will reflect object-based attentional control. In Experiment 1, where search displays always contained only one object with target-matching features, targets and incorrect conjunction objects elicited identical N2pc and SPCN components, demonstrating that attentional guidance was entirely feature-based. In Experiment 2, where targets and incorrect conjunction objects could appear in the same display, clear evidence for object-based attentional control was found. The target N2pc became larger than the N2pc to incorrect conjunction objects from 250 ms poststimulus, and only targets elicited SPCN components. This demonstrates that after an initial feature-based guidance phase, object-based templates are activated when they are required to distinguish target and nontarget objects. These templates modulate visual processing and control access to working memory, and their activation may coincide with the start of feature integration processes. Results also suggest that while multiple feature templates can be activated concurrently, only a single object-based target template can guide attention at any given time. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Auditory memory can be object based.

Science.gov (United States)

Dyson, Benjamin J; Ishfaq, Feraz

2008-04-01

Identifying how memories are organized remains a fundamental issue in psychology. Previous work has shown that visual short-term memory is organized according to the object of origin, with participants being better at retrieving multiple pieces of information from the same object than from different objects. However, it is not yet clear whether similar memory structures are employed for other modalities, such as audition. Under analogous conditions in the auditory domain, we found that short-term memories for sound can also be organized according to object, with a same-object advantage being demonstrated for the retrieval of information in an auditory scene defined by two complex sounds overlapping in both space and time. Our results provide support for the notion of an auditory object, in addition to the continued identification of similar processing constraints across visual and auditory domains. The identification of modality-independent organizational principles of memory, such as object-based coding, suggests possible mechanisms by which the human processing system remembers multimodal experiences.

Utilization-based object recognition in confined spaces

Science.gov (United States)

Shirkhodaie, Amir; Telagamsetti, Durga; Chan, Alex L.

2017-05-01

Recognizing substantially occluded objects in confined spaces is a very challenging problem for ground-based persistent surveillance systems. In this paper, we discuss the ontology inference of occluded object recognition in the context of in-vehicle group activities (IVGA) and describe an approach that we refer to as utilization-based object recognition method. We examine the performance of three types of classifiers tailored for the recognition of objects with partial visibility, namely, (1) Hausdorff Distance classifier, (2) Hamming Network classifier, and (3) Recurrent Neural Network classifier. In order to train these classifiers, we have generated multiple imagery datasets containing a mixture of common objects appearing inside a vehicle with full or partial visibility and occultation. To generate dynamic interactions between multiple people, we model the IVGA scenarios using a virtual simulation environment, in which a number of simulated actors perform a variety of IVGA tasks independently or jointly. This virtual simulation engine produces the much needed imagery datasets for the verification and validation of the efficiency and effectiveness of the selected object recognizers. Finally, we improve the performance of these object recognizers by incorporating human gestural information that differentiates various object utilization or handling methods through the analyses of dynamic human-object interactions (HOI), human-human interactions (HHI), and human-vehicle interactions (HVI) in the context of IVGA.

Pixel multiplexing technique for real-time three-dimensional-imaging laser detection and ranging system using four linear-mode avalanche photodiodes

Energy Technology Data Exchange (ETDEWEB)

Xu, Fan; Wang, Yuanqing, E-mail: yqwang@nju.edu.cn; Li, Fenfang [School of Electronic Science and Engineering, Nanjing University, Nanjing 210046 (China)

2016-03-15

The avalanche-photodiode-array (APD-array) laser detection and ranging (LADAR) system has been continually developed owing to its superiority of nonscanning, large field of view, high sensitivity, and high precision. However, how to achieve higher-efficient detection and better integration of the LADAR system for real-time three-dimensional (3D) imaging continues to be a problem. In this study, a novel LADAR system using four linear mode APDs (LmAPDs) is developed for high-efficient detection by adopting a modulation and multiplexing technique. Furthermore, an automatic control system for the array LADAR system is proposed and designed by applying the virtual instrumentation technique. The control system aims to achieve four functions: synchronization of laser emission and rotating platform, multi-channel synchronous data acquisition, real-time Ethernet upper monitoring, and real-time signal processing and 3D visualization. The structure and principle of the complete system are described in the paper. The experimental results demonstrate that the LADAR system is capable of achieving real-time 3D imaging on an omnidirectional rotating platform under the control of the virtual instrumentation system. The automatic imaging LADAR system utilized only 4 LmAPDs to achieve 256-pixel-per-frame detection with by employing 64-bit demodulator. Moreover, the lateral resolution is ∼15 cm and range accuracy is ∼4 cm root-mean-square error at a distance of ∼40 m.

Stereovision-Based Object Segmentation for Automotive Applications

Directory of Open Access Journals (Sweden)

Fu Shan

2005-01-01

Full Text Available Obstacle detection and classification in a complex urban area are highly demanding, but desirable for pedestrian protection, stop & go, and enhanced parking aids. The most difficult task for the system is to segment objects from varied and complicated background. In this paper, a novel position-based object segmentation method has been proposed to solve this problem. According to the method proposed, object segmentation is performed in two steps: in depth map ( - plane and in layered images ( - planes. The stereovision technique is used to reconstruct image points and generate the depth map. Objects are detected in the depth map. Afterwards, the original edge image is separated into different layers based on the distance of detected objects. Segmentation performed in these layered images can be easier and more reliable. It has been proved that the proposed method offers robust detection of potential obstacles and accurate measurement of their location and size.

Ultrabroadband optical chirp linearization for precision metrology applications.

Science.gov (United States)

Roos, Peter A; Reibel, Randy R; Berg, Trenton; Kaylor, Brant; Barber, Zeb W; Babbitt, Wm Randall

2009-12-01

We demonstrate precise linearization of ultrabroadband laser frequency chirps via a fiber-based self-heterodyne technique to enable extremely high-resolution, frequency-modulated cw laser-radar (LADAR) and a wide range of other metrology applications. Our frequency chirps cover bandwidths up to nearly 5 THz with frequency errors as low as 170 kHz, relative to linearity. We show that this performance enables 31-mum transform-limited LADAR range resolution (FWHM) and 86 nm range precisions over a 1.5 m range baseline. Much longer range baselines are possible but are limited by atmospheric turbulence and fiber dispersion.

Coding Transparency in Object-Based Video

DEFF Research Database (Denmark)

Aghito, Shankar Manuel; Forchhammer, Søren

2006-01-01

A novel algorithm for coding gray level alpha planes in object-based video is presented. The scheme is based on segmentation in multiple layers. Different coders are specifically designed for each layer. In order to reduce the bit rate, cross-layer redundancies as well as temporal correlation are...

A Secure and Robust Object-Based Video Authentication System

Directory of Open Access Journals (Sweden)

He Dajun

2004-01-01

Full Text Available An object-based video authentication system, which combines watermarking, error correction coding (ECC, and digital signature techniques, is presented for protecting the authenticity between video objects and their associated backgrounds. In this system, a set of angular radial transformation (ART coefficients is selected as the feature to represent the video object and the background, respectively. ECC and cryptographic hashing are applied to those selected coefficients to generate the robust authentication watermark. This content-based, semifragile watermark is then embedded into the objects frame by frame before MPEG4 coding. In watermark embedding and extraction, groups of discrete Fourier transform (DFT coefficients are randomly selected, and their energy relationships are employed to hide and extract the watermark. The experimental results demonstrate that our system is robust to MPEG4 compression, object segmentation errors, and some common object-based video processing such as object translation, rotation, and scaling while securely preventing malicious object modifications. The proposed solution can be further incorporated into public key infrastructure (PKI.

HI-CLASS on AEOS: A Large Aperture Laser Radar for Space Surveillance/ Situational Awareness Investigations

National Research Council Canada - National Science Library

Uroden, M

2001-01-01

...) laser radar systems at MSSS. The paper reviews the first generation kilowatt class ladar/lidar HI-CLASS/LBD systems as the foundation for a second-generation ladar system that was developed under the AFRL/DE ALVA program...

Good and Bad Objects : Cardinality-Based Rules

NARCIS (Netherlands)

Dimitrov, D.A.; Borm, P.E.M.; Hendrickx, R.L.P.

2003-01-01

We consider the problem of ranking sets of objects, the members of which are mutually compatible.Assuming that each object is either good or bad, we axiomatically characterize three cardinality-based rules which arise naturally in this dichotomous setting.They are what we call the symmetric

Retrieving top-k prestige-based relevant spatial web objects

DEFF Research Database (Denmark)

Cao, Xin; Cong, Gao; Jensen, Christian S.

2010-01-01

The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous...... of prestige-based relevance to capture both the textual relevance of an object to a query and the effects of nearby objects. Based on this, a new type of query, the Location-aware top-k Prestige-based Text retrieval (LkPT) query, is proposed that retrieves the top-k spatial web objects ranked according...... to both prestige-based relevance and location proximity. We propose two algorithms that compute LkPT queries. Empirical studies with real-world spatial data demonstrate that LkPT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby...

A C++ Class for Rule-Base Objects

Directory of Open Access Journals (Sweden)

William J. Grenney

1992-01-01

Full Text Available A C++ class, called Tripod, was created as a tool to assist with the development of rule-base decision support systems. The Tripod class contains data structures for the rule-base and member functions for operating on the data. The rule-base is defined by three ASCII files. These files are translated by a preprocessor into a single file that is located when a rule-base object is instantiated. The Tripod class was tested as part of a proto-type decision support system (DSS for winter highway maintenance in the Intermountain West. The DSS is composed of two principal modules: the main program, called the wrapper, and a Tripod rule-base object. The wrapper is a procedural module that interfaces with remote sensors and an external meterological database. The rule-base contains the logic for advising an inexperienced user and for assisting with the decision making process.

An object-based visual attention model for robotic applications.

Science.gov (United States)

Yu, Yuanlong; Mann, George K I; Gosine, Raymond G

2010-10-01

By extending integrated competition hypothesis, this paper presents an object-based visual attention model, which selects one object of interest using low-dimensional features, resulting that visual perception starts from a fast attentional selection procedure. The proposed attention model involves seven modules: learning of object representations stored in a long-term memory (LTM), preattentive processing, top-down biasing, bottom-up competition, mediation between top-down and bottom-up ways, generation of saliency maps, and perceptual completion processing. It works in two phases: learning phase and attending phase. In the learning phase, the corresponding object representation is trained statistically when one object is attended. A dual-coding object representation consisting of local and global codings is proposed. Intensity, color, and orientation features are used to build the local coding, and a contour feature is employed to constitute the global coding. In the attending phase, the model preattentively segments the visual field into discrete proto-objects using Gestalt rules at first. If a task-specific object is given, the model recalls the corresponding representation from LTM and deduces the task-relevant feature(s) to evaluate top-down biases. The mediation between automatic bottom-up competition and conscious top-down biasing is then performed to yield a location-based saliency map. By combination of location-based saliency within each proto-object, the proto-object-based saliency is evaluated. The most salient proto-object is selected for attention, and it is finally put into the perceptual completion processing module to yield a complete object region. This model has been applied into distinct tasks of robots: detection of task-specific stationary and moving objects. Experimental results under different conditions are shown to validate this model.

Tracking target objects orbiting earth using satellite-based telescopes

Science.gov (United States)

De Vries, Willem H; Olivier, Scot S; Pertica, Alexander J

2014-10-14

A system for tracking objects that are in earth orbit via a constellation or network of satellites having imaging devices is provided. An object tracking system includes a ground controller and, for each satellite in the constellation, an onboard controller. The ground controller receives ephemeris information for a target object and directs that ephemeris information be transmitted to the satellites. Each onboard controller receives ephemeris information for a target object, collects images of the target object based on the expected location of the target object at an expected time, identifies actual locations of the target object from the collected images, and identifies a next expected location at a next expected time based on the identified actual locations of the target object. The onboard controller processes the collected image to identify the actual location of the target object and transmits the actual location information to the ground controller.

Underwater Object Segmentation Based on Optical Features

Directory of Open Access Journals (Sweden)

Zhe Chen

2018-01-01

Full Text Available Underwater optical environments are seriously affected by various optical inputs, such as artificial light, sky light, and ambient scattered light. The latter two can block underwater object segmentation tasks, since they inhibit the emergence of objects of interest and distort image information, while artificial light can contribute to segmentation. Artificial light often focuses on the object of interest, and, therefore, we can initially identify the region of target objects if the collimation of artificial light is recognized. Based on this concept, we propose an optical feature extraction, calculation, and decision method to identify the collimated region of artificial light as a candidate object region. Then, the second phase employs a level set method to segment the objects of interest within the candidate region. This two-phase structure largely removes background noise and highlights the outline of underwater objects. We test the performance of the method with diverse underwater datasets, demonstrating that it outperforms previous methods.

Conditioning 3D object-based models to dense well data

Science.gov (United States)

Wang, Yimin C.; Pyrcz, Michael J.; Catuneanu, Octavian; Boisvert, Jeff B.

2018-06-01

Object-based stochastic simulation models are used to generate categorical variable models with a realistic representation of complicated reservoir heterogeneity. A limitation of object-based modeling is the difficulty of conditioning to dense data. One method to achieve data conditioning is to apply optimization techniques. Optimization algorithms can utilize an objective function measuring the conditioning level of each object while also considering the geological realism of the object. Here, an objective function is optimized with implicit filtering which considers constraints on object parameters. Thousands of objects conditioned to data are generated and stored in a database. A set of objects are selected with linear integer programming to generate the final realization and honor all well data, proportions and other desirable geological features. Although any parameterizable object can be considered, objects from fluvial reservoirs are used to illustrate the ability to simultaneously condition multiple types of geologic features. Channels, levees, crevasse splays and oxbow lakes are parameterized based on location, path, orientation and profile shapes. Functions mimicking natural river sinuosity are used for the centerline model. Channel stacking pattern constraints are also included to enhance the geological realism of object interactions. Spatial layout correlations between different types of objects are modeled. Three case studies demonstrate the flexibility of the proposed optimization-simulation method. These examples include multiple channels with high sinuosity, as well as fragmented channels affected by limited preservation. In all cases the proposed method reproduces input parameters for the object geometries and matches the dense well constraints. The proposed methodology expands the applicability of object-based simulation to complex and heterogeneous geological environments with dense sampling.

Object-based Landslide Mapping: Examples, Challenges and Opportunities

Science.gov (United States)

Hölbling, Daniel; Eisank, Clemens; Friedl, Barbara; Chang, Kang-Tsung; Tsai, Tsai-Tsung; Birkefeldt Møller Pedersen, Gro; Betts, Harley; Cigna, Francesca; Chiang, Shou-Hao; Aubrey Robson, Benjamin; Bianchini, Silvia; Füreder, Petra; Albrecht, Florian; Spiekermann, Raphael; Weinke, Elisabeth; Blaschke, Thomas; Phillips, Chris

2016-04-01

Over the last decade, object-based image analysis (OBIA) has been increasingly used for mapping landslides that occur after triggering events such as heavy rainfall. The increasing availability and quality of Earth Observation (EO) data in terms of temporal, spatial and spectral resolution allows for comprehensive mapping of landslides at multiple scales. Most often very high resolution (VHR) or high resolution (HR) optical satellite images are used in combination with a digital elevation model (DEM) and its products such as slope and curvature. Semi-automated object-based mapping makes use of various characteristics of image objects that are derived through segmentation. OBIA enables numerous spectral, spatial, contextual and textural image object properties to be applied during an analysis. This is especially useful when mapping complex natural features such as landslides and constitutes an advantage over pixel-based image analysis. However, several drawbacks in the process of object-based landslide mapping have not been overcome yet. The developed classification routines are often rather complex and limited regarding their transferability across areas and sensors. There is still more research needed to further improve present approaches and to fully exploit the capabilities of OBIA for landslide mapping. In this study several examples of object-based landslide mapping from various geographical regions with different characteristics are presented. Examples from the Austrian and Italian Alps are shown, whereby one challenge lies in the detection of small-scale landslides on steep slopes while preventing the classification of false positives with similar spectral properties (construction areas, utilized land, etc.). Further examples feature landslides mapped in Iceland, where the differentiation of landslides from other landscape-altering processes in a highly dynamic volcanic landscape poses a very distinct challenge, and in Norway, which is exposed to multiple

Multi-objective Search-based Mobile Testing

OpenAIRE

Mao, K.

2017-01-01

Despite the tremendous popularity of mobile applications, mobile testing still relies heavily on manual testing. This thesis presents mobile test automation approaches based on multi-objective search. We introduce three approaches: Sapienz (for native Android app testing), Octopuz (for hybrid/web JavaScript app testing) and Polariz (for using crowdsourcing to support search-based mobile testing). These three approaches represent the primary scientific and technical contributions of the thesis...

Paramecium: An Extensible Object-Based Kernel

NARCIS (Netherlands)

van Doorn, L.; Homburg, P.; Tanenbaum, A.S.

1995-01-01

In this paper we describe the design of an extensible kernel, called Paramecium. This kernel uses an object-based software architecture which together with instance naming, late binding and explicit overrides enables easy reconfiguration. Determining which components reside in the kernel protection

A Framework-Based Environment for Object-Oriented Scientific Codes

Directory of Open Access Journals (Sweden)

Robert A. Ballance

1993-01-01

Full Text Available Frameworks are reusable object-oriented designs for domain-specific programs. In our estimation, frameworks are the key to productivity and reuse. However, frameworks require increased support from the programming environment. A framework-based environment must include design aides and project browsers that can mediate between the user and the framework. A framework-based approach also places new requirements on conventional tools such as compilers. This article explores the impact of object-oriented frameworks upon a programming environment, in the context of object-oriented finite element and finite difference codes. The role of tools such as design aides and project browsers is discussed, and the impact of a framework-based approach upon compilers is examined. Examples are drawn from our prototype C++ based environment.

A review of supervised object-based land-cover image classification

Science.gov (United States)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial

The modulation of inhibition of return by object-internal structure: implications for theories of object-based attentional selection.

Science.gov (United States)

Reppa, Irene; Leek, E Charles

2003-06-01

Recently, Vecera, Behrmann, and McGoldrick (2000), using a divided-attention task, reported that targets are detected more accurately when they occur on the same structural part of an object, suggesting that attention can be directed toward object-internal features. We present converging evidence using the object-based inhibition of return (IOR) paradigm as an implicit measure of selection. The results show that IOR is attenuated when cues and targets appear on the same part of an object relative to when they are separated by a part boundary. These findings suggest that object-based mechanisms of selection can operate over shape representations that make explicit information about object-internal structure.

Learning based particle filtering object tracking for visible-light systems.

Science.gov (United States)

Sun, Wei

2015-10-01

We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.

A Dynamic Object Behavior Model and Implementation Based on Computational Reflection

Institute of Scientific and Technical Information of China (English)

HE Cheng-wan; HE Fei; HE Ke-qing

2005-01-01

A dynamic object behavior model based on computational reflection is proposed. This model consists of function level and meta level, the meta objects in meta level manage the base objects and behaviors in function level, including dynamic binding and unbinding of base object and behavior.We implement this model with RoleJava Language, which is our self linguistic extension of the Java Language. Meta Objects are generated automatically at compile-time, this makes the reflecton mechanism transparent to programmers. Finally an example applying this model to a banking system is presented.

Color Independent Components Based SIFT Descriptors for Object/Scene Classification

Science.gov (United States)

Ai, Dan-Ni; Han, Xian-Hua; Ruan, Xiang; Chen, Yen-Wei

In this paper, we present a novel color independent components based SIFT descriptor (termed CIC-SIFT) for object/scene classification. We first learn an efficient color transformation matrix based on independent component analysis (ICA), which is adaptive to each category in a database. The ICA-based color transformation can enhance contrast between the objects and the background in an image. Then we compute CIC-SIFT descriptors over all three transformed color independent components. Since the ICA-based color transformation can boost the objects and suppress the background, the proposed CIC-SIFT can extract more effective and discriminative local features for object/scene classification. The comparison is performed among seven SIFT descriptors, and the experimental classification results show that our proposed CIC-SIFT is superior to other conventional SIFT descriptors.

A Learning Object Approach To Evidence based learning

Directory of Open Access Journals (Sweden)

Zabin Visram

2005-06-01

Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

Feature-based and object-based attention orientation during short-term memory maintenance.

Science.gov (United States)

Ku, Yixuan

2015-12-01

Top-down attention biases the short-term memory (STM) processing at multiple stages. Orienting attention during the maintenance period of STM by a retrospective cue (retro-cue) strengthens the representation of the cued item and improves the subsequent STM performance. In a recent article, Backer et al. (Backer KC, Binns MA, Alain C. J Neurosci 35: 1307-1318, 2015) extended these findings from the visual to the auditory domain and combined electroencephalography to dissociate neural mechanisms underlying feature-based and object-based attention orientation. Both event-related potentials and neural oscillations explained the behavioral benefits of retro-cues and favored the theory that feature-based and object-based attention orientation were independent. Copyright © 2015 the American Physiological Society.

Learning Object Retrieval and Aggregation Based on Learning Styles

Science.gov (United States)

Ramirez-Arellano, Aldo; Bory-Reyes, Juan; Hernández-Simón, Luis Manuel

2017-01-01

The main goal of this article is to develop a Management System for Merging Learning Objects (msMLO), which offers an approach that retrieves learning objects (LOs) based on students' learning styles and term-based queries, which produces a new outcome with a better score. The msMLO faces the task of retrieving LOs via two steps: The first step…

Integration of object-oriented knowledge representation with the CLIPS rule based system

Science.gov (United States)

Logie, David S.; Kamil, Hasan

1990-01-01

The paper describes a portion of the work aimed at developing an integrated, knowledge based environment for the development of engineering-oriented applications. An Object Representation Language (ORL) was implemented in C++ which is used to build and modify an object-oriented knowledge base. The ORL was designed in such a way so as to be easily integrated with other representation schemes that could effectively reason with the object base. Specifically, the integration of the ORL with the rule based system C Language Production Systems (CLIPS), developed at the NASA Johnson Space Center, will be discussed. The object-oriented knowledge representation provides a natural means of representing problem data as a collection of related objects. Objects are comprised of descriptive properties and interrelationships. The object-oriented model promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects. Data is inherited through an object network via the relationship links. Together, the two schemes complement each other in that the object-oriented approach efficiently handles problem data while the rule based knowledge is used to simulate the reasoning process. Alone, the object based knowledge is little more than an object-oriented data storage scheme; however, the CLIPS inference engine adds the mechanism to directly and automatically reason with that knowledge. In this hybrid scheme, the expert system dynamically queries for data and can modify the object base with complete access to all the functionality of the ORL from rules.

Sub-OBB based object recognition and localization algorithm using range images

International Nuclear Information System (INIS)

Hoang, Dinh-Cuong; Chen, Liang-Chia; Nguyen, Thanh-Hung

2017-01-01

This paper presents a novel approach to recognize and estimate pose of the 3D objects in cluttered range images. The key technical breakthrough of the developed approach can enable robust object recognition and localization under undesirable condition such as environmental illumination variation as well as optical occlusion to viewing the object partially. First, the acquired point clouds are segmented into individual object point clouds based on the developed 3D object segmentation for randomly stacked objects. Second, an efficient shape-matching algorithm called Sub-OBB based object recognition by using the proposed oriented bounding box (OBB) regional area-based descriptor is performed to reliably recognize the object. Then, the 3D position and orientation of the object can be roughly estimated by aligning the OBB of segmented object point cloud with OBB of matched point cloud in a database generated from CAD model and 3D virtual camera. To detect accurate pose of the object, the iterative closest point (ICP) algorithm is used to match the object model with the segmented point clouds. From the feasibility test of several scenarios, the developed approach is verified to be feasible for object pose recognition and localization. (paper)

Knowledge-based simulation using object-oriented programming

Science.gov (United States)

Sidoran, Karen M.

1993-01-01

Simulations have become a powerful mechanism for understanding and modeling complex phenomena. Their results have had substantial impact on a broad range of decisions in the military, government, and industry. Because of this, new techniques are continually being explored and developed to make them even more useful, understandable, extendable, and efficient. One such area of research is the application of the knowledge-based methods of artificial intelligence (AI) to the computer simulation field. The goal of knowledge-based simulation is to facilitate building simulations of greatly increased power and comprehensibility by making use of deeper knowledge about the behavior of the simulated world. One technique for representing and manipulating knowledge that has been enhanced by the AI community is object-oriented programming. Using this technique, the entities of a discrete-event simulation can be viewed as objects in an object-oriented formulation. Knowledge can be factual (i.e., attributes of an entity) or behavioral (i.e., how the entity is to behave in certain circumstances). Rome Laboratory's Advanced Simulation Environment (RASE) was developed as a research vehicle to provide an enhanced simulation development environment for building more intelligent, interactive, flexible, and realistic simulations. This capability will support current and future battle management research and provide a test of the object-oriented paradigm for use in large scale military applications.

Robust object tacking based on self-adaptive search area

Science.gov (United States)

Dong, Taihang; Zhong, Sheng

2018-02-01

Discriminative correlation filter (DCF) based trackers have recently achieved excellent performance with great computational efficiency. However, DCF based trackers suffer boundary effects, which result in the unstable performance in challenging situations exhibiting fast motion. In this paper, we propose a novel method to mitigate this side-effect in DCF based trackers. We change the search area according to the prediction of target motion. When the object moves fast, broad search area could alleviate boundary effects and reserve the probability of locating object. When the object moves slowly, narrow search area could prevent effect of useless background information and improve computational efficiency to attain real-time performance. This strategy can impressively soothe boundary effects in situations exhibiting fast motion and motion blur, and it can be used in almost all DCF based trackers. The experiments on OTB benchmark show that the proposed framework improves the performance compared with the baseline trackers.

Autocorrelation based reconstruction of two-dimensional binary objects

International Nuclear Information System (INIS)

Mejia-Barbosa, Y.; Castaneda, R.

2005-10-01

A method for reconstructing two-dimensional binary objects from its autocorrelation function is discussed. The objects consist of a finite set of identical elements. The reconstruction algorithm is based on the concept of class of element pairs, defined as the set of element pairs with the same separation vector. This concept allows to solve the redundancy introduced by the element pairs of each class. It is also shown that different objects, consisting of an equal number of elements and the same classes of pairs, provide Fraunhofer diffraction patterns with identical intensity distributions. However, the method predicts all the possible objects that produce the same Fraunhofer pattern. (author)

Model-based object classification using unification grammars and abstract representations

Science.gov (United States)

Liburdy, Kathleen A.; Schalkoff, Robert J.

1993-04-01

The design and implementation of a high level computer vision system which performs object classification is described. General object labelling and functional analysis require models of classes which display a wide range of geometric variations. A large representational gap exists between abstract criteria such as `graspable' and current geometric image descriptions. The vision system developed and described in this work addresses this problem and implements solutions based on a fusion of semantics, unification, and formal language theory. Object models are represented using unification grammars, which provide a framework for the integration of structure and semantics. A methodology for the derivation of symbolic image descriptions capable of interacting with the grammar-based models is described and implemented. A unification-based parser developed for this system achieves object classification by determining if the symbolic image description can be unified with the abstract criteria of an object model. Future research directions are indicated.

OBEST: The Object-Based Event Scenario Tree Methodology

International Nuclear Information System (INIS)

WYSS, GREGORY D.; DURAN, FELICIA A.

2001-01-01

Event tree analysis and Monte Carlo-based discrete event simulation have been used in risk assessment studies for many years. This report details how features of these two methods can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology with some of the best features of each. The resultant Object-Based Event Scenarios Tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible (especially those that exhibit inconsistent or variable event ordering, which are difficult to represent in an event tree analysis). Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST method uses a recursive algorithm to solve the object model and identify all possible scenarios and their associated probabilities. Since scenario likelihoods are developed directly by the solution algorithm, they need not be computed by statistical inference based on Monte Carlo observations (as required by some discrete event simulation methods). Thus, OBEST is not only much more computationally efficient than these simulation methods, but it also discovers scenarios that have extremely low probabilities as a natural analytical result--scenarios that would likely be missed by a Monte Carlo-based method. This report documents the OBEST methodology, the demonstration software that implements it, and provides example OBEST models for several different application domains, including interactions among failing interdependent infrastructure systems, circuit analysis for fire risk evaluation in nuclear power plants, and aviation safety studies

Bindings in working memory: The role of object-based attention.

Science.gov (United States)

Gao, Zaifeng; Wu, Fan; Qiu, Fangfang; He, Kaifeng; Yang, Yue; Shen, Mowei

2017-02-01

Over the past decade, it has been debated whether retaining bindings in working memory (WM) requires more attention than retaining constituent features, focusing on domain-general attention and space-based attention. Recently, we proposed that retaining bindings in WM needs more object-based attention than retaining constituent features (Shen, Huang, & Gao, 2015, Journal of Experimental Psychology: Human Perception and Performance, doi: 10.1037/xhp0000018 ). However, only unitized visual bindings were examined; to establish the role of object-based attention in retaining bindings in WM, more emperical evidence is required. We tested 4 new bindings that had been suggested requiring no more attention than the constituent features in the WM maintenance phase: The two constituent features of binding were stored in different WM modules (cross-module binding, Experiment 1), from auditory and visual modalities (cross-modal binding, Experiment 2), or temporally (cross-time binding, Experiments 3) or spatially (cross-space binding, Experiments 4-6) separated. In the critical condition, we added a secondary object feature-report task during the delay interval of the change-detection task, such that the secondary task competed for object-based attention with the to-be-memorized stimuli. If more object-based attention is required for retaining bindings than for retaining constituent features, the secondary task should impair the binding performance to a larger degree relative to the performance of constituent features. Indeed, Experiments 1-6 consistently revealed a significantly larger impairment for bindings than for the constituent features, suggesting that object-based attention plays a pivotal role in retaining bindings in WM.

Research on moving object detection based on frog's eyes

Science.gov (United States)

Fu, Hongwei; Li, Dongguang; Zhang, Xinyuan

2008-12-01

On the basis of object's information processing mechanism with frog's eyes, this paper discussed a bionic detection technology which suitable for object's information processing based on frog's vision. First, the bionics detection theory by imitating frog vision is established, it is an parallel processing mechanism which including pick-up and pretreatment of object's information, parallel separating of digital image, parallel processing, and information synthesis. The computer vision detection system is described to detect moving objects which has special color, special shape, the experiment indicates that it can scheme out the detecting result in the certain interfered background can be detected. A moving objects detection electro-model by imitating biologic vision based on frog's eyes is established, the video simulative signal is digital firstly in this system, then the digital signal is parallel separated by FPGA. IN the parallel processing, the video information can be caught, processed and displayed in the same time, the information fusion is taken by DSP HPI ports, in order to transmit the data which processed by DSP. This system can watch the bigger visual field and get higher image resolution than ordinary monitor systems. In summary, simulative experiments for edge detection of moving object with canny algorithm based on this system indicate that this system can detect the edge of moving objects in real time, the feasibility of bionic model was fully demonstrated in the engineering system, and it laid a solid foundation for the future study of detection technology by imitating biologic vision.

Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

Directory of Open Access Journals (Sweden)

L. DJEROU,

2012-01-01

Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

Attribute-based classification for zero-shot visual object categorization.

Science.gov (United States)

Lampert, Christoph H; Nickisch, Hannes; Harmeling, Stefan

2014-03-01

We study the problem of object recognition for categories for which we have no training examples, a task also called zero--data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.

Category-based attentional guidance can operate in parallel for multiple target objects.

Science.gov (United States)

Jenkins, Michael; Grubert, Anna; Eimer, Martin

2018-04-30

The question whether the control of attention during visual search is always feature-based or can also be based on the category of objects remains unresolved. Here, we employed the N2pc component as an on-line marker for target selection processes to compare the efficiency of feature-based and category-based attentional guidance. Two successive displays containing pairs of real-world objects (line drawings of kitchen or clothing items) were separated by a 10 ms SOA. In Experiment 1, target objects were defined by their category. In Experiment 2, one specific visual object served as target (exemplar-based search). On different trials, targets appeared either in one or in both displays, and participants had to report the number of targets (one or two). Target N2pc components were larger and emerged earlier during exemplar-based search than during category-based search, demonstrating the superior efficiency of feature-based attentional guidance. On trials where target objects appeared in both displays, both targets elicited N2pc components that overlapped in time, suggesting that attention was allocated in parallel to these target objects. Critically, this was the case not only in the exemplar-based task, but also when targets were defined by their category. These results demonstrate that attention can be guided by object categories, and that this type of category-based attentional control can operate concurrently for multiple target objects. Copyright © 2018 Elsevier B.V. All rights reserved.

Virtual Surveyor based Object Extraction from Airborne LiDAR data

Science.gov (United States)

Habib, Md. Ahsan

Topographic feature detection of land cover from LiDAR data is important in various fields - city planning, disaster response and prevention, soil conservation, infrastructure or forestry. In recent years, feature classification, compliant with Object-Based Image Analysis (OBIA) methodology has been gaining traction in remote sensing and geographic information science (GIS). In OBIA, the LiDAR image is first divided into meaningful segments called object candidates. This results, in addition to spectral values, in a plethora of new information such as aggregated spectral pixel values, morphology, texture, context as well as topology. Traditional nonparametric segmentation methods rely on segmentations at different scales to produce a hierarchy of semantically significant objects. Properly tuned scale parameters are, therefore, imperative in these methods for successful subsequent classification. Recently, some progress has been made in the development of methods for tuning the parameters for automatic segmentation. However, researchers found that it is very difficult to automatically refine the tuning with respect to each object class present in the scene. Moreover, due to the relative complexity of real-world objects, the intra-class heterogeneity is very high, which leads to over-segmentation. Therefore, the method fails to deliver correctly many of the new segment features. In this dissertation, a new hierarchical 3D object segmentation algorithm called Automatic Virtual Surveyor based Object Extracted (AVSOE) is presented. AVSOE segments objects based on their distinct geometric concavity/convexity. This is achieved by strategically mapping the sloping surface, which connects the object to its background. Further analysis produces hierarchical decomposition of objects to its sub-objects at a single scale level. Extensive qualitative and qualitative results are presented to demonstrate the efficacy of this hierarchical segmentation approach.

Object manipulation facilitates kind-based object individuation of shape-similar objects

DEFF Research Database (Denmark)

Kingo, Osman Skjold; Krøjgaard, Peter

2011-01-01

Five experiments investigated the importance of shape and object manipulation when 12-month-olds were given the task of individuating objects representing exemplars of kinds in an event-mapping design. In Experiments 1 and 2, results of the study from Xu, Carey, and Quint (2004, Experiment 4) wer...

Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach

Directory of Open Access Journals (Sweden)

Muhammad Kamal

2011-10-01

Full Text Available Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM and linear spectral unmixing (LSU for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA. The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57, the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41, and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67. Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments.

Effect of Feature Dimensionality on Object-based Land Cover ...

African Journals Online (AJOL)

Geographic object-based image analysis (GEOBIA) allows the easy integration of such additional features into the classification process. This paper compares the performance of three supervised classifiers in a GEOBIA environment as an increasing number of object features are included as classification input.

A REGION-BASED MULTI-SCALE APPROACH FOR OBJECT-BASED IMAGE ANALYSIS

Directory of Open Access Journals (Sweden)

T. Kavzoglu

2016-06-01

Full Text Available Within the last two decades, object-based image analysis (OBIA considering objects (i.e. groups of pixels instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features. Although there are several parameters (scale, shape, compactness and band weights to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient. Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than image-based single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

Unsupervised motion-based object segmentation refined by color

Science.gov (United States)

Piek, Matthijs C.; Braspenning, Ralph; Varekamp, Chris

2003-06-01

For various applications, such as data compression, structure from motion, medical imaging and video enhancement, there is a need for an algorithm that divides video sequences into independently moving objects. Because our focus is on video enhancement and structure from motion for consumer electronics, we strive for a low complexity solution. For still images, several approaches exist based on colour, but these lack in both speed and segmentation quality. For instance, colour-based watershed algorithms produce a so-called oversegmentation with many segments covering each single physical object. Other colour segmentation approaches exist which somehow limit the number of segments to reduce this oversegmentation problem. However, this often results in inaccurate edges or even missed objects. Most likely, colour is an inherently insufficient cue for real world object segmentation, because real world objects can display complex combinations of colours. For video sequences, however, an additional cue is available, namely the motion of objects. When different objects in a scene have different motion, the motion cue alone is often enough to reliably distinguish objects from one another and the background. However, because of the lack of sufficient resolution of efficient motion estimators, like the 3DRS block matcher, the resulting segmentation is not at pixel resolution, but at block resolution. Existing pixel resolution motion estimators are more sensitive to noise, suffer more from aperture problems or have less correspondence to the true motion of objects when compared to block-based approaches or are too computationally expensive. From its tendency to oversegmentation it is apparent that colour segmentation is particularly effective near edges of homogeneously coloured areas. On the other hand, block-based true motion estimation is particularly effective in heterogeneous areas, because heterogeneous areas improve the chance a block is unique and thus decrease the

Optical Aperture Synthesis Object's Information Extracting Based on Wavelet Denoising

International Nuclear Information System (INIS)

Fan, W J; Lu, Y

2006-01-01

Wavelet denoising is studied to improve OAS(optical aperture synthesis) object's Fourier information extracting. Translation invariance wavelet denoising based on Donoho wavelet soft threshold denoising is researched to remove Pseudo-Gibbs in wavelet soft threshold image. OAS object's information extracting based on translation invariance wavelet denoising is studied. The study shows that wavelet threshold denoising can improve the precision and the repetition of object's information extracting from interferogram, and the translation invariance wavelet denoising information extracting is better than soft threshold wavelet denoising information extracting

Teaching object concepts for XML-based representations.

Energy Technology Data Exchange (ETDEWEB)

Kelsey, R. L. (Robert L.)

2002-01-01

Students learned about object-oriented design concepts and knowledge representation through the use of a set of toy blocks. The blocks represented a limited and focused domain of knowledge and one that was physical and tangible. The blocks helped the students to better visualize, communicate, and understand the domain of knowledge as well as how to perform object decomposition. The blocks were further abstracted to an engineering design kit for water park design. This helped the students to work on techniques for abstraction and conceptualization. It also led the project from tangible exercises into software and programming exercises. Students employed XML to create object-based knowledge representations and Java to use the represented knowledge. The students developed and implemented software allowing a lay user to design and create their own water slide and then to take a simulated ride on their slide.

Revisiting child-based objections to commercial surrogacy.

Science.gov (United States)

Hanna, Jason K M

2010-09-01

Many critics of commercial surrogate motherhood argue that it violates the rights of children. In this paper, I respond to several versions of this objection. The most common version claims that surrogacy involves child-selling. I argue that while proponents of surrogacy have generally failed to provide an adequate response to this objection, it can be overcome. After showing that the two most prominent arguments for the child-selling objection fail, I explain how the commissioning couple can acquire parental rights by paying the surrogate only for her reproductive labor. My explanation appeals to the idea that parental rights are acquired by those who have claims over the reproductive labor that produces the child, not necessarily by those who actually perform the labor. This account clarifies how commercial surrogacy differs from commercial adoption. In the final section of the paper, I consider and reject three further child-based objections to commercial surrogacy: that it establishes a market in children's attributes, that it requires courts to stray from the best interests standard in determining custodial rights, and that it requires the surrogate to neglect her parental responsibilities. Since each of these objections fails, children's rights probably do not pose an obstacle to the acceptability of commercial surrogacy arrangements.

