WorldWideScience
1

Supercomputer-based advanced ladar imaging simulator (ALIS)  

Science.gov (United States)

The Advanced Ladar Imaging Simulator (ALIS) is a comprehensive multi-dimensional laser radar system simulator that models complex atmospheric scenes combined with high-resolution solid object scenes. The primary functions of ALIS are to serve as a laser radar sensor design tool, data product generator for exploitation, and a decision aid for implementing system designs. This paper focuses on the software structure of the simulator and the challenges that it presents. The ambient atmospheric scene generation is augmented with built-in approximate plume models or with external large-scale Navier-Stokes computational fluid dynamics plume models. The mixed atmosphere and solid object scene is generated via an adaptively meshed, over-sampled voxel representation predicated jointly on the sensor capabilities and scene complexity. To our knowledge, ALIS goes beyond previous ladar simulators with detailed atmospheric turbulence effects and time-dependent plume dispersion models for direct and coherent detection frequency-agile transceivers. ALIS models a wide range of ladar architectures, treating laser coherence properties, receiver electronics noise/transfer functions, and electronics/photon statistical noise. It provides complex amplitude ladar echo "range cubes" (all range reports along a given line-of-sight) for the composite atmosphere-solid scene. The model complexity and its capability to process large (>109) voxel count scenes is accommodated with a portable, scalable software architecture that supports single processors to fine-grained parallel supercomputers.

Smith, Duane D.; Nichols, Terry L.; Gatt, Philip; Lee, Kotik K.; Sicking, Charles; Seida, Steven B.; Coker, Charles F.; Perry, Kimberly M.; Coker, Jason S.

2004-01-01

2

Three-dimensional material identification and hazard detection with shortwave infrared supercontinuum based spectral ladar  

Science.gov (United States)

This paper presents new experimental results from a prototype Spectral LADAR, which combines active multispectral and 3D time-of-flight point cloud imaging. The physical domain unification of these imaging modalities based on a pulse modulated supercontinuum source enables substantially higher fidelity images of obscured targets compared to the data domain fusion of passive hyperspectral cameras and conventional LADAR imagers. Spectral LADAR produces 3D spectral point clouds with unambiguously associated 3D image points and spectral vectors, promoting improved object classification performance in cluttered scenes. The 3D shape and material spectral signature of objects may be acquired in daylight or darkness, behind common glass, and behind obscurants such as foliage and camouflage. These capabilities are demonstrated by data obtained from test scenes. These scenes include plastic mine-like objects obscured by foliage, distinction of hazardous explosives inside plastic containers versus innocuous decoy materials, and 3D spectral imaging behind ordinary glass windows. These scenes, at effective ranges of approximately 40 meters, are imaged with nanosecond-regime optical pulses spanning 1.08 ?m to 1.62 ?m divided into 25 independently ranged spectral bands. The resultant point cloud is spectrally classified according to material type. In contrast to other active spectral imaging techniques, Spectral LADAR is well suited to operate at high pixel and frame rates and at considerable stand-off distances. A combination of favorable attributes, including eye safe wavelengths, relatively small apertures, and very short (single pulse) receiver integration time, bear the potential for this technique to be used on robotic platforms for on-the-move imaging and high area coverage rates.

Powers, Michael A.

2012-06-01

3

Active and attentive LADAR scanning for automatic target recognition  

OpenAIRE

In this work we examine the dynamic implications of active and attentive scanning for LADAR based automatic target/object recognition and show that a dynamically constrained, scanner based, ATR system's ability to identify objects in real-time is improved through attentive scanning. By actively and attentively scanning only salient regions of an image at the density required for recognition, the amount of time it takes to find a target object in a random scene is reduced. A LADAR scanner's at...

Mamanakis, M.; Fullmer, R. R.; Pack, R. T.; Budge, S. E.

2008-01-01

4

Spectral ladar as a UGV navigation sensor  

Science.gov (United States)

We demonstrate new results using our Spectral LADAR prototype, which highlight the benefits of this sensor for Unmanned Ground Vehicle (UGV) navigation applications. This sensor is an augmentation of conventional LADAR and uses a polychromatic source to obtain range-resolved 3D spectral point clouds. These point cloud images can be used to identify objects based on combined spatial and spectral features in three dimensions and at long standoff range. The Spectral LADAR transmits nanosecond supercontinuum pulses generated in a photonic crystal fiber. Backscatter from distant targets is dispersed into 25 spectral bands, where each spectral band is independently range resolved with multiple return pulse recognition. Our new results show that Spectral LADAR can spectrally differentiate hazardous terrain (mud) from favorable driving surfaces (dry ground). This is a critical capability, since in UGV contexts mud is potentially hazardous, requires modified vehicle dynamics, and is difficult to identify based on 3D spatial signatures. Additionally, we demonstrate the benefits of range resolved spectral imaging, where highly cluttered 3D images of scenes (e.g. containing camouflage, foliage) are spectrally unmixed by range separation and segmented accordingly. Spectral LADAR can achieve this unambiguously and without the need for stereo correspondence, sub-pixel detection algorithms, or multi-sensor registration and data fusion.

Powers, Michael A.; Davis, Christopher C.

2011-06-01

5

Pose recognition of articulated target based on ladar range image with elastic shape analysis  

Science.gov (United States)

Elastic shape analysis is introduced for pose recognition of articulated target which is based on small samples of ladar range images. Shape deformations caused by poses changes represented as closed elastic curves given by the square-root velocity function geodesics are used to quantify shape differences and the