Spanish Tourist Behaviour: A Specific Objective-base Segmantation

OpenAIRE

González, Pablo Rodríguez; Molina, Oscar

2009-01-01

This work uses data from the Spanish Tourism Demand Segments Survey (N=6900) conducted by the IESA-CSIC for Turismo Andaluz, SA. The objective of the paper is to develop a statistical segmentation or typology of Spanish tourists based on objective aspects of tourist behaviour measured in the survey including destinations visited, theme of the trip, lodging, transportation and travel group. Initial categorical data are reduced using multiple correspondence analysis and grouped through cluster ...

Object-Based Image Analysis Beyond Remote Sensing - the Human Perspective

Science.gov (United States)

Blaschke, T.; Lang, S.; Tiede, D.; Papadakis, M.; Györi, A.

2016-06-01

We introduce a prototypical methodological framework for a place-based GIS-RS system for the spatial delineation of place while incorporating spatial analysis and mapping techniques using methods from different fields such as environmental psychology, geography, and computer science. The methodological lynchpin for this to happen - when aiming to delineate place in terms of objects - is object-based image analysis (OBIA).

A Novel Abandoned Object Detection System Based on Three-Dimensional Image Information

Directory of Open Access Journals (Sweden)

Yiliang Zeng

2015-03-01

Full Text Available A new idea of an abandoned object detection system for road traffic surveillance systems based on three-dimensional image information is proposed in this paper to prevent traffic accidents. A novel Binocular Information Reconstruction and Recognition (BIRR algorithm is presented to implement the new idea. As initial detection, suspected abandoned objects are detected by the proposed static foreground region segmentation algorithm based on surveillance video from a monocular camera. After detection of suspected abandoned objects, three-dimensional (3D information of the suspected abandoned object is reconstructed by the proposed theory about 3D object information reconstruction with images from a binocular camera. To determine whether the detected object is hazardous to normal road traffic, road plane equation and height of suspected-abandoned object are calculated based on the three-dimensional information. Experimental results show that this system implements fast detection of abandoned objects and this abandoned object system can be used for road traffic monitoring and public area surveillance.

SVM Classifiers: The Objects Identification on the Base of Their Hyperspectral Features

Directory of Open Access Journals (Sweden)

Demidova Liliya

2017-01-01

Full Text Available The problem of the objects identification on the base of their hyperspectral features has been considered. It is offered to use the SVM classifiers on the base of the modified PSO algorithm, adapted to specifics of the problem of the objects identification on the base of their hyperspectral features. The results of the objects identification on the base of their hyperspectral features with using of the SVM classifiers have been presented.

A Semantic-Based Indexing for Indoor Moving Objects

OpenAIRE

Tingting Ben; Xiaolin Qin; Ning Wang

2014-01-01

The increasing availability of indoor positioning, driven by techniques like RFID, Bluetooth, and smart phones, enables a variety of indoor location-based services (LBSs). Efficient queries based on semantic-constraint in indoor spaces play an important role in supporting and boosting LBSs. However, the existing indoor index techniques cannot support these semantic constraints-based queries. To solve this problem, this paper addresses the challenge of indexing moving objects in indoor spaces,...

Model-Based Software Testing for Object-Oriented Software

Science.gov (United States)

Biju, Soly Mathew

2008-01-01

Model-based testing is one of the best solutions for testing object-oriented software. It has a better test coverage than other testing styles. Model-based testing takes into consideration behavioural aspects of a class, which are usually unchecked in other testing methods. An increase in the complexity of software has forced the software industry…

The Web-based Module of Changes in Objects

Science.gov (United States)

Triayomi, R.

2017-09-01

To understand the changes of substances contained in such a kind of substance and substance characteristics then need a deep study of the concept. In this concept is expected to understand the changes of objects such as substance type and substance characteristics. Types of substances and characteristics of substances through physical changes and chemical changes and means of separation consisting of two or more substances. The principle of separation of the mixture is based on differences in physical properties of its constituents, such as substances, particle size, melting point, boiling point, magnetic properties, solubility, and so forth. This study aims to produce a web-based module of changes in objects that are valid, practical, and have effectiveness of student learning outcomes and activities on natural science learning. The experiment was conducted on 30 children in South Sumatera. The case of the development of the learning module of change of the object is influenced by the child’s understanding of the concept. Expected to be adapted by world teachers.

Speckle-learning-based object recognition through scattering media.

Science.gov (United States)

Ando, Takamasa; Horisaki, Ryoichi; Tanida, Jun

2015-12-28

We experimentally demonstrated object recognition through scattering media based on direct machine learning of a number of speckle intensity images. In the experiments, speckle intensity images of amplitude or phase objects on a spatial light modulator between scattering plates were captured by a camera. We used the support vector machine for binary classification of the captured speckle intensity images of face and non-face data. The experimental results showed that speckles are sufficient for machine learning.

Online decoding of object-based attention using real-time fMRI.

Science.gov (United States)

Niazi, Adnan M; van den Broek, Philip L C; Klanke, Stefan; Barth, Markus; Poel, Mannes; Desain, Peter; van Gerven, Marcel A J

2014-01-01

Visual attention is used to selectively filter relevant information depending on current task demands and goals. Visual attention is called object-based attention when it is directed to coherent forms or objects in the visual field. This study used real-time functional magnetic resonance imaging for moment-to-moment decoding of attention to spatially overlapped objects belonging to two different object categories. First, a whole-brain classifier was trained on pictures of faces and places. Subjects then saw transparently overlapped pictures of a face and a place, and attended to only one of them while ignoring the other. The category of the attended object, face or place, was decoded on a scan-by-scan basis using the previously trained decoder. The decoder performed at 77.6% accuracy indicating that despite competing bottom-up sensory input, object-based visual attention biased neural patterns towards that of the attended object. Furthermore, a comparison between different classification approaches indicated that the representation of faces and places is distributed rather than focal. This implies that real-time decoding of object-based attention requires a multivariate decoding approach that can detect these distributed patterns of cortical activity. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Segmentation of object-based video of gaze communication

DEFF Research Database (Denmark)

Aghito, Shankar Manuel; Stegmann, Mikkel Bille; Forchhammer, Søren

2005-01-01

Aspects of video communication based on gaze interaction are considered. The overall idea is to use gaze interaction to control video, e.g. for video conferencing. Towards this goal, animation of a facial mask is demonstrated. The animation is based on images using Active Appearance Models (AAM......). Good quality reproduction of (low-resolution) coded video of an animated facial mask as low as 10-20 kbit/s using MPEG-4 object based video is demonstated....

Affordance estimation for vision-based object replacement on a humanoid robot

DEFF Research Database (Denmark)

Mustafa, Wail; Wächter, Mirko; Szedmak, Sandor

2016-01-01

In this paper, we address the problem of finding replacements of missing objects, involved in the execution of manipulation tasks. Our approach is based on estimating functional affordances for the unknown objects in order to propose replacements. We use a vision-based affordance estimation syste...

Image-based querying of urban knowledge databases

Science.gov (United States)

Cho, Peter; Bae, Soonmin; Durand, Fredo

2009-05-01

We extend recent automated computer vision algorithms to reconstruct the global three-dimensional structures for photos and videos shot at fixed points in outdoor city environments. Mosaics of digital stills and embedded videos are georegistered by matching a few of their 2D features with 3D counterparts in aerial ladar imagery. Once image planes are aligned with world maps, abstract urban knowledge can propagate from the latter into the former. We project geotagged annotations from a 3D map into a 2D video stream and demonstrate their tracking buildings and streets in a clip with significant panning motion. We also present an interactive tool which enables users to select city features of interest in video frames and retrieve their geocoordinates and ranges. Implications of this work for future augmented reality systems based upon mobile smart phones are discussed.

Covert orienting in the split brain: Right hemisphere specialization for object-based attention.

Science.gov (United States)

Kingstone, Alan

2015-12-18

The present paper takes as its starting point Phil Bryden's long-standing interest in human attention and the role it can play in laterality effects. Past split-brain research has suggested that object-based attention is lateralized to the left hemisphere [e.g., Egly, R., Rafal, R. D., Driver, J., & Starreveld, Y. (1994). Covert orienting in the split brain reveals hemispheric specialization for object-based attention. Psychological Science, 5(6), 380-382]. The task used to isolate object-based attention in that previous work, however, has been found wanting [Vecera, S. P. (1994). Grouped locations and object-based attention: Comment on Egly, Driver, and Rafal (1994). Journal of Experimental Psychology: General, 123(3), 316-320]; and indeed, subsequent research with healthy participants using a different task has suggested that object-based attention is lateralized to the opposite right hemisphere (RH) [Valsangkar-Smyth, M. A., Donovan, C. L., Sinnett, S., Dawson, M. R., & Kingstone, A. (2004). Hemispheric performance in object-based attention. Psychonomic Bulletin & Review, 11(1), 84-91]. The present study tested the same split-brain as Egly, Rafal, et al. (1994) but used the object-based attention task introduced by Valsangkar-Smyth et al. (2004). The results confirm that object-based attention is lateralized to the RH. They also suggest that subcortical interhemispheric competition may occur and be dominated by the RH.

A low-cost, high-resolution, video-rate imaging optical radar

Energy Technology Data Exchange (ETDEWEB)

Sackos, J.T.; Nellums, R.O.; Lebien, S.M.; Diegert, C.F. [Sandia National Labs., Albuquerque, NM (United States); Grantham, J.W.; Monson, T. [Air Force Research Lab., Eglin AFB, FL (United States)

1998-04-01

Sandia National Laboratories has developed a unique type of portable low-cost range imaging optical radar (laser radar or LADAR). This innovative sensor is comprised of an active floodlight scene illuminator and an image intensified CCD camera receiver. It is a solid-state device (no moving parts) that offers significant size, performance, reliability, and simplicity advantages over other types of 3-D imaging sensors. This unique flash LADAR is based on low cost, commercially available hardware, and is well suited for many government and commercial uses. This paper presents an update of Sandia`s development of the Scannerless Range Imager technology and applications, and discusses the progress that has been made in evolving the sensor into a compact, low, cost, high-resolution, video rate Laser Dynamic Range Imager.

Agent-based Algorithm for Spatial Distribution of Objects

KAUST Repository

Collier, Nathan

2012-06-02

In this paper we present an agent-based algorithm for the spatial distribution of objects. The algorithm is a generalization of the bubble mesh algorithm, initially created for the point insertion stage of the meshing process of the finite element method. The bubble mesh algorithm treats objects in space as bubbles, which repel and attract each other. The dynamics of each bubble are approximated by solving a series of ordinary differential equations. We present numerical results for a meshing application as well as a graph visualization application.

D Modelling and Interactive Web-Based Visualization of Cultural Heritage Objects

Science.gov (United States)

Koeva, M. N.

2016-06-01

Nowadays, there are rapid developments in the fields of photogrammetry, laser scanning, computer vision and robotics, together aiming to provide highly accurate 3D data that is useful for various applications. In recent years, various LiDAR and image-based techniques have been investigated for 3D modelling because of their opportunities for fast and accurate model generation. For cultural heritage preservation and the representation of objects that are important for tourism and their interactive visualization, 3D models are highly effective and intuitive for present-day users who have stringent requirements and high expectations. Depending on the complexity of the objects for the specific case, various technological methods can be applied. The selected objects in this particular research are located in Bulgaria - a country with thousands of years of history and cultural heritage dating back to ancient civilizations. This motivates the preservation, visualisation and recreation of undoubtedly valuable historical and architectural objects and places, which has always been a serious challenge for specialists in the field of cultural heritage. In the present research, comparative analyses regarding principles and technological processes needed for 3D modelling and visualization are presented. The recent problems, efforts and developments in interactive representation of precious objects and places in Bulgaria are presented. Three technologies based on real projects are described: (1) image-based modelling using a non-metric hand-held camera; (2) 3D visualization based on spherical panoramic images; (3) and 3D geometric and photorealistic modelling based on architectural CAD drawings. Their suitability for web-based visualization are demonstrated and compared. Moreover the possibilities for integration with additional information such as interactive maps, satellite imagery, sound, video and specific information for the objects are described. This comparative study

Object recognition based on Google's reverse image search and image similarity

Science.gov (United States)

Horváth, András.

2015-12-01

Image classification is one of the most challenging tasks in computer vision and a general multiclass classifier could solve many different tasks in image processing. Classification is usually done by shallow learning for predefined objects, which is a difficult task and very different from human vision, which is based on continuous learning of object classes and one requires years to learn a large taxonomy of objects which are not disjunct nor independent. In this paper I present a system based on Google image similarity algorithm and Google image database, which can classify a large set of different objects in a human like manner, identifying related classes and taxonomies.

Object based implicit contextual learning: a study of eye movements.

Science.gov (United States)

van Asselen, Marieke; Sampaio, Joana; Pina, Ana; Castelo-Branco, Miguel

2011-02-01

Implicit contextual cueing refers to a top-down mechanism in which visual search is facilitated by learned contextual features. In the current study we aimed to investigate the mechanism underlying implicit contextual learning using object information as a contextual cue. Therefore, we measured eye movements during an object-based contextual cueing task. We demonstrated that visual search is facilitated by repeated object information and that this reduction in response times is associated with shorter fixation durations. This indicates that by memorizing associations between objects in our environment we can recognize objects faster, thereby facilitating visual search.

DMD-based multi-object spectrograph on Galileo telescope

Science.gov (United States)

Zamkotsian, Frederic; Spano, Paolo; Lanzoni, Patrick; Bon, William; Riva, Marco; Nicastro, Luciano; Molinari, Emilio; Di Marcantonio, Paolo; Zerbi, Filippo; Valenziano, Luca

2013-03-01

Next-generation infrared astronomical instrumentation for ground-based and space telescopes could be based on MOEMS programmable slit masks for multi-object spectroscopy (MOS). This astronomical technique is used extensively to investigate the formation and evolution of galaxies. We propose to develop a 2048x1080 DMD-based MOS instrument to be mounted on the Galileo telescope and called BATMAN. A two-arm instrument has been designed for providing in parallel imaging and spectroscopic capabilities. The two arms with F/4 on the DMD are mounted on a common bench, and an upper bench supports the detectors thanks to two independent hexapods. Very good optical quality on the DMD and the detectors will be reached. ROBIN, a BATMAN demonstrator, has been designed, realized and integrated. It permits to determine the instrument integration procedure, including optics and mechanics integration, alignment procedure and optical quality. First images have been obtained and measured. A DMD pattern manager has been developed in order to generate any slit mask according to the list of objects to be observed; spectra have been generated and measured. Observation strategies will be studied and demonstrated for the scientific optimization strategy over the whole FOV. BATMAN on the sky is of prime importance for characterizing the actual performance of this new family of MOS instruments, as well as investigating the operational procedures on astronomical objects. This instrument will be placed on the Telescopio Nazionale Galileo at the beginning of next year, in 2014.

Research on Daily Objects Detection Based on Deep Neural Network

Science.gov (United States)

Ding, Sheng; Zhao, Kun

2018-03-01

With the rapid development of deep learning, great breakthroughs have been made in the field of object detection. In this article, the deep learning algorithm is applied to the detection of daily objects, and some progress has been made in this direction. Compared with traditional object detection methods, the daily objects detection method based on deep learning is faster and more accurate. The main research work of this article: 1. collect a small data set of daily objects; 2. in the TensorFlow framework to build different models of object detection, and use this data set training model; 3. the training process and effect of the model are improved by fine-tuning the model parameters.

Object-based landslide detection in different geographic regions

Science.gov (United States)

Friedl, Barbara; Hölbling, Daniel; Eisank, Clemens; Blaschke, Thomas

2015-04-01

Landslides occur in almost all mountainous regions of the world and rank among the most severe natural hazards. In the last decade - according to the world disaster report 2014 published by the International Federation of Red Cross and Red Crescent Societies (IRFC) - more than 9.000 people were killed by mass movements, more than 3.2 million people were affected and the total amount of disaster estimated damage accounts to more than 1.700 million US dollars. The application of remote sensing data for mapping landslides can contribute to post-disaster reconstruction or hazard mitigation, either by providing rapid information about the spatial distribution and location of landslides in the aftermath of triggering events or by creating and updating landslide inventories. This is especially valid for remote and inaccessible areas, where information on landslides is often lacking. However, reliable methods are needed for extracting timely and relevant information about landslides from remote sensing data. In recent years, novel methods such as object-based image analysis (OBIA) have been successfully employed for semi-automated landslide mapping. Several studies revealed that OBIA frequently outperforms pixel-based approaches, as a range of image object properties (spectral, spatial, morphometric, contextual) can be exploited during the analysis. However, object-based methods are often tailored to specific study areas, and thus, the transferability to regions with different geological settings, is often limited. The present case study evaluates the transferability and applicability of an OBIA approach for landslide detection in two distinct regions, i.e. the island of Taiwan and Austria. In Taiwan, sub-areas in the Baichi catchment in the North and in the Huaguoshan catchment in the southern-central part of the island are selected; in Austria, landslide-affected sites in the Upper Salzach catchment in the federal state of Salzburg are investigated. For both regions

Geophysics-based method of locating a stationary earth object

Science.gov (United States)

Daily, Michael R [Albuquerque, NM; Rohde, Steven B [Corrales, NM; Novak, James L [Albuquerque, NM

2008-05-20

A geophysics-based method for determining the position of a stationary earth object uses the periodic changes in the gravity vector of the earth caused by the sun- and moon-orbits. Because the local gravity field is highly irregular over a global scale, a model of local tidal accelerations can be compared to actual accelerometer measurements to determine the latitude and longitude of the stationary object.

Fast region-based object detection and tracking using correlation of features

CSIR Research Space (South Africa)

Senekal, F

2010-11-01

Full Text Available and track a target object (or objects) over a series of digital images. Visual target tracking can be accomplished by feature-based or region-based approaches. In feature-based approaches, interest points are calculated in a digital image, and a local...-time performance based on the computational power that is available on a specific platform. To further reduce the computational requirements, process- ing is restricted to the region of interest (ROI). The region of interest is provided as an input parameter...

Nanoscale synthesis and characterization of graphene-based objects

Directory of Open Access Journals (Sweden)

Daisuke Fujita

2011-01-01

Full Text Available Graphene-based nano-objects such as nanotrenches, nanowires, nanobelts and nanoscale superstructures have been grown by surface segregation and precipitation on carbon-doped mono- and polycrystalline nickel substrates in ultrahigh vacuum. The dominant morphologies of the nano-objects were nanowire and nanosheet. Nucleation of graphene sheets occurred at surface defects such as step edges and resulted in the directional growth of nanowires. Surface analysis by scanning tunneling microscopy (STM has clarified the structure and functionality of the novel nano-objects at atomic resolution. Nanobelts were detected consisting of bilayer graphene sheets with a nanoscale width and a length of several microns. Moiré patterns and one-dimensional reconstruction were observed on multilayer graphite terraces. As a useful functionality, application to repairable high-resolution STM probes is demonstrated.

Fragmented perception: slower space-based but faster object-based attention in recent-onset psychosis with and without Schizophrenia.

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Henderikus G O M Smid

Full Text Available BACKGROUND: Schizophrenia is associated with impairments of the perception of objects, but how this affects higher cognitive functions, whether this impairment is already present after recent onset of psychosis, and whether it is specific for schizophrenia related psychosis, is not clear. We therefore tested the hypothesis that because schizophrenia is associated with impaired object perception, schizophrenia patients should differ in shifting attention between objects compared to healthy controls. To test this hypothesis, a task was used that allowed us to separately observe space-based and object-based covert orienting of attention. To examine whether impairment of object-based visual attention is related to higher order cognitive functions, standard neuropsychological tests were also administered. METHOD: Patients with recent onset psychosis and normal controls performed the attention task, in which space- and object-based attention shifts were induced by cue-target sequences that required reorienting of attention within an object, or reorienting attention between objects. RESULTS: Patients with and without schizophrenia showed slower than normal spatial attention shifts, but the object-based component of attention shifts in patients was smaller than normal. Schizophrenia was specifically associated with slowed right-to-left attention shifts. Reorienting speed was significantly correlated with verbal memory scores in controls, and with visual attention scores in patients, but not with speed-of-processing scores in either group. CONCLUSIONS: deficits of object-perception and spatial attention shifting are not only associated with schizophrenia, but are common to all psychosis patients. Schizophrenia patients only differed by having abnormally slow right-to-left visual field reorienting. Deficits of object-perception and spatial attention shifting are already present after recent onset of psychosis. Studies investigating visual spatial

AN OBJECT-BASED METHOD FOR CHINESE LANDFORM TYPES CLASSIFICATION

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H. Ding

2016-06-01

Full Text Available Landform classification is a necessary task for various fields of landscape and regional planning, for example for landscape evaluation, erosion studies, hazard prediction, et al. This study proposes an improved object-based classification for Chinese landform types using the factor importance analysis of random forest and the gray-level co-occurrence matrix (GLCM. In this research, based on 1km DEM of China, the combination of the terrain factors extracted from DEM are selected by correlation analysis and Sheffield's entropy method. Random forest classification tree is applied to evaluate the importance of the terrain factors, which are used as multi-scale segmentation thresholds. Then the GLCM is conducted for the knowledge base of classification. The classification result was checked by using the 1:4,000,000 Chinese Geomorphological Map as reference. And the overall classification accuracy of the proposed method is 5.7% higher than ISODATA unsupervised classification, and 15.7% higher than the traditional object-based classification method.

Object-based attention underlies the rehearsal of feature binding in visual working memory.

Science.gov (United States)

Shen, Mowei; Huang, Xiang; Gao, Zaifeng

2015-04-01

Feature binding is a core concept in many research fields, including the study of working memory (WM). Over the past decade, it has been debated whether keeping the feature binding in visual WM consumes more visual attention than the constituent single features. Previous studies have only explored the contribution of domain-general attention or space-based attention in the binding process; no study so far has explored the role of object-based attention in retaining binding in visual WM. We hypothesized that object-based attention underlay the mechanism of rehearsing feature binding in visual WM. Therefore, during the maintenance phase of a visual WM task, we inserted a secondary mental rotation (Experiments 1-3), transparent motion (Experiment 4), or an object-based feature report task (Experiment 5) to consume the object-based attention available for binding. In line with the prediction of the object-based attention hypothesis, Experiments 1-5 revealed a more significant impairment for binding than for constituent single features. However, this selective binding impairment was not observed when inserting a space-based visual search task (Experiment 6). We conclude that object-based attention underlies the rehearsal of binding representation in visual WM. (c) 2015 APA, all rights reserved.

Object-based selection from spatially-invariant representations: evidence from a feature-report task.

Science.gov (United States)

Matsukura, Michi; Vecera, Shaun P

2011-02-01

Attention selects objects as well as locations. When attention selects an object's features, observers identify two features from a single object more accurately than two features from two different objects (object-based effect of attention; e.g., Duncan, Journal of Experimental Psychology: General, 113, 501-517, 1984). Several studies have demonstrated that object-based attention can operate at a late visual processing stage that is independent of objects' spatial information (Awh, Dhaliwal, Christensen, & Matsukura, Psychological Science, 12, 329-334, 2001; Matsukura & Vecera, Psychonomic Bulletin & Review, 16, 529-536, 2009; Vecera, Journal of Experimental Psychology: General, 126, 14-18, 1997; Vecera & Farah, Journal of Experimental Psychology: General, 123, 146-160, 1994). In the present study, we asked two questions regarding this late object-based selection mechanism. In Part I, we investigated how observers' foreknowledge of to-be-reported features allows attention to select objects, as opposed to individual features. Using a feature-report task, a significant object-based effect was observed when to-be-reported features were known in advance but not when this advance knowledge was absent. In Part II, we examined what drives attention to select objects rather than individual features in the absence of observers' foreknowledge of to-be-reported features. Results suggested that, when there was no opportunity for observers to direct their attention to objects that possess to-be-reported features at the time of stimulus presentation, these stimuli must retain strong perceptual cues to establish themselves as separate objects.

An Ada-based preprocessor language for concurrent object oriented programming

International Nuclear Information System (INIS)

Almulla, M.; Al-Haddad, M.; Loeper, H.

2001-01-01

In this paper, implementation issues of concurrent-objected programming using Ada 95 are addressed. Ada is not a pure object-oriented language; in order to make it so, a uniform template for structuring object classes is proposed. The template constitutes a basis for an Ada-based preprocessor language that handles concurrent object-oriented programming. The preprocessor accepts Ada-like object-oriented programs (object classes, subclasses and main program) as input and produces Ada 95 concurrent object-oriented program units as output. The preprocessor language has the advantage of adding a new component to the class specification called the protocol, which specifies the order for requesting methods f an object. The preprocessor also touches on the extensibility of object classes issue. It supports defining class hierarchies by inheritance and aggregation. In addition, the preprocessor language supports the re-use of Ada packages, which are not necessarily written according to the object-oriented approach. The paper also investigates the definition of circular dependent object classes and proposes a solution for introducing a collection of classes. (author)

Drone-based Object Counting by Spatially Regularized Regional Proposal Network

OpenAIRE

Hsieh, Meng-Ru; Lin, Yen-Liang; Hsu, Winston H.

2017-01-01

Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification). In addition, most of prior work mainly focus on counting objects in static environments with fixed cameras. Motivated by the advent of unmanned flying vehicles (i.e., drones), we are interested in detecting and counting objects in such dynamic environments. We propose Layout Prop...

Multi-Objective Design Optimization of an Over-Constrained Flexure-Based Amplifier

Directory of Open Access Journals (Sweden)

Yuan Ni

2015-07-01

Full Text Available The optimizing design for enhancement of the micro performance of manipulator based on analytical models is investigated in this paper. By utilizing the established uncanonical linear homogeneous equations, the quasi-static analytical model of the micro-manipulator is built, and the theoretical calculation results are tested by FEA simulations. To provide a theoretical basis for a micro-manipulator being used in high-precision engineering applications, this paper investigates the modal property based on the analytical model. Based on the finite element method, with multipoint constraint equations, the model is built and the results have a good match with the simulation. The following parametric influences studied show that the influences of other objectives on one objective are complicated.  Consequently, the multi-objective optimization by the derived analytical models is carried out to find out the optimal solutions of the manipulator. Besides the inner relationships among these design objectives during the optimization process are discussed.

Interrupted object-based updating of reach program leads to a negative compatibility effect.

Science.gov (United States)

Vainio, Lari

2009-07-01

The author investigated how the motor program elicited by an object's orientation is updated by object-based information while a participant reaches for the object. Participants selected the hand of response according to the thickness of the graspable object and then reached toward the location in which the object appeared. Reach initiation times decreased when the handle of the object was oriented toward the responding hand. This positive compatibility effect turned into a negative compatibility effect (NCE) during reach execution when the object was removed from the display 300 ms after object onset or replaced with a mask at movement onset. The results demonstrate that interrupted object-based updating of an ongoing reach movement triggers the NCE.

KNOWLEDGE-BASED OBJECT DETECTION IN LASER SCANNING POINT CLOUDS

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F. Boochs

2012-07-01

Full Text Available Object identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools available for use. Such modeling algorithms are data-driven and concentrate on specific features of the objects, being accessible to numerical models. We present an approach that brings the human expert knowledge about the scene, the objects inside, and their representation by the data and the behavior of algorithms to the machine. This “understanding” enables the machine to assist human interpretation of the scene inside the point cloud. Furthermore, it allows the machine to understand possibilities and limitations of algorithms and to take this into account within the processing chain. This not only assists the researchers in defining optimal processing steps, but also provides suggestions when certain changes or new details emerge from the point cloud. Our approach benefits from the advancement in knowledge technologies within the Semantic Web framework. This advancement has provided a strong base for applications based on knowledge management. In the article we will present and describe the knowledge technologies used for our approach such as Web Ontology Language (OWL, used for formulating the knowledge base and the Semantic Web Rule Language (SWRL with 3D processing and topologic built-ins, aiming to combine geometrical analysis of 3D point clouds, and specialists’ knowledge of the scene and algorithmic processing.

Knowledge-Based Object Detection in Laser Scanning Point Clouds

Science.gov (United States)

Boochs, F.; Karmacharya, A.; Marbs, A.

2012-07-01

Object identification and object processing in 3D point clouds have always posed challenges in terms of effectiveness and efficiency. In practice, this process is highly dependent on human interpretation of the scene represented by the point cloud data, as well as the set of modeling tools available for use. Such modeling algorithms are data-driven and concentrate on specific features of the objects, being accessible to numerical models. We present an approach that brings the human expert knowledge about the scene, the objects inside, and their representation by the data and the behavior of algorithms to the machine. This "understanding" enables the machine to assist human interpretation of the scene inside the point cloud. Furthermore, it allows the machine to understand possibilities and limitations of algorithms and to take this into account within the processing chain. This not only assists the researchers in defining optimal processing steps, but also provides suggestions when certain changes or new details emerge from the point cloud. Our approach benefits from the advancement in knowledge technologies within the Semantic Web framework. This advancement has provided a strong base for applications based on knowledge management. In the article we will present and describe the knowledge technologies used for our approach such as Web Ontology Language (OWL), used for formulating the knowledge base and the Semantic Web Rule Language (SWRL) with 3D processing and topologic built-ins, aiming to combine geometrical analysis of 3D point clouds, and specialists' knowledge of the scene and algorithmic processing.

Vision-based autonomous grasping of unknown piled objects

International Nuclear Information System (INIS)

Johnson, R.K.

1994-01-01

Computer vision techniques have been used to develop a vision-based grasping capability for autonomously picking and placing unknown piled objects. This work is currently being applied to the problem of hazardous waste sorting in support of the Department of Energy's Mixed Waste Operations Program

Geographic Object-Based Image Analysis - Towards a new paradigm.

Science.gov (United States)

Blaschke, Thomas; Hay, Geoffrey J; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis - GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ' per-pixel paradigm ' and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm.

Context based Coding of Quantized Alpha Planes for Video Objects

DEFF Research Database (Denmark)

Aghito, Shankar Manuel; Forchhammer, Søren

2002-01-01

In object based video, each frame is a composition of objects that are coded separately. The composition is performed through the alpha plane that represents the transparency of the object. We present an alternative to MPEG-4 for coding of alpha planes that considers their specific properties....... Comparisons in terms of rate and distortion are provided, showing that the proposed coding scheme for still alpha planes is better than the algorithms for I-frames used in MPEG-4....

Prism adaptation does not alter object-based attention in healthy participants

Science.gov (United States)

Bultitude, Janet H.

2013-01-01

Hemispatial neglect (‘neglect’) is a disabling condition that can follow damage to the right side of the brain, in which patients show difficulty in responding to or orienting towards objects and events that occur on the left side of space. Symptoms of neglect can manifest in both space- and object-based frames of reference. Although patients can show a combination of these two forms of neglect, they are considered separable and have distinct neurological bases. In recent years considerable evidence has emerged to demonstrate that spatial symptoms of neglect can be reduced by an intervention called prism adaptation. Patients point towards objects viewed through prismatic lenses that shift the visual image to the right. Approximately five minutes of repeated pointing results in a leftward recalibration of pointing and improved performance on standard clinical tests for neglect. The understanding of prism adaptation has also been advanced through studies of healthy participants, in whom adaptation to leftward prismatic shifts results in temporary neglect-like performance. Here we examined the effect of prism adaptation on the performance of healthy participants who completed a computerised test of space- and object-based attention. Participants underwent adaptation to leftward- or rightward-shifting prisms, or performed neutral pointing according to a between-groups design. Significant pointing after-effects were found for both prism groups, indicating successful adaptation. In addition, the results of the computerised test revealed larger reaction-time costs associated with shifts of attention between two objects compared to shifts of attention within the same object, replicating previous work. However there were no differences in the performance of the three groups, indicating that prism adaptation did not influence space- or object-based attention for this task. When combined with existing literature, the results are consistent with the proposal that prism

Object Extraction in Cluttered Environments via a P300-Based IFCE

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Xiaoqian Mao

2017-01-01

Full Text Available One of the fundamental issues for robot navigation is to extract an object of interest from an image. The biggest challenges for extracting objects of interest are how to use a machine to model the objects in which a human is interested and extract them quickly and reliably under varying illumination conditions. This article develops a novel method for segmenting an object of interest in a cluttered environment by combining a P300-based brain computer interface (BCI and an improved fuzzy color extractor (IFCE. The induced P300 potential identifies the corresponding region of interest and obtains the target of interest for the IFCE. The classification results not only represent the human mind but also deliver the associated seed pixel and fuzzy parameters to extract the specific objects in which the human is interested. Then, the IFCE is used to extract the corresponding objects. The results show that the IFCE delivers better performance than the BP network or the traditional FCE. The use of a P300-based IFCE provides a reliable solution for assisting a computer in identifying an object of interest within images taken under varying illumination intensities.

Object tracking system using a VSW algorithm based on color and point features

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Lim Hye-Youn

2011-01-01

Full Text Available Abstract An object tracking system using a variable search window (VSW algorithm based on color and feature points is proposed. A meanshift algorithm is an object tracking technique that works according to color probability distributions. An advantage of this algorithm based on color is that it is robust to specific color objects; however, a disadvantage is that it is sensitive to non-specific color objects due to illumination and noise. Therefore, to offset this weakness, it presents the VSW algorithm based on robust feature points for the accurate tracking of moving objects. The proposed method extracts the feature points of a detected object which is the region of interest (ROI, and generates a VSW using the given information which is the positions of extracted feature points. The goal of this paper is to achieve an efficient and effective object tracking system that meets the accurate tracking of moving objects. Through experiments, the object tracking system is implemented that it performs more precisely than existing techniques.

A Learning Object Approach To Evidence based learning

OpenAIRE

Zabin Visram; Bruce Elson; Patricia Reynolds

2005-01-01

This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has mea...

Studies on combined model based on functional objectives of large scale complex engineering

Science.gov (United States)

Yuting, Wang; Jingchun, Feng; Jiabao, Sun

2018-03-01

As various functions were included in large scale complex engineering, and each function would be conducted with completion of one or more projects, combined projects affecting their functions should be located. Based on the types of project portfolio, the relationship of projects and their functional objectives were analyzed. On that premise, portfolio projects-technics based on their functional objectives were introduced, then we studied and raised the principles of portfolio projects-technics based on the functional objectives of projects. In addition, The processes of combined projects were also constructed. With the help of portfolio projects-technics based on the functional objectives of projects, our research findings laid a good foundation for management of large scale complex engineering portfolio management.

Child's objection to non-beneficial research: capacity and distress based models.

Science.gov (United States)

Waligora, Marcin; Różyńska, Joanna; Piasecki, Jan

2016-03-01

A child's objection, refusal and dissent regarding participation in non-beneficial biomedical research must be respected, even when the parents or legal representatives have given their permission. There is, however, no consensus on the definition and criteria of a meaningful and valid child's objection. The aim of this article is to clarify this issue. In the first part we describe the problems of a child's assent in research. In the second part we distinguish and analyze two models of a child's objection to research: the capacity-based model and the distress-based model. In the last part we present arguments for a broader and unified understanding of a child's objection within regulations and practices. This will strengthen children's rights and facilitate the entire process of assessment of research protocols.

Fuzzy-Rule-Based Object Identification Methodology for NAVI System

Directory of Open Access Journals (Sweden)

Yaacob Sazali

2005-01-01

Full Text Available We present an object identification methodology applied in a navigation assistance for visually impaired (NAVI system. The NAVI has a single board processing system (SBPS, a digital video camera mounted headgear, and a pair of stereo earphones. The captured image from the camera is processed by the SBPS to generate a specially structured stereo sound suitable for vision impaired people in understanding the presence of objects/obstacles in front of them. The image processing stage is designed to identify the objects in the captured image. Edge detection and edge-linking procedures are applied in the processing of image. A concept of object preference is included in the image processing scheme and this concept is realized using a fuzzy-rule base. The blind users are trained with the stereo sound produced by NAVI for achieving a collision-free autonomous navigation.

Fuzzy-Rule-Based Object Identification Methodology for NAVI System

Science.gov (United States)

Nagarajan, R.; Sainarayanan, G.; Yaacob, Sazali; Porle, Rosalyn R.

2005-12-01

We present an object identification methodology applied in a navigation assistance for visually impaired (NAVI) system. The NAVI has a single board processing system (SBPS), a digital video camera mounted headgear, and a pair of stereo earphones. The captured image from the camera is processed by the SBPS to generate a specially structured stereo sound suitable for vision impaired people in understanding the presence of objects/obstacles in front of them. The image processing stage is designed to identify the objects in the captured image. Edge detection and edge-linking procedures are applied in the processing of image. A concept of object preference is included in the image processing scheme and this concept is realized using a fuzzy-rule base. The blind users are trained with the stereo sound produced by NAVI for achieving a collision-free autonomous navigation.

Do silhouettes and photographs produce fundamentally different object-based correspondence effects?

Science.gov (United States)

Proctor, Robert W; Lien, Mei-Ching; Thompson, Lane

2017-12-01

When participants classify pictures of objects as upright or inverted with a left or right keypress, responses are faster if the response location (left/right) corresponds with the location of a handle (left/right) than if it does not. This result has typically been attributed to a grasping affordance (automatic activation of muscles associated with grasping the object with the ipsilateral hand), but several findings have indicated instead that the effect is a spatial correspondence effect, much like the Simon effect for object location. Pappas (2014) reported evidence he interpreted as showing that spatial coding predominates with silhouettes of objects, whereas photographs of objects yield affordance-based effects. We conducted two experiments similar to those of Pappas, using frying pans as stimuli, with our two experiments differing in whether the entire object was centered on the display screen or the base was centered. When the objects were centered, a positive correspondence effect relative to the handle was evident for the silhouettes but a negative correspondence effect for the photographs. When the base was centered, the handle was clearly located to the left or right side of the display, and both silhouettes and photographs produced correspondence effects of similar size relative to the handle location. Despite the main results being counter to the grasping affordance hypothesis, response-time distribution analyses suggest that, instead of activating automatically at fast responses, an effector-specific component of the hypothesized type may come into play for responses that are selected after the handle location has been identified. Copyright © 2017 Elsevier B.V. All rights reserved.

Model-based recognition of 3-D objects by geometric hashing technique

International Nuclear Information System (INIS)

Severcan, M.; Uzunalioglu, H.

1992-09-01

A model-based object recognition system is developed for recognition of polyhedral objects. The system consists of feature extraction, modelling and matching stages. Linear features are used for object descriptions. Lines are obtained from edges using rotation transform. For modelling and recognition process, geometric hashing method is utilized. Each object is modelled using 2-D views taken from the viewpoints on the viewing sphere. A hidden line elimination algorithm is used to find these views from the wire frame model of the objects. The recognition experiments yielded satisfactory results. (author). 8 refs, 5 figs

Home-Explorer: Ontology-Based Physical Artifact Search and Hidden Object Detection System

Directory of Open Access Journals (Sweden)

Bin Guo

2008-01-01

Full Text Available A new system named Home-Explorer that searches and finds physical artifacts in a smart indoor environment is proposed. The view on which it is based is artifact-centered and uses sensors attached to the everyday artifacts (called smart objects in the real world. This paper makes two main contributions: First, it addresses, the robustness of the embedded sensors, which is seldom discussed in previous smart artifact research. Because sensors may sometimes be broken or fail to work under certain conditions, smart objects become hidden ones. However, current systems provide no mechanism to detect and manage objects when this problem occurs. Second, there is no common context infrastructure for building smart artifact systems, which makes it difficult for separately developed applications to interact with each other and uneasy for them to share and reuse knowledge. Unlike previous systems, Home-Explorer builds on an ontology-based knowledge infrastructure named Sixth-Sense, which makes it easy for the system to interact with other applications or agents also based on this ontology. The hidden object problem is also reflected in our ontology, which enables Home-Explorer to deal with both smart objects and hidden objects. A set of rules for deducing an object's status or location information and for locating hidden objects are described and evaluated.

3D MODELLING AND INTERACTIVE WEB-BASED VISUALIZATION OF CULTURAL HERITAGE OBJECTS

Directory of Open Access Journals (Sweden)

M. N. Koeva

2016-06-01

Full Text Available Nowadays, there are rapid developments in the fields of photogrammetry, laser scanning, computer vision and robotics, together aiming to provide highly accurate 3D data that is useful for various applications. In recent years, various LiDAR and image-based techniques have been investigated for 3D modelling because of their opportunities for fast and accurate model generation. For cultural heritage preservation and the representation of objects that are important for tourism and their interactive visualization, 3D models are highly effective and intuitive for present-day users who have stringent requirements and high expectations. Depending on the complexity of the objects for the specific case, various technological methods can be applied. The selected objects in this particular research are located in Bulgaria – a country with thousands of years of history and cultural heritage dating back to ancient civilizations. \\this motivates the preservation, visualisation and recreation of undoubtedly valuable historical and architectural objects and places, which has always been a serious challenge for specialists in the field of cultural heritage. In the present research, comparative analyses regarding principles and technological processes needed for 3D modelling and visualization are presented. The recent problems, efforts and developments in interactive representation of precious objects and places in Bulgaria are presented. Three technologies based on real projects are described: (1 image-based modelling using a non-metric hand-held camera; (2 3D visualization based on spherical panoramic images; (3 and 3D geometric and photorealistic modelling based on architectural CAD drawings. Their suitability for web-based visualization are demonstrated and compared. Moreover the possibilities for integration with additional information such as interactive maps, satellite imagery, sound, video and specific information for the objects are described. This

Real time object localization based on histogram of s-RGB

Science.gov (United States)

Mudjirahardjo, Panca; Suyono, Hadi; Setyawan, Raden Arief

2017-09-01

Object localization is the first task in pattern detection and recognition. This task is very important due to it reduces the searching time to the interest object. In this paper we introduce our novel method of object localization based on color feature. Our novel method is a histogram of s-RGB. This histogram is used in the training phase to determine the color dominant in the initial Region of Interest (ROI). Then this information is used to label the interest object. To reduce noise and localize the interest object, we apply the row and column density function of pixels. The comparison result with some processes, our system gives a best result and takes a short computation time of 26.56 ms, in the video rate of 15 frames per second (fps).

Region-Based Image Retrieval Using an Object Ontology and Relevance Feedback

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Kompatsiaris Ioannis

2004-01-01

Full Text Available An image retrieval methodology suited for search in large collections of heterogeneous images is presented. The proposed approach employs a fully unsupervised segmentation algorithm to divide images into regions and endow the indexing and retrieval system with content-based functionalities. Low-level descriptors for the color, position, size, and shape of each region are subsequently extracted. These arithmetic descriptors are automatically associated with appropriate qualitative intermediate-level descriptors, which form a simple vocabulary termed object ontology. The object ontology is used to allow the qualitative definition of the high-level concepts the user queries for (semantic objects, each represented by a keyword and their relations in a human-centered fashion. When querying for a specific semantic object (or objects, the intermediate-level descriptor values associated with both the semantic object and all image regions in the collection are initially compared, resulting in the rejection of most image regions as irrelevant. Following that, a relevance feedback mechanism, based on support vector machines and using the low-level descriptors, is invoked to rank the remaining potentially relevant image regions and produce the final query results. Experimental results and comparisons demonstrate, in practice, the effectiveness of our approach.

Activity in human visual and parietal cortex reveals object-based attention in working memory.

Science.gov (United States)

Peters, Benjamin; Kaiser, Jochen; Rahm, Benjamin; Bledowski, Christoph

2015-02-25

Visual attention enables observers to select behaviorally relevant information based on spatial locations, features, or objects. Attentional selection is not limited to physically present visual information, but can also operate on internal representations maintained in working memory (WM) in service of higher-order cognition. However, only little is known about whether attention to WM contents follows the same principles as attention to sensory stimuli. To address this question, we investigated in humans whether the typically observed effects of object-based attention in perception are also evident for object-based attentional selection of internal object representations in WM. In full accordance with effects in visual perception, the key behavioral and neuronal characteristics of object-based attention were observed in WM. Specifically, we found that reaction times were shorter when shifting attention to memory positions located on the currently attended object compared with equidistant positions on a different object. Furthermore, functional magnetic resonance imaging and multivariate pattern analysis of visuotopic activity in visual (areas V1-V4) and parietal cortex revealed that directing attention to one position of an object held in WM also enhanced brain activation for other positions on the same object, suggesting that attentional selection in WM activates the entire object. This study demonstrated that all characteristic features of object-based attention are present in WM and thus follows the same principles as in perception. Copyright © 2015 the authors 0270-6474/15/353360-10$15.00/0.

Object Detection Based on Template Matching through Use of Best-So-Far ABC

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Anan Banharnsakun

2014-01-01

Full Text Available Best-so-far ABC is a modified version of the artificial bee colony (ABC algorithm used for optimization tasks. This algorithm is one of the swarm intelligence (SI algorithms proposed in recent literature, in which the results demonstrated that the best-so-far ABC can produce higher quality solutions with faster convergence than either the ordinary ABC or the current state-of-the-art ABC-based algorithm. In this work, we aim to apply the best-so-far ABC-based approach for object detection based on template matching by using the difference between the RGB level histograms corresponding to the target object and the template object as the objective function. Results confirm that the proposed method was successful in both detecting objects and optimizing the time used to reach the solution.

Performance Evaluation of Java Based Object Relational Mapping Tools

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Shoaib Mahmood Bhatti

2013-04-01

Full Text Available Object persistency is the hot issue in the form of ORM (Object Relational Mapping tools in industry as developers use these tools during software development. This paper presents the performance evaluation of Java based ORM tools. For this purpose, Hibernate, Ebean and TopLinkhave been selected as the ORM tools which are popular and open source. Their performance has been measured from execution point of view. The results show that ORM tools are the good option for the developers considering the system throughput in shorter setbacks and they can be used efficiently and effectively for performing mapping of the objects into the relational dominated world of database, thus creating a hope for a better and well dominated future of this technology.

Multi-objective decision-making model based on CBM for an aircraft fleet

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Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

Object Detection Based on Fast/Faster RCNN Employing Fully Convolutional Architectures

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Yun Ren

2018-01-01

Full Text Available Modern object detectors always include two major parts: a feature extractor and a feature classifier as same as traditional object detectors. The deeper and wider convolutional architectures are adopted as the feature extractor at present. However, many notable object detection systems such as Fast/Faster RCNN only consider simple fully connected layers as the feature classifier. In this paper, we declare that it is beneficial for the detection performance to elaboratively design deep convolutional networks (ConvNets of various depths for feature classification, especially using the fully convolutional architectures. In addition, this paper also demonstrates how to employ the fully convolutional architectures in the Fast/Faster RCNN. Experimental results show that a classifier based on convolutional layer is more effective for object detection than that based on fully connected layer and that the better detection performance can be achieved by employing deeper ConvNets as the feature classifier.

Possibility of object recognition using Altera's model based design approach

International Nuclear Information System (INIS)

Tickle, A J; Harvey, P K; Smith, J S; Wu, F

2009-01-01

Object recognition is an image processing task of finding a given object in a selected image or video sequence. Object recognition can be divided into two areas: one of these is decision-theoretic and deals with patterns described by quantitative descriptors, for example such as length, area, shape and texture. With this Graphical User Interface Circuitry (GUIC) methodology employed here being relatively new for object recognition systems, the aim of this work is to identify if the developed circuitry can detect certain shapes or strings within the target image. A much smaller reference image feeds the preset data for identification, tests are conducted for both binary and greyscale and the additional mathematical morphology to highlight the area within the target image with the object(s) are located is also presented. This then provides proof that basic recognition methods are valid and would allow the progression to developing decision-theoretical and learning based approaches using GUICs for use in multidisciplinary tasks.

Personalised Learning Object System Based on Self-Regulated Learning Theories

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Ali Alharbi

2014-06-01

Full Text Available Self-regulated learning has become an important construct in education research in the last few years. Selfregulated learning in its simple form is the learner’s ability to monitor and control the learning process. There is increasing research in the literature on how to support students become more self-regulated learners. However, the advancement in the information technology has led to paradigm changes in the design and development of educational content. The concept of learning object instructional technology has emerged as a result of this shift in educational technology paradigms. This paper presents the results of a study that investigated the potential educational effectiveness of a pedagogical framework based on the self-regulated learning theories to support the design of learning object systems to help computer science students. A prototype learning object system was developed based on the contemporary research on self-regulated learning. The system was educationally evaluated in a quasi-experimental study over two semesters in a core programming languages concepts course. The evaluation revealed that a learning object system that takes into consideration contemporary research on self-regulated learning can be an effective learning environment to support computer science education.

Techniques for Efficient Tracking of Road-Network-Based Moving Objects

DEFF Research Database (Denmark)

Civilis, Alminas; Jensen, Christian Søndergaard; Saltenis, Simonas

With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects....... The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents...... algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and it proposes to use acceleration profiles together with the routes. Using real GPS...

Techniques for efficient road-network-based tracking of moving objects

DEFF Research Database (Denmark)

Civilis, A.; Jensen, Christian Søndergaard; Pakalnis, Stardas

2005-01-01

With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects....... The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents...... algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and it proposes to use acceleration profiles together with the routes. Using real GPS...

A new user-assisted segmentation and tracking technique for an object-based video editing system

Science.gov (United States)

Yu, Hong Y.; Hong, Sung-Hoon; Lee, Mike M.; Choi, Jae-Gark

2004-03-01

This paper presents a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the user-guided and selected objects are continuously separated from the unselected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on these results, we have developed objects based video editing system with several convenient editing functions.

METHOD OF GROUP OBJECTS FORMING FOR SPACE-BASED REMOTE SENSING OF THE EARTH

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A. N. Grigoriev

2015-07-01

Full Text Available Subject of Research. Research findings of the specific application of space-based optical-electronic and radar means for the Earth remote sensing are considered. The subject matter of the study is the current planning of objects survey on the underlying surface in order to increase the effectiveness of sensing system due to the rational use of its resources. Method. New concept of a group object, stochastic swath and stochastic length of the route is introduced. The overview of models for single, group objects and their parameters is given. The criterion for the existence of the group object based on two single objects is formulated. The method for group objects formation while current survey planning has been developed and its description is presented. The method comprises several processing stages for data about objects with the calculation of new parameters, the stochastic characteristics of space means and validates the spatial size of the object value of the stochastic swath and stochastic length of the route. The strict mathematical description of techniques for model creation of a group object based on data about a single object and onboard special complex facilities in difficult conditions of registration of spatial data is given. Main Results. The developed method is implemented on the basis of modern geographic information system in the form of a software tool layout with advanced tools of processing and analysis of spatial data in vector format. Experimental studies of the forming method for the group of objects were carried out on a different real object environment using the parameters of modern national systems of the Earth remote sensing detailed observation Canopus-B and Resurs-P. Practical Relevance. The proposed models and method are focused on practical implementation using vector spatial data models and modern geoinformation technologies. Practical value lies in the reduction in the amount of consumable resources by means of

Online decoding of object-based attention using real-time fMRI

NARCIS (Netherlands)

Niazi, A.M.; Broek, P.L.C. van den; Klanke, S.; Barth, M.; Poel, M.; Gerven, M.A.J. van

2014-01-01

Visual attention is used to selectively filter relevant information depending on current task demands and goals. Visual attention is called object-based attention when it is directed to coherent forms or objects in the visual field. This study used real-time functional magnetic resonance imaging for

Online decoding of object-based attention using real-time fMRI

NARCIS (Netherlands)

Niazi, Adnan M.; van den Broek, Philip L.C.; Klanke, Stefan; Barth, Markus; Poel, Mannes; Desain, Peter; van Gerven, Marcel A.J.

Visual attention is used to selectively filter relevant information depending on current task demands and goals. Visual attention is called object-based attention when it is directed to coherent forms or objects in the visual field. This study used real-time functional magnetic resonance imaging for

USE OF IMAGE BASED MODELLING FOR DOCUMENTATION OF INTRICATELY SHAPED OBJECTS

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M. Marčiš

2016-06-01

Full Text Available In the documentation of cultural heritage, we can encounter three dimensional shapes and structures which are complicated to measure. Such objects are for example spiral staircases, timber roof trusses, historical furniture or folk costume where it is nearly impossible to effectively use the traditional surveying or the terrestrial laser scanning due to the shape of the object, its dimensions and the crowded environment. The actual methods of digital photogrammetry can be very helpful in such cases with the emphasis on the automated processing of the extensive image data. The created high resolution 3D models and 2D orthophotos are very important for the documentation of architectural elements and they can serve as an ideal base for the vectorization and 2D drawing documentation. This contribution wants to describe the various usage of image based modelling in specific interior spaces and specific objects. The advantages and disadvantages of the photogrammetric measurement of such objects in comparison to other surveying methods are reviewed.

Internet of Things based on smart objects technology, middleware and applications

CERN Document Server

Trunfio, Paolo

2014-01-01

The Internet of Things (IoT) usually refers to a world-wide network of interconnected heterogeneous objects (sensors, actuators, smart devices, smart objects, RFID, embedded computers, etc) uniquely addressable, based on standard communication protocols. Beyond such a definition, it is emerging a new definition of IoT seen as a loosely coupled, decentralized system of cooperating smart objects (SOs). A SO is an autonomous, physical digital object augmented with sensing/actuating, processing, storing, and networking capabilities. SOs are able to sense/actuate, store, and interpret information created within themselves and around the neighbouring external world where they are situated, act on their own, cooperate with each other, and exchange information with other kinds of electronic devices and human users. However, such SO-oriented IoT raises many in-the-small and in-the-large issues involving SO programming, IoT system architecture/middleware and methods/methodologies for the development of SO-based applica...

Use of Image Based Modelling for Documentation of Intricately Shaped Objects

Science.gov (United States)

Marčiš, M.; Barták, P.; Valaška, D.; Fraštia, M.; Trhan, O.

2016-06-01

In the documentation of cultural heritage, we can encounter three dimensional shapes and structures which are complicated to measure. Such objects are for example spiral staircases, timber roof trusses, historical furniture or folk costume where it is nearly impossible to effectively use the traditional surveying or the terrestrial laser scanning due to the shape of the object, its dimensions and the crowded environment. The actual methods of digital photogrammetry can be very helpful in such cases with the emphasis on the automated processing of the extensive image data. The created high resolution 3D models and 2D orthophotos are very important for the documentation of architectural elements and they can serve as an ideal base for the vectorization and 2D drawing documentation. This contribution wants to describe the various usage of image based modelling in specific interior spaces and specific objects. The advantages and disadvantages of the photogrammetric measurement of such objects in comparison to other surveying methods are reviewed.

Additivity of Feature-based and Symmetry-based Grouping Effects in Multiple Object Tracking

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Chundi eWang

2016-05-01

Full Text Available Multiple object tracking (MOT is an attentional process wherein people track several moving targets among several distractors. Symmetry, an important indicator of regularity, is a general spatial pattern observed in natural and artificial scenes. According to the laws of perceptual organization proposed by Gestalt psychologists, regularity is a principle of perceptual grouping, such as similarity and closure. A great deal of research reported that feature-based similarity grouping (e.g., grouping based on color, size, or shape among targets in MOT tasks can improve tracking performance. However, no additive feature-based grouping effects have been reported where the tracking objects had two or more features. Additive effect refers to a greater grouping effect produced by grouping based on multiple cues instead of one cue. Can spatial symmetry produce a similar grouping effect similar to that of feature similarity in MOT tasks? Are the grouping effects based on symmetry and feature similarity additive? This study includes four experiments to address these questions. The results of Experiments 1 and 2 demonstrated the automatic symmetry-based grouping effects. More importantly, an additive grouping effect of symmetry and feature similarity was observed in Experiments 3 and 4. Our findings indicate that symmetry can produce an enhanced grouping effect in MOT and facilitate the grouping effect based on color or shape similarity. The where and what pathways might have played an important role in the additive grouping effect.

Visual working memory for global, object, and part-based information.

Science.gov (United States)

Patterson, Michael D; Bly, Benjamin Martin; Porcelli, Anthony J; Rypma, Bart

2007-06-01

We investigated visual working memory for novel objects and parts of novel objects. After a delay period, participants showed strikingly more accurate performance recognizing a single whole object than the parts of that object. This bias to remember whole objects, rather than parts, persisted even when the division between parts was clearly defined and the parts were disconnected from each other so that, in order to remember the single whole object, the participants needed to mentally combine the parts. In addition, the bias was confirmed when the parts were divided by color. These experiments indicated that holistic perceptual-grouping biases are automatically used to organize storage in visual working memory. In addition, our results suggested that the bias was impervious to top-down consciously directed control, because when task demands were manipulated through instruction and catch trials, the participants still recognized whole objects more quickly and more accurately than their parts. This bias persisted even when the whole objects were novel and the parts were familiar. We propose that visual working memory representations depend primarily on the global configural properties of whole objects, rather than part-based representations, even when the parts themselves can be clearly perceived as individual objects. This global configural bias beneficially reduces memory load on a capacity-limited system operating in a complex visual environment, because fewer distinct items must be remembered.

Multi-objective mixture-based iterated density estimation evolutionary algorithms

NARCIS (Netherlands)

Thierens, D.; Bosman, P.A.N.

2001-01-01

We propose an algorithm for multi-objective optimization using a mixture-based iterated density estimation evolutionary algorithm (MIDEA). The MIDEA algorithm is a prob- abilistic model building evolutionary algo- rithm that constructs at each generation a mixture of factorized probability

Object-Based Attention on Social Units: Visual Selection of Hands Performing a Social Interaction.

Science.gov (United States)

Yin, Jun; Xu, Haokui; Duan, Jipeng; Shen, Mowei

2018-05-01

Traditionally, objects of attention are characterized either as full-fledged entities or either as elements grouped by Gestalt principles. Because humans appear to use social groups as units to explain social activities, we proposed that a socially defined group, according to social interaction information, would also be a possible object of attentional selection. This hypothesis was examined using displays with and without handshaking interactions. Results demonstrated that object-based attention, which was measured by an object-specific attentional advantage (i.e., shorter response times to targets on a single object), was extended to two hands performing a handshake but not to hands that did not perform meaningful social interactions, even when they did perform handshake-like actions. This finding cannot be attributed to the familiarity of the frequent co-occurrence of two handshaking hands. Hence, object-based attention can select a grouped object whose parts are connected within a meaningful social interaction. This finding implies that object-based attention is constrained by top-down information.

An object-oriented classification method of high resolution imagery based on improved AdaTree

International Nuclear Information System (INIS)

Xiaohe, Zhang; Liang, Zhai; Jixian, Zhang; Huiyong, Sang

2014-01-01

With the popularity of the application using high spatial resolution remote sensing image, more and more studies paid attention to object-oriented classification on image segmentation as well as automatic classification after image segmentation. This paper proposed a fast method of object-oriented automatic classification. First, edge-based or FNEA-based segmentation was used to identify image objects and the values of most suitable attributes of image objects for classification were calculated. Then a certain number of samples from the image objects were selected as training data for improved AdaTree algorithm to get classification rules. Finally, the image objects could be classified easily using these rules. In the AdaTree, we mainly modified the final hypothesis to get classification rules. In the experiment with WorldView2 image, the result of the method based on AdaTree showed obvious accuracy and efficient improvement compared with the method based on SVM with the kappa coefficient achieving 0.9242

Efficient view based 3-D object retrieval using Hidden Markov Model

Science.gov (United States)

Jain, Yogendra Kumar; Singh, Roshan Kumar

2013-12-01

Recent research effort has been dedicated to view based 3-D object retrieval, because of highly discriminative property of 3-D object and has multi view representation. The state-of-art method is highly depending on their own camera array setting for capturing views of 3-D object and use complex Zernike descriptor, HAC for representative view selection which limit their practical application and make it inefficient for retrieval. Therefore, an efficient and effective algorithm is required for 3-D Object Retrieval. In order to move toward a general framework for efficient 3-D object retrieval which is independent of camera array setting and avoidance of representative view selection, we propose an Efficient View Based 3-D Object Retrieval (EVBOR) method using Hidden Markov Model (HMM). In this framework, each object is represented by independent set of view, which means views are captured from any direction without any camera array restriction. In this, views are clustered (including query view) to generate the view cluster, which is then used to build the query model with HMM. In our proposed method, HMM is used in twofold: in the training (i.e. HMM estimate) and in the retrieval (i.e. HMM decode). The query model is trained by using these view clusters. The EVBOR query model is worked on the basis of query model combining with HMM. The proposed approach remove statically camera array setting for view capturing and can be apply for any 3-D object database to retrieve 3-D object efficiently and effectively. Experimental results demonstrate that the proposed scheme has shown better performance than existing methods. [Figure not available: see fulltext.

Geographic Object-Based Image Analysis: Towards a new paradigm

NARCIS (Netherlands)

Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.A.|info:eu-repo/dai/nl/224281216; Queiroz Feitosa, R.; van der Meer, F.D.|info:eu-repo/dai/nl/138940908; van der Werff, H.M.A.; van Coillie, F.; Tiede, A.

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature

Combining the Pixel-based and Object-based Methods for Building Change Detection Using High-resolution Remote Sensing Images

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ZHANG Zhiqiang

2018-01-01

Full Text Available Timely and accurate change detection of buildings provides important information for urban planning and management.Accompanying with the rapid development of satellite remote sensing technology,detecting building changes from high-resolution remote sensing images have received wide attention.Given that pixel-based methods of change detection often lead to low accuracy while object-based methods are complicated for uses,this research proposes a method that combines pixel-based and object-based methods for detecting building changes from high-resolution remote sensing images.First,based on the multiple features extracted from the high-resolution images,a random forest classifier is applied to detect changed building at the pixel level.Then,a segmentation method is applied to segement the post-phase remote sensing image and to get post-phase image objects.Finally,both changed building at the pixel level and post-phase image objects are fused to recognize the changed building objects.Multi-temporal QuickBird images are used as experiment data for building change detection with high-resolution remote sensing images,the results indicate that the proposed method could reduce the influence of environmental difference,such as light intensity and view angle,on building change detection,and effectively improve the accuracies of building change detection.

Benchmarking the Applicability of Ontology in Geographic Object-Based Image Analysis

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Sachit Rajbhandari

2017-11-01

Full Text Available In Geographic Object-based Image Analysis (GEOBIA, identification of image objects is normally achieved using rule-based classification techniques supported by appropriate domain knowledge. However, GEOBIA currently lacks a systematic method to formalise the domain knowledge required for image object identification. Ontology provides a representation vocabulary for characterising domain-specific classes. This study proposes an ontological framework that conceptualises domain knowledge in order to support the application of rule-based classifications. The proposed ontological framework is tested with a landslide case study. The Web Ontology Language (OWL is used to construct an ontology in the landslide domain. The segmented image objects with extracted features are incorporated into the ontology as instances. The classification rules are written in Semantic Web Rule Language (SWRL and executed using a semantic reasoner to assign instances to appropriate landslide classes. Machine learning techniques are used to predict new threshold values for feature attributes in the rules. Our framework is compared with published work on landslide detection where ontology was not used for the image classification. Our results demonstrate that a classification derived from the ontological framework accords with non-ontological methods. This study benchmarks the ontological method providing an alternative approach for image classification in the case study of landslides.

Working memory capacity accounts for the ability to switch between object-based and location-based allocation of visual attention.

Science.gov (United States)

Bleckley, M Kathryn; Foster, Jeffrey L; Engle, Randall W

2015-04-01

Bleckley, Durso, Crutchfield, Engle, and Khanna (Psychonomic Bulletin & Review, 10, 884-889, 2003) found that visual attention allocation differed between groups high or low in working memory capacity (WMC). High-span, but not low-span, subjects showed an invalid-cue cost during a letter localization task in which the letter appeared closer to fixation than the cue, but not when the letter appeared farther from fixation than the cue. This suggests that low-spans allocated attention as a spotlight, whereas high-spans allocated their attention to objects. In this study, we tested whether utilizing object-based visual attention is a resource-limited process that is difficult for low-span individuals. In the first experiment, we tested the uses of object versus location-based attention with high and low-span subjects, with half of the subjects completing a demanding secondary load task. Under load, high-spans were no longer able to use object-based visual attention. A second experiment supported the hypothesis that these differences in allocation were due to high-spans using object-based allocation, whereas low-spans used location-based allocation.

The role of space and time in object-based visual search

NARCIS (Netherlands)

Schreij, D.B.B.; Olivers, C.N.L.

2013-01-01

Recently we have provided evidence that observers more readily select a target from a visual search display if the motion trajectory of the display object suggests that the observer has dealt with it before. Here we test the prediction that this object-based memory effect on search breaks down if

Geographic Object-Based Image Analysis – Towards a new paradigm

Science.gov (United States)

Blaschke, Thomas; Hay, Geoffrey J.; Kelly, Maggi; Lang, Stefan; Hofmann, Peter; Addink, Elisabeth; Queiroz Feitosa, Raul; van der Meer, Freek; van der Werff, Harald; van Coillie, Frieke; Tiede, Dirk

2014-01-01

The amount of scientific literature on (Geographic) Object-based Image Analysis – GEOBIA has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use GIS-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science (GIScience). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether GEOBIA can be regarded as a paradigm according to this definition. We crystallize core concepts of GEOBIA, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and GEOBIA are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that GEOBIA is a new and evolving paradigm. PMID:24623958

A NEW FRAMEWORK FOR OBJECT-BASED IMAGE ANALYSIS BASED ON SEGMENTATION SCALE SPACE AND RANDOM FOREST CLASSIFIER

Directory of Open Access Journals (Sweden)

A. Hadavand

2015-12-01

Full Text Available In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS, a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.

Random walk-based similarity measure method for patterns in complex object

Directory of Open Access Journals (Sweden)

Liu Shihu

2017-04-01

Full Text Available This paper discusses the similarity of the patterns in complex objects. The complex object is composed both of the attribute information of patterns and the relational information between patterns. Bearing in mind the specificity of complex object, a random walk-based similarity measurement method for patterns is constructed. In this method, the reachability of any two patterns with respect to the relational information is fully studied, and in the case of similarity of patterns with respect to the relational information can be calculated. On this bases, an integrated similarity measurement method is proposed, and algorithms 1 and 2 show the performed calculation procedure. One can find that this method makes full use of the attribute information and relational information. Finally, a synthetic example shows that our proposed similarity measurement method is validated.

Object-based target templates guide attention during visual search

OpenAIRE

Berggren, Nick; Eimer, Martin

2018-01-01

During visual search, attention is believed to be controlled in a strictly feature-based fashion, without any guidance by object-based target representations. To challenge this received view, we measured electrophysiological markers of attentional selection (N2pc component) and working memory (SPCN) in search tasks where two possible targets were defined by feature conjunctions (e.g., blue circles and green squares). Critically, some search displays also contained nontargets with two target f...

JTpack90: A parallel, object-based, Fortran 90 linear algebra package

Energy Technology Data Exchange (ETDEWEB)

Turner, J.A.; Kothe, D.B. [Los Alamos National Lab., NM (United States); Ferrell, R.C. [Cambridge Power Computing Associates, Ltd., Brookline, MA (United States)

1997-03-01

The authors have developed an object-based linear algebra package, currently with emphasis on sparse Krylov methods, driven primarily by needs of the Los Alamos National Laboratory parallel unstructured-mesh casting simulation tool Telluride. Support for a number of sparse storage formats, methods, and preconditioners have been implemented, driven primarily by application needs. They describe the object-based Fortran 90 approach, which enhances maintainability, performance, and extensibility, the parallelization approach using a new portable gather/scatter library (PGSLib), current capabilities and future plans, and present preliminary performance results on a variety of platforms.

Attention-spreading based on hierarchical spatial representations for connected objects.

Science.gov (United States)

Kasai, Tetsuko

2010-01-01

Attention selects objects or groups as the most fundamental unit, and this may be achieved through a process in which attention automatically spreads throughout their entire region. Previously, we found that a lateralized potential relative to an attended hemifield at occipito-temporal electrode sites reflects attention-spreading in response to connected bilateral stimuli [Kasai, T., & Kondo, M. Electrophysiological correlates of attention-spreading in visual grouping. NeuroReport, 18, 93-98, 2007]. The present study examined the nature of object representations by manipulating the extent of grouping through connectedness, while controlling the symmetrical structure of bilateral stimuli. The electrophysiological results of two experiments consistently indicated that attention was guided twice in association with perceptual grouping in the early phase (N1, 150-200 msec poststimulus) and with the unity of an object in the later phase (N2pc, 310/330-390 msec). This suggests that there are two processes in object-based spatial selection, and these are discussed with regard to their cognitive mechanisms and object representations.

Generative Learning Objects Instantiated with Random Numbers Based Expressions

Directory of Open Access Journals (Sweden)

Ciprian Bogdan Chirila

2015-12-01

Full Text Available The development of interactive e-learning content requires special skills like programming techniques, web integration, graphic design etc. Generally, online educators do not possess such skills and their e-learning products tend to be static like presentation slides and textbooks. In this paper we propose a new interactive model of generative learning objects as a compromise betweenstatic, dull materials and dynamic, complex software e-learning materials developed by specialized teams. We find that random numbers based automatic initialization learning objects increases content diversity, interactivity thus enabling learners’ engagement. The resulted learning object model is at a limited level of complexity related to special e-learning software, intuitive and capable of increasing learners’ interactivity, engagement and motivation through dynamic content. The approach was applied successfully on several computer programing disciplines.

Quality Evaluation in Wireless Imaging Using Feature-Based Objective Metrics

OpenAIRE

Engelke, Ulrich; Zepernick, Hans-Jürgen

2007-01-01

This paper addresses the evaluation of image quality in the context of wireless systems using feature-based objective metrics. The considered metrics comprise of a weighted combination of feature values that are used to quantify the extend by which the related artifacts are present in a processed image. In view of imaging applications in mobile radio and wireless communication systems, reduced-reference objective quality metrics are investigated for quantifying user-perceived quality. The exa...

Simple Ontology of Manipulation Actions based on Hand-Object Relations

DEFF Research Database (Denmark)

Wörgötter, Florentin; Aksoy, E. E.; Krüger, Norbert

2013-01-01

and time. For this we use as temporal anchor points those moments where two objects (or hand and object) touch or un-touch each other during a manipulation. We show that by this one can define a relatively small tree-like manipulation ontology. We find less than 30 fundamental manipulations. The temporal...... and encoded. Examples of manipulations recognition and execution by a robot based on this representation are given at the end of this study....

A General Polygon-based Deformable Model for Object Recognition

DEFF Research Database (Denmark)

Jensen, Rune Fisker; Carstensen, Jens Michael

1999-01-01

We propose a general scheme for object localization and recognition based on a deformable model. The model combines shape and image properties by warping a arbitrary prototype intensity template according to the deformation in shape. The shape deformations are constrained by a probabilistic distr...

An Object-Oriented Architecture for a Web-Based CAI System.

Science.gov (United States)

Nakabayashi, Kiyoshi; Hoshide, Takahide; Seshimo, Hitoshi; Fukuhara, Yoshimi

This paper describes the design and implementation of an object-oriented World Wide Web-based CAI (Computer-Assisted Instruction) system. The goal of the design is to provide a flexible CAI/ITS (Intelligent Tutoring System) framework with full extendibility and reusability, as well as to exploit Web-based software technologies such as JAVA, ASP (a…

Knowledge-Based Topic Model for Unsupervised Object Discovery and Localization.

Science.gov (United States)

Niu, Zhenxing; Hua, Gang; Wang, Le; Gao, Xinbo

Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object instances from a given image collection without any supervision. Previous work has attempted to tackle this problem with vanilla topic models, such as latent Dirichlet allocation (LDA). However, in those methods no prior knowledge for the given image collection is exploited to facilitate object discovery. On the other hand, the topic models used in those methods suffer from the topic coherence issue-some inferred topics do not have clear meaning, which limits the final performance of object discovery. In this paper, prior knowledge in terms of the so-called must-links are exploited from Web images on the Internet. Furthermore, a novel knowledge-based topic model, called LDA with mixture of Dirichlet trees, is proposed to incorporate the must-links into topic modeling for object discovery. In particular, to better deal with the polysemy phenomenon of visual words, the must-link is re-defined as that one must-link only constrains one or some topic(s) instead of all topics, which leads to significantly improved topic coherence. Moreover, the must-links are built and grouped with respect to specific object classes, thus the must-links in our approach are semantic-specific , which allows to more efficiently exploit discriminative prior knowledge from Web images. Extensive experiments validated the efficiency of our proposed approach on several data sets. It is shown that our method significantly improves topic coherence and outperforms the unsupervised methods for object discovery and localization. In addition, compared with discriminative methods, the naturally existing object classes in the given image collection can be subtly discovered, which makes our approach well suited for realistic applications of unsupervised object discovery.Unsupervised object discovery and localization is to discover some dominant object classes and localize all of object

Moving object detection in video satellite image based on deep learning

Science.gov (United States)

Zhang, Xueyang; Xiang, Junhua

2017-11-01

Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

Probabilistic active recognition of multiple objects using Hough-based geometric matching features

CSIR Research Space (South Africa)

Govender, N

2015-01-01

Full Text Available be recognized simultaneously, and occlusion and clutter (through distracter objects) is common. We propose a representation for object viewpoints using Hough transform based geometric matching features, which are robust in such circumstances. We show how...

Lossy to lossless object-based coding of 3-D MRI data.

Science.gov (United States)

Menegaz, Gloria; Thiran, Jean-Philippe

2002-01-01

We propose a fully three-dimensional (3-D) object-based coding system exploiting the diagnostic relevance of the different regions of the volumetric data for rate allocation. The data are first decorrelated via a 3-D discrete wavelet transform. The implementation via the lifting steps scheme allows to map integer-to-integer values, enabling lossless coding, and facilitates the definition of the object-based inverse transform. The coding process assigns disjoint segments of the bitstream to the different objects, which can be independently accessed and reconstructed at any up-to-lossless quality. Two fully 3-D coding strategies are considered: embedded zerotree coding (EZW-3D) and multidimensional layered zero coding (MLZC), both generalized for region of interest (ROI)-based processing. In order to avoid artifacts along region boundaries, some extra coefficients must be encoded for each object. This gives rise to an overheading of the bitstream with respect to the case where the volume is encoded as a whole. The amount of such extra information depends on both the filter length and the decomposition depth. The system is characterized on a set of head magnetic resonance images. Results show that MLZC and EZW-3D have competitive performances. In particular, the best MLZC mode outperforms the others state-of-the-art techniques on one of the datasets for which results are available in the literature.

Concept of development of integrated computer - based control system for 'Ukryttia' object

International Nuclear Information System (INIS)

Buyal'skij, V.M.; Maslov, V.P.

2003-01-01

The structural concept of Chernobyl NPP 'Ukryttia' Object's integrated computer - based control system development is presented on the basis of general concept of integrated Computer - based Control System (CCS) design process for organizing and technical management subjects.The concept is aimed at state-of-the-art architectural design technique application and allows using modern computer-aided facilities for functional model,information (logical and physical) models development,as well as for system object model under design

Aerodynamic multi-objective integrated optimization based on principal component analysis

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Jiangtao HUANG

2017-08-01

Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

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Rafał Dreżewski

2017-08-01

Full Text Available Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

A wavelet-based Bayesian framework for 3D object segmentation in microscopy

Science.gov (United States)

Pan, Kangyu; Corrigan, David; Hillebrand, Jens; Ramaswami, Mani; Kokaram, Anil

2012-03-01

In confocal microscopy, target objects are labeled with fluorescent markers in the living specimen, and usually appear with irregular brightness in the observed images. Also, due to the existence of out-of-focus objects in the image, the segmentation of 3-D objects in the stack of image slices captured at different depth levels of the specimen is still heavily relied on manual analysis. In this paper, a novel Bayesian model is proposed for segmenting 3-D synaptic objects from given image stack. In order to solve the irregular brightness and out-offocus problems, the segmentation model employs a likelihood using the luminance-invariant 'wavelet features' of image objects in the dual-tree complex wavelet domain as well as a likelihood based on the vertical intensity profile of the image stack in 3-D. Furthermore, a smoothness 'frame' prior based on the a priori knowledge of the connections of the synapses is introduced to the model for enhancing the connectivity of the synapses. As a result, our model can successfully segment the in-focus target synaptic object from a 3D image stack with irregular brightness.

Robust object tracking techniques for vision-based 3D motion analysis applications

Science.gov (United States)

Knyaz, Vladimir A.; Zheltov, Sergey Y.; Vishnyakov, Boris V.

2016-04-01

Automated and accurate spatial motion capturing of an object is necessary for a wide variety of applications including industry and science, virtual reality and movie, medicine and sports. For the most part of applications a reliability and an accuracy of the data obtained as well as convenience for a user are the main characteristics defining the quality of the motion capture system. Among the existing systems for 3D data acquisition, based on different physical principles (accelerometry, magnetometry, time-of-flight, vision-based), optical motion capture systems have a set of advantages such as high speed of acquisition, potential for high accuracy and automation based on advanced image processing algorithms. For vision-based motion capture accurate and robust object features detecting and tracking through the video sequence are the key elements along with a level of automation of capturing process. So for providing high accuracy of obtained spatial data the developed vision-based motion capture system "Mosca" is based on photogrammetric principles of 3D measurements and supports high speed image acquisition in synchronized mode. It includes from 2 to 4 technical vision cameras for capturing video sequences of object motion. The original camera calibration and external orientation procedures provide the basis for high accuracy of 3D measurements. A set of algorithms as for detecting, identifying and tracking of similar targets, so for marker-less object motion capture is developed and tested. The results of algorithms' evaluation show high robustness and high reliability for various motion analysis tasks in technical and biomechanics applications.

Object detection system based on multimodel saliency maps

Science.gov (United States)

Guo, Ya'nan; Luo, Chongfan; Ma, Yide

2017-03-01

Detection of visually salient image regions is extensively applied in computer vision and computer graphics, such as object detection, adaptive compression, and object recognition, but any single model always has its limitations to various images, so in our work, we establish a method based on multimodel saliency maps to detect the object, which intelligently absorbs the merits of various individual saliency detection models to achieve promising results. The method can be roughly divided into three steps: in the first step, we propose a decision-making system to evaluate saliency maps obtained by seven competitive methods and merely select the three most valuable saliency maps; in the second step, we introduce heterogeneous PCNN algorithm to obtain three prime foregrounds; and then a self-designed nonlinear fusion method is proposed to merge these saliency maps; at last, the adaptive improved and simplified PCNN model is used to detect the object. Our proposed method can constitute an object detection system for different occasions, which requires no training, is simple, and highly efficient. The proposed saliency fusion technique shows better performance over a broad range of images and enriches the applicability range by fusing different individual saliency models, this proposed system is worthy enough to be called a strong model. Moreover, the proposed adaptive improved SPCNN model is stemmed from the Eckhorn's neuron model, which is skilled in image segmentation because of its biological background, and in which all the parameters are adaptive to image information. We extensively appraise our algorithm on classical salient object detection database, and the experimental results demonstrate that the aggregation of saliency maps outperforms the best saliency model in all cases, yielding highest precision of 89.90%, better recall rates of 98.20%, greatest F-measure of 91.20%, and lowest mean absolute error value of 0.057, the value of proposed saliency evaluation

SUSTAINABILITY LOGISTICS BASING SCIENCE AND TECHNOLOGY OBJECTIVE DEMONSTRATION; SELECTED TECHNOLOGY ASSESSMENT

Science.gov (United States)

2018-03-22

BASING SCIENCE AND TECHNOLOGY OBJECTIVE – DEMONSTRATION; SELECTED TECHNOLOGY ASSESSMENT by Gregg J. Gildea Paul D. Carpenter Benjamin J...Campbell William F. Harris* Michael A. McCluskey** and José A. Miletti*** *General Dynamics Information Technology Fairfax, VA 22030 **Maneuver...SCIENCE AND TECHNOLOGY OBJECTIVE – DEMONSTRATION; SELECTED TECHNOLOGY ASSESSMENT 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT

Personalised learning object based on multi-agent model and learners’ learning styles

Directory of Open Access Journals (Sweden)

Noppamas Pukkhem

2011-09-01

Full Text Available A multi-agent model is proposed in which learning styles and a word analysis technique to create a learning object recommendation system are used. On the basis of a learning style-based design, a concept map combination model is proposed to filter out unsuitable learning concepts from a given course. Our learner model classifies learners into eight styles and implements compatible computational methods consisting of three recommendations: i non-personalised, ii preferred feature-based, and iii neighbour-based collaborative filtering. The analysis of preference error (PE was performed by comparing the actual preferred learning object with the predicted one. In our experiments, the feature-based recommendation algorithm has the fewest PE.

An object recognition method based on fuzzy theory and BP networks

Science.gov (United States)

Wu, Chuan; Zhu, Ming; Yang, Dong

2006-01-01

It is difficult to choose eigenvectors when neural network recognizes object. It is possible that the different object eigenvectors is similar or the same object eigenvectors is different under scaling, shifting, rotation if eigenvectors can not be chosen appropriately. In order to solve this problem, the image is edged, the membership function is reconstructed and a new threshold segmentation method based on fuzzy theory is proposed to get the binary image. Moment invariant of binary image is extracted and normalized. Some time moment invariant is too small to calculate effectively so logarithm of moment invariant is taken as input eigenvectors of BP network. The experimental results demonstrate that the proposed approach could recognize the object effectively, correctly and quickly.

Query and Update Efficient B+-Tree Based Indexing of Moving Objects

DEFF Research Database (Denmark)

Jensen, Christian Søndergaard; Lin, Dan; Ooi, Beng Chin

2004-01-01

. This motivates the design of a solution that enables the B+-tree to manage moving objects. We represent moving-object locations as vectors that are timestamped based on their update time. By applying a novel linearization technique to these values, it is possible to index the resulting values using a single B...... are streamed to a database. Indexes for moving objects must support queries efficiently, but must also support frequent updates. Indexes based on minimum bounding regions (MBRs) such as the R-tree exhibit high concurrency overheads during node splitting, and each individual update is known to be quite costly......+-tree that partitions values according to their timestamp and otherwise preserves spatial proximity. We develop algorithms for range and k nearest neighbor queries, as well as continuous queries. The proposal can be grafted into existing database systems cost effectively. An extensive experimental study explores...

Interactive object modelling based on piecewise planar surface patches.

Science.gov (United States)

Prankl, Johann; Zillich, Michael; Vincze, Markus

2013-06-01

Detecting elements such as planes in 3D is essential to describe objects for applications such as robotics and augmented reality. While plane estimation is well studied, table-top scenes exhibit a large number of planes and methods often lock onto a dominant plane or do not estimate 3D object structure but only homographies of individual planes. In this paper we introduce MDL to the problem of incrementally detecting multiple planar patches in a scene using tracked interest points in image sequences. Planar patches are reconstructed and stored in a keyframe-based graph structure. In case different motions occur, separate object hypotheses are modelled from currently visible patches and patches seen in previous frames. We evaluate our approach on a standard data set published by the Visual Geometry Group at the University of Oxford [24] and on our own data set containing table-top scenes. Results indicate that our approach significantly improves over the state-of-the-art algorithms.

Interactive object modelling based on piecewise planar surface patches☆

Science.gov (United States)

Prankl, Johann; Zillich, Michael; Vincze, Markus

2013-01-01

Detecting elements such as planes in 3D is essential to describe objects for applications such as robotics and augmented reality. While plane estimation is well studied, table-top scenes exhibit a large number of planes and methods often lock onto a dominant plane or do not estimate 3D object structure but only homographies of individual planes. In this paper we introduce MDL to the problem of incrementally detecting multiple planar patches in a scene using tracked interest points in image sequences. Planar patches are reconstructed and stored in a keyframe-based graph structure. In case different motions occur, separate object hypotheses are modelled from currently visible patches and patches seen in previous frames. We evaluate our approach on a standard data set published by the Visual Geometry Group at the University of Oxford [24] and on our own data set containing table-top scenes. Results indicate that our approach significantly improves over the state-of-the-art algorithms. PMID:24511219

Roadside Multiple Objects Extraction from Mobile Laser Scanning Point Cloud Based on DBN

Directory of Open Access Journals (Sweden)

LUO Haifeng

2018-02-01

Full Text Available This paper proposed an novel algorithm for exploring deep belief network (DBN architectures to extract and recognize roadside facilities (trees,cars and traffic poles from mobile laser scanning (MLS point cloud.The proposed methods firstly partitioned the raw MLS point cloud into blocks and then removed the ground and building points.In order to partition the off-ground objects into individual objects,off-ground points were organized into an Octree structure and clustered into candidate objects based on connected component.To improve segmentation performance on clusters containing overlapped objects,a refining processing using a voxel-based normalized cut was then implemented.In addition,multi-view features descriptor was generated for each independent roadside facilities based on binary images.Finally,a deep belief network (DBN was trained to extract trees,cars and traffic pole objects.Experiments are undertaken to evaluate the validities of the proposed method with two datasets acquired by Lynx Mobile Mapper System.The precision of trees,cars and traffic poles objects extraction results respectively was 97.31%,97.79% and 92.78%.The recall was 98.30%,98.75% and 96.77% respectively.The quality is 95.70%,93.81% and 90.00%.And the F1 measure was 97.80%,96.81% and 94.73%.

Full Waveform Analysis for Long-Range 3D Imaging Laser Radar

Directory of Open Access Journals (Sweden)

Wallace AndrewM

2010-01-01

Full Text Available The new generation of 3D imaging systems based on laser radar (ladar offers significant advantages in defense and security applications. In particular, it is possible to retrieve 3D shape information directly from the scene and separate a target from background or foreground clutter by extracting a narrow depth range from the field of view by range gating, either in the sensor or by postprocessing. We discuss and demonstrate the applicability of full-waveform ladar to produce multilayer 3D imagery, in which each pixel produces a complex temporal response that describes the scene structure. Such complexity caused by multiple and distributed reflection arises in many relevant scenarios, for example in viewing partially occluded targets, through semitransparent materials (e.g., windows and through distributed reflective media such as foliage. We demonstrate our methodology on 3D image data acquired by a scanning time-of-flight system, developed in our own laboratories, which uses the time-correlated single-photon counting technique.

Nonlinear automatic landing control of unmanned aerial vehicles on moving platforms via a 3D laser radar

Energy Technology Data Exchange (ETDEWEB)

Hervas, Jaime Rubio; Tang, Hui [School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, 639798 (Singapore); Reyhanoglu, Mahmut [Physical Sciences Department, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114 (United States)

2014-12-10

This paper presents a motion tracking and control system for automatically landing Unmanned Aerial Vehicles (UAVs) on an oscillating platform using Laser Radar (LADAR) observations. The system itself is assumed to be mounted on a ship deck. A full nonlinear mathematical model is first introduced for the UAV. The ship motion is characterized by a Fourier transform based method which includes a realistic characterization of the sea waves. LADAR observation models are introduced and an algorithm to process those observations for yielding the relative state between the vessel and the UAV is presented, from which the UAV's state relative to an inertial frame can be obtained and used for feedback purposes. A sliding mode control algorithm is derived for tracking a landing trajectory defined by a set of desired waypoints. An extended Kalman filter (EKF) is proposed to account for process and observation noises in the design of a state estimator. The effectiveness of the control algorithm is illustrated through a simulation example.

Nonlinear automatic landing control of unmanned aerial vehicles on moving platforms via a 3D laser radar

International Nuclear Information System (INIS)

Hervas, Jaime Rubio; Tang, Hui; Reyhanoglu, Mahmut

2014-01-01

This paper presents a motion tracking and control system for automatically landing Unmanned Aerial Vehicles (UAVs) on an oscillating platform using Laser Radar (LADAR) observations. The system itself is assumed to be mounted on a ship deck. A full nonlinear mathematical model is first introduced for the UAV. The ship motion is characterized by a Fourier transform based method which includes a realistic characterization of the sea waves. LADAR observation models are introduced and an algorithm to process those observations for yielding the relative state between the vessel and the UAV is presented, from which the UAV's state relative to an inertial frame can be obtained and used for feedback purposes. A sliding mode control algorithm is derived for tracking a landing trajectory defined by a set of desired waypoints. An extended Kalman filter (EKF) is proposed to account for process and observation noises in the design of a state estimator. The effectiveness of the control algorithm is illustrated through a simulation example

Top-down attention based on object representation and incremental memory for knowledge building and inference.

Science.gov (United States)

Kim, Bumhwi; Ban, Sang-Woo; Lee, Minho

2013-10-01

Humans can efficiently perceive arbitrary visual objects based on an incremental learning mechanism with selective attention. This paper proposes a new task specific top-down attention model to locate a target object based on its form and color representation along with a bottom-up saliency based on relativity of primitive visual features and some memory modules. In the proposed model top-down bias signals corresponding to the target form and color features are generated, which draw the preferential attention to the desired object by the proposed selective attention model in concomitance with the bottom-up saliency process. The object form and color representation and memory modules have an incremental learning mechanism together with a proper object feature representation scheme. The proposed model includes a Growing Fuzzy Topology Adaptive Resonance Theory (GFTART) network which plays two important roles in object color and form biased attention; one is to incrementally learn and memorize color and form features of various objects, and the other is to generate a top-down bias signal to localize a target object by focusing on the candidate local areas. Moreover, the GFTART network can be utilized for knowledge inference which enables the perception of new unknown objects on the basis of the object form and color features stored in the memory during training. Experimental results show that the proposed model is successful in focusing on the specified target objects, in addition to the incremental representation and memorization of various objects in natural scenes. In addition, the proposed model properly infers new unknown objects based on the form and color features of previously trained objects. Copyright © 2013 Elsevier Ltd. All rights reserved.

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

Science.gov (United States)

Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

2017-01-01

Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization

International Nuclear Information System (INIS)

Punnathanam, Varun; Kotecha, Prakash

2017-01-01

Highlights: • Efficient multi-objective optimization algorithm F-YYPO demonstrated. • Three Stirling engine applications with a total of eight cases. • Improvements in the objective function values of up to 30%. • Superior to the popularly used gamultiobj of MATLAB. • F-YYPO has extremely low time complexity. - Abstract: In this work, we demonstrate the performance of Front-based Yin-Yang-Pair Optimization (F-YYPO) to solve multi-objective problems related to Stirling engine systems. The performance of F-YYPO is compared with that of (i) a recently proposed multi-objective optimization algorithm (Multi-Objective Grey Wolf Optimizer) and (ii) an algorithm popularly employed in literature due to its easy accessibility (MATLAB’s inbuilt multi-objective Genetic Algorithm function: gamultiobj). We consider three Stirling engine based optimization problems: (i) the solar-dish Stirling engine system which considers objectives of output power, thermal efficiency and rate of entropy generation; (ii) Stirling engine thermal model which considers the associated irreversibility of the cycle with objectives of output power, thermal efficiency and pressure drop; and finally (iii) an experimentally validated polytropic finite speed thermodynamics based Stirling engine model also with objectives of output power and pressure drop. We observe F-YYPO to be significantly more effective as compared to its competitors in solving the problems, while requiring only a fraction of the computational time required by the other algorithms.

Prioritization of pavement maintenance sections using objective based Analytic Hierarchy Process

Directory of Open Access Journals (Sweden)

Sarfaraz Ahmed

2017-03-01

Full Text Available The application of Analytic Hierarchy Process (AHP method for the prioritization of pavement maintenance sections is widespread now-a-days. Although the evaluation of pavement maintenance section through AHP method is simple, where the relative importance (on Saaty’s scale assigned to each parameter in the hierarchy varies between the experts (transportation professionals consulted, which leads to discrepancies in the final rankings of the sections’, due to the subjectivity in the process. Further, experts base their decisions solely on their experience while consideration is not given to the actual quantitative physical condition of the roads. To overcome these difficulties an objective based AHP method is proposed in this study, where pairwise comparison values are assigned based on the collected field data from a road network in Mumbai city, consisting of 28 road sections. The final ranking list of candidate sections takes into consideration the priority weight of alternatives, which reflect the road conditions. The solution of priority ratings of AHP method is compared with the corresponding solution of road condition index method, a traditional pavement maintenance procedure. The findings of the present study suggest that objective based AHP method is more suitable for the prioritization of pavement maintenance of roads. Keywords: Prioritization, Analytic Hierarchy Process, Road condition index, Objective method, Rating and ranking

Object-Based Image Analysis in Wetland Research: A Review

Directory of Open Access Journals (Sweden)

Iryna Dronova

2015-05-01

Full Text Available The applications of object-based image analysis (OBIA in remote sensing studies of wetlands have been growing over recent decades, addressing tasks from detection and delineation of wetland bodies to comprehensive analyses of within-wetland cover types and their change. Compared to pixel-based approaches, OBIA offers several important benefits to wetland analyses related to smoothing of the local noise, incorporating meaningful non-spectral features for class separation and accounting for landscape hierarchy of wetland ecosystem organization and structure. However, there has been little discussion on whether unique challenges of wetland environments can be uniformly addressed by OBIA across different types of data, spatial scales and research objectives, and to what extent technical and conceptual aspects of this framework may themselves present challenges in a complex wetland setting. This review presents a synthesis of 73 studies that applied OBIA to different types of remote sensing data, spatial scale and research objectives. It summarizes the progress and scope of OBIA uses in wetlands, key benefits of this approach, factors related to accuracy and uncertainty in its applications and the main research needs and directions to expand the OBIA capacity in the future wetland studies. Growing demands for higher-accuracy wetland characterization at both regional and local scales together with advances in very high resolution remote sensing and novel tasks in wetland restoration monitoring will likely continue active exploration of the OBIA potential in these diverse and complex environments.

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

Science.gov (United States)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

A Novel Object Tracking Algorithm Based on Compressed Sensing and Entropy of Information

Directory of Open Access Journals (Sweden)

Ding Ma

2015-01-01

Full Text Available Object tracking has always been a hot research topic in the field of computer vision; its purpose is to track objects with specific characteristics or representation and estimate the information of objects such as their locations, sizes, and rotation angles in the current frame. Object tracking in complex scenes will usually encounter various sorts of challenges, such as location change, dimension change, illumination change, perception change, and occlusion. This paper proposed a novel object tracking algorithm based on compressed sensing and information entropy to address these challenges. First, objects are characterized by the Haar (Haar-like and ORB features. Second, the dimensions of computation space of the Haar and ORB features are effectively reduced through compressed sensing. Then the above-mentioned features are fused based on information entropy. Finally, in the particle filter framework, an object location was obtained by selecting candidate object locations in the current frame from the local context neighboring the optimal locations in the last frame. Our extensive experimental results demonstrated that this method was able to effectively address the challenges of perception change, illumination change, and large area occlusion, which made it achieve better performance than existing approaches such as MIL and CT.

Object-based implicit learning in visual search: perceptual segmentation constrains contextual cueing.

Science.gov (United States)

Conci, Markus; Müller, Hermann J; von Mühlenen, Adrian

2013-07-09

In visual search, detection of a target is faster when it is presented within a spatial layout of repeatedly encountered nontarget items, indicating that contextual invariances can guide selective attention (contextual cueing; Chun & Jiang, 1998). However, perceptual regularities may interfere with contextual learning; for instance, no contextual facilitation occurs when four nontargets form a square-shaped grouping, even though the square location predicts the target location (Conci & von Mühlenen, 2009). Here, we further investigated potential causes for this interference-effect: We show that contextual cueing can reliably occur for targets located within the region of a segmented object, but not for targets presented outside of the object's boundaries. Four experiments demonstrate an object-based facilitation in contextual cueing, with a modulation of context-based learning by relatively subtle grouping cues including closure, symmetry, and spatial regularity. Moreover, the lack of contextual cueing for targets located outside the segmented region was due to an absence of (latent) learning of contextual layouts, rather than due to an attentional bias towards the grouped region. Taken together, these results indicate that perceptual segmentation provides a basic structure within which contextual scene regularities are acquired. This in turn argues that contextual learning is constrained by object-based selection.

Dissociating object-based from egocentric transformations in mental body rotation: effect of stimuli size.

Science.gov (United States)

Habacha, Hamdi; Moreau, David; Jarraya, Mohamed; Lejeune-Poutrain, Laure; Molinaro, Corinne

2018-01-01

The effect of stimuli size on the mental rotation of abstract objects has been extensively investigated, yet its effect on the mental rotation of bodily stimuli remains largely unexplored. Depending on the experimental design, mentally rotating bodily stimuli can elicit object-based transformations, relying mainly on visual processes, or egocentric transformations, which typically involve embodied motor processes. The present study included two mental body rotation tasks requiring either a same-different or a laterality judgment, designed to elicit object-based or egocentric transformations, respectively. Our findings revealed shorter response times for large-sized stimuli than for small-sized stimuli only for greater angular disparities, suggesting that the more unfamiliar the orientations of the bodily stimuli, the more stimuli size affected mental processing. Importantly, when comparing size transformation times, results revealed different patterns of size transformation times as a function of angular disparity between object-based and egocentric transformations. This indicates that mental size transformation and mental rotation proceed differently depending on the mental rotation strategy used. These findings are discussed with respect to the different spatial manipulations involved during object-based and egocentric transformations.

Object Detection and Tracking-Based Camera Calibration for Normalized Human Height Estimation

Directory of Open Access Journals (Sweden)

Jaehoon Jung

2016-01-01

Full Text Available This paper presents a normalized human height estimation algorithm using an uncalibrated camera. To estimate the normalized human height, the proposed algorithm detects a moving object and performs tracking-based automatic camera calibration. The proposed method consists of three steps: (i moving human detection and tracking, (ii automatic camera calibration, and (iii human height estimation and error correction. The proposed method automatically calibrates camera by detecting moving humans and estimates the human height using error correction. The proposed method can be applied to object-based video surveillance systems and digital forensic.

Water Detection Based on Object Reflections

Science.gov (United States)

Rankin, Arturo L.; Matthies, Larry H.

2012-01-01

Water bodies are challenging terrain hazards for terrestrial unmanned ground vehicles (UGVs) for several reasons. Traversing through deep water bodies could cause costly damage to the electronics of UGVs. Additionally, a UGV that is either broken down due to water damage or becomes stuck in a water body during an autonomous operation will require rescue, potentially drawing critical resources away from the primary operation and increasing the operation cost. Thus, robust water detection is a critical perception requirement for UGV autonomous navigation. One of the properties useful for detecting still water bodies is that their surface acts as a horizontal mirror at high incidence angles. Still water bodies in wide-open areas can be detected by geometrically locating the exact pixels in the sky that are reflecting on candidate water pixels on the ground, predicting if ground pixels are water based on color similarity to the sky and local terrain features. But in cluttered areas where reflections of objects in the background dominate the appearance of the surface of still water bodies, detection based on sky reflections is of marginal value. Specifically, this software attempts to solve the problem of detecting still water bodies on cross-country terrain in cluttered areas at low cost.

Object based data access at the D0 experiment

International Nuclear Information System (INIS)

Fuess, S.

1995-11-01

The D OE Experiment at Fermilab is currently participating in the FNAL Computing Division's ''Computing for Analysis Project'' (CAP) to investigate object based data storage and access. Following a short description of the CAP system architecture, the D OE data model is explored. A brief discussion of the method of operation of the CAP system leads into a concluding section

Archive Design Based on Planets Inspired Logical Object Model

DEFF Research Database (Denmark)

Zierau, Eld; Johansen, Anders

2008-01-01

We describe a proposal for a logical data model based on preliminary work the Planets project In OAIS terms the main areas discussed are related to the introduction of a logical data model for representing the past, present and future versions of the digital object associated with the Archival St...... Storage Package for the publications deposited by our client repositories....

BATMAN: a DMD-based multi-object spectrograph on Galileo telescope

Science.gov (United States)

Zamkotsian, Frederic; Spano, Paolo; Lanzoni, Patrick; Ramarijaona, Harald; Moschetti, Manuele; Riva, Marco; Bon, William; Nicastro, Luciano; Molinari, Emilio; Cosentino, Rosario; Ghedina, Adriano; Gonzalez, Manuel; Di Marcantonio, Paolo; Coretti, Igor; Cirami, Roberto; Zerbi, Filippo; Valenziano, Luca

2014-07-01

Next-generation infrared astronomical instrumentation for ground-based and space telescopes could be based on MOEMS programmable slit masks for multi-object spectroscopy (MOS). This astronomical technique is used extensively to investigate the formation and evolution of galaxies. We are developing a 2048x1080 Digital-Micromirror-Device-based (DMD) MOS instrument to be mounted on the Galileo telescope and called BATMAN. A two-arm instrument has been designed for providing in parallel imaging and spectroscopic capabilities. The field of view (FOV) is 6.8 arcmin x 3.6 arcmin with a plate scale of 0.2 arcsec per micromirror. The wavelength range is in the visible and the spectral resolution is R=560 for 1 arcsec object (typical slit size). The two arms will have 2k x 4k CCD detectors. ROBIN, a BATMAN demonstrator, has been designed, realized and integrated. It permits to determine the instrument integration procedure, including optics and mechanics integration, alignment procedure and optical quality. First images and spectra have been obtained and measured: typical spot diameters are within 1.5 detector pixels, and spectra generated by one micro-mirror slits are displayed with this optical quality over the whole visible wavelength range. Observation strategies are studied and demonstrated for the scientific optimization strategy over the whole FOV. BATMAN on the sky is of prime importance for characterizing the actual performance of this new family of MOS instruments, as well as investigating the operational procedures on astronomical objects. This instrument will be placed on the Telescopio Nazionale Galileo mid-2015.

Objective Audio Quality Assessment Based on Spectro-Temporal Modulation Analysis

OpenAIRE

Guo, Ziyuan

2011-01-01

Objective audio quality assessment is an interdisciplinary research area that incorporates audiology and machine learning. Although much work has been made on the machine learning aspect, the audiology aspect also deserves investigation. This thesis proposes a non-intrusive audio quality assessment algorithm, which is based on an auditory model that simulates human auditory system. The auditory model is based on spectro-temporal modulation analysis of spectrogram, which has been proven to be ...

Automatic feature-based grouping during multiple object tracking.

Science.gov (United States)

Erlikhman, Gennady; Keane, Brian P; Mettler, Everett; Horowitz, Todd S; Kellman, Philip J

2013-12-01

Contour interpolation automatically binds targets with distractors to impair multiple object tracking (Keane, Mettler, Tsoi, & Kellman, 2011). Is interpolation special in this regard or can other features produce the same effect? To address this question, we examined the influence of eight features on tracking: color, contrast polarity, orientation, size, shape, depth, interpolation, and a combination (shape, color, size). In each case, subjects tracked 4 of 8 objects that began as undifferentiated shapes, changed features as motion began (to enable grouping), and returned to their undifferentiated states before halting. We found that intertarget grouping improved performance for all feature types except orientation and interpolation (Experiment 1 and Experiment 2). Most importantly, target-distractor grouping impaired performance for color, size, shape, combination, and interpolation. The impairments were, at times, large (>15% decrement in accuracy) and occurred relative to a homogeneous condition in which all objects had the same features at each moment of a trial (Experiment 2), and relative to a "diversity" condition in which targets and distractors had different features at each moment (Experiment 3). We conclude that feature-based grouping occurs for a variety of features besides interpolation, even when irrelevant to task instructions and contrary to the task demands, suggesting that interpolation is not unique in promoting automatic grouping in tracking tasks. Our results also imply that various kinds of features are encoded automatically and in parallel during tracking.

Attribute and topology based change detection in a constellation of previously detected objects

Science.gov (United States)

Paglieroni, David W.; Beer, Reginald N.

2016-01-19

A system that applies attribute and topology based change detection to networks of objects that were detected on previous scans of a structure, roadway, or area of interest. The attributes capture properties or characteristics of the previously detected objects, such as location, time of detection, size, elongation, orientation, etc. The topology of the network of previously detected objects is maintained in a constellation database that stores attributes of previously detected objects and implicitly captures the geometrical structure of the network. A change detection system detects change by comparing the attributes and topology of new objects detected on the latest scan to the constellation database of previously detected objects.

Creating Usage Context-Based Object Similarities to Boost Recommender Systems in Technology Enhanced Learning

Science.gov (United States)

Niemann, Katja; Wolpers, Martin

2015-01-01

In this paper, we introduce a new way of detecting semantic similarities between learning objects by analysing their usage in web portals. Our approach relies on the usage-based relations between the objects themselves rather then on the content of the learning objects or on the relations between users and learning objects. We then take this new…

Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

Science.gov (United States)

Omagari, Hiroki; Higashino, Shin-Ichiro

2018-04-01

In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

Birth of the Object: Detection of Objectness and Extraction of Object Shape through Object Action Complexes

DEFF Research Database (Denmark)

Kraft, Dirk; Pugeault, Nicolas; Baseski, Emre

2008-01-01

We describe a process in which the segmentation of objects as well as the extraction of the object shape becomes realized through active exploration of a robot vision system. In the exploration process, two behavioral modules that link robot actions to the visual and haptic perception of objects...... interact. First, by making use of an object independent grasping mechanism, physical control over potential objects can be gained. Having evaluated the initial grasping mechanism as being successful, a second behavior extracts the object shape by making use of prediction based on the motion induced...... system, knowledge about its own embodiment as well as knowledge about geometric relationships such as rigid body motion. This prior knowledge allows the extraction of representations that are semantically richer compared to many other approaches....

Ontology-Based Retrieval of Spatially Related Objects for Location Based Services

Science.gov (United States)

Haav, Hele-Mai; Kaljuvee, Aivi; Luts, Martin; Vajakas, Toivo

Advanced Location Based Service (LBS) applications have to integrate information stored in GIS, information about users' preferences (profile) as well as contextual information and information about application itself. Ontology engineering provides methods to semantically integrate several data sources. We propose an ontology-driven LBS development framework: the paper describes the architecture of ontologies and their usage for retrieval of spatially related objects relevant to the user. Our main contribution is to enable personalised ontology driven LBS by providing a novel approach for defining personalised semantic spatial relationships by means of ontologies. The approach is illustrated by an industrial case study.

A study on software-based sensing technology for multiple object control in AR video.

Science.gov (United States)

Jung, Sungmo; Song, Jae-Gu; Hwang, Dae-Joon; Ahn, Jae Young; Kim, Seoksoo

2010-01-01

Researches on Augmented Reality (AR) have recently received attention. With these, the Machine-to-Machine (M2M) market has started to be active and there are numerous efforts to apply this to real life in all sectors of society. To date, the M2M market has applied the existing marker-based AR technology in entertainment, business and other industries. With the existing marker-based AR technology, a designated object can only be loaded on the screen from one marker and a marker has to be added to load on the screen the same object again. This situation creates a problem where the relevant marker'should be extracted and printed in screen so that loading of the multiple objects is enabled. However, since the distance between markers will not be measured in the process of detecting and copying markers, the markers can be overlapped and thus the objects would not be augmented. To solve this problem, a circle having the longest radius needs to be created from a focal point of a marker to be copied, so that no object is copied within the confines of the circle. In this paper, software-based sensing technology for multiple object detection and loading using PPHT has been developed and overlapping marker control according to multiple object control has been studied using the Bresenham and Mean Shift algorithms.

A Study on Software-based Sensing Technology for Multiple Object Control in AR Video

Directory of Open Access Journals (Sweden)

Seoksoo Kim

2010-11-01

Full Text Available Researches on Augmented Reality (AR have recently received attention. With these, the Machine-to-Machine (M2M market has started to be active and there are numerous efforts to apply this to real life in all sectors of society. To date, the M2M market has applied the existing marker-based AR technology in entertainment, business and other industries. With the existing marker-based AR technology, a designated object can only be loaded on the screen from one marker and a marker has to be added to load on the screen the same object again. This situation creates a problem where the relevant marker should be extracted and printed in screen so that loading of the multiple objects is enabled. However, since the distance between markers will not be measured in the process of detecting and copying markers, the markers can be overlapped and thus the objects would not be augmented. To solve this problem, a circle having the longest radius needs to be created from a focal point of a marker to be copied, so that no object is copied within the confines of the circle. In this paper, software-based sensing technology for multiple object detection and loading using PPHT has been developed and overlapping marker control according to multiple object control has been studied using the Bresenham and Mean Shift algorithms.

Effect of objective function on multi-objective inverse planning of radiation therapy

International Nuclear Information System (INIS)

Li Guoli; Wu Yican; Song Gang; Wang Shifang

2006-01-01

There are two kinds of objective functions in radiotherapy inverse planning: dose distribution-based and Dose-Volume Histogram (DVH)-based functions. The treatment planning in our days is still a trial and error process because the multi-objective problem is solved by transforming it into a single objective problem using a specific set of weights for each object. This work investigates the problem of objective function setting based on Pareto multi-optimization theory, and compares the effect on multi-objective inverse planning of those two kinds of objective functions including calculation time, converge speed, etc. The basis of objective function setting on inverse planning is discussed. (authors)

Parachuting from fixed objects: descriptive study of 106 fatal events in BASE jumping 1981-2006.

Science.gov (United States)

Westman, A; Rosén, M; Berggren, P; Björnstig, U

2008-06-01

To analyse the characteristics of fatal incidents in fixed object sport parachuting (building, antenna, span, earth (BASE) jumping) and create a basis for prevention. Descriptive epidemiological study. Data on reported fatal injury events (n = 106) worldwide in 1981-2006 retrieved from the BASE fatality list. Human, equipment and environmental factors. Identification of typical fatal incident and injury mechanisms for each of the four fixed object types of BASE jumping (building, antenna, span, earth). Human factors included parachutist free fall instability (loss of body control before parachute deployment), free fall acrobatics and deployment failure by the parachutist. Equipment factors included pilot chute malfunction and parachute malfunction. In cliff jumping (BASE object type E), parachute opening towards the object jumped was the most frequent equipment factor. Environmental factors included poor visibility, strong or turbulent winds, cold and water. The overall annual fatality risk for all object types during the year 2002 was estimated at about one fatality per 60 participants. Participants in BASE jumping should target risk factors with training and technical interventions. The mechanisms described in this study should be used by rescue units to improve the management of incidents.

An Object-Oriented Information Model for Policy-based Management of Distributed Applications

NARCIS (Netherlands)

Diaz, G.; Gay, V.C.J.; Horlait, E.; Hamza, M.H.

2002-01-01

This paper presents an object-oriented information model to support a policy-based management for distributed multimedia applications. The information base contains application-level information about the users, the applications, and their profile. Our Information model is described in details and

Can state-of-the-art HVS-based objective image quality criteria be used for image reconstruction techniques based on ROI analysis?

Science.gov (United States)

Dostal, P.; Krasula, L.; Klima, M.

2012-06-01

Various image processing techniques in multimedia technology are optimized using visual attention feature of the human visual system. Spatial non-uniformity causes that different locations in an image are of different importance in terms of perception of the image. In other words, the perceived image quality depends mainly on the quality of important locations known as regions of interest. The performance of such techniques is measured by subjective evaluation or objective image quality criteria. Many state-of-the-art objective metrics are based on HVS properties; SSIM, MS-SSIM based on image structural information, VIF based on the information that human brain can ideally gain from the reference image or FSIM utilizing the low-level features to assign the different importance to each location in the image. But still none of these objective metrics utilize the analysis of regions of interest. We solve the question if these objective metrics can be used for effective evaluation of images reconstructed by processing techniques based on ROI analysis utilizing high-level features. In this paper authors show that the state-of-the-art objective metrics do not correlate well with subjective evaluation while the demosaicing based on ROI analysis is used for reconstruction. The ROI were computed from "ground truth" visual attention data. The algorithm combining two known demosaicing techniques on the basis of ROI location is proposed to reconstruct the ROI in fine quality while the rest of image is reconstructed with low quality. The color image reconstructed by this ROI approach was compared with selected demosaicing techniques by objective criteria and subjective testing. The qualitative comparison of the objective and subjective results indicates that the state-of-the-art objective metrics are still not suitable for evaluation image processing techniques based on ROI analysis and new criteria is demanded.

Learning-based stochastic object models for characterizing anatomical variations

Science.gov (United States)

Dolly, Steven R.; Lou, Yang; Anastasio, Mark A.; Li, Hua

2018-03-01

It is widely known that the optimization of imaging systems based on objective, task-based measures of image quality via computer-simulation requires the use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in human anatomy within a specified ensemble of patients remains a challenging task. Previously reported numerical anatomic models lack the ability to accurately model inter-patient and inter-organ variations in human anatomy among a broad patient population, mainly because they are established on image data corresponding to a few of patients and individual anatomic organs. This may introduce phantom-specific bias into computer-simulation studies, where the study result is heavily dependent on which phantom is used. In certain applications, however, databases of high-quality volumetric images and organ contours are available that can facilitate this SOM development. In this work, a novel and tractable methodology for learning a SOM and generating numerical phantoms from a set of volumetric training images is developed. The proposed methodology learns geometric attribute distributions (GAD) of human anatomic organs from a broad patient population, which characterize both centroid relationships between neighboring organs and anatomic shape similarity of individual organs among patients. By randomly sampling the learned centroid and shape GADs with the constraints of the respective principal attribute variations learned from the training data, an ensemble of stochastic objects can be created. The randomness in organ shape and position reflects the learned variability of human anatomy. To demonstrate the methodology, a SOM of an adult male pelvis is computed and examples of corresponding numerical phantoms are created.

An Innovative SIFT-Based Method for Rigid Video Object Recognition

Directory of Open Access Journals (Sweden)

Jie Yu

2014-01-01

Full Text Available This paper presents an innovative SIFT-based method for rigid video object recognition (hereafter called RVO-SIFT. Just like what happens in the vision system of human being, this method makes the object recognition and feature updating process organically unify together, using both trajectory and feature matching, and thereby it can learn new features not only in the training stage but also in the recognition stage, which can improve greatly the completeness of the video object’s features automatically and, in turn, increases the ratio of correct recognition drastically. The experimental results on real video sequences demonstrate its surprising robustness and efficiency.

The Study of Object-Oriented Motor Imagery Based on EEG Suppression.

Directory of Open Access Journals (Sweden)

Lili Li

Full Text Available Motor imagery is a conventional method for brain computer interface and motor learning. To avoid the great individual difference of the motor imagery ability, object-oriented motor imagery was applied, and the effects were studied. Kinesthetic motor imagery and visual observation were administered to 15 healthy volunteers. The EEG during cue-based simple imagery (SI, object-oriented motor imagery (OI, non-object-oriented motor imagery (NI and visual observation (VO was recorded. Study results showed that OI and NI presented significant contralateral suppression in mu rhythm (p 0.05. Compared with NI, OI showed significant difference (p < 0.05 in mu rhythm and weak significant difference (p = 0.0612 in beta rhythm over the contralateral hemisphere. The ability of motor imagery can be reflected by the suppression degree of mu and beta frequencies which are the motor related rhythms. Thus, greater enhancement of activation in mirror neuron system is involved in response to object-oriented motor imagery. The object-oriented motor imagery is favorable for improvement of motor imagery ability.

Explicit area-based accuracy assessment for mangrove tree crown delineation using Geographic Object-Based Image Analysis (GEOBIA)

Science.gov (United States)

Kamal, Muhammad; Johansen, Kasper

2017-10-01

Effective mangrove management requires spatially explicit information of mangrove tree crown map as a basis for ecosystem diversity study and health assessment. Accuracy assessment is an integral part of any mapping activities to measure the effectiveness of the classification approach. In geographic object-based image analysis (GEOBIA) the assessment of the geometric accuracy (shape, symmetry and location) of the created image objects from image segmentation is required. In this study we used an explicit area-based accuracy assessment to measure the degree of similarity between the results of the classification and reference data from different aspects, including overall quality (OQ), user's accuracy (UA), producer's accuracy (PA) and overall accuracy (OA). We developed a rule set to delineate the mangrove tree crown using WorldView-2 pan-sharpened image. The reference map was obtained by visual delineation of the mangrove tree crowns boundaries form a very high-spatial resolution aerial photograph (7.5cm pixel size). Ten random points with a 10 m radius circular buffer were created to calculate the area-based accuracy assessment. The resulting circular polygons were used to clip both the classified image objects and reference map for area comparisons. In this case, the area-based accuracy assessment resulted 64% and 68% for the OQ and OA, respectively. The overall quality of the calculation results shows the class-related area accuracy; which is the area of correctly classified as tree crowns was 64% out of the total area of tree crowns. On the other hand, the overall accuracy of 68% was calculated as the percentage of all correctly classified classes (tree crowns and canopy gaps) in comparison to the total class area (an entire image). Overall, the area-based accuracy assessment was simple to implement and easy to interpret. It also shows explicitly the omission and commission error variations of object boundary delineation with colour coded polygons.

Localized probability of improvement for kriging based multi-objective optimization

Science.gov (United States)

Li, Yinjiang; Xiao, Song; Barba, Paolo Di; Rotaru, Mihai; Sykulski, Jan K.

2017-12-01

The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

Objective video quality assessment method for freeze distortion based on freeze aggregation

Science.gov (United States)

Watanabe, Keishiro; Okamoto, Jun; Kurita, Takaaki

2006-01-01

With the development of the broadband network, video communications such as videophone, video distribution, and IPTV services are beginning to become common. In order to provide these services appropriately, we must manage them based on subjective video quality, in addition to designing a network system based on it. Currently, subjective quality assessment is the main method used to quantify video quality. However, it is time-consuming and expensive. Therefore, we need an objective quality assessment technology that can estimate video quality from video characteristics effectively. Video degradation can be categorized into two types: spatial and temporal. Objective quality assessment methods for spatial degradation have been studied extensively, but methods for temporal degradation have hardly been examined even though it occurs frequently due to network degradation and has a large impact on subjective quality. In this paper, we propose an objective quality assessment method for temporal degradation. Our approach is to aggregate multiple freeze distortions into an equivalent freeze distortion and then derive the objective video quality from the equivalent freeze distortion. Specifically, our method considers the total length of all freeze distortions in a video sequence as the length of the equivalent single freeze distortion. In addition, we propose a method using the perceptual characteristics of short freeze distortions. We verified that our method can estimate the objective video quality well within the deviation of subjective video quality.

An object oriented framework of EPICS for MicroTCA based control system

International Nuclear Information System (INIS)

Geng, Z.

2012-01-01

EPICS (Experimental Physics and Industrial Control System) is a distributed control system platform which has been widely used for large scientific devices control like particle accelerators and fusion plant. EPICS has introduced object oriented (C ++ ) interfaces to most of the core services. But the major part of EPICS, the run-time database, only provides C interfaces, which is hard to involve the EPICS record concerned data and routines in the object oriented architecture of the software. This paper presents an object oriented framework which contains some abstract classes to encapsulate the EPICS record concerned data and routines in C ++ classes so that full OOA (Objected Oriented Analysis) and OOD (Object Oriented Design) methodologies can be used for EPICS IOC design. We also present a dynamic device management scheme for the hot swap capability of the MicroTCA based control system. (authors)

Development of national competency-based learning objectives "Medical Informatics" for undergraduate medical education.

Science.gov (United States)

Röhrig, R; Stausberg, J; Dugas, M

2013-01-01

The aim of this project is to develop a catalogue of competency-based learning objectives "Medical Informatics" for undergraduate medical education (abbreviated NKLM-MI in German). The development followed a multi-level annotation and consensus process. For each learning objective a reason why a physician needs this competence was required. In addition, each objective was categorized according to the competence context (A = covered by medical informatics, B = core subject of medical informatics, C = optional subject of medical informatics), the competence level (1 = referenced knowledge, 2 = applied knowledge, 3 = routine knowledge) and a CanMEDS competence role (medical expert, communicator, collaborator, manager, health advocate, professional, scholar). Overall 42 objectives in seven areas (medical documentation and information processing, medical classifications and terminologies, information systems in healthcare, health telematics and telemedicine, data protection and security, access to medical knowledge and medical signal-/image processing) were identified, defined and consented. With the NKLM-MI the competences in the field of medical informatics vital to a first year resident physician are identified, defined and operationalized. These competencies are consistent with the recommendations of the International Medical Informatics Association (IMIA). The NKLM-MI will be submitted to the National Competence-Based Learning Objectives for Undergraduate Medical Education. The next step is implementation of these objectives by the faculties.

Deep Learning for Detection of Object-Based Forgery in Advanced Video

Directory of Open Access Journals (Sweden)

Ye Yao

2017-12-01

Full Text Available Passive video forensics has drawn much attention in recent years. However, research on detection of object-based forgery, especially for forged video encoded with advanced codec frameworks, is still a great challenge. In this paper, we propose a deep learning-based approach to detect object-based forgery in the advanced video. The presented deep learning approach utilizes a convolutional neural network (CNN to automatically extract high-dimension features from the input image patches. Different from the traditional CNN models used in computer vision domain, we let video frames go through three preprocessing layers before being fed into our CNN model. They include a frame absolute difference layer to cut down temporal redundancy between video frames, a max pooling layer to reduce computational complexity of image convolution, and a high-pass filter layer to enhance the residual signal left by video forgery. In addition, an asymmetric data augmentation strategy has been established to get a similar number of positive and negative image patches before the training. The experiments have demonstrated that the proposed CNN-based model with the preprocessing layers has achieved excellent results.

Effect Analysis of Early Warning for Abandoned Object on Highway Based on Internet-of-Vehicles CA Model

Directory of Open Access Journals (Sweden)

Juan Bao

2018-01-01

Full Text Available An early warning on the highway will effectively reduce traffic accidents. Considering the influence of an abandoned object on driving behavior, a Visual-based Asymmetric Two-lane Cellular Automata model with Abandoned Object (V-ATCA-AO and an Internet-of-Vehicles-based Asymmetric Two-lane Cellular Automata model with Abandoned Object (IoV-ATCA-AO are proposed. Based on the two models, two types of traffic accidents caused by an abandoned object are analyzed: rear-end collision caused by the abandoned object ahead and collision of the vehicle with the abandoned object. Simulation results show the following: (1 the accidents occur when the road density is smaller, while the accidents will not occur when the density is larger. The results are different from the rear-end collision rate curve without abandoned object in a single lane. (2 Compared with the visual-based avoidance pattern in V-ATCA-AO, the Internet-of-Vehicles-based avoidance pattern in IoV-ATCA-AO can create an early warning for the abandoned object and tell the vehicle to make an earlier lane change and decelerate in advance, thereby significantly reducing the accident rate. (3 Spatiotemporal characteristics in front of the abandoned object directly affect the accident rate: the less the “stability” of a traffic jam in front of the abandoned object, the higher the accident rate.

Soft object deformation monitoring and learning for model-based robotic hand manipulation.

Science.gov (United States)

Cretu, Ana-Maria; Payeur, Pierre; Petriu, Emil M

2012-06-01

This paper discusses the design and implementation of a framework that automatically extracts and monitors the shape deformations of soft objects from a video sequence and maps them with force measurements with the goal of providing the necessary information to the controller of a robotic hand to ensure safe model-based deformable object manipulation. Measurements corresponding to the interaction force at the level of the fingertips and to the position of the fingertips of a three-finger robotic hand are associated with the contours of a deformed object tracked in a series of images using neural-network approaches. The resulting model captures the behavior of the object and is able to predict its behavior for previously unseen interactions without any assumption on the object's material. The availability of such models can contribute to the improvement of a robotic hand controller, therefore allowing more accurate and stable grasp while providing more elaborate manipulation capabilities for deformable objects. Experiments performed for different objects, made of various materials, reveal that the method accurately captures and predicts the object's shape deformation while the object is submitted to external forces applied by the robot fingers. The proposed method is also fast and insensitive to severe contour deformations, as well as to smooth changes in lighting, contrast, and background.

A foreground object features-based stereoscopic image visual comfort assessment model

Science.gov (United States)

Jin, Xin; Jiang, G.; Ying, H.; Yu, M.; Ding, S.; Peng, Z.; Shao, F.

2014-11-01

Since stereoscopic images provide observers with both realistic and discomfort viewing experience, it is necessary to investigate the determinants of visual discomfort. By considering that foreground object draws most attention when human observing stereoscopic images. This paper proposes a new foreground object based visual comfort assessment (VCA) metric. In the first place, a suitable segmentation method is applied to disparity map and then the foreground object is ascertained as the one having the biggest average disparity. In the second place, three visual features being average disparity, average width and spatial complexity of foreground object are computed from the perspective of visual attention. Nevertheless, object's width and complexity do not consistently influence the perception of visual comfort in comparison with disparity. In accordance with this psychological phenomenon, we divide the whole images into four categories on the basis of different disparity and width, and exert four different models to more precisely predict its visual comfort in the third place. Experimental results show that the proposed VCA metric outperformance other existing metrics and can achieve a high consistency between objective and subjective visual comfort scores. The Pearson Linear Correlation Coefficient (PLCC) and Spearman Rank Order Correlation Coefficient (SROCC) are over 0.84 and 0.82, respectively.

Analysis of manufacturing based on object oriented discrete event simulation

Directory of Open Access Journals (Sweden)

Eirik Borgen

1990-01-01

Full Text Available This paper describes SIMMEK, a computer-based tool for performing analysis of manufacturing systems, developed at the Production Engineering Laboratory, NTH-SINTEF. Its main use will be in analysis of job shop type of manufacturing. But certain facilities make it suitable for FMS as well as a production line manufacturing. This type of simulation is very useful in analysis of any types of changes that occur in a manufacturing system. These changes may be investments in new machines or equipment, a change in layout, a change in product mix, use of late shifts, etc. The effects these changes have on for instance the throughput, the amount of VIP, the costs or the net profit, can be analysed. And this can be done before the changes are made, and without disturbing the real system. Simulation takes into consideration, unlike other tools for analysis of manufacturing systems, uncertainty in arrival rates, process and operation times, and machine availability. It also shows the interaction effects a job which is late in one machine, has on the remaining machines in its route through the layout. It is these effects that cause every production plan not to be fulfilled completely. SIMMEK is based on discrete event simulation, and the modeling environment is object oriented. The object oriented models are transformed by an object linker into data structures executable by the simulation kernel. The processes of the entity objects, i.e. the products, are broken down to events and put into an event list. The user friendly graphical modeling environment makes it possible for end users to build models in a quick and reliable way, using terms from manufacturing. Various tests and a check of model logic are helpful functions when testing validity of the models. Integration with software packages, with business graphics and statistical functions, is convenient in the result presentation phase.

A prototype distributed object-oriented architecture for image-based automatic laser alignment

International Nuclear Information System (INIS)

Stout, E.A.; Kamm, V.J.M.; Spann, J.M.; Van Arsdall, P.J.

1996-01-01

Designing a computer control system for the National Ignition Facility (NIF) is a complex undertaking because of the system's large size and its distributed nature. The controls team is addressing that complexity by adopting the object-oriented programming paradigm, designing reusable software frameworks, and using the Common Object Request Broker Architecture (CORBA) for distribution. A prototype system for image-based automatic laser alignment has been developed to evaluate and gain experience with CORBA and OOP in a small distributed system. The prototype is also important in evaluating alignment concepts, image processing techniques, speed and accuracy of automatic alignment objectives for the NIF, and control hardware for aligment devices. The prototype system has met its inital objectives and provides a basis for continued development

Fusion of Pixel-based and Object-based Features for Road Centerline Extraction from High-resolution Satellite Imagery

Directory of Open Access Journals (Sweden)

CAO Yungang

2016-10-01

Full Text Available A novel approach for road centerline extraction from high spatial resolution satellite imagery is proposed by fusing both pixel-based and object-based features. Firstly, texture and shape features are extracted at the pixel level, and spectral features are extracted at the object level based on multi-scale image segmentation maps. Then, extracted multiple features are utilized in the fusion framework of Dempster-Shafer evidence theory to roughly identify the road network regions. Finally, an automatic noise removing algorithm combined with the tensor voting strategy is presented to accurately extract the road centerline. Experimental results using high-resolution satellite imageries with different scenes and spatial resolutions showed that the proposed approach compared favorably with the traditional methods, particularly in the aspect of eliminating the salt noise and conglutination phenomenon.

Three-dimensional model-based object recognition and segmentation in cluttered scenes.

Science.gov (United States)

Mian, Ajmal S; Bennamoun, Mohammed; Owens, Robyn

2006-10-01

Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency.

A flexible object-based software framework for modeling complex systems with interacting natural and societal processes.

Energy Technology Data Exchange (ETDEWEB)

Christiansen, J. H.

2000-06-15

The Dynamic Information Architecture System (DIAS) is a flexible, extensible, object-based framework for developing and maintaining complex multidisciplinary simulations. The DIAS infrastructure makes it feasible to build and manipulate complex simulation scenarios in which many thousands of objects can interact via dozens to hundreds of concurrent dynamic processes. The flexibility and extensibility of the DIAS software infrastructure stem mainly from (1) the abstraction of object behaviors, (2) the encapsulation and formalization of model functionality, and (3) the mutability of domain object contents. DIAS simulation objects are inherently capable of highly flexible and heterogeneous spatial realizations. Geospatial graphical representation of DIAS simulation objects is addressed via the GeoViewer, an object-based GIS toolkit application developed at ANL. DIAS simulation capabilities have been extended by inclusion of societal process models generated by the Framework for Addressing Cooperative Extended Transactions (FACET), another object-based framework developed at Argonne National Laboratory. By using FACET models to implement societal behaviors of individuals and organizations within larger DIAS-based natural systems simulations, it has become possible to conveniently address a broad range of issues involving interaction and feedback among natural and societal processes. Example DIAS application areas discussed in this paper include a dynamic virtual oceanic environment, detailed simulation of clinical, physiological, and logistical aspects of health care delivery, and studies of agricultural sustainability of urban centers under environmental stress in ancient Mesopotamia.

SUPPORT VECTOR MACHINE CLASSIFICATION OF OBJECT-BASED DATA FOR CROP MAPPING, USING MULTI-TEMPORAL LANDSAT IMAGERY

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R. Devadas

2012-07-01

Full Text Available Crop mapping and time series analysis of agronomic cycles are critical for monitoring land use and land management practices, and analysing the issues of agro-environmental impacts and climate change. Multi-temporal Landsat data can be used to analyse decadal changes in cropping patterns at field level, owing to its medium spatial resolution and historical availability. This study attempts to develop robust remote sensing techniques, applicable across a large geographic extent, for state-wide mapping of cropping history in Queensland, Australia. In this context, traditional pixel-based classification was analysed in comparison with image object-based classification using advanced supervised machine-learning algorithms such as Support Vector Machine (SVM. For the Darling Downs region of southern Queensland we gathered a set of Landsat TM images from the 2010–2011 cropping season. Landsat data, along with the vegetation index images, were subjected to multiresolution segmentation to obtain polygon objects. Object-based methods enabled the analysis of aggregated sets of pixels, and exploited shape-related and textural variation, as well as spectral characteristics. SVM models were chosen after examining three shape-based parameters, twenty-three textural parameters and ten spectral parameters of the objects. We found that the object-based methods were superior to the pixel-based methods for classifying 4 major landuse/land cover classes, considering the complexities of within field spectral heterogeneity and spectral mixing. Comparative analysis clearly revealed that higher overall classification accuracy (95% was observed in the object-based SVM compared with that of traditional pixel-based classification (89% using maximum likelihood classifier (MLC. Object-based classification also resulted speckle-free images. Further, object-based SVM models were used to classify different broadacre crop types for summer and winter seasons. The influence of

Real-Time Projection-Based Augmented Reality System for Dynamic Objects in the Performing Arts

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Jaewoon Lee

2015-02-01

Full Text Available This paper describes the case study of applying projection-based augmented reality, especially for dynamic objects in live performing shows, such as plays, dancing, or musicals. Our study aims to project imagery correctly inside the silhouettes of flexible objects, in other words, live actors or the surface of actor’s costumes; the silhouette transforms its own shape frequently. To realize this work, we implemented a special projection system based on the real-time masking technique, that is to say real-time projection-based augmented reality system for dynamic objects in performing arts. We installed the sets on a stage for live performance, and rehearsed particular scenes of a musical. In live performance, using projection-based augmented reality technology enhances technical and theatrical aspects which were not possible with existing video projection techniques. The projected images on the surfaces of actor’s costume could not only express the particular scene of a performance more effectively, but also lead the audience to an extraordinary visual experience.

Understanding of Object Detection Based on CNN Family and YOLO

Science.gov (United States)

Du, Juan

2018-04-01

As a key use of image processing, object detection has boomed along with the unprecedented advancement of Convolutional Neural Network (CNN) and its variants since 2012. When CNN series develops to Faster Region with CNN (R-CNN), the Mean Average Precision (mAP) has reached 76.4, whereas, the Frame Per Second (FPS) of Faster R-CNN remains 5 to 18 which is far slower than the real-time effect. Thus, the most urgent requirement of object detection improvement is to accelerate the speed. Based on the general introduction to the background and the core solution CNN, this paper exhibits one of the best CNN representatives You Only Look Once (YOLO), which breaks through the CNN family’s tradition and innovates a complete new way of solving the object detection with most simple and high efficient way. Its fastest speed has achieved the exciting unparalleled result with FPS 155, and its mAP can also reach up to 78.6, both of which have surpassed the performance of Faster R-CNN greatly. Additionally, compared with the latest most advanced solution, YOLOv2 achieves an excellent tradeoff between speed and accuracy as well as an object detector with strong generalization ability to represent the whole image.

3D shape measurement of moving object with FFT-based spatial matching

Science.gov (United States)

Guo, Qinghua; Ruan, Yuxi; Xi, Jiangtao; Song, Limei; Zhu, Xinjun; Yu, Yanguang; Tong, Jun

2018-03-01

This work presents a new technique for 3D shape measurement of moving object in translational motion, which finds applications in online inspection, quality control, etc. A low-complexity 1D fast Fourier transform (FFT)-based spatial matching approach is devised to obtain accurate object displacement estimates, and it is combined with single shot fringe pattern prolometry (FPP) techniques to achieve high measurement performance with multiple captured images through coherent combining. The proposed technique overcomes some limitations of existing ones. Specifically, the placement of marks on object surface and synchronization between projector and camera are not needed, the velocity of the moving object is not required to be constant, and there is no restriction on the movement trajectory. Both simulation and experimental results demonstrate the effectiveness of the proposed technique.

Comparison Effectiveness of Pixel Based Classification and Object Based Classification Using High Resolution Image In Floristic Composition Mapping (Study Case: Gunung Tidar Magelang City)

Science.gov (United States)

Ardha Aryaguna, Prama; Danoedoro, Projo

2016-11-01

Developments of analysis remote sensing have same way with development of technology especially in sensor and plane. Now, a lot of image have high spatial and radiometric resolution, that's why a lot information. Vegetation object analysis such floristic composition got a lot advantage of that development. Floristic composition can be interpreted using a lot of method such pixel based classification and object based classification. The problems for pixel based method on high spatial resolution image are salt and paper who appear in result of classification. The purpose of this research are compare effectiveness between pixel based classification and object based classification for composition vegetation mapping on high resolution image Worldview-2. The results show that pixel based classification using majority 5×5 kernel windows give the highest accuracy between another classifications. The highest accuracy is 73.32% from image Worldview-2 are being radiometric corrected level surface reflectance, but for overall accuracy in every class, object based are the best between another methods. Reviewed from effectiveness aspect, pixel based are more effective then object based for vegetation composition mapping in Tidar forest.

Real-time object tracking system based on field-programmable gate array and convolution neural network

Directory of Open Access Journals (Sweden)

Congyi Lyu

2016-12-01

Full Text Available Vision-based object tracking has lots of applications in robotics, like surveillance, navigation, motion capturing, and so on. However, the existing object tracking systems still suffer from the challenging problem of high computation consumption in the image processing algorithms. The problem can prevent current systems from being used in many robotic applications which have limitations of payload and power, for example, micro air vehicles. In these applications, the central processing unit- or graphics processing unit-based computers are not good choices due to the high weight and power consumption. To address the problem, this article proposed a real-time object tracking system based on field-programmable gate array, convolution neural network, and visual servo technology. The time-consuming image processing algorithms, such as distortion correction, color space convertor, and Sobel edge, Harris corner features detector, and convolution neural network were redesigned using the programmable gates in field-programmable gate array. Based on the field-programmable gate array-based image processing, an image-based visual servo controller was designed to drive a two degree of freedom manipulator to track the target in real time. Finally, experiments on the proposed system were performed to illustrate the effectiveness of the real-time object tracking system.

Artificial Mangrove Species Mapping Using Pléiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms

Directory of Open Access Journals (Sweden)

Dezhi Wang

2018-02-01

Full Text Available In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pléiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms—decision tree (DT, support vector machine (SVM, and random forest (RF—were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%; for object-based image analysis, RF could achieve the highest overall accuracy (82.40%, and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar’s test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1 as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT could well be identified. However, for complex and easily

Waveform inversion with exponential damping using a deconvolution-based objective function

KAUST Repository

Choi, Yun Seok

2016-09-06

The lack of low frequency components in seismic data usually leads full waveform inversion into the local minima of its objective function. An exponential damping of the data, on the other hand, generates artificial low frequencies, which can be used to admit long wavelength updates for waveform inversion. Another feature of exponential damping is that the energy of each trace also exponentially decreases with source-receiver offset, where the leastsquare misfit function does not work well. Thus, we propose a deconvolution-based objective function for waveform inversion with an exponential damping. Since the deconvolution filter includes a division process, it can properly address the unbalanced energy levels of the individual traces of the damped wavefield. Numerical examples demonstrate that our proposed FWI based on the deconvolution filter can generate a convergent long wavelength structure from the artificial low frequency components coming from an exponential damping.

Delineation of wetland areas from high resolution WorldView-2 data by object-based method

International Nuclear Information System (INIS)

Hassan, N; Hamid, J R A; Adnan, N A; Jaafar, M

2014-01-01

Various classification methods are available that can be used to delineate land cover types. Object-based is one of such methods for delineating the land cover from satellite imageries. This paper focuses on the digital image processing aspects of discriminating wetland areas via object-based method using high resolution satellite multispectral WorldView-2 image data taken over part of Penang Island region. This research is an attempt to improve the wetland area delineation in conjunction with a range of classification techniques which can be applied to satellite data with high spatial and spectral resolution such as World View 2. The intent is to determine a suitable approach to delineate and map these wetland areas more appropriately. There are common parameters to take into account that are pivotal in object-based method which are the spatial resolution and the range of spectral channels of the imaging sensor system. The preliminary results of the study showed object-based analysis is capable of delineating wetland region of interest with an accuracy that is acceptable to the required tolerance for land cover classification

A comparison of moving object detection methods for real-time moving object detection

Science.gov (United States)

Roshan, Aditya; Zhang, Yun

2014-06-01

Moving object detection has a wide variety of applications from traffic monitoring, site monitoring, automatic theft identification, face detection to military surveillance. Many methods have been developed across the globe for moving object detection, but it is very difficult to find one which can work globally in all situations and with different types of videos. The purpose of this paper is to evaluate existing moving object detection methods which can be implemented in software on a desktop or laptop, for real time object detection. There are several moving object detection methods noted in the literature, but few of them are suitable for real time moving object detection. Most of the methods which provide for real time movement are further limited by the number of objects and the scene complexity. This paper evaluates the four most commonly used moving object detection methods as background subtraction technique, Gaussian mixture model, wavelet based and optical flow based methods. The work is based on evaluation of these four moving object detection methods using two (2) different sets of cameras and two (2) different scenes. The moving object detection methods have been implemented using MatLab and results are compared based on completeness of detected objects, noise, light change sensitivity, processing time etc. After comparison, it is observed that optical flow based method took least processing time and successfully detected boundary of moving objects which also implies that it can be implemented for real-time moving object detection.

Vanpool trip planning based on evolutionary multiple objective optimization

Science.gov (United States)

Zhao, Ming; Yang, Disheng; Feng, Shibing; Liu, Hengchang

2017-08-01

Carpool and vanpool draw a lot of researchers’ attention, which is the emphasis of this paper. A concrete vanpool operation definition is given, based on the given definition, this paper tackles vanpool operation optimization using user experience decline index(UEDI). This paper is focused on making each user having identical UEDI and the system having minimum sum of all users’ UEDI. Three contributions are made, the first contribution is a vanpool operation scheme diagram, each component of the scheme is explained in detail. The second contribution is getting all customer’s UEDI as a set, standard deviation and sum of all users’ UEDI set are used as objectives in multiple objective optimization to decide trip start address, trip start time and trip destination address. The third contribution is a trip planning algorithm, which tries to minimize the sum of all users’ UEDI. Geographical distribution of the charging stations and utilization rate of the charging stations are considered in the trip planning process.

Context based Coding of Binary Shapes by Object Boundary Straightness Analysis

DEFF Research Database (Denmark)

Aghito, Shankar Manuel; Forchhammer, Søren

2004-01-01

A new lossless compression scheme for bilevel images targeted at binary shapes of image and video objects is presented. The scheme is based on a local analysis of the digital straightness of the causal part of the object boundary, which is used in the context definition for arithmetic encoding....... Tested on individual images of binary shapes and binary layers of digital maps the algorithm outperforms PWC, JBIG and MPEG-4 CAE. On the binary shapes the code lengths are reduced by 21%, 25%, and 42%, respectively. On the maps the reductions are 34%, 32%, and 59%, respectively. The algorithm is also...

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Science.gov (United States)

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

Object-based processes in the planning of goal-directed hand movements

NARCIS (Netherlands)

Bekkering, H.; Pratt, J.

2004-01-01

Theories in motor control suggest that the parameters specified during the planning of goal-directed hand movements to a visual target are defined in spatial parameters like direction and amplitude. Recent findings in the visual attention literature, however, argue widely for early object-based

Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data

Directory of Open Access Journals (Sweden)

Xiaolong Liu

2015-01-01

Full Text Available Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR data in an object-based image analysis (OBIA paradigm. The aims of this work were the following: (i to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species’ separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI with a 1 m spatial resolution and the Canopy Height Model (CHM data derived from the LiDAR data acquired by the airborne Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR, VHR/CHM, and minimum noise fractional transformed data (MNF/CHM were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that

Image de-noising based on mathematical morphology and multi-objective particle swarm optimization

Science.gov (United States)

Dou, Liyun; Xu, Dan; Chen, Hao; Liu, Yicheng

2017-07-01

To overcome the problem of image de-noising, an efficient image de-noising approach based on mathematical morphology and multi-objective particle swarm optimization (MOPSO) is proposed in this paper. Firstly, constructing a series and parallel compound morphology filter based on open-close (OC) operation and selecting a structural element with different sizes try best to eliminate all noise in a series link. Then, combining multi-objective particle swarm optimization (MOPSO) to solve the parameters setting of multiple structural element. Simulation result shows that our algorithm can achieve a superior performance compared with some traditional de-noising algorithm.

Multi-objective experimental design for (13)C-based metabolic flux analysis.

Science.gov (United States)

Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

2015-10-01

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective

Development of an FPGA Based Embedded System for High Speed Object Tracking

Directory of Open Access Journals (Sweden)

Chandrashekar MATHAM

2010-01-01

Full Text Available This paper deals with the development and implementation of system on chip (SOC for object tracking using histograms. To acquire the distance and velocity information of moving vehicles such as military tanks, to identify the type of target within the range from 100 m to 3 km and to estimate the movements of the vehicle. The VHDL code is written for the above objectives and implemented using Xilinx’s VERTEX-4 based PCI card family.

Measurement of a discontinuous object based on a dual-frequency grating

Institute of Scientific and Technical Information of China (English)

Qiao Nao-Sheng; Cai Xin-Hua; Yao Chun-Mei

2009-01-01

The dual-frequency grating measurement theory is proposed in order to carry out the measurement of a discontinuous object. Firstly, the reason why frequency spectra are produced by low frequency gratings and high frequency gratings in the field of frequency is analysed, and the relationship between the wrapped-phase and the unwrappingphase is discussed. Secondly, a method to combine the advantages of the two kinds of gratings is proposed: one stripe is produced in the mutation part of the object measured by a suitable low frequency grating designed by MATLAB, then the phase produced by the low frequency grating need not be unfolded. The integer series of stripes is produced by a high frequency grating designed by MATLAB based on the frequency ratio of the two kinds of gratings and the high frequency wrapped-phase, and the high frequency unwrapping-phase is then obtained. In order to verify the correctness of the theoretical analysis, a steep discontinuous object of 600×600 pixels and 10.00 mm in height is simulated and a discontinuous object of ladder shape which is 32.00 mm in height is used in experiment. Both the simulation and the experiment can restore the discontinuous object height accurately by using the dual-frequency grating measurement theory.

When action turns into words. Activation of motor-based knowledge during categorization of manipulable objects

DEFF Research Database (Denmark)

Gerlach, Christian; Law, Ian; Paulson, Olaf B

2002-01-01

Functional imaging studies have demonstrated that processing of man-made objects activate the left ventral premotor cortex, which is known to be concerned with motor function. This has led to the suggestion that the comprehension of man-made objects may rely on motor-based knowledge of object uti...

An Integrative Object-Based Image Analysis Workflow for Uav Images

Science.gov (United States)

Yu, Huai; Yan, Tianheng; Yang, Wen; Zheng, Hong

2016-06-01

In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya'an earthquake demonstrate the effectiveness and efficiency of our proposed method.

AN INTEGRATIVE OBJECT-BASED IMAGE ANALYSIS WORKFLOW FOR UAV IMAGES

Directory of Open Access Journals (Sweden)

H. Yu

2016-06-01

Full Text Available In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA. More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC. Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake demonstrate the effectiveness and efficiency of our proposed method.

A hybrid evolutionary algorithm for multi-objective anatomy-based dose optimization in high-dose-rate brachytherapy

International Nuclear Information System (INIS)

Lahanas, M; Baltas, D; Zamboglou, N

2003-01-01

Multiple objectives must be considered in anatomy-based dose optimization for high-dose-rate brachytherapy and a large number of parameters must be optimized to satisfy often competing objectives. For objectives expressed solely in terms of dose variances, deterministic gradient-based algorithms can be applied and a weighted sum approach is able to produce a representative set of non-dominated solutions. As the number of objectives increases, or non-convex objectives are used, local minima can be present and deterministic or stochastic algorithms such as simulated annealing either cannot be used or are not efficient. In this case we employ a modified hybrid version of the multi-objective optimization algorithm NSGA-II. This, in combination with the deterministic optimization algorithm, produces a representative sample of the Pareto set. This algorithm can be used with any kind of objectives, including non-convex, and does not require artificial importance factors. A representation of the trade-off surface can be obtained with more than 1000 non-dominated solutions in 2-5 min. An analysis of the solutions provides information on the possibilities available using these objectives. Simple decision making tools allow the selection of a solution that provides a best fit for the clinical goals. We show an example with a prostate implant and compare results obtained by variance and dose-volume histogram (DVH) based objectives

Analytical Model of Doppler Spectra of Light Backscattered from Rotating Convex Bodies of Revolution in the Global Cartesian Coordinate System

International Nuclear Information System (INIS)

Yan-Jun, Gong; Zhen-Sen, Wu; Jia-Ji, Wu

2009-01-01

We present an analytical model of Doppler spectra in backscattering from arbitrary rough convex bodies of revolution rotating around their axes in the global Cartesian coordinate system. This analytical model is applied to analyse Doppler spectra in backscatter from two cones and two cylinders, as well as two ellipsoids of revolution. We numerically analyse the influences of attitude and geometry size of objects on Doppler spectra. The analytical model can give contribution of the surface roughness, attitude and geometry size of convex bodies of revolution to Doppler spectra and may contribute to laser Doppler velocimetry as well as ladar applications

Object Persistence: A Framework Based On Design Patterns

OpenAIRE

Kienzle, Jörg; Romanovsky, Alexander

2000-01-01

The poster presents a framework for providing object persistence in object-oriented programming languages without modifying the run-time system or the language itself. The framework does not rely on any kind of special programming language features. It only uses basic object-oriented programming techniques, and is therefore implementable in any object-oriented programming language.

Image-based tracking system for vibration measurement of a rotating object using a laser scanning vibrometer

Energy Technology Data Exchange (ETDEWEB)

Kim, Dongkyu, E-mail: akein@gist.ac.kr; Khalil, Hossam; Jo, Youngjoon; Park, Kyihwan, E-mail: khpark@gist.ac.kr [School of Mechatronics, Gwangju Institute of Science and Technology, Buk-gu, Gwangju, South Korea, 500-712 (Korea, Republic of)

2016-06-28

An image-based tracking system using laser scanning vibrometer is developed for vibration measurement of a rotating object. The proposed system unlike a conventional one can be used where the position or velocity sensor such as an encoder cannot be attached to an object. An image processing algorithm is introduced to detect a landmark and laser beam based on their colors. Then, through using feedback control system, the laser beam can track a rotating object.

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement.

Science.gov (United States)

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Bai, Xiangzhi; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-03-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment.

A computer graphics based model for scattering from objects of arbitrary shapes in the optical region

Science.gov (United States)

Goel, Narendra S.; Rozehnal, Ivan; Thompson, Richard L.

1991-01-01

A computer-graphics-based model, named DIANA, is presented for generation of objects of arbitrary shape and for calculating bidirectional reflectances and scattering from them, in the visible and infrared region. The computer generation is based on a modified Lindenmayer system approach which makes it possible to generate objects of arbitrary shapes and to simulate their growth, dynamics, and movement. Rendering techniques are used to display an object on a computer screen with appropriate shading and shadowing and to calculate the scattering and reflectance from the object. The technique is illustrated with scattering from canopies of simulated corn plants.

A recurrent neural model for proto-object based contour integration and figure-ground segregation.

Science.gov (United States)

Hu, Brian; Niebur, Ernst

2017-12-01

Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594-6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492-1499 2007; Chen et al. Neuron, 82(3), 682-694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach

Science.gov (United States)

Alizadeh, Mohammad Reza; Nikoo, Mohammad Reza; Rakhshandehroo, Gholam Reza

2017-08-01

Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

An Approach of Dynamic Object Removing for Indoor Mapping Based on UGV SLAM

Directory of Open Access Journals (Sweden)

Jian Tang

2015-07-01

Full Text Available The study of indoor mapping for Location Based Service (LBS becomes more and more popular in recent years. LiDAR SLAM based mapping method seems to be a promising indoor mapping solution. However, there are some dynamic objects such as pedestrians, indoor vehicles, etc. existing in the raw LiDAR range data. They have to be removal for mapping purpose. In this paper, a new approach of dynamic object removing called Likelihood Grid Voting (LGV is presented. It is a model free method and takes full advantage of the high scanning rate of LiDAR, which is moving at a relative low speed in indoor environment. In this method, a counting grid is allocated for recording the occupation of map position by laser scans. The lower counter value of this position can be recognized as dynamic objects and the point cloud will be removed from map. This work is a part of algorithms in our self- developed Unmanned Ground Vehicles (UGV simultaneous localization and Mapping (SLAM system- NAVIS. Field tests are carried in an indoor parking place with NAVIS to evaluate the effectiveness of the proposed method. The result shows that all the small size objects like pedestrians can be detected and removed quickly; large size of objects like cars can be detected and removed partly.

Pre-conceptual-schema-based patterns for deriving key performance indicators from strategic objectives

Directory of Open Access Journals (Sweden)

Carlos Mario Zapata Jaramillo

2017-05-01

Full Text Available Performance measurement is crucial for achieving business success. Moreover, such success is also related to the fulfillment of the organizational strategic objectives. Hence, an adequate determination of relevant performance indicators—or key performance indicators (KPIs—and their relationships to organizational objectives is needed. Even though several approaches for treating KPIs and objective-KPI relationships have been proposed, they exhibit some drawbacks associated with the lack of reusability and traceability. We attempt to fill this gap by proposing a set of patterns based on pre-conceptual schemas for supporting the systematic derivation of KPIs and their relationships to organizational objectives. In this way, the proposed patterns guarantee a reusable and traceable derivation process of a set of candidate KPIs from organizational strategic objectives. Lastly, we provide a lab study in order to illustrate the usefulness of this proposal.

A comparison of an energy/economic-based against an exergoeconomic-based multi-objective optimisation for low carbon building energy design

International Nuclear Information System (INIS)

García Kerdan, Iván; Raslan, Rokia; Ruyssevelt, Paul; Morillón Gálvez, David

2017-01-01

This study presents a comparison of the optimisation of building energy retrofit strategies from two different perspectives: an energy/economic-based analysis and an exergy/exergoeconomic-based analysis. A recently retrofitted community centre is used as a case study. ExRET-Opt, a novel building energy/exergy simulation tool with multi-objective optimisation capabilities based on NSGA-II is used to run both analysis. The first analysis, based on the 1st Law only, simultaneously optimises building energy use and design's Net Present Value (NPV). The second analysis, based on the 1st and the 2nd Laws, simultaneously optimises exergy destructions and the exergoeconomic cost-benefit index. Occupant thermal comfort is considered as a common objective function for both approaches. The aim is to assess the difference between the methods and calculate the performance among main indicators, considering the same decision variables and constraints. Outputs show that the inclusion of exergy/exergoeconomics as objective functions into the optimisation procedure has resulted in similar 1st Law and thermal comfort outputs, while providing solutions with less environmental impact under similar capital investments. This outputs demonstrate how the 1st Law is only a necessary calculation while the utilisation of the 1st and 2nd Laws becomes a sufficient condition for the analysis and design of low carbon buildings. - Highlights: • The study compares an energy-based and an exergy-based building design optimisation. • Occupant thermal comfort is considered as a common objective function. • A comparison of thermodynamic outputs is made against the actual retrofit design. • Under similar constraints, second law optimisation presents better overall results. • Exergoeconomic optimisation solutions improves building exergy efficiency to double.

Multi-Objective Ant Colony Optimization Based on the Physarum-Inspired Mathematical Model for Bi-Objective Traveling Salesman Problems.

Directory of Open Access Journals (Sweden)

Zili Zhang

Full Text Available Bi-objective Traveling Salesman Problem (bTSP is an important field in the operations research, its solutions can be widely applied in the real world. Many researches of Multi-objective Ant Colony Optimization (MOACOs have been proposed to solve bTSPs. However, most of MOACOs suffer premature convergence. This paper proposes an optimization strategy for MOACOs by optimizing the initialization of pheromone matrix with the prior knowledge of Physarum-inspired Mathematical Model (PMM. PMM can find the shortest route between two nodes based on the positive feedback mechanism. The optimized algorithms, named as iPM-MOACOs, can enhance the pheromone in the short paths and promote the search ability of ants. A series of experiments are conducted and experimental results show that the proposed strategy can achieve a better compromise solution than the original MOACOs for solving bTSPs.

Optimization of object tracking based on enhanced imperialist ...

African Journals Online (AJOL)

. Tracking moving object(s) in video/image frame sequences in cluttered scenes usually results in complications and hence performance degradation. This is attributable to complexity in partial and full object occlusions and scene illumination ...

Thickness and clearance visualization based on distance field of 3D objects

Directory of Open Access Journals (Sweden)

Masatomo Inui

2015-07-01

Full Text Available This paper proposes a novel method for visualizing the thickness and clearance of 3D objects in a polyhedral representation. The proposed method uses the distance field of the objects in the visualization. A parallel algorithm is developed for constructing the distance field of polyhedral objects using the GPU. The distance between a voxel and the surface polygons of the model is computed many times in the distance field construction. Similar sets of polygons are usually selected as close polygons for close voxels. By using this spatial coherence, a parallel algorithm is designed to compute the distances between a cluster of close voxels and the polygons selected by the culling operation so that the fast shared memory mechanism of the GPU can be fully utilized. The thickness/clearance of the objects is visualized by distributing points on the visible surfaces of the objects and painting them with a unique color corresponding to the thickness/clearance values at those points. A modified ray casting method is developed for computing the thickness/clearance using the distance field of the objects. A system based on these algorithms can compute the distance field of complex objects within a few minutes for most cases. After the distance field construction, thickness/clearance visualization at a near interactive rate is achieved.

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings.

Science.gov (United States)

Su, Nan; Yan, Yiming; Qiu, Mingjie; Zhao, Chunhui; Wang, Liguo

2018-03-29

In this paper, we proposed a novel object-based dense matching method specially for the high-precision disparity map of building objects in urban areas, which can maintain accurate object structure characteristics. The proposed framework mainly includes three stages. Firstly, an improved edge line extraction method is proposed for the edge segments to fit closely to building outlines. Secondly, a fusion method is proposed for the outlines under the constraint of straight lines, which can maintain the building structural attribute with parallel or vertical edges, which is very useful for the dense matching method. Finally, we proposed an edge constraint and outline compensation (ECAOC) dense matching method to maintain building object structural characteristics in the disparity map. In the proposed method, the improved edge lines are used to optimize matching search scope and matching template window, and the high-precision building outlines are used to compensate the shape feature of building objects. Our method can greatly increase the matching accuracy of building objects in urban areas, especially at building edges. For the outline extraction experiments, our fusion method verifies the superiority and robustness on panchromatic images of different satellites and different resolutions. For the dense matching experiments, our ECOAC method shows great advantages for matching accuracy of building objects in urban areas compared with three other methods.

Object-Based Dense Matching Method for Maintaining Structure Characteristics of Linear Buildings

Directory of Open Access Journals (Sweden)

Nan Su

2018-03-01

Full Text Available In this paper, we proposed a novel object-based dense matching method specially for the high-precision disparity map of building objects in urban areas, which can maintain accurate object structure characteristics. The proposed framework mainly includes three stages. Firstly, an improved edge line extraction method is proposed for the edge segments to fit closely to building outlines. Secondly, a fusion method is proposed for the outlines under the constraint of straight lines, which can maintain the building structural attribute with parallel or vertical edges, which is very useful for the dense matching method. Finally, we proposed an edge constraint and outline compensation (ECAOC dense matching method to maintain building object structural characteristics in the disparity map. In the proposed method, the improved edge lines are used to optimize matching search scope and matching template window, and the high-precision building outlines are used to compensate the shape feature of building objects. Our method can greatly increase the matching accuracy of building objects in urban areas, especially at building edges. For the outline extraction experiments, our fusion method verifies the superiority and robustness on panchromatic images of different satellites and different resolutions. For the dense matching experiments, our ECOAC method shows great advantages for matching accuracy of building objects in urban areas compared with three other methods.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Directory of Open Access Journals (Sweden)

Marko Budinich

Full Text Available Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA and multi-objective flux variability analysis (MO-FVA. Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity that take place at the ecosystem scale.

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

Science.gov (United States)

Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua

2014-01-01

To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252

Network Structures in a Society Composed of Individuals with Utilities DependingStudy of Object-Oriented Model for the Knowledge Base System

OpenAIRE

Mingwei, Zhao; Yanzhong, Dang

2005-01-01

Based on the analysis of object-oriented model, knowledge base and knowledge base system by using theories on object-oriented and knowledge base, the relationships between object-oriented model and knowledge base are discussed in this paper. The architecture of object-oriented knowledge system is proposed and the Rule-Case-Based Reasoning knowledge base system is designed.

Cascade Boosting-Based Object Detection from High-Level Description to Hardware Implementation

Directory of Open Access Journals (Sweden)

K. Khattab

2009-01-01

Full Text Available Object detection forms the first step of a larger setup for a wide variety of computer vision applications. The focus of this paper is the implementation of a real-time embedded object detection system while relying on high-level description language such as SystemC. Boosting-based object detection algorithms are considered as the fastest accurate object detection algorithms today. However, the implementation of a real time solution for such algorithms is still a challenge. A new parallel implementation, which exploits the parallelism and the pipelining in these algorithms, is proposed. We show that using a SystemC description model paired with a mainstream automatic synthesis tool can lead to an efficient embedded implementation. We also display some of the tradeoffs and considerations, for this implementation to be effective. This implementation proves capable of achieving 42 fps for 320×240 images as well as bringing regularity in time consuming.

Modeling and query the uncertainty of network constrained moving objects based on RFID data

Science.gov (United States)

Han, Liang; Xie, Kunqing; Ma, Xiujun; Song, Guojie

2007-06-01

The management of network constrained moving objects is more and more practical, especially in intelligent transportation system. In the past, the location information of moving objects on network is collected by GPS, which cost high and has the problem of frequent update and privacy. The RFID (Radio Frequency IDentification) devices are used more and more widely to collect the location information. They are cheaper and have less update. And they interfere in the privacy less. They detect the id of the object and the time when moving object passed by the node of the network. They don't detect the objects' exact movement in side the edge, which lead to a problem of uncertainty. How to modeling and query the uncertainty of the network constrained moving objects based on RFID data becomes a research issue. In this paper, a model is proposed to describe the uncertainty of network constrained moving objects. A two level index is presented to provide efficient access to the network and the data of movement. The processing of imprecise time-slice query and spatio-temporal range query are studied in this paper. The processing includes four steps: spatial filter, spatial refinement, temporal filter and probability calculation. Finally, some experiments are done based on the simulated data. In the experiments the performance of the index is studied. The precision and recall of the result set are defined. And how the query arguments affect the precision and recall of the result set is also discussed.

A bio-inspired method and system for visual object-based attention and segmentation

Science.gov (United States)

Huber, David J.; Khosla, Deepak

2010-04-01

This paper describes a method and system of human-like attention and object segmentation in visual scenes that (1) attends to regions in a scene in their rank of saliency in the image, (2) extracts the boundary of an attended proto-object based on feature contours, and (3) can be biased to boost the attention paid to specific features in a scene, such as those of a desired target object in static and video imagery. The purpose of the system is to identify regions of a scene of potential importance and extract the region data for processing by an object recognition and classification algorithm. The attention process can be performed in a default, bottom-up manner or a directed, top-down manner which will assign a preference to certain features over others. One can apply this system to any static scene, whether that is a still photograph or imagery captured from video. We employ algorithms that are motivated by findings in neuroscience, psychology, and cognitive science to construct a system that is novel in its modular and stepwise approach to the problems of attention and region extraction, its application of a flooding algorithm to break apart an image into smaller proto-objects based on feature density, and its ability to join smaller regions of similar features into larger proto-objects. This approach allows many complicated operations to be carried out by the system in a very short time, approaching real-time. A researcher can use this system as a robust front-end to a larger system that includes object recognition and scene understanding modules; it is engineered to function over a broad range of situations and can be applied to any scene with minimal tuning from the user.

Sapphire: Canada's Answer to Space-Based Surveillance of Orbital Objects

Science.gov (United States)

Maskell, P.; Oram, L.

The Canadian Department of National Defence is in the process of developing the Canadian Space Surveillance System (CSSS) as the main focus of the Surveillance of Space (SofS) Project. The CSSS consists of two major elements: the Sapphire System and the Sensor System Operations Centre (SSOC). The space segment of the Sapphire System is comprised of the Sapphire Satellite - an autonomous spacecraft with an electro-optical payload which will act as a contributing sensor to the United States (US) Space Surveillance Network (SSN). It will operate in a circular, sunsynchronous orbit at an altitude of approximately 750 kilometers and image a minimum of 360 space objects daily in orbits ranging from 6,000 to 40,000 kilometers in altitude. The ground segment of the Sapphire System is composed of a Spacecraft Control Center (SCC), a Satellite Processing and Scheduling Facility (SPSF), and the Sapphire Simulator. The SPSF will be responsible for data transmission, reception, and processing while the SCC will serve to control and monitor the Sapphire Satellite. Surveillance data will be received from Sapphire through two ground stations. Following processing by the SPSF, the surveillance data will then be forwarded to the SSOC. The SSOC will function as the interface between the Sapphire System and the US Joint Space Operations Center (JSpOC). The JSpOC coordinates input from various sensors around the world, all of which are a part of the SSN. The SSOC will task the Sapphire System daily and provide surveillance data to the JSpOC for correlation with data from other SSN sensors. This will include orbital parameters required to predict future positions of objects to be tracked. The SSOC receives daily tasking instructions from the JSpOC to determine which objects the Sapphire spacecraft is required to observe. The advantage of this space-based sensor over ground-based telescopes is that weather and time of day are not factors affecting observation. Thus, space-based optical

Application of In-Segment Multiple Sampling in Object-Based Classification

Directory of Open Access Journals (Sweden)

Nataša Đurić

2014-12-01

Full Text Available When object-based analysis is applied to very high-resolution imagery, pixels within the segments reveal large spectral inhomogeneity; their distribution can be considered complex rather than normal. When normality is violated, the classification methods that rely on the assumption of normally distributed data are not as successful or accurate. It is hard to detect normality violations in small samples. The segmentation process produces segments that vary highly in size; samples can be very big or very small. This paper investigates whether the complexity within the segment can be addressed using multiple random sampling of segment pixels and multiple calculations of similarity measures. In order to analyze the effect sampling has on classification results, statistics and probability value equations of non-parametric two-sample Kolmogorov-Smirnov test and parametric Student’s t-test are selected as similarity measures in the classification process. The performance of both classifiers was assessed on a WorldView-2 image for four land cover classes (roads, buildings, grass and trees and compared to two commonly used object-based classifiers—k-Nearest Neighbor (k-NN and Support Vector Machine (SVM. Both proposed classifiers showed a slight improvement in the overall classification accuracies and produced more accurate classification maps when compared to the ground truth image.

Semantic-Based Concurrency Control for Object-Oriented Database Systems Supporting Real-Time Applications

National Research Council Canada - National Science Library

Lee, Juhnyoung; Son, Sang H

1994-01-01

.... This paper investigates major issues in designing semantic-based concurrency control for object-oriented database systems supporting real-time applications, and it describes approaches to solving...

Simplified production of multimedia based radiological learning objects using the flash format

International Nuclear Information System (INIS)

Jedrusik, P.; Preisack, M.; Dammann, F.

2005-01-01

Purpose: evaluation of the applicability of the flash format for the production of radiological learning objects used in an e-learning environment. Material and methods: five exemplary learning objects with different didactic purposes referring to radiological diagnostics are presented. They have been intended for the use within the multimedia, internet-based e-learning environment LaMedica. Interactive learning objects were composed using the Flash 5.0 software (Macromedia, San Francisco, USA) on the basis of digital CT and MR images, digitized conventional radiographs and different graphical elements prepared as TIFF files or in a vector graphics format. Results: after a short phase of initial skill adaptation training, a radiologist author was soon able to create independently all learning objects. The import of different types of images and graphical elements was carried out without complications. Despite manifold design options, handling of the program is easy due to clear arrangement and structure, thus enabling the creation of simple as well as complex learning objects that provided a high degree of attractiveness and interaction. Data volume and bandwidth demand for online use was significantly reduced by the flash format compression without a substantial loss of visual quality. (orig.)

Electron beam for preservation of biodeteriorated cultural heritage paper-based objects

Science.gov (United States)

Chmielewska-Śmietanko, Dagmara; Gryczka, Urszula; Migdał, Wojciech; Kopeć, Kamil

2018-02-01

Unsuitable storage conditions or accidents such as floods can present a serious threat for large quantities of book making them prone to attack by harmful microorganisms. The microbiological degradation of archives and book collections can be efficiently inhibited with irradiation processing. Application of EB irradiation to book and archive collections can also be a very effective alternative to the commonly used ethylene oxide treatment, which is toxic to the human and natural environment. In this study was evaluated the influence of EB irradiation used for microbiological decontamination process on paper-based objects. Three different kinds of paper (Whatman CHR 1, office paper and newsprint paper) were treated with 0.4, 1, 2, 5, 10 and 25 kGy electron beam irradiation. Optical and mechanical properties of different sorts of paper treated with e-beam, before and after the radiation process were studied. These results, which correlated with absorbed radiation doses effective for the elimination of Aspergillus niger (A. niger) allowed to determine that EB irradiation with absorbed radiation dose of 5 kGy ensures safe decontamination of different sorts of paper-based objects.

A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

International Nuclear Information System (INIS)

Khoroshiltseva, Marina; Slanzi, Debora; Poli, Irene

2016-01-01

Highlights: • We present a multi-objective optimization algorithm for shading design. • We combine Harmony search and Pareto-based procedures. • Thermal and daylighting performances of external shading were considered. • We applied the optimization process to a residential social housing in Madrid. - Abstract: In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions.

Two-year-olds exclude novel objects as potential referents of novel words based on pragmatics.

Science.gov (United States)

Grassmann, Susanne; Stracke, Marén; Tomasello, Michael

2009-09-01

Many studies have established that children tend to exclude objects for which they already have a name as potential referents of novel words. In the current study we asked whether this exclusion can be triggered by social-pragmatic context alone without pre-existing words as blockers. Two-year-old children watched an adult looking at a novel object while saying a novel word with excitement. In one condition the adult had not seen the object beforehand, and so the children interpreted the adult's utterance as referring to the gazed-at object. In another condition the adult and child had previously played jointly with the gazed-at object. In this case, children less often assumed that the adult was referring to the object but rather they searched for an alternative referent--presumably because they inferred that the gazed-at object was old news in their common ground with the adult and so not worthy of excited labeling. Since this inference based on exclusion is highly similar to that underlying the Principle of Contrast/Mutual Exclusivity, we propose that this principle is not purely lexical but rather is based on children's understanding of how and why people direct one another's attention to things either with or without language.

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

Directory of Open Access Journals (Sweden)

Ming Xue

2014-02-01

Full Text Available To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

Aerial surveillance based on hierarchical object classification for ground target detection

Science.gov (United States)

Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

2015-03-01

Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

Incorporating social benefits in multi-objective optimization of forest-based bioenergy and biofuel supply chains

International Nuclear Information System (INIS)

Cambero, Claudia; Sowlati, Taraneh

2016-01-01

Highlights: • Quantified social benefits of forest- based biomass supply chain. • Developed multi-objective optimization model. • Incorporated social benefits into multi-objective model. • Solved the model using the AUGMECON method. • Applied the model to a case study in Canada. - Abstract: Utilization of forest and wood residues to produce bioenergy and biofuels could generate additional revenue streams for forestry companies, reduce their environmental impacts and generate new development opportunities for forest-dependent communities. Further development of forest-based biorefineries entails addressing complexities and challenges related to biomass procurement, logistics, technologies, and sustainability. Numerous optimization models have been proposed for the economic and environmental design of biomass-to-bioenergy or biofuel supply chains. A few of them also maximized the job creation potential of the supply chain through the use of employment multipliers. The use of a total job creation indicator as the social optimization objective implies that all new jobs generate the same level of social benefit. In this paper, we quantify the potential social benefit of new forest-based biorefinery supply chains considering different impacts of new jobs based on their type and location. This social benefit is incorporated into a multi-objective mixed integer linear programming model that maximizes the social benefit, net present value and greenhouse gas emission saving potential of a forest-based biorefinery supply chain. The applicability of the model is illustrated through a case study in the interior region of British Columbia, Canada where different utilization paths for available forest and wood residues are investigated. The multi-objective optimization model is solved using a Pareto-generating method. The analysis of the generated set of Pareto-optimal solutions show a trade-off between the net present value of the supply chain and the other two

Object-Based Classification as an Alternative Approach to the Traditional Pixel-Based Classification to Identify Potential Habitat of the Grasshopper Sparrow

Science.gov (United States)

Jobin, Benoît; Labrecque, Sandra; Grenier, Marcelle; Falardeau, Gilles

2008-01-01

The traditional method of identifying wildlife habitat distribution over large regions consists of pixel-based classification of satellite images into a suite of habitat classes used to select suitable habitat patches. Object-based classification is a new method that can achieve the same objective based on the segmentation of spectral bands of the image creating homogeneous polygons with regard to spatial or spectral characteristics. The segmentation algorithm does not solely rely on the single pixel value, but also on shape, texture, and pixel spatial continuity. The object-based classification is a knowledge base process where an interpretation key is developed using ground control points and objects are assigned to specific classes according to threshold values of determined spectral and/or spatial attributes. We developed a model using the eCognition software to identify suitable habitats for the Grasshopper Sparrow, a rare and declining species found in southwestern Québec. The model was developed in a region with known breeding sites and applied on other images covering adjacent regions where potential breeding habitats may be present. We were successful in locating potential habitats in areas where dairy farming prevailed but failed in an adjacent region covered by a distinct Landsat scene and dominated by annual crops. We discuss the added value of this method, such as the possibility to use the contextual information associated to objects and the ability to eliminate unsuitable areas in the segmentation and land cover classification processes, as well as technical and logistical constraints. A series of recommendations on the use of this method and on conservation issues of Grasshopper Sparrow habitat is also provided.

Mobile object retrieval in server-based image databases

Science.gov (United States)

Manger, D.; Pagel, F.; Widak, H.

2013-05-01

The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to search for similar objects in an own image database. As the computational performance and the memory capacity of mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for example, if the images are represented with global image features or if the search is done using EXIF or textual metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend is needed. In this work, we present a content-based image retrieval system with a client server architecture working with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the most similar images of the database highlighting the visual information which is common with the query image. Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased image retrieval.

Objective-lens-free Fiber-based Position Detection with Nanometer Resolution in a Fiber Optical Trapping System.

Science.gov (United States)

Ti, Chaoyang; Ho-Thanh, Minh-Tri; Wen, Qi; Liu, Yuxiang

2017-10-13

Position detection with high accuracy is crucial for force calibration of optical trapping systems. Most existing position detection methods require high-numerical-aperture objective lenses, which are bulky, expensive, and difficult to miniaturize. Here, we report an affordable objective-lens-free, fiber-based position detection scheme with 2 nm spatial resolution and 150 MHz bandwidth. This fiber based detection mechanism enables simultaneous trapping and force measurements in a compact fiber optical tweezers system. In addition, we achieved more reliable signal acquisition with less distortion compared with objective based position detection methods, thanks to the light guiding in optical fibers and small distance between the fiber tips and trapped particle. As a demonstration of the fiber based detection, we used the fiber optical tweezers to apply a force on a cell membrane and simultaneously measure the cellular response.

Invariant object recognition based on the generalized discrete radon transform

Science.gov (United States)

Easley, Glenn R.; Colonna, Flavia

2004-04-01

We introduce a method for classifying objects based on special cases of the generalized discrete Radon transform. We adjust the transform and the corresponding ridgelet transform by means of circular shifting and a singular value decomposition (SVD) to obtain a translation, rotation and scaling invariant set of feature vectors. We then use a back-propagation neural network to classify the input feature vectors. We conclude with experimental results and compare these with other invariant recognition methods.

Vision-based overlay of a virtual object into real scene for designing room interior

Science.gov (United States)

Harasaki, Shunsuke; Saito, Hideo

2001-10-01

In this paper, we introduce a geometric registration method for augmented reality (AR) and an application system, interior simulator, in which a virtual (CG) object can be overlaid into a real world space. Interior simulator is developed as an example of an AR application of the proposed method. Using interior simulator, users can visually simulate the location of virtual furniture and articles in the living room so that they can easily design the living room interior without placing real furniture and articles, by viewing from many different locations and orientations in real-time. In our system, two base images of a real world space are captured from two different views for defining a projective coordinate of object 3D space. Then each projective view of a virtual object in the base images are registered interactively. After such coordinate determination, an image sequence of a real world space is captured by hand-held camera with tracking non-metric measured feature points for overlaying a virtual object. Virtual objects can be overlaid onto the image sequence by taking each relationship between the images. With the proposed system, 3D position tracking device, such as magnetic trackers, are not required for the overlay of virtual objects. Experimental results demonstrate that 3D virtual furniture can be overlaid into an image sequence of the scene of a living room nearly at video rate (20 frames per second).

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

Science.gov (United States)

Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

A 3-Step Algorithm Using Region-Based Active Contours for Video Objects Detection

Directory of Open Access Journals (Sweden)

Stéphanie Jehan-Besson

2002-06-01

Full Text Available We propose a 3-step algorithm for the automatic detection of moving objects in video sequences using region-based active contours. First, we introduce a very full general framework for region-based active contours with a new Eulerian method to compute the evolution equation of the active contour from a criterion including both region-based and boundary-based terms. This framework can be easily adapted to various applications, thanks to the introduction of functions named descriptors of the different regions. With this new Eulerian method based on shape optimization principles, we can easily take into account the case of descriptors depending upon features globally attached to the regions. Second, we propose a 3-step algorithm for detection of moving objects, with a static or a mobile camera, using region-based active contours. The basic idea is to hierarchically associate temporal and spatial information. The active contour evolves with successively three sets of descriptors: a temporal one, and then two spatial ones. The third spatial descriptor takes advantage of the segmentation of the image in intensity homogeneous regions. User interaction is reduced to the choice of a few parameters at the beginning of the process. Some experimental results are supplied.

[Object-oriented stand type classification based on the combination of multi-source remote sen-sing data].

Science.gov (United States)

Mao, Xue Gang; Wei, Jing Yu

2017-11-01

The recognition of forest type is one of the key problems in forest resource monitoring. The Radarsat-2 data and QuickBird remote sensing image were used for object-based classification to study the object-based forest type classification and recognition based on the combination of multi-source remote sensing data. In the process of object-based classification, three segmentation schemes (segmentation with QuickBird remote sensing image only, segmentation with Radarsat-2 data only, segmentation with combination of QuickBird and Radarsat-2) were adopted. For the three segmentation schemes, ten segmentation scale parameters were adopted (25-250, step 25), and modified Euclidean distance 3 index was further used to evaluate the segmented results to determine the optimal segmentation scheme and segmentation scale. Based on the optimal segmented result, three forest types of Chinese fir, Masson pine and broad-leaved forest were classified and recognized using Support Vector Machine (SVM) classifier with Radial Basis Foundation (RBF) kernel according to different feature combinations of topography, height, spectrum and common features. The results showed that the combination of Radarsat-2 data and QuickBird remote sensing image had its advantages of object-based forest type classification over using Radarsat-2 data or QuickBird remote sensing image only. The optimal scale parameter for QuickBirdRadarsat-2 segmentation was 100, and at the optimal scale, the accuracy of object-based forest type classification was the highest (OA=86%, Kappa=0.86), when using all features which were extracted from two kinds of data resources. This study could not only provide a reference for forest type recognition using multi-source remote sensing data, but also had a practical significance for forest resource investigation and monitoring.

A Real-Time Method to Estimate Speed of Object Based on Object Detection and Optical Flow Calculation

Science.gov (United States)

Liu, Kaizhan; Ye, Yunming; Li, Xutao; Li, Yan

2018-04-01

In recent years Convolutional Neural Network (CNN) has been widely used in computer vision field and makes great progress in lots of contents like object detection and classification. Even so, combining Convolutional Neural Network, which means making multiple CNN frameworks working synchronously and sharing their output information, could figure out useful message that each of them cannot provide singly. Here we introduce a method to real-time estimate speed of object by combining two CNN: YOLOv2 and FlowNet. In every frame, YOLOv2 provides object size; object location and object type while FlowNet providing the optical flow of whole image. On one hand, object size and object location help to select out the object part of optical flow image thus calculating out the average optical flow of every object. On the other hand, object type and object size help to figure out the relationship between optical flow and true speed by means of optics theory and priori knowledge. Therefore, with these two key information, speed of object can be estimated. This method manages to estimate multiple objects at real-time speed by only using a normal camera even in moving status, whose error is acceptable in most application fields like manless driving or robot vision.

Functional Object Analysis

DEFF Research Database (Denmark)

Raket, Lars Lau

We propose a direction it the field of statistics which we will call functional object analysis. This subfields considers the analysis of functional objects defined on continuous domains. In this setting we will focus on model-based statistics, with a particularly emphasis on mixed......-effect formulations, where the observed functional signal is assumed to consist of both fixed and random functional effects. This thesis takes the initial steps toward the development of likelihood-based methodology for functional objects. We first consider analysis of functional data defined on high...

Adobe Boxes: Locating Object Proposals Using Object Adobes.

Science.gov (United States)

Fang, Zhiwen; Cao, Zhiguo; Xiao, Yang; Zhu, Lei; Yuan, Junsong

2016-09-01

Despite the previous efforts of object proposals, the detection rates of the existing approaches are still not satisfactory enough. To address this, we propose Adobe Boxes to efficiently locate the potential objects with fewer proposals, in terms of searching the object adobes that are the salient object parts easy to be perceived. Because of the visual difference between the object and its surroundings, an object adobe obtained from the local region has a high probability to be a part of an object, which is capable of depicting the locative information of the proto-object. Our approach comprises of three main procedures. First, the coarse object proposals are acquired by employing randomly sampled windows. Then, based on local-contrast analysis, the object adobes are identified within the enlarged bounding boxes that correspond to the coarse proposals. The final object proposals are obtained by converging the bounding boxes to tightly surround the object adobes. Meanwhile, our object adobes can also refine the detection rate of most state-of-the-art methods as a refinement approach. The extensive experiments on four challenging datasets (PASCAL VOC2007, VOC2010, VOC2012, and ILSVRC2014) demonstrate that the detection rate of our approach generally outperforms the state-of-the-art methods, especially with relatively small number of proposals. The average time consumed on one image is about 48 ms, which nearly meets the real-time requirement.

Partial Evaluation for Class-Based Object-Oriented Languages

DEFF Research Database (Denmark)

Schultz, Ulrik Pagh

2001-01-01

Object-oriented programming facilitates the development of generic software, but at a significant cost in terms of performance. We apply partial evaluation to object-oriented programs, to automatically map generic software into specific implementations. In this paper we give a concise, formal...... description of a simple partial evaluator for a minimal object-oriented language, and give directions for extending this partial evaluator to handle realistic programs....

Multi-Level Sensor Fusion Algorithm Approach for BMD Interceptor Applications

National Research Council Canada - National Science Library

Allen, Doug

1998-01-01

... through fabrication and testing of advanced sensor hardware concepts and advanced sensor fusion algorithms. Advanced sensor concepts include onboard LADAR in conjunction with a multi-color passive IR sensor...

Geographic object-based delineation of neighborhoods of Accra, Ghana using QuickBird satellite imagery.

Science.gov (United States)

Stow, Douglas A; Lippitt, Christopher D; Weeks, John R

2010-08-01

The objective was to test GEographic Object-based Image Analysis (GEOBIA) techniques for delineating neighborhoods of Accra, Ghana using QuickBird multispectral imagery. Two approaches to aggregating census enumeration areas (EAs) based on image-derived measures of vegetation objects were tested: (1) merging adjacent EAs according to vegetation measures and (2) image segmentation. Both approaches exploit readily available functions within commercial GEOBIA software. Image-derived neighborhood maps were compared to a reference map derived by spatial clustering of slum index values (from census data), to provide a relative assessment of potential map utility. A size-constrained iterative segmentation approach to aggregation was more successful than standard image segmentation or feature merge techniques. The segmentation approaches account for size and shape characteristics, enabling more realistic neighborhood boundaries to be delineated. The percentage of vegetation patches within each EA yielded more realistic delineation of potential neighborhoods than mean vegetation patch size per EA.

Portfolio optimization using fundamental indicators based on multi-objective EA

CERN Document Server

Silva, Antonio Daniel; Horta, Nuno

2016-01-01

This work presents a new approach to portfolio composition in the stock market. It incorporates a fundamental approach using financial ratios and technical indicators with a Multi-Objective Evolutionary Algorithms to choose the portfolio composition with two objectives the return and the risk. Two different chromosomes are used for representing different investment models with real constraints equivalents to the ones faced by managers of mutual funds, hedge funds, and pension funds. To validate the present solution two case studies are presented for the SP&500 for the period June 2010 until end of 2012. The simulations demonstrates that stock selection based on financial ratios is a combination that can be used to choose the best companies in operational terms, obtaining returns above the market average with low variances in their returns. In this case the optimizer found stocks with high return on investment in a conjunction with high rate of growth of the net income and a high profit margin. To obtain s...

Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

Directory of Open Access Journals (Sweden)

Wenbiao Li

2016-04-01

Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

Science.gov (United States)

Toure, Sory I.; Stow, Douglas A.; Weeks, John R.; Kumar, Sunil

2013-01-01

The classification of image-objects is usually done using parametric statistical measures of central tendency and/or dispersion (e.g., mean or standard deviation). The objectives of this study were to analyze digital number histograms of image objects and evaluate classifications measures exploiting characteristic signatures of such histograms. Two histograms matching classifiers were evaluated and compared to the standard nearest neighbor to mean classifier. An ADS40 airborne multispectral image of San Diego, California was used for assessing the utility of curve matching classifiers in a geographic object-based image analysis (GEOBIA) approach. The classifications were performed with data sets having 0.5 m, 2.5 m, and 5 m spatial resolutions. Results show that histograms are reliable features for characterizing classes. Also, both histogram matching classifiers consistently performed better than the one based on the standard nearest neighbor to mean rule. The highest classification accuracies were produced with images having 2.5 m spatial resolution. PMID:24403648

Shadow Detection Based on Regions of Light Sources for Object Extraction in Nighttime Video

Directory of Open Access Journals (Sweden)

Gil-beom Lee

2017-03-01

Full Text Available Intelligent video surveillance systems detect pre-configured surveillance events through background modeling, foreground and object extraction, object tracking, and event detection. Shadow regions inside video frames sometimes appear as foreground objects, interfere with ensuing processes, and finally degrade the event detection performance of the systems. Conventional studies have mostly used intensity, color, texture, and geometric information to perform shadow detection in daytime video, but these methods lack the capability of removing shadows in nighttime video. In this paper, a novel shadow detection algorithm for nighttime video is proposed; this algorithm partitions each foreground object based on the object’s vertical histogram and screens out shadow objects by validating their orientations heading toward regions of light sources. From the experimental results, it can be seen that the proposed algorithm shows more than 93.8% shadow removal and 89.9% object extraction rates for nighttime video sequences, and the algorithm outperforms conventional shadow removal algorithms designed for daytime videos.

Multi-objective PSO based optimal placement of solar power DG in radial distribution system

Directory of Open Access Journals (Sweden)

Mahesh Kumar

2017-06-01

Full Text Available Ever increasing trend of electricity demand, fossil fuel depletion and environmental issues request the integration of renewable energy into the distribution system. The optimal planning of renewable distributed generation (DG is much essential for ensuring maximum benefits. Hence, this paper proposes the optimal placement of probabilistic based solar power DG into the distribution system. The two objective functions such as power loss reduction and voltage stability index improvement are optimized. The power balance and voltage limits are kept as constraints of the problem. The non-sorting pare to-front based multi-objective particle swarm optimization (MOPSO technique is proposed on standard IEEE 33 radial distribution test system.

Neurocomputational bases of object and face recognition.

OpenAIRE

Biederman, I; Kalocsai, P

1997-01-01

A number of behavioural phenomena distinguish the recognition of faces and objects, even when members of a set of objects are highly similar. Because faces have the same parts in approximately the same relations, individuation of faces typically requires specification of the metric variation in a holistic and integral representation of the facial surface. The direct mapping of a hypercolumn-like pattern of activation onto a representation layer that preserves relative spatial filter values in...

Optical encryption of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography

Science.gov (United States)

Wang, Ying; Liu, Qi; Wang, Jun; Wang, Qiong-Hua

2018-03-01

We present an optical encryption method of multiple three-dimensional objects based on multiple interferences and single-pixel digital holography. By modifying the Mach–Zehnder interferometer, the interference of the multiple objects beams and the one reference beam is used to simultaneously encrypt multiple objects into a ciphertext. During decryption, each three-dimensional object can be decrypted independently without having to decrypt other objects. Since the single-pixel digital holography based on compressive sensing theory is introduced, the encrypted data of this method is effectively reduced. In addition, recording fewer encrypted data can greatly reduce the bandwidth of network transmission. Moreover, the compressive sensing essentially serves as a secret key that makes an intruder attack invalid, which means that the system is more secure than the conventional encryption method. Simulation results demonstrate the feasibility of the proposed method and show that the system has good security performance. Project supported by the National Natural Science Foundation of China (Grant Nos. 61405130 and 61320106015).

Invariant Visual Object and Face Recognition: Neural and Computational Bases, and a Model, VisNet.

Science.gov (United States)

Rolls, Edmund T

2012-01-01

Neurophysiological evidence for invariant representations of objects and faces in the primate inferior temporal visual cortex is described. Then a computational approach to how invariant representations are formed in the brain is described that builds on the neurophysiology. A feature hierarchy model in which invariant representations can be built by self-organizing learning based on the temporal and spatial statistics of the visual input produced by objects as they transform in the world is described. VisNet can use temporal continuity in an associative synaptic learning rule with a short-term memory trace, and/or it can use spatial continuity in continuous spatial transformation learning which does not require a temporal trace. The model of visual processing in the ventral cortical stream can build representations of objects that are invariant with respect to translation, view, size, and also lighting. The model has been extended to provide an account of invariant representations in the dorsal visual system of the global motion produced by objects such as looming, rotation, and object-based movement. The model has been extended to incorporate top-down feedback connections to model the control of attention by biased competition in, for example, spatial and object search tasks. The approach has also been extended to account for how the visual system can select single objects in complex visual scenes, and how multiple objects can be represented in a scene. The approach has also been extended to provide, with an additional layer, for the development of representations of spatial scenes of the type found in the hippocampus.

Object Classification Based on Analysis of Spectral Characteristics of Seismic Signal Envelopes

Science.gov (United States)

Morozov, Yu. V.; Spektor, A. A.

2017-11-01

A method for classifying moving objects having a seismic effect on the ground surface is proposed which is based on statistical analysis of the envelopes of received signals. The values of the components of the amplitude spectrum of the envelopes obtained applying Hilbert and Fourier transforms are used as classification criteria. Examples illustrating the statistical properties of spectra and the operation of the seismic classifier are given for an ensemble of objects of four classes (person, group of people, large animal, vehicle). It is shown that the computational procedures for processing seismic signals are quite simple and can therefore be used in real-time systems with modest requirements for computational resources.

A policy-based multi-objective optimisation framework for residential distributed energy system design★

Directory of Open Access Journals (Sweden)

Wouters Carmen

2017-01-01

Full Text Available Distributed energy systems (DES are increasingly being introduced as solutions to alleviate conventional energy system challenges related to energy security, climate change and increasing demands. From a technological and economic perspective, distributed energy resources are already becoming viable. The question still remains as to how these technologies and practices can be “best” selected, sized and integrated within consumer areas. To aid decision-makers and enable widespread DES adoption, a strategic superstructure design framework is therefore still required that ensures balancing of multiple stakeholder interests and fits in with liberalised energy system objectives of competition, security of supply and sustainability. Such a design framework is presented in this work. An optimisation-based approach for the design of neighbourhood-based DES is developed that enables meeting their yearly electricity, heating and cooling needs by appropriately selecting, sizing and locating technologies and energy interactions. A pool of poly-generation and storage technologies is hereto considered combined with local energy sharing between participating prosumers through thermal pipeline design and microgrid operation, and, a bi-directional connection with the central distribution grid. A superstructure mixed-integer linear programming approach (MILP is proposed to trade off three minimisation objectives in the design process: total annualised cost, annual CO2 emissions and electrical system unavailability, aligned with the three central energy system objectives. The developed model is applied on a small South Australian neighbourhood. The approach enables identifying “knee-point” neighbourhood energy system designs through Pareto trade-offs between objectives and serves to inform decision-makers about the impact of policy objectives on DES development strategies.

An object-oriented feature-based design system face-based detection of feature interactions

International Nuclear Information System (INIS)

Ariffin Abdul Razak

1999-01-01

This paper presents an object-oriented, feature-based design system which supports the integration of design and manufacture by ensuring that part descriptions fully account for any feature interactions. Manufacturing information is extracted from the feature descriptions in the form of volumes and Tool Access Directions, TADs. When features interact, both volumes and TADs are updated. This methodology has been demonstrated by developing a prototype system in which ACIS attributes are used to record feature information within the data structure of the solid model. The system implemented in the C++ programming language and embedded in a menu-driven X-windows user interface to the ACIS 3D Toolkit. (author)

Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

DEFF Research Database (Denmark)

Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas

2018-01-01

Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored...... in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments...

Shadow detection of moving objects based on multisource information in Internet of things

Science.gov (United States)

Ma, Zhen; Zhang, De-gan; Chen, Jie; Hou, Yue-xian

2017-05-01

Moving object detection is an important part in intelligent video surveillance under the banner of Internet of things. The detection of moving target's shadow is also an important step in moving object detection. On the accuracy of shadow detection will affect the detection results of the object directly. Based on the variety of shadow detection method, we find that only using one feature can't make the result of detection accurately. Then we present a new method for shadow detection which contains colour information, the invariance of optical and texture feature. Through the comprehensive analysis of the detecting results of three kinds of information, the shadow was effectively determined. It gets ideal effect in the experiment when combining advantages of various methods.

An electrophysiological study of the object-based correspondence effect: is the effect triggered by an intended grasping action?

Science.gov (United States)

Lien, Mei-Ching; Jardin, Elliott; Proctor, Robert W

2013-11-01

We examined Goslin, Dixon, Fischer, Cangelosi, and Ellis's (Psychological Science 23:152-157, 2012) claim that the object-based correspondence effect (i.e., faster keypress responses when the orientation of an object's graspable part corresponds with the response location than when it does not) is the result of object-based attention (vision-action binding). In Experiment 1, participants determined the category of a centrally located object (kitchen utensil vs. tool), as in Goslin et al.'s study. The handle orientation (left vs. right) did or did not correspond with the response location (left vs. right). We found no correspondence effect on the response times (RTs) for either category. The effect was also not evident in the P1 and N1 components of the event-related potentials, which are thought to reflect the allocation of early visual attention. This finding was replicated in Experiment 2 for centrally located objects, even when the object was presented 45 times (33 more times than in Exp. 1). Critically, the correspondence effects on RTs, P1s, and N1s emerged only when the object was presented peripherally, so that the object handle was clearly located to the left or right of fixation. Experiment 3 provided further evidence that the effect was observed only for the base-centered objects, in which the handle was clearly positioned to the left or right of center. These findings contradict those of Goslin et al. and provide no evidence that an intended grasping action modulates visual attention. Instead, the findings support the spatial-coding account of the object-based correspondence effect.

Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

Directory of Open Access Journals (Sweden)

Yan Sun

2015-09-01

Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

Science.gov (United States)

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to

Consumer-based technology for distribution of surgical videos for objective evaluation.

Science.gov (United States)

Gonzalez, Ray; Martinez, Jose M; Lo Menzo, Emanuele; Iglesias, Alberto R; Ro, Charles Y; Madan, Atul K

2012-08-01

The Global Operative Assessment of Laparoscopic Skill (GOALS) is one validated metric utilized to grade laparoscopic skills and has been utilized to score recorded operative videos. To facilitate easier viewing of these recorded videos, we are developing novel techniques to enable surgeons to view these videos. The objective of this study is to determine the feasibility of utilizing widespread current consumer-based technology to assist in distributing appropriate videos for objective evaluation. Videos from residents were recorded via a direct connection from the camera processor via an S-video output via a cable into a hub to connect to a standard laptop computer via a universal serial bus (USB) port. A standard consumer-based video editing program was utilized to capture the video and record in appropriate format. We utilized mp4 format, and depending on the size of the file, the videos were scaled down (compressed), their format changed (using a standard video editing program), or sliced into multiple videos. Standard available consumer-based programs were utilized to convert the video into a more appropriate format for handheld personal digital assistants. In addition, the videos were uploaded to a social networking website and video sharing websites. Recorded cases of laparoscopic cholecystectomy in a porcine model were utilized. Compression was required for all formats. All formats were accessed from home computers, work computers, and iPhones without difficulty. Qualitative analyses by four surgeons demonstrated appropriate quality to grade for these formats. Our preliminary results show promise that, utilizing consumer-based technology, videos can be easily distributed to surgeons to grade via GOALS via various methods. Easy accessibility may help make evaluation of resident videos less complicated and cumbersome.

MOPSO-based multi-objective TSO planning considering uncertainties

DEFF Research Database (Denmark)

Wang, Qi; Zhang, Chunyu; Ding, Yi

2014-01-01

factors, i.e. load growth, generation capacity and line faults, and aims to enhance the transmission system via the multi-objective TSO planning (MOTP) approach. The proposed MOTP approach optimizes three objectives simultaneously, namely the probabilistic available transfer capability (PATC), investment...... cost and power outage cost. A two-phase MOPSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity ofPareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach...

Combining TerraSAR-X and SPOT-5 data for object-based landslide detection

Science.gov (United States)

Friedl, B.; Hölbling, D.; Füreder, P.

2012-04-01

Landslide detection and classification is an essential requirement in pre- and post-disaster hazard analysis. In earlier studies landslide detection often was achieved through time-consuming and cost-intensive field surveys and visual orthophoto interpretation. Recent studies show that Earth Observation (EO) data offer new opportunities for fast, reliable and accurate landslide detection and classification, which may conduce to an effective landslide monitoring and landslide hazard management. To ensure the fast recognition and classification of landslides at a regional scale, a (semi-)automated object-based landslide detection approach is established for a study site situated in the Huaguoshan catchment, Southern Taiwan. The study site exhibits a high vulnerability to landslides and debris flows, which are predominantly typhoon-induced. Through the integration of optical satellite data (SPOT-5 with 2.5 m GSD), SAR (Synthetic Aperture Radar) data (TerraSAR-X Spotlight with 2.95 m GSD) and digital elevation information (DEM with 5 m GSD) including its derived products (e.g. slope, curvature, flow accumulation) landslides may be examined in a more efficient way as if relying on single data sources only. The combination of optical and SAR data in an object-based image analysis (OBIA) domain for landslide detection and classification has not been investigated so far, even if SAR imagery show valuable properties for landslide detection, which differ from optical data (e.g. high sensitivity to surface roughness and soil moisture). The main purpose of this study is to recognize and analyze existing landslides by applying object-based image analysis making use of eCognition software. OBIA provides a framework for examining features defined by spectral, spatial, textural, contextual as well as hierarchical properties. Objects are derived through image segmentation and serve as input for the classification process, which relies on transparent rulesets, representing knowledge

Cognitive object recognition system (CORS)

Science.gov (United States)

Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

2010-04-01

We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

THE METHOD OF GEOMETRIC CALIBRATION OF OPTOELECTRONIC SYSTEMS BASED ON ELECTRONIC TEST OBJECT

Directory of Open Access Journals (Sweden)

D. A. Kozhevnikov

2017-01-01

Full Text Available Designing remote sensing of the Earth devices is requires a lot of attention to evaluation lens distortion level and providing the required accuracy values of geometric calibration of optoelectronic systems at all. Test- objects known as most common tools for optical systems geometric calibration. The purpose of the research was creating an automatically method of distortion correction coefficients calculating with a 3 μm precision in the measurement process. The method of geometric calibration of the internal orientation elements of the optical system based on the electronic test object is proposed. The calculation of the test string brightness image from its multispectral image and filtered signal extrema position determination are presented. Ratio of magnitude of the distortion and interval center is given. Three variants of electronic test-objects with different step and element size are considered. Оptimal size of calibration element was defined as 3×3 pixels due to shape of the subpixels with the aspect ratio of the radiating areas about 1 : 3. It is advisable to use IPS as an electronic test object template. An experimental test and measurement stand functional diagram based on the collimator and optical bench «OSK-2CL» is showed. It was determined that test objects with a grid spacing of 4 and 8 pixels can’t provide tolerable image because of non-collimated emission of active sites and scattering on optical surfaces – the shape of the elements is substantially disrupted. Test-object with a 12 pixels grid spacing was used to distortion level analyzing as most suitable.Ratio of coordinate increment and element number graphs for two photographic lenses (Canon EF-S 17-85 f/4-5.6 IS USM and EF-S 18-55 f/3.5-5.6 IS II are presented. A calculation of the distortion values in edge zones was held, which were respectively 43 μm and 51.6 μm. The technique and algorithm of software implementation is described. Possible directions of the

An Object-Relational Ifc Storage Model Based on Oracle Database

Science.gov (United States)

Li, Hang; Liu, Hua; Liu, Yong; Wang, Yuan

2016-06-01

With the building models are getting increasingly complicated, the levels of collaboration across professionals attract more attention in the architecture, engineering and construction (AEC) industry. In order to adapt the change, buildingSMART developed Industry Foundation Classes (IFC) to facilitate the interoperability between software platforms. However, IFC data are currently shared in the form of text file, which is defective. In this paper, considering the object-based inheritance hierarchy of IFC and the storage features of different database management systems (DBMS), we propose a novel object-relational storage model that uses Oracle database to store IFC data. Firstly, establish the mapping rules between data types in IFC specification and Oracle database. Secondly, design the IFC database according to the relationships among IFC entities. Thirdly, parse the IFC file and extract IFC data. And lastly, store IFC data into corresponding tables in IFC database. In experiment, three different building models are selected to demonstrate the effectiveness of our storage model. The comparison of experimental statistics proves that IFC data are lossless during data exchange.

A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images

Science.gov (United States)

Keyport, Ren N.; Oommen, Thomas; Martha, Tapas R.; Sajinkumar, K. S.; Gierke, John S.

2018-02-01

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification.

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

Science.gov (United States)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

Correspondence of perceived vs. objective proximity to parks and their relationship to park-based physical activity

Directory of Open Access Journals (Sweden)

Kaczynski Andrew T

2009-08-01

Full Text Available Abstract Background Parks are key environmental resources for encouraging population-level physical activity (PA. In measuring availability of parks, studies have employed both self-reported and objective indicators of proximity, with little correspondence observed between these two types of measures. However, little research has examined how the degree of correspondence between self-reported and objectively-measured distance to parks is influenced by individual, neighborhood, and park-related variables, or which type of measure is more strongly related to physical activity outcomes. Methods We used data from 574 respondents who reported the distance to their closest park and compared this with objective measurements of proximity to the closest park. Both indicators were dichotomized as having or not having a park within 750 m. Audits of all park features within this distance were also conducted and other personal characteristics and neighborhood context variables (safety, connectedness, aesthetics were gleaned from participants' survey responses. Participants also completed detailed seven-day PA log booklets from which measures of neighborhood-based and park-based PA were derived. Results Agreement was poor in that only 18% of respondents achieved a match between perceived and objective proximity to the closest park (kappa = 0.01. Agreement was higher among certain subgroups, especially those who reported engaging in at least some park-based PA. As well, respondents with a greater number of parks nearby, whose closest park had more features, and whose closest park contained a playground or wooded area were more likely to achieve a match. Having a ball diamond or soccer field in the closest park was negatively related to achieving a match between perceived and objective proximity. Finally, engaging in at least some park-based PA was not related to either perceived or objective proximity to a park, but was more likely when a match between and

Genetic Particle Swarm Optimization–Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm. PMID:27483285

Genetic Particle Swarm Optimization-Based Feature Selection for Very-High-Resolution Remotely Sensed Imagery Object Change Detection.

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

In the field of multiple features Object-Based Change Detection (OBCD) for very-high-resolution remotely sensed images, image objects have abundant features and feature selection affects the precision and efficiency of OBCD. Through object-based image analysis, this paper proposes a Genetic Particle Swarm Optimization (GPSO)-based feature selection algorithm to solve the optimization problem of feature selection in multiple features OBCD. We select the Ratio of Mean to Variance (RMV) as the fitness function of GPSO, and apply the proposed algorithm to the object-based hybrid multivariate alternative detection model. Two experiment cases on Worldview-2/3 images confirm that GPSO can significantly improve the speed of convergence, and effectively avoid the problem of premature convergence, relative to other feature selection algorithms. According to the accuracy evaluation of OBCD, GPSO is superior at overall accuracy (84.17% and 83.59%) and Kappa coefficient (0.6771 and 0.6314) than other algorithms. Moreover, the sensitivity analysis results show that the proposed algorithm is not easily influenced by the initial parameters, but the number of features to be selected and the size of the particle swarm would affect the algorithm. The comparison experiment results reveal that RMV is more suitable than other functions as the fitness function of GPSO-based feature selection algorithm.

Optical Landing Hazard Sensor, Phase II

Data.gov (United States)

National Aeronautics and Space Administration — Visidyne's Phase I effort has established through modeling and analysis that a unique concept for an active optical 3-D Imager (or Imaging LADAR) has high potential...

Optical Landing Hazard Sensor, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — Visidyne proposes to investigate an active optical 3D imaging LADAR as the sensor for an automated Landing Hazard Avoidance system for spacecraft landing on the Moon...

A Cultured Learning Environment: Implementing a Problem- and Service-Based Microbiology Capstone Course to Assess Process- and Skill-Based Learning Objectives

Science.gov (United States)

Watson, Rachel M.; Willford, John D.; Pfeifer, Mariel A.

2018-01-01

In this study, a problem-based capstone course was designed to assess the University of Wyoming Microbiology Program's skill-based and process-based student learning objectives. Students partnered with a local farm, a community garden, and a free downtown clinic in order to conceptualize, propose, perform, and present studies addressing problems…

A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA Utilizing Raster Attribute Tables

Directory of Open Access Journals (Sweden)

Daniel Clewley

2014-06-01

Full Text Available A modular system for performing Geographic Object-Based Image Analysis (GEOBIA, using entirely open source (General Public License compatible software, is presented based around representing objects as raster clumps and storing attributes as a raster attribute table (RAT. The system utilizes a number of libraries, developed by the authors: The Remote Sensing and GIS Library (RSGISLib, the Raster I/O Simplification (RIOS Python Library, the KEA image format and TuiView image viewer. All libraries are accessed through Python, providing a common interface on which to build processing chains. Three examples are presented, to demonstrate the capabilities of the system: (1 classification of mangrove extent and change in French Guiana; (2 a generic scheme for the classification of the UN-FAO land cover classification system (LCCS and their subsequent translation to habitat categories; and (3 a national-scale segmentation for Australia. The system presented provides similar functionality to existing GEOBIA packages, but is more flexible, due to its modular environment, capable of handling complex classification processes and applying them to larger datasets.

The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem

Science.gov (United States)

Aytaç Adalı, Esra; Tuş Işık, Ayşegül

2017-06-01

A decision making process requires the values of conflicting objectives for alternatives and the selection of the best alternative according to the needs of decision makers. Multi-objective optimization methods may provide solution for this selection. In this paper it is aimed to present the laptop selection problem based on MOORA plus full multiplicative form (MULTIMOORA) and multi-objective optimization on the basis of simple ratio analysis (MOOSRA) which are relatively new multi-objective optimization methods. The novelty of this paper is solving this problem with the MULTIMOORA and MOOSRA methods for the first time.

Model of Recommendation System for for Indexing and Retrieving the Learning Object based on Multiagent System

Directory of Open Access Journals (Sweden)

Ronaldo Lima Rocha Campos

2012-07-01

Full Text Available This paper proposes a multiagent system application model for indexing, retrieving and recommendation learning objects stored in different and heterogeneous repositories. The objects within these repositories are described by filled fields using different metadata standards. The searching mechanism covers several different learning object repositories and the same object can be described in these repositories by the use of different types of fields. Aiming to improve accuracy and coverage in terms of recovering a learning object and improve the signification of the results we propose an information retrieval model based on the multiagent system approach and an ontological model to describe the knowledge domain covered.

An UML Statechart Diagram-Based MM-Path Generation Approach for Object-Oriented Integration Testing

OpenAIRE

Ruilian Zhao; Ling Lin

2008-01-01

MM-Path, an acronym for Method/Message Path, describes the dynamic interactions between methods in object-oriented systems. This paper discusses the classifications of MM-Path, based on the characteristics of object-oriented software. We categorize it according to the generation reasons, the effect scope and the composition of MM-Path. A formalized representation of MM-Path is also proposed, which has considered the influence of state on response method sequences of messages. .Moreover, an au...

Retrospective Cues Based on Object Features Improve Visual Working Memory Performance in Older Adults

OpenAIRE

Gilchrist, Amanda L.; Duarte, Audrey; Verhaeghen, Paul

2015-01-01

Research with younger adults has shown that retrospective cues can be used to orient top-down attention toward relevant items in working memory. We examined whether older adults could take advantage of these cues to improve memory performance. Younger and older adults were presented with visual arrays of five colored shapes; during maintenance, participants were either presented with an informative cue based on an object feature (here, object shape or color) that would be probed, or with an u...

Image objects detection based on boosting neural network

NARCIS (Netherlands)

Liang, N.; Hegt, J.A.; Mladenov, V.M.

2010-01-01

This paper discusses the problem of object area detection of video frames. The goal is to design a pixel accurate detector for grass, which could be used for object adaptive video enhancement. A boosting neural network is used for creating such a detector. The resulted detector uses both textural

Repurposing learning object components

NARCIS (Netherlands)

Verbert, K.; Jovanovic, J.; Gasevic, D.; Duval, E.; Meersman, R.

2005-01-01

This paper presents an ontology-based framework for repurposing learning object components. Unlike the usual practice where learning object components are assembled manually, the proposed framework enables on-the-fly access and repurposing of learning object components. The framework supports two

Probabilistic safety assessment model in consideration of human factors based on object-oriented bayesian networks

International Nuclear Information System (INIS)

Zhou Zhongbao; Zhou Jinglun; Sun Quan

2007-01-01

Effect of Human factors on system safety is increasingly serious, which is often ignored in traditional probabilistic safety assessment methods however. A new probabilistic safety assessment model based on object-oriented Bayesian networks is proposed in this paper. Human factors are integrated into the existed event sequence diagrams. Then the classes of the object-oriented Bayesian networks are constructed which are converted to latent Bayesian networks for inference. Finally, the inference results are integrated into event sequence diagrams for probabilistic safety assessment. The new method is applied to the accident of loss of coolant in a nuclear power plant. the results show that the model is not only applicable to real-time situation assessment, but also applicable to situation assessment based certain amount of information. The modeling complexity is kept down and the new method is appropriate to large complex systems due to the thoughts of object-oriented. (authors)

A novel lobster-eye imaging system based on Schmidt-type objective for X-ray-backscattering inspection

International Nuclear Information System (INIS)

Xu, Jie; Wang, Xin; Zhan, Qi; Huang, Shengling; Chen, Yifan; Mu, Baozhong

2016-01-01

This paper presents a novel lobster-eye imaging system for X-ray-backscattering inspection. The system was designed by modifying the Schmidt geometry into a treble-lens structure in order to reduce the resolution difference between the vertical and horizontal directions, as indicated by ray-tracing simulations. The lobster-eye X-ray imaging system is capable of operating over a wide range of photon energies up to 100 keV. In addition, the optics of the lobster-eye X-ray imaging system was tested to verify that they meet the requirements. X-ray-backscattering imaging experiments were performed in which T-shaped polymethyl-methacrylate objects were imaged by the lobster-eye X-ray imaging system based on both the double-lens and treble-lens Schmidt objectives. The results show similar resolution of the treble-lens Schmidt objective in both the vertical and horizontal directions. Moreover, imaging experiments were performed using a second treble-lens Schmidt objective with higher resolution. The results show that for a field of view of over 200 mm and with a 500 mm object distance, this lobster-eye X-ray imaging system based on a treble-lens Schmidt objective offers a spatial resolution of approximately 3 mm.

An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration

Energy Technology Data Exchange (ETDEWEB)

Niknam, Taher [Electronic and Electrical Engineering Department, Shiraz University of Technology, Shiraz (Iran)

2009-08-15

This paper introduces a robust searching hybrid evolutionary algorithm to solve the multi-objective Distribution Feeder Reconfiguration (DFR). The main objective of the DFR is to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and balance the loads on the feeders. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. This paper presents a new approach based on norm3 for the DFR problem. In the proposed method, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. Since the proposed DFR is a multi objective and non-differentiable optimization problem, a new hybrid evolutionary algorithm (EA) based on the combination of the Honey Bee Mating Optimization (HBMO) and the Discrete Particle Swarm Optimization (DPSO), called DPSO-HBMO, is implied to solve it. The results of the proposed reconfiguration method are compared with the solutions obtained by other approaches, the original DPSO and HBMO over different distribution test systems. (author)

Herbig-Haro objects

International Nuclear Information System (INIS)

Schwartz, R.D.

1983-01-01

Progress in the understanding of Herbig-Haro (HH) objects is reviewed. The results of optical studies of the proper motions and alignments, variability, and polarization of HH objects and the results of spectroscopic studies are discussed. Ground-based infrared studies and far-infrared observations are reviewed. Findings on the properties of molecular clouds associated with HH objects, on gas flows associated with HH IR stars, on maser emission, and on radio continuum observations are considered. A history of proposed excitation mechanisms for HH objects is briefly presented, and the salient shock-wave calculations aimed at synthesizing the spectra of HH objects are summarized along with hypotheses that have been advanced about the origin of the objects. 141 references

A neural network model of semantic memory linking feature-based object representation and words.

Science.gov (United States)

Cuppini, C; Magosso, E; Ursino, M

2009-06-01

Recent theories in cognitive neuroscience suggest that semantic memory is a distributed process, which involves many cortical areas and is based on a multimodal representation of objects. The aim of this work is to extend a previous model of object representation to realize a semantic memory, in which sensory-motor representations of objects are linked with words. The model assumes that each object is described as a collection of features, coded in different cortical areas via a topological organization. Features in different objects are segmented via gamma-band synchronization of neural oscillators. The feature areas are further connected with a lexical area, devoted to the representation of words. Synapses among the feature areas, and among the lexical area and the feature areas are trained via a time-dependent Hebbian rule, during a period in which individual objects are presented together with the corresponding words. Simulation results demonstrate that, during the retrieval phase, the network can deal with the simultaneous presence of objects (from sensory-motor inputs) and words (from acoustic inputs), can correctly associate objects with words and segment objects even in the presence of incomplete information. Moreover, the network can realize some semantic links among words representing objects with shared features. These results support the idea that semantic memory can be described as an integrated process, whose content is retrieved by the co-activation of different multimodal regions. In perspective, extended versions of this model may be used to test conceptual theories, and to provide a quantitative assessment of existing data (for instance concerning patients with neural deficits).

Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

Directory of Open Access Journals (Sweden)

Lei Qin

2014-05-01

Full Text Available We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences.

Object-based Encoding in Visual Working Memory: Evidence from Memory-driven Attentional Capture.

Science.gov (United States)

Gao, Zaifeng; Yu, Shixian; Zhu, Chengfeng; Shui, Rende; Weng, Xuchu; Li, Peng; Shen, Mowei

2016-03-09

Visual working memory (VWM) adopts a specific manner of object-based encoding (OBE) to extract perceptual information: Whenever one feature-dimension is selected for entry into VWM, the others are also extracted. Currently most studies revealing OBE probed an 'irrelevant-change distracting effect', where changes of irrelevant-features dramatically affected the performance of the target feature. However, the existence of irrelevant-feature change may affect participants' processing manner, leading to a false-positive result. The current study conducted a strict examination of OBE in VWM, by probing whether irrelevant-features guided the deployment of attention in visual search. The participants memorized an object's colour yet ignored shape and concurrently performed a visual-search task. They searched for a target line among distractor lines, each embedded within a different object. One object in the search display could match the shape, colour, or both dimensions of the memory item, but this object never contained the target line. Relative to a neutral baseline, where there was no match between the memory and search displays, search time was significantly prolonged in all match conditions, regardless of whether the memory item was displayed for 100 or 1000 ms. These results suggest that task-irrelevant shape was extracted into VWM, supporting OBE in VWM.

Object-Oriented Type Systems

DEFF Research Database (Denmark)

Schwartzbach, Michael Ignatieff; Palsberg, Jens

a type system that generalizes and explains them. The theory is based on an idealized object-oriented language called BOPL (Basic Object Programming Language), containing common features of the above languages. A type system, type inference algorithm, and typings of inheritance and genericity......Object-Oriented Type Systems Jens Palsberg and Michael I. Schwartzbach Aarhus University, Denmark Type systems are required to ensure reliability and efficiency of software. For object-oriented languages, typing is an especially challenging problem because of inheritance, assignment, and late...... are provided for BOPL. Throughout, the results are related to the languages on which BOPL is based. This text offers advanced undergraduates and professional software developers a sound understanding of the key aspects of object-oriented type systems. All algorithms are implemented in a freely available...

Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

Science.gov (United States)

Alam, Md Golam Rabiul; Abedin, Sarder Fakhrul; Al Ameen, Moshaddique; Hong, Choong Seon

2016-01-01

Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study. PMID:27608023

[Object-oriented segmentation and classification of forest gap based on QuickBird remote sensing image.

Science.gov (United States)

Mao, Xue Gang; Du, Zi Han; Liu, Jia Qian; Chen, Shu Xin; Hou, Ji Yu

2018-01-01

Traditional field investigation and artificial interpretation could not satisfy the need of forest gaps extraction at regional scale. High spatial resolution remote sensing image provides the possibility for regional forest gaps extraction. In this study, we used object-oriented classification method to segment and classify forest gaps based on QuickBird high resolution optical remote sensing image in Jiangle National Forestry Farm of Fujian Province. In the process of object-oriented classification, 10 scales (10-100, with a step length of 10) were adopted to segment QuickBird remote sensing image; and the intersection area of reference object (RA or ) and intersection area of segmented object (RA os ) were adopted to evaluate the segmentation result at each scale. For segmentation result at each scale, 16 spectral characteristics and support vector machine classifier (SVM) were further used to classify forest gaps, non-forest gaps and others. The results showed that the optimal segmentation scale was 40 when RA or was equal to RA os . The accuracy difference between the maximum and minimum at different segmentation scales was 22%. At optimal scale, the overall classification accuracy was 88% (Kappa=0.82) based on SVM classifier. Combining high resolution remote sensing image data with object-oriented classification method could replace the traditional field investigation and artificial interpretation method to identify and classify forest gaps at regional scale.

Vision-based robotic system for object agnostic placing operations

DEFF Research Database (Denmark)

Rofalis, Nikolaos; Nalpantidis, Lazaros; Andersen, Nils Axel

2016-01-01

Industrial robots are part of almost all modern factories. Even though, industrial robots nowadays manipulate objects of a huge variety in different environments, exact knowledge about both of them is generally assumed. The aim of this work is to investigate the ability of a robotic system to ope...... to the system, neither for the objects nor for the placing box. The experimental evaluation of the developed robotic system shows that a combination of seemingly simple modules and strategies can provide effective solution to the targeted problem....... to operate within an unknown environment manipulating unknown objects. The developed system detects objects, finds matching compartments in a placing box, and ultimately grasps and places the objects there. The developed system exploits 3D sensing and visual feature extraction. No prior knowledge is provided...

Object-Based Change Detection Using High-Resolution Remotely Sensed Data and GIS

Science.gov (United States)

Sofina, N.; Ehlers, M.

2012-08-01

High resolution remotely sensed images provide current, detailed, and accurate information for large areas of the earth surface which can be used for change detection analyses. Conventional methods of image processing permit detection of changes by comparing remotely sensed multitemporal images. However, for performing a successful analysis it is desirable to take images from the same sensor which should be acquired at the same time of season, at the same time of a day, and - for electro-optical sensors - in cloudless conditions. Thus, a change detection analysis could be problematic especially for sudden catastrophic events. A promising alternative is the use of vector-based maps containing information about the original urban layout which can be related to a single image obtained after the catastrophe. The paper describes a methodology for an object-based search of destroyed buildings as a consequence of a natural or man-made catastrophe (e.g., earthquakes, flooding, civil war). The analysis is based on remotely sensed and vector GIS data. It includes three main steps: (i) generation of features describing the state of buildings; (ii) classification of building conditions; and (iii) data import into a GIS. One of the proposed features is a newly developed 'Detected Part of Contour' (DPC). Additionally, several features based on the analysis of textural information corresponding to the investigated vector objects are calculated. The method is applied to remotely sensed images of areas that have been subjected to an earthquake. The results show the high reliability of the DPC feature as an indicator for change.

Context-based object-of-interest detection for a generic traffic surveillance analysis system

NARCIS (Netherlands)

Bao, X.; Javanbakhti, S.; Zinger, S.; Wijnhoven, R.G.J.; With, de P.H.N.

2014-01-01

We present a new traffic surveillance video analysis system, focusing on building a framework with robust and generic techniques, based on both scene understanding and moving object-of-interest detection. Since traffic surveillance is widely applied, we want to design a single system that can be

Trusted Objects

International Nuclear Information System (INIS)

CAMPBELL, PHILIP L.; PIERSON, LYNDON G.; WITZKE, EDWARD L.

1999-01-01

In the world of computers a trusted object is a collection of possibly-sensitive data and programs that can be allowed to reside and execute on a computer, even on an adversary's machine. Beyond the scope of one computer we believe that network-based agents in high-consequence and highly reliable applications will depend on this approach, and that the basis for such objects is what we call ''faithful execution.''

Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes

Science.gov (United States)

Feizizadeh, Bakhtiar; Blaschke, Thomas; Tiede, Dirk; Moghaddam, Mohammad Hossein Rezaei

2017-09-01

This article presents a method of object-based image analysis (OBIA) for landslide delineation and landslide-related change detection from multi-temporal satellite images. It uses both spatial and spectral information on landslides, through spectral analysis, shape analysis, textural measurements using a gray-level co-occurrence matrix (GLCM), and fuzzy logic membership functionality. Following an initial segmentation step, particular combinations of various information layers were investigated to generate objects. This was achieved by applying multi-resolution segmentation to IRS-1D, SPOT-5, and ALOS satellite imagery in sequential steps of feature selection and object classification, and using slope and flow direction derivatives from a digital elevation model together with topographically-oriented gray level co-occurrence matrices. Fuzzy membership values were calculated for 11 different membership functions using 20 landslide objects from a landslide training data. Six fuzzy operators were used for the final classification and the accuracies of the resulting landslide maps were compared. A Fuzzy Synthetic Evaluation (FSE) approach was adapted for validation of the results and for an accuracy assessment using the landslide inventory database. The FSE approach revealed that the AND operator performed best with an accuracy of 93.87% for 2005 and 94.74% for 2011, closely followed by the MEAN Arithmetic operator, while the OR and AND (*) operators yielded relatively low accuracies. An object-based change detection was then applied to monitor landslide-related changes that occurred in northern Iran between 2005 and 2011. Knowledge rules to detect possible landslide-related changes were developed by evaluating all possible landslide-related objects for both time steps.

Flash 3D Rendezvous and Docking Sensor, Phase I

Data.gov (United States)

National Aeronautics and Space Administration — 3D Flash Ladar is a breakthrough technology for many emerging and existing 3D vision areas, and sensor improvements will have an impact on nearly all these fields....

Breaking object correspondence across saccadic eye movements deteriorates object recognition

Directory of Open Access Journals (Sweden)

Christian H. Poth

2015-12-01

Full Text Available Visual perception is based on information processing during periods of eye fixations that are interrupted by fast saccadic eye movements. The ability to sample and relate information on task-relevant objects across fixations implies that correspondence between presaccadic and postsaccadic objects is established. Postsaccadic object information usually updates and overwrites information on the corresponding presaccadic object. The presaccadic object representation is then lost. In contrast, the presaccadic object is conserved when object correspondence is broken. This helps transsaccadic memory but it may impose attentional costs on object recognition. Therefore, we investigated how breaking object correspondence across the saccade affects postsaccadic object recognition. In Experiment 1, object correspondence was broken by a brief postsaccadic blank screen. Observers made a saccade to a peripheral object which was displaced during the saccade. This object reappeared either immediately after the saccade or after the blank screen. Within the postsaccadic object, a letter was briefly presented (terminated by a mask. Observers reported displacement direction and letter identity in different blocks. Breaking object correspondence by blanking improved displacement identification but deteriorated postsaccadic letter recognition. In Experiment 2, object correspondence was broken by changing the object’s contrast-polarity. There were no object displacements and observers only reported letter identity. Again, breaking object correspondence deteriorated postsaccadic letter recognition. These findings identify transsaccadic object correspondence as a key determinant of object recognition across the saccade. This is in line with the recent hypothesis that breaking object correspondence results in separate representations of presaccadic and postsaccadic objects which then compete for limited attentional processing resources (Schneider, 2013. Postsaccadic

Modeling a terminology-based electronic nursing record system: an object-oriented approach.

Science.gov (United States)

Park, Hyeoun-Ae; Cho, InSook; Byeun, NamSoo

2007-10-01

The aim of this study was to present our perspectives on healthcare information analysis at a conceptual level and the lessons learned from our experience with the development of a terminology-based enterprise electronic nursing record system - which was one of components in an EMR system at a tertiary teaching hospital in Korea - using an object-oriented system analysis and design concept. To ensure a systematic approach and effective collaboration, the department of nursing constituted a system modeling team comprising a project manager, systems analysts, user representatives, an object-oriented methodology expert, and healthcare informaticists (including the authors). A rational unified process (RUP) and the Unified Modeling Language were used as a development process and for modeling notation, respectively. From the scenario and RUP approach, user requirements were formulated into use case sets and the sequence of activities in the scenario was depicted in an activity diagram. The structure of the system was presented in a class diagram. This approach allowed us to identify clearly the structural and behavioral states and important factors of a terminology-based ENR system (e.g., business concerns and system design concerns) according to the viewpoints of both domain and technical experts.

Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

Science.gov (United States)

Gao, Yan; Marpu, Prashanth; Morales Manila, Luis M.

2014-11-01

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

Directory of Open Access Journals (Sweden)

Xianju Li

2015-07-01

Full Text Available For identification of forested landslides, most studies focus on knowledge-based and pixel-based analysis (PBA of LiDar data, while few studies have examined (semi- automated methods and object-based image analysis (OBIA. Moreover, most of them are focused on soil-covered areas with gentle hillslopes. In bedrock-covered mountains with steep and rugged terrain, it is so difficult to identify landslides that there is currently no research on whether combining semi-automated methods and OBIA with only LiDar derivatives could be more effective. In this study, a semi-automatic object-based landslide identification approach was developed and implemented in a forested area, the Three Gorges of China. Comparisons of OBIA and PBA, two different machine learning algorithms and their respective sensitivity to feature selection (FS, were first investigated. Based on the classification result, the landslide inventory was finally obtained according to (1 inclusion of holes encircled by the landslide body; (2 removal of isolated segments, and (3 delineation of closed envelope curves for landslide objects by manual digitizing operation. The proposed method achieved the following: (1 the filter features of surface roughness were first applied for calculating object features, and proved useful; (2 FS improved classification accuracy and reduced features; (3 the random forest algorithm achieved higher accuracy and was less sensitive to FS than a support vector machine; (4 compared to PBA, OBIA was more sensitive to FS, remarkably reduced computing time, and depicted more contiguous terrain segments; (5 based on the classification result with an overall accuracy of 89.11% ± 0.03%, the obtained inventory map was consistent with the referenced landslide inventory map, with a position mismatch value of 9%. The outlined approach would be helpful for forested landslide identification in steep and rugged terrain.

An object-based approach for tree species extraction from digital orthophoto maps

Science.gov (United States)

Jamil, Akhtar; Bayram, Bulent

2018-05-01

Tree segmentation is an active and ongoing research area in the field of photogrammetry and remote sensing. It is more challenging due to both intra-class and inter-class similarities among various tree species. In this study, we exploited various statistical features for extraction of hazelnut trees from 1 : 5000 scaled digital orthophoto maps. Initially, the non-vegetation areas were eliminated using traditional normalized difference vegetation index (NDVI) followed by application of mean shift segmentation for transforming the pixels into meaningful homogeneous objects. In order to eliminate false positives, morphological opening and closing was employed on candidate objects. A number of heuristics were also derived to eliminate unwanted effects such as shadow and bounding box aspect ratios, before passing them into the classification stage. Finally, a knowledge based decision tree was constructed to distinguish the hazelnut trees from rest of objects which include manmade objects and other type of vegetation. We evaluated the proposed methodology on 10 sample orthophoto maps obtained from Giresun province in Turkey. The manually digitized hazelnut tree boundaries were taken as reference data for accuracy assessment. Both manually digitized and segmented tree borders were converted into binary images and the differences were calculated. According to the obtained results, the proposed methodology obtained an overall accuracy of more than 85 % for all sample images.

Depth Value Pre-Processing for Accurate Transfer Learning Based RGB-D Object Recognition

DEFF Research Database (Denmark)

Aakerberg, Andreas; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund

2017-01-01

of an existing deeplearning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we showthat encoding the depth values as colorized surface normals is beneficial, when the model is initialized withweights learned from training on ImageNet data. Additionally, we show...

Geomorphological change detection using object-based feature extraction from multi-temporal LIDAR data

NARCIS (Netherlands)

Seijmonsbergen, A.C.; Anders, N.S.; Bouten, W.; Feitosa, R.Q.; da Costa, G.A.O.P.; de Almeida, C.M.; Fonseca, L.M.G.; Kux, H.J.H.

2012-01-01

Multi-temporal LiDAR DTMs are used for the development and testing of a method for geomorphological change analysis in western Austria. Our test area is located on a mountain slope in the Gargellen Valley in western Austria. Six geomorphological features were mapped by using stratified Object-Based

Learning Mathematics by Designing, Programming, and Investigating with Interactive, Dynamic Computer-Based Objects

Science.gov (United States)

Marshall, Neil; Buteau, Chantal

2014-01-01

As part of their undergraduate mathematics curriculum, students at Brock University learn to create and use computer-based tools with dynamic, visual interfaces, called Exploratory Objects, developed for the purpose of conducting pure or applied mathematical investigations. A student's Development Process Model of creating and using an Exploratory…

6DoF object pose measurement by a monocular manifold-based pattern recognition technique

International Nuclear Information System (INIS)

Kouskouridas, Rigas; Charalampous, Konstantinos; Gasteratos, Antonios

2012-01-01

In this paper, a novel solution to the compound problem of object recognition and 3D pose estimation is presented. An accurate measurement of the geometrical configuration of a recognized target, relative to a known coordinate system, is of fundamental importance and constitutes a prerequisite for several applications such as robot grasping or obstacle avoidance. The proposed method lays its foundations on the following assumptions: (a) the same object captured under varying viewpoints and perspectives represents data that could be projected onto a well-established and highly distinguishable subspace; (b) totally different objects observed under the same viewpoints and perspectives share identical 3D pose that can be sufficiently modeled to produce a generalized model. Toward this end, we propose an advanced architecture that allows both recognizing patterns and providing efficient solution for 6DoF pose estimation. We employ a manifold modeling architecture that is grounded on a part-based representation of an object, which in turn, is accomplished via an unsupervised clustering of the extracted visual cues. The main contributions of the proposed framework are: (a) the proposed part-based architecture requires minimum supervision, compared to other contemporary solutions, whilst extracting new features encapsulating both appearance and geometrical attributes of the objects; (b) contrary to related projects that extract high-dimensional data, thus, increasing the complexity of the system, the proposed manifold modeling approach makes use of low dimensionality input vectors; (c) the formulation of a novel input–output space mapping that outperforms the existing dimensionality reduction schemes. Experimental results justify our theoretical claims and demonstrate the superiority of our method comparing to other related contemporary projects. (paper)

Object-based Morphological Building Index for Building Extraction from High Resolution Remote Sensing Imagery

Directory of Open Access Journals (Sweden)

LIN Xiangguo

2017-06-01

Full Text Available Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. In this article, an object-based morphological building index (OBMBI is constructed based on both image segmentation and graph-based top-hat reconstruction, and OBMBI is used for building extraction from high resolution remote sensing images. First, bidirectional mapping relationship between pixels, objects and graph-nodes are constructed. Second, the OBMBI image is built based on both graph-based top-hat reconstruction and the above mapping relationship. Third, a binary thresholding is performed on the OBMBI image, and the binary image is converted into vector format to derive the building polygons. Finally, the post-processing is made to optimize the extracted building polygons. Two images, including an aerial image and a panchromatic satellite image, are used to test both the proposed method and classic PanTex method. The experimental results suggest that our proposed method has a higher accuracy in building extraction than the classic PanTex method. On average, the correctness, the completeness and the quality of our method are respectively 9.49%, 11.26% and 14.11% better than those of the PanTex.

Multi-objective optimum design of fast tool servo based on improved differential evolution algorithm

International Nuclear Information System (INIS)

Zhu, Zhiwei; Zhou, Xiaoqin; Liu, Qiang; Zhao, Shaoxin

2011-01-01

The flexure-based mechanism is a promising realization of fast tool servo (FTS), and the optimum determination of flexure hinge parameters is one of the most important elements in the FTS design. This paper presents a multi-objective optimization approach to optimizing the dimension and position parameters of the flexure-based mechanism, which is based on the improved differential evolution algorithm embedding chaos and nonlinear simulated anneal algorithm. The results of optimum design show that the proposed algorithm has excellent performance and a well-balanced compromise is made between two conflicting objectives, the stroke and natural frequency of the FTS mechanism. The validation tests based on finite element analysis (FEA) show good agreement with the results obtained by using the proposed theoretical algorithm of this paper. Finally, a series of experimental tests are conducted to validate the design process and assess the performance of the FTS mechanism. The designed FTS reaches up to a stroke of 10.25 μm with at least 2 kHz bandwidth. Both of the FEA and experimental results demonstrate that the parameters of the flexure-based mechanism determined by the proposed approaches can achieve the specified performance and the proposed approach is suitable for the optimum design of FTS mechanism and of excellent performances

[Electrophysiological bases of semantic processing of objects].

Science.gov (United States)

Kahlaoui, Karima; Baccino, Thierry; Joanette, Yves; Magnié, Marie-Noële

2007-02-01

How pictures and words are stored and processed in the human brain constitute a long-standing question in cognitive psychology. Behavioral studies have yielded a large amount of data addressing this issue. Generally speaking, these data show that there are some interactions between the semantic processing of pictures and words. However, behavioral methods can provide only limited insight into certain findings. Fortunately, Event-Related Potential (ERP) provides on-line cues about the temporal nature of cognitive processes and contributes to the exploration of their neural substrates. ERPs have been used in order to better understand semantic processing of words and pictures. The main objective of this article is to offer an overview of the electrophysiologic bases of semantic processing of words and pictures. Studies presented in this article showed that the processing of words is associated with an N 400 component, whereas pictures elicited both N 300 and N 400 components. Topographical analysis of the N 400 distribution over the scalp is compatible with the idea that both image-mediated concrete words and pictures access an amodal semantic system. However, given the distinctive N 300 patterns, observed only during picture processing, it appears that picture and word processing rely upon distinct neuronal networks, even if they end up activating more or less similar semantic representations.

Fuzzy preference based interactive fuzzy physical programming and its application in multi-objective optimization

International Nuclear Information System (INIS)

Zhang, Xu; Huang, Hong Zhong; Yu, Lanfeng

2006-01-01

Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer

Cost and quality effectiveness of objective-based and statistically-based quality control for volatile organic compounds analyses of gases

International Nuclear Information System (INIS)

Bennett, J.T.; Crowder, C.A.; Connolly, M.J.

1994-01-01

Gas samples from drums of radioactive waste at the Department of Energy (DOE) Idaho National Engineering Laboratory are being characterized for 29 volatile organic compounds to determine the feasibility of storing the waste in DOE's Waste Isolation Pilot Plant (WIPP) in Carlsbad, New Mexico. Quality requirements for the gas chromatography (GC) and GC/mass spectrometry chemical methods used to analyze the waste are specified in the Quality Assurance Program Plan for the WIPP Experimental Waste Characterization Program. Quality requirements consist of both objective criteria (data quality objectives, DQOs) and statistical criteria (process control). The DQOs apply to routine sample analyses, while the statistical criteria serve to determine and monitor precision and accuracy (P ampersand A) of the analysis methods and are also used to assign upper confidence limits to measurement results close to action levels. After over two years and more than 1000 sample analyses there are two general conclusions concerning the two approaches to quality control: (1) Objective criteria (e.g., ± 25% precision, ± 30% accuracy) based on customer needs and the usually prescribed criteria for similar EPA- approved methods are consistently attained during routine analyses. (2) Statistical criteria based on short term method performance are almost an order of magnitude more stringent than objective criteria and are difficult to satisfy following the same routine laboratory procedures which satisfy the objective criteria. A more cost effective and representative approach to establishing statistical method performances criteria would be either to utilize a moving average of P ampersand A from control samples over a several month time period or to determine within a sample variation by one-way analysis of variance of several months replicate sample analysis results or both. Confidence intervals for results near action levels could also be determined by replicate analysis of the sample in

Non-sky polarization-based dehazing algorithm for non-specular objects using polarization difference and global scene feature.

Science.gov (United States)

Qu, Yufu; Zou, Zhaofan

2017-10-16

Photographic images taken in foggy or hazy weather (hazy images) exhibit poor visibility and detail because of scattering and attenuation of light caused by suspended particles, and therefore, image dehazing has attracted considerable research attention. The current polarization-based dehazing algorithms strongly rely on the presence of a "sky area", and thus, the selection of model parameters is susceptible to external interference of high-brightness objects and strong light sources. In addition, the noise of the restored image is large. In order to solve these problems, we propose a polarization-based dehazing algorithm that does not rely on the sky area ("non-sky"). First, a linear polarizer is used to collect three polarized images. The maximum- and minimum-intensity images are then obtained by calculation, assuming the polarization of light emanating from objects is negligible in most scenarios involving non-specular objects. Subsequently, the polarization difference of the two images is used to determine a sky area and calculate the infinite atmospheric light value. Next, using the global features of the image, and based on the assumption that the airlight and object radiance are irrelevant, the degree of polarization of the airlight (DPA) is calculated by solving for the optimal solution of the correlation coefficient equation between airlight and object radiance; the optimal solution is obtained by setting the right-hand side of the equation to zero. Then, the hazy image is subjected to dehazing. Subsequently, a filtering denoising algorithm, which combines the polarization difference information and block-matching and 3D (BM3D) filtering, is designed to filter the image smoothly. Our experimental results show that the proposed polarization-based dehazing algorithm does not depend on whether the image includes a sky area and does not require complex models. Moreover, the dehazing image except specular object scenarios is superior to those obtained by Tarel

Well-Being and Objectivity

Directory of Open Access Journals (Sweden)

Jakub Bożydar Wiśniewski

2011-03-01

Full Text Available In this paper, I investigate the issue of whether there exists an objective element of well-being, completely independent of anyone’s desires, interests and preferences. After rejecting health-based and convention-based approaches to objectivity, I conclude that the element in question consists in respecting autonomy, voluntariness of every purposive agent and the principle of non-aggression.

Object-based classification of global undersea topography and geomorphological features from the SRTM30_PLUS data

Science.gov (United States)

Dekavalla, Maria; Argialas, Demetre

2017-07-01

The analysis of undersea topography and geomorphological features provides necessary information to related disciplines and many applications. The development of an automated knowledge-based classification approach of undersea topography and geomorphological features is challenging due to their multi-scale nature. The aim of the study is to develop and evaluate an automated knowledge-based OBIA approach to: i) decompose the global undersea topography to multi-scale regions of distinct morphometric properties, and ii) assign the derived regions to characteristic geomorphological features. First, the global undersea topography was decomposed through the SRTM30_PLUS bathymetry data to the so-called morphometric objects of discrete morphometric properties and spatial scales defined by data-driven methods (local variance graphs and nested means) and multi-scale analysis. The derived morphometric objects were combined with additional relative topographic position information computed with a self-adaptive pattern recognition method (geomorphons), and auxiliary data and were assigned to characteristic undersea geomorphological feature classes through a knowledge base, developed from standard definitions. The decomposition of the SRTM30_PLUS data to morphometric objects was considered successful for the requirements of maximizing intra-object and inter-object heterogeneity, based on the near zero values of the Moran's I and the low values of the weighted variance index. The knowledge-based classification approach was tested for its transferability in six case studies of various tectonic settings and achieved the efficient extraction of 11 undersea geomorphological feature classes. The classification results for the six case studies were compared with the digital global seafloor geomorphic features map (GSFM). The 11 undersea feature classes and their producer's accuracies in respect to the GSFM relevant areas were Basin (95%), Continental Shelf (94.9%), Trough (88

Channels as Objects in Concurrent Object-Oriented Programming

Directory of Open Access Journals (Sweden)

Joana Campos

2011-10-01

Full Text Available There is often a sort of a protocol associated to each class, stating when and how certain methods should be called. Given that this protocol is, if at all, described in the documentation accompanying the class, current mainstream object-oriented languages cannot provide for the verification of client code adherence against the sought class behaviour. We have defined a class-based concurrent object-oriented language that formalises such protocols in the form of usage types. Usage types are attached to class definitions, allowing for the specification of (1 the available methods, (2 the tests clients must perform on the result of methods, and (3 the object status - linear or shared - all of which depend on the object's state. Our work extends the recent approach on modular session types by eliminating channel operations, and defining the method call as the single communication primitive in both sequential and concurrent settings. In contrast to previous works, we define a single category for objects, instead of distinct categories for linear and for shared objects, and let linear objects evolve into shared ones. We introduce a standard sync qualifier to prevent thread interference in certain operations on shared objects. We formalise the language syntax, the operational semantics, and a type system that enforces by static typing that methods are called only when available, and by a single client if so specified in the usage type. We illustrate the language via a complete example.

Volumetric Medical Image Coding: An Object-based, Lossy-to-lossless and Fully Scalable Approach

Science.gov (United States)

Danyali, Habibiollah; Mertins, Alfred

2011-01-01

In this article, an object-based, highly scalable, lossy-to-lossless 3D wavelet coding approach for volumetric medical image data (e.g., magnetic resonance (MR) and computed tomography (CT)) is proposed. The new method, called 3DOBHS-SPIHT, is based on the well-known set partitioning in the hierarchical trees (SPIHT) algorithm and supports both quality and resolution scalability. The 3D input data is grouped into groups of slices (GOS) and each GOS is encoded and decoded as a separate unit. The symmetric tree definition of the original 3DSPIHT is improved by introducing a new asymmetric tree structure. While preserving the compression efficiency, the new tree structure allows for a small size of each GOS, which not only reduces memory consumption during the encoding and decoding processes, but also facilitates more efficient random access to certain segments of slices. To achieve more compression efficiency, the algorithm only encodes the main object of interest in each 3D data set, which can have any arbitrary shape, and ignores the unnecessary background. The experimental results on some MR data sets show the good performance of the 3DOBHS-SPIHT algorithm for multi-resolution lossy-to-lossless coding. The compression efficiency, full scalability, and object-based features of the proposed approach, beside its lossy-to-lossless coding support, make it a very attractive candidate for volumetric medical image information archiving and transmission applications. PMID:22606653

Object-Based Visual Attention in 8-Month-Old Infants: Evidence from an Eye-Tracking Study

Science.gov (United States)

Bulf, Hermann; Valenza, Eloisa

2013-01-01

Visual attention is one of the infant's primary tools for gathering relevant information from the environment for further processing and learning. The space-based component of visual attention in infants has been widely investigated; however, the object-based component of visual attention has received scarce interest. This scarcity is…

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

Science.gov (United States)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

Multi-objective Calibration of DHSVM Based on Hydrologic Key Elements in Jinhua River Basin, East China

Science.gov (United States)

Pan, S.; Liu, L.; Xu, Y. P.

2017-12-01

Abstract: In physically based distributed hydrological model, large number of parameters, representing spatial heterogeneity of watershed and various processes in hydrologic cycle, are involved. For lack of calibration module in Distributed Hydrology Soil Vegetation Model, this study developed a multi-objective calibration module using Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) and based on parallel computing of Linux cluster for DHSVM (ɛP-DHSVM). In this study, two hydrologic key elements (i.e., runoff and evapotranspiration) are used as objectives in multi-objective calibration of model. MODIS evapotranspiration obtained by SEBAL is adopted to fill the gap of lack of observation for evapotranspiration. The results show that good performance of runoff simulation in single objective calibration cannot ensure good simulation performance of other hydrologic key elements. Self-developed ɛP-DHSVM model can make multi-objective calibration more efficiently and effectively. The running speed can be increased by more than 20-30 times via applying ɛP-DHSVM. In addition, runoff and evapotranspiration can be simulated very well simultaneously by ɛP-DHSVM, with superior values for two efficiency coefficients (0.74 for NS of runoff and 0.79 for NS of evapotranspiration, -10.5% and -8.6% for PBIAS of runoff and evapotranspiration respectively).

Three-Dimensional Stereo Reconstruction and Sensor Registration With Application to the Development of a Multi-Sensor Database

National Research Council Canada - National Science Library

Oberle, William

2002-01-01

... and the transformations between the camera system and other sensor, vehicle, and world coordinate systems. Results indicate that the measured stereo and ladar data are susceptible to large errors that affect the accuracy of the calculated transformations.

MULTIPLE OBJECTS

Directory of Open Access Journals (Sweden)

A. A. Bosov

2015-04-01

Full Text Available Purpose. The development of complicated techniques of production and management processes, information systems, computer science, applied objects of systems theory and others requires improvement of mathematical methods, new approaches for researches of application systems. And the variety and diversity of subject systems makes necessary the development of a model that generalizes the classical sets and their development – sets of sets. Multiple objects unlike sets are constructed by multiple structures and represented by the structure and content. The aim of the work is the analysis of multiple structures, generating multiple objects, the further development of operations on these objects in application systems. Methodology. To achieve the objectives of the researches, the structure of multiple objects represents as constructive trio, consisting of media, signatures and axiomatic. Multiple object is determined by the structure and content, as well as represented by hybrid superposition, composed of sets, multi-sets, ordered sets (lists and heterogeneous sets (sequences, corteges. Findings. In this paper we study the properties and characteristics of the components of hybrid multiple objects of complex systems, proposed assessments of their complexity, shown the rules of internal and external operations on objects of implementation. We introduce the relation of arbitrary order over multiple objects, we define the description of functions and display on objects of multiple structures. Originality.In this paper we consider the development of multiple structures, generating multiple objects.Practical value. The transition from the abstract to the subject of multiple structures requires the transformation of the system and multiple objects. Transformation involves three successive stages: specification (binding to the domain, interpretation (multiple sites and particularization (goals. The proposed describe systems approach based on hybrid sets

Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems

International Nuclear Information System (INIS)

Chen, Gonggui; Liu, Lilan; Song, Peizhu; Du, Yangwei

2014-01-01

Highlights: • New method for MOORPD problem using MOCIPSO and MOIPSO approaches. • Constrain-prior Pareto-dominance method is proposed to meet the constraints. • The limits of the apparent power flow of transmission line are considered. • MOORPD model is built up for MOORPD problem. • The achieved results by MOCIPSO and MOIPSO approaches are better than MOPSO method. - Abstract: Multi-objective optimal reactive power dispatch (MOORPD) seeks to not only minimize power losses, but also improve the stability of power system simultaneously. In this paper, the static voltage stability enhancement is achieved through incorporating L index in MOORPD problem. Chaotic improved PSO-based multi-objective optimization (MOCIPSO) and improved PSO-based multi-objective optimization (MOIPSO) approaches are proposed for solving complex multi-objective, mixed integer nonlinear problems such as minimization of power losses and L index in power systems simultaneously. In MOCIPSO and MOIPSO based optimization approaches, crossover operator is proposed to enhance PSO diversity and improve their global searching capability, and for MOCIPSO based optimization approach, chaotic sequences based on logistic map instead of random sequences is introduced to PSO for enhancing exploitation capability. In the two approaches, constrain-prior Pareto-dominance method (CPM) is proposed to meet the inequality constraints on state variables, the sorting and crowding distance methods are considered to maintain a well distributed Pareto optimal solutions, and moreover, fuzzy set theory is employed to extract the best compromise solution over the Pareto optimal curve. The proposed approaches have been examined and tested in the IEEE 30 bus and the IEEE 57 bus power systems. The performances of MOCIPSO, MOIPSO, and multi-objective PSO (MOPSO) approaches are compared with respect to multi-objective performance measures. The simulation results are promising and confirm the ability of MOCIPSO and

Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

NARCIS (Netherlands)

Pratomo, J.; Kuffer, M.; Martinez, Javier; Kohli, D.

2017-01-01

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

DEFF Research Database (Denmark)

Zhang, Chunyu; Ding, Yi; Wu, Qiuwei

2013-01-01

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...

Detection of Water Hazards for Autonomous Robotic Vehicles

Science.gov (United States)

Matthes, Larry; Belluta, Paolo; McHenry, Michael

2006-01-01

Four methods of detection of bodies of water are under development as means to enable autonomous robotic ground vehicles to avoid water hazards when traversing off-road terrain. The methods involve processing of digitized outputs of optoelectronic sensors aboard the vehicles. It is planned to implement these methods in hardware and software that would operate in conjunction with the hardware and software for navigation and for avoidance of solid terrain obstacles and hazards. The first method, intended for use during the day, is based on the observation that, under most off-road conditions, reflections of sky from water are easily discriminated from the adjacent terrain by their color and brightness, regardless of the weather and of the state of surface waves on the water. Accordingly, this method involves collection of color imagery by a video camera and processing of the image data by an algorithm that classifies each pixel as soil, water, or vegetation according to its color and brightness values (see figure). Among the issues that arise is the fact that in the presence of reflections of objects on the opposite shore, it is difficult to distinguish water by color and brightness alone. Another issue is that once a body of water has been identified by means of color and brightness, its boundary must be mapped for use in navigation. Techniques for addressing these issues are under investigation. The second method, which is not limited by time of day, is based on the observation that ladar returns from bodies of water are usually too weak to be detected. In this method, ladar scans of the terrain are analyzed for returns and the absence thereof. In appropriate regions, the presence of water can be inferred from the absence of returns. Under some conditions in which reflections from the bottom are detectable, ladar returns could, in principle, be used to determine depth. The third method involves the recognition of bodies of water as dark areas in short

The associations between objectively-determined and self-reported urban form characteristics and neighborhood-based walking in adults.

Science.gov (United States)

Jack, Elizabeth; McCormack, Gavin R

2014-06-04

Self-reported and objectively-determined neighborhood built characteristics are associated with physical activity, yet little is known about their combined influence on walking. This study: 1) compared self-reported measures of the neighborhood built environment between objectively-determined low, medium, and high walkable neighborhoods; 2) estimated the relative associations between self-reported and objectively-determined neighborhood characteristics and walking and; 3) examined the extent to which the objectively-determined built environment moderates the association between self-reported measures of the neighborhood built environment and walking. A random cross-section of 1875 Canadian adults completed a telephone-interview and postal questionnaire capturing neighborhood walkability, neighborhood-based walking, socio-demographic characteristics, walking attitudes, and residential self-selection. Walkability of each respondent's neighborhood was objectively-determined (low [LW], medium [MW], and high walkable [HW]). Covariate-adjusted regression models estimated the associations between weekly participation and duration in transportation and recreational walking and self-reported and objectively-determined walkability. Compared with objectively-determined LW neighborhoods, respondents in HW neighborhoods positively perceived access to services, street connectivity, pedestrian infrastructure, and utilitarian and recreation destination mix, but negatively perceived motor vehicle traffic and crime related safety. Compared with residents of objectively-determined LW neighborhoods, residents of HW neighborhoods were more likely (p spend more time, per week (193 min/wk) transportation walking. Perceived access to services, street connectivity, motor vehicle safety, and mix of recreational destinations were also significantly associated with transportation walking. With regard to interactions, HW x utilitarian destination mix was positively associated with

Objectively Quantifying Radiation Esophagitis With Novel Computed Tomography–Based Metrics

Energy Technology Data Exchange (ETDEWEB)

Niedzielski, Joshua S., E-mail: jsniedzielski@mdanderson.org [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States); Yang, Jinzhong [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States); Stingo, Francesco [Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); Martel, Mary K.; Mohan, Radhe [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States); Gomez, Daniel R. [Department of Radiation Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); Briere, Tina M. [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States); Liao, Zhongxing [Department of Radiation Oncology, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); Court, Laurence E. [Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, Texas (United States); University of Texas Houston Graduate School of Biomedical Science, Houston, Texas (United States)

2016-02-01

Purpose: To study radiation-induced esophageal expansion as an objective measure of radiation esophagitis in patients with non-small cell lung cancer (NSCLC) treated with intensity modulated radiation therapy. Methods and Materials: Eighty-five patients had weekly intra-treatment CT imaging and esophagitis scoring according to Common Terminlogy Criteria for Adverse Events 4.0, (24 Grade 0, 45 Grade 2, and 16 Grade 3). Nineteen esophageal expansion metrics based on mean, maximum, spatial length, and volume of expansion were calculated as voxel-based relative volume change, using the Jacobian determinant from deformable image registration between the planning and weekly CTs. An anatomic variability correction method was validated and applied to these metrics to reduce uncertainty. An analysis of expansion metrics and radiation esophagitis grade was conducted using normal tissue complication probability from univariate logistic regression and Spearman rank for grade 2 and grade 3 esophagitis endpoints, as well as the timing of expansion and esophagitis grade. Metrics' performance in classifying esophagitis was tested with receiver operating characteristic analysis. Results: Expansion increased with esophagitis grade. Thirteen of 19 expansion metrics had receiver operating characteristic area under the curve values >0.80 for both grade 2 and grade 3 esophagitis endpoints, with the highest performance from maximum axial expansion (MaxExp1) and esophageal length with axial expansion ≥30% (LenExp30%) with area under the curve values of 0.93 and 0.91 for grade 2, 0.90 and 0.90 for grade 3 esophagitis, respectively. Conclusions: Esophageal expansion may be a suitable objective measure of esophagitis, particularly maximum axial esophageal expansion and esophageal length with axial expansion ≥30%, with 2.1 Jacobian value and 98.6 mm as the metric value for 50% probability of grade 3 esophagitis. The uncertainty in esophageal Jacobian calculations can be reduced

Multi-agent system for Knowledge-based recommendation of Learning Objects

Directory of Open Access Journals (Sweden)

Paula Andrea RODRÍGUEZ MARÍN

2015-12-01

Full Text Available Learning Object (LO is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.Learning Object (LO is a content unit being used within virtual learning environments, which -once found and retrieved- may assist students in the teaching - learning process. Such LO search and retrieval are recently supported and enhanced by data mining techniques. In this sense, clustering can be used to find groups holding similar LOs so that from obtained groups, knowledge-based recommender systems (KRS can recommend more adapted and relevant LOs. In particular, prior knowledge come from LOs previously selected, liked and ranked by the student to whom the recommendation will be performed. In this paper, we present a KRS for LOs, which uses a conventional clustering technique, namely K-means, aimed at finding similar LOs and delivering resources adapted to a specific student. Obtained promising results show that proposed KRS is able to both retrieve relevant LO and improve the recommendation precision.

Objectively Quantifying Radiation Esophagitis With Novel Computed Tomography–Based Metrics

International Nuclear Information System (INIS)

Niedzielski, Joshua S.; Yang, Jinzhong; Stingo, Francesco; Martel, Mary K.; Mohan, Radhe; Gomez, Daniel R.; Briere, Tina M.; Liao, Zhongxing; Court, Laurence E.

2016-01-01

Purpose: To study radiation-induced esophageal expansion as an objective measure of radiation esophagitis in patients with non-small cell lung cancer (NSCLC) treated with intensity modulated radiation therapy. Methods and Materials: Eighty-five patients had weekly intra-treatment CT imaging and esophagitis scoring according to Common Terminlogy Criteria for Adverse Events 4.0, (24 Grade 0, 45 Grade 2, and 16 Grade 3). Nineteen esophageal expansion metrics based on mean, maximum, spatial length, and volume of expansion were calculated as voxel-based relative volume change, using the Jacobian determinant from deformable image registration between the planning and weekly CTs. An anatomic variability correction method was validated and applied to these metrics to reduce uncertainty. An analysis of expansion metrics and radiation esophagitis grade was conducted using normal tissue complication probability from univariate logistic regression and Spearman rank for grade 2 and grade 3 esophagitis endpoints, as well as the timing of expansion and esophagitis grade. Metrics' performance in classifying esophagitis was tested with receiver operating characteristic analysis. Results: Expansion increased with esophagitis grade. Thirteen of 19 expansion metrics had receiver operating characteristic area under the curve values >0.80 for both grade 2 and grade 3 esophagitis endpoints, with the highest performance from maximum axial expansion (MaxExp1) and esophageal length with axial expansion ≥30% (LenExp30%) with area under the curve values of 0.93 and 0.91 for grade 2, 0.90 and 0.90 for grade 3 esophagitis, respectively. Conclusions: Esophageal expansion may be a suitable objective measure of esophagitis, particularly maximum axial esophageal expansion and esophageal length with axial expansion ≥30%, with 2.1 Jacobian value and 98.6 mm as the metric value for 50% probability of grade 3 esophagitis. The uncertainty in esophageal Jacobian calculations can be reduced

Fast processing of microscopic images using object-based extended depth of field.

Science.gov (United States)

Intarapanich, Apichart; Kaewkamnerd, Saowaluck; Pannarut, Montri; Shaw, Philip J; Tongsima, Sissades

2016-12-22

Microscopic analysis requires that foreground objects of interest, e.g. cells, are in focus. In a typical microscopic specimen, the foreground objects may lie on different depths of field necessitating capture of multiple images taken at different focal planes. The extended depth of field (EDoF) technique is a computational method for merging images from different depths of field into a composite image with all foreground objects in focus. Composite images generated by EDoF can be applied in automated image processing and pattern recognition systems. However, current algorithms for EDoF are computationally intensive and impractical, especially for applications such as medical diagnosis where rapid sample turnaround is important. Since foreground objects typically constitute a minor part of an image, the EDoF technique could be made to work much faster if only foreground regions are processed to make the composite image. We propose a novel algorithm called object-based extended depths of field (OEDoF) to address this issue. The OEDoF algorithm consists of four major modules: 1) color conversion, 2) object region identification, 3) good contrast pixel identification and 4) detail merging. First, the algorithm employs color conversion to enhance contrast followed by identification of foreground pixels. A composite image is constructed using only these foreground pixels, which dramatically reduces the computational time. We used 250 images obtained from 45 specimens of confirmed malaria infections to test our proposed algorithm. The resulting composite images with all in-focus objects were produced using the proposed OEDoF algorithm. We measured the performance of OEDoF in terms of image clarity (quality) and processing time. The features of interest selected by the OEDoF algorithm are comparable in quality with equivalent regions in images processed by the state-of-the-art complex wavelet EDoF algorithm; however, OEDoF required four times less processing time. This

Full Waveform Inversion Using an Energy-Based Objective Function with Efficient Calculation of the Gradient

KAUST Repository

Choi, Yun Seok

2017-05-26

Full waveform inversion (FWI) using an energy-based objective function has the potential to provide long wavelength model information even without low frequency in the data. However, without the back-propagation method (adjoint-state method), its implementation is impractical for the model size of general seismic survey. We derive the gradient of the energy-based objective function using the back-propagation method to make its FWI feasible. We also raise the energy signal to the power of a small positive number to properly handle the energy signal imbalance as a function of offset. Examples demonstrate that the proposed FWI algorithm provides a convergent long wavelength structure model even without low-frequency information, which can be used as a good starting model for the subsequent conventional FWI.

Survey of Object-Based Data Reduction Techniques in Observational Astronomy

Directory of Open Access Journals (Sweden)

Łukasik Szymon

2016-01-01

Full Text Available Dealing with astronomical observations represents one of the most challenging areas of big data analytics. Besides huge variety of data types, dynamics related to continuous data flow from multiple sources, handling enormous volumes of data is essential. This paper provides an overview of methods aimed at reducing both the number of features/attributes as well as data instances. It concentrates on data mining approaches not related to instruments and observation tools instead working on processed object-based data. The main goal of this article is to describe existing datasets on which algorithms are frequently tested, to characterize and classify available data reduction algorithms and identify promising solutions capable of addressing present and future challenges in astronomy.

Object-Based Assessment of Satellite Precipitation Products

Directory of Open Access Journals (Sweden)

Jingjing Li

2016-06-01

Full Text Available An object-based verification approach is employed to assess the performance of the commonly used high-resolution satellite precipitation products: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN, Climate Prediction center MORPHing technique (CMORPH, and Tropical Rainfall Measurement Mission (TRMM Multi-Satellite Precipitation Analysis (TMPA 3B42RT. The evaluation of the satellite precipitation products focuses on the skill of depicting the geometric features of the localized precipitation areas. Seasonal variability of the performances of these products against the ground observations is investigated through the examples of warm and cold seasons. It is found that PERSIANN is capable of depicting the orientation of the localized precipitation areas in both seasons. CMORPH has the ability to capture the sizes of the localized precipitation areas and performs the best in the overall assessment for both seasons. 3B42RT is capable of depicting the location of the precipitation areas for both seasons. In addition, all of the products perform better on capturing the sizes and centroids of precipitation areas in the warm season than in the cold season, while they perform better on depicting the intersection area and orientation in the cold season than in the warm season. These products are more skillful on correctly detecting the localized precipitation areas against the observations in the warm season than in the cold season.

A Vision for Spaceflight Reliability: NASA's Objectives Based Strategy

Science.gov (United States)

Groen, Frank; Evans, John; Hall, Tony

2015-01-01

In defining the direction for a new Reliability and Maintainability standard, OSMA has extracted the essential objectives that our programs need, to undertake a reliable mission. These objectives have been structured to lead mission planning through construction of an objective hierarchy, which defines the critical approaches for achieving high reliability and maintainability (R M). Creating a hierarchy, as a basis for assurance implementation, is a proven approach; yet, it holds the opportunity to enable new directions, as NASA moves forward in tackling the challenges of space exploration.

Contextual System of Symbol Structural Recognition based on an Object-Process Methodology

OpenAIRE

Delalandre, Mathieu

2005-01-01

We present in this paper a symbol recognition system for the graphic documents. This one is based on a contextual approach for symbol structural recognition exploiting an Object-Process Methodology. It uses a processing library composed of structural recognition processings and contextual evaluation processings. These processings allow our system to deal with the multi-representation of symbols. The different processings are controlled, in an automatic way, by an inference engine during the r...

Laser radar IV; Proceedings of the Meeting, Orlando, FL, Mar. 29, 30, 1989

Science.gov (United States)

Becherer, Richard J.

1989-09-01

Various papers on laser radars are presented. Individual topics considered include: frequency chirp of a low-pressure hybrid TE CO2 laser, design of a high-power isotopic CO2 laser amplifier, monolithic beam steering for large aperture laser radar, laser radar receiver using a Digicon detector, all-solid-state CO2 laser driver, noise in an acoustooptic-modulated laser source, laser signature prediction using the Value computer program, laser radar acquisition and tracking, concept of a moving target indicator search ladar, system design philosophy for laser radar wavelength determination, imaging three-frequency CO2 laser radar, backscatter-modulation semiconductor laser radar, three-dimensional imaging using a single laser pulse, design and manufacture of a high-resolution laser radar scanner, calculations of vibrational signatures for coherent ladar, coherent subaperture ultraviolet imagery, and range-Doppler resolution degradation associated with amplitude distortion.

Object-oriented communications

International Nuclear Information System (INIS)

Chapman, L.J.

1989-01-01

OOC is a high-level communications protocol based on the object-oriented paradigm. OOC's syntax, semantics, and pragmatics balance simplicity and expressivity for controls environments. While natural languages are too complex, computer protocols are often insufficiently expressive. An object-oriented communications philosophy provides a base for building the necessary high-level communications primitives like I don't understand and the current value of X is K. OOC is sufficiently flexible to express data acquisition, control requests, alarm messages, and error messages in a straightforward generic way. It can be used in networks, for inter-task communication, and even for intra-task communication

Formal Transformations from Graphically-Based Object-Oriented Representations to Theory-Based Specifications

Science.gov (United States)

1996-06-01

for Software Synthesis." KBSE 󈨡. IEEE, 1993. 51. Kang, Kyo C., et al. Feature-Oriented Domain Analysis ( FODA ) Feasibility Study. Technical Report...Algebra. New York, NY: Chelsea Publishing Company , 1993. 72. Martin, James. Principles of Object-Oriented Analysis and Design. Englewood Cliffs, New...and usefulness in domain analysis and modeling. Rumbaugh uses three distinct views to describe a domain: (1) the object model describes structural

Sustainability Logistics Basing - Science and Technology Objective - Demonstration; Industry Assessment and Demonstration Final Report

Science.gov (United States)

2017-08-14

TECHNICAL REPORT AD ________________ NATICK/TR-17/019 SUSTAINABILITY ...LOGISTICS BASING – SCIENCE & TECHNOLOGY OBJECTIVE – DEMONSTRATION; INDUSTRY ASSESSMENT AND DEMONSTRATION FINAL REPORT by Elizabeth D. Swisher and...Benjamin J. Campbell August 2017 Final Report December 2014 – February 2016 Approved for public release; distribution is

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

Directory of Open Access Journals (Sweden)

R. Venkata Rao

2014-01-01

Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

Smartphone-based objective monitoring in bipolar disorder

DEFF Research Database (Denmark)

Faurholt-Jepsen, Maria; Bauer, Michael; Kessing, Lars Vedel

2018-01-01

, anxiety, substance abuse, eating disorder, schizophrenia and bipolar disorder have been developed and used. The present paper presents the status and findings from studies using automatically generated objective smartphone data in the monitoring of bipolar disorder, and addresses considerations...

FPGA-Based HD Camera System for the Micropositioning of Biomedical Micro-Objects Using a Contactless Micro-Conveyor

Directory of Open Access Journals (Sweden)

Elmar Yusifli

2017-03-01

Full Text Available With recent advancements, micro-object contactless conveyers are becoming an essential part of the biomedical sector. They help avoid any infection and damage that can occur due to external contact. In this context, a smart micro-conveyor is devised. It is a Field Programmable Gate Array (FPGA-based system that employs a smart surface for conveyance along with an OmniVision complementary metal-oxide-semiconductor (CMOS HD camera for micro-object position detection and tracking. A specific FPGA-based hardware design and VHSIC (Very High Speed Integrated Circuit Hardware Description Language (VHDL implementation are realized. It is done without employing any Nios processor or System on a Programmable Chip (SOPC builder based Central Processing Unit (CPU core. It keeps the system efficient in terms of resource utilization and power consumption. The micro-object positioning status is captured with an embedded FPGA-based camera driver and it is communicated to the Image Processing, Decision Making and Command (IPDC module. The IPDC is programmed in C++ and can run on a Personal Computer (PC or on any appropriate embedded system. The IPDC decisions are sent back to the FPGA, which pilots the smart surface accordingly. In this way, an automated closed-loop system is employed to convey the micro-object towards a desired location. The devised system architecture and implementation principle is described. Its functionality is also verified. Results have confirmed the proper functionality of the developed system, along with its outperformance compared to other solutions.

Metrological characterization of 3D imaging devices

Science.gov (United States)

Guidi, G.

2013-04-01

Manufacturers often express the performance of a 3D imaging device in various non-uniform ways for the lack of internationally recognized standard requirements for metrological parameters able to identify the capability of capturing a real scene. For this reason several national and international organizations in the last ten years have been developing protocols for verifying such performance. Ranging from VDI/VDE 2634, published by the Association of German Engineers and oriented to the world of mechanical 3D measurements (triangulation-based devices), to the ASTM technical committee E57, working also on laser systems based on direct range detection (TOF, Phase Shift, FM-CW, flash LADAR), this paper shows the state of the art about the characterization of active range devices, with special emphasis on measurement uncertainty, accuracy and resolution. Most of these protocols are based on special objects whose shape and size are certified with a known level of accuracy. By capturing the 3D shape of such objects with a range device, a comparison between the measured points and the theoretical shape they should represent is possible. The actual deviations can be directly analyzed or some derived parameters can be obtained (e.g. angles between planes, distances between barycenters of spheres rigidly connected, frequency domain parameters, etc.). This paper shows theoretical aspects and experimental results of some novel characterization methods applied to different categories of active 3D imaging devices based on both principles of triangulation and direct range detection.

Quasi-objects, Cult Objects and Fashion Objects

DEFF Research Database (Denmark)

Andersen, Bjørn Schiermer

2011-01-01

This article attempts to rehabilitate the concept of fetishism and to contribute to the debate on the social role of objects as well as to fashion theory. Extrapolating from Michel Serres’ theory of the quasi-objects, I distinguish two phenomenologies possessing almost opposite characteristics. T...... as a unique opportunity for studying the interchange between these two forms of fetishism and their respective phenomenologies. Finally, returning to Serres, I briefly consider the theoretical consequences of introducing the fashion object as a quasi-object.......This article attempts to rehabilitate the concept of fetishism and to contribute to the debate on the social role of objects as well as to fashion theory. Extrapolating from Michel Serres’ theory of the quasi-objects, I distinguish two phenomenologies possessing almost opposite characteristics....... These two phenomenologies are, so I argue, essential to quasi-object theory, yet largely ignored by Serres’ sociological interpreters. They correspond with the two different theories of fetishism found in Marx and Durkheim, respectively. In the second half of the article, I introduce the fashion object...

[Location selection for Shenyang urban parks based on GIS and multi-objective location allocation model].

Science.gov (United States)

Zhou, Yuan; Shi, Tie-Mao; Hu, Yuan-Man; Gao, Chang; Liu, Miao; Song, Lin-Qi

2011-12-01

Based on geographic information system (GIS) technology and multi-objective location-allocation (LA) model, and in considering of four relatively independent objective factors (population density level, air pollution level, urban heat island effect level, and urban land use pattern), an optimized location selection for the urban parks within the Third Ring of Shenyang was conducted, and the selection results were compared with the spatial distribution of existing parks, aimed to evaluate the rationality of the spatial distribution of urban green spaces. In the location selection of urban green spaces in the study area, the factor air pollution was most important, and, compared with single objective factor, the weighted analysis results of multi-objective factors could provide optimized spatial location selection of new urban green spaces. The combination of GIS technology with LA model would be a new approach for the spatial optimizing of urban green spaces.

Building and Programming a Smart Robotic System for Distinguishing Objects Based on their Shape and Colour

Science.gov (United States)

Sharari, T. M.

2015-03-01

This paper presents a robotic system designed for holding and placing objects based on their colour and shape. The presented robot is given a complete set of instructions of positions and orientation angles for each manipulation motion. The main feature in this paper is that the developed robot used a combination of vision and motion systems for holding and placing the work-objects, mounted on the flat work-plane, based on their shapes and colors. This combination improves the flexibility of manipulation which may help eliminate the use of some expensive manipulation tasks in a variety of industrial applications. The robotic system presented in this paper is designed as an educational robot that possesses the ability for holding-and-placing operations with limited load. To process the various instructions for holding and placing the work objects, a main control unit - Manipulation Control Unit (MCU) is used as well as a slave unit that performed the actual instructions from the MCU.

Fast generation of video holograms of three-dimensional moving objects using a motion compensation-based novel look-up table.

Science.gov (United States)

Kim, Seung-Cheol; Dong, Xiao-Bin; Kwon, Min-Woo; Kim, Eun-Soo

2013-05-06

A novel approach for fast generation of video holograms of three-dimensional (3-D) moving objects using a motion compensation-based novel-look-up-table (MC-N-LUT) method is proposed. Motion compensation has been widely employed in compression of conventional 2-D video data because of its ability to exploit high temporal correlation between successive video frames. Here, this concept of motion-compensation is firstly applied to the N-LUT based on its inherent property of shift-invariance. That is, motion vectors of 3-D moving objects are extracted between the two consecutive video frames, and with them motions of the 3-D objects at each frame are compensated. Then, through this process, 3-D object data to be calculated for its video holograms are massively reduced, which results in a dramatic increase of the computational speed of the proposed method. Experimental results with three kinds of 3-D video scenarios reveal that the average number of calculated object points and the average calculation time for one object point of the proposed method, have found to be reduced down to 86.95%, 86.53% and 34.99%, 32.30%, respectively compared to those of the conventional N-LUT and temporal redundancy-based N-LUT (TR-N-LUT) methods.

A Moving Object Detection Algorithm Based on Color Information

International Nuclear Information System (INIS)

Fang, X H; Xiong, W; Hu, B J; Wang, L T

2006-01-01

This paper designed a new algorithm of moving object detection for the aim of quick moving object detection and orientation, which used a pixel and its neighbors as an image vector to represent that pixel and modeled different chrominance component pixel as a mixture of Gaussians, and set up different mixture model of Gauss for different YUV chrominance components. In order to make full use of the spatial information, color segmentation and background model were combined. Simulation results show that the algorithm can detect intact moving objects even when the foreground has low contrast with background

Retrospective cues based on object features improve visual working memory performance in older adults.

Science.gov (United States)

Gilchrist, Amanda L; Duarte, Audrey; Verhaeghen, Paul

2016-01-01

Research with younger adults has shown that retrospective cues can be used to orient top-down attention toward relevant items in working memory. We examined whether older adults could take advantage of these cues to improve memory performance. Younger and older adults were presented with visual arrays of five colored shapes; during maintenance, participants were presented either with an informative cue based on an object feature (here, object shape or color) that would be probed, or with an uninformative, neutral cue. Although older adults were less accurate overall, both age groups benefited from the presentation of an informative, feature-based cue relative to a neutral cue. Surprisingly, we also observed differences in the effectiveness of shape versus color cues and their effects upon post-cue memory load. These results suggest that older adults can use top-down attention to remove irrelevant items from visual working memory, provided that task-relevant features function as cues.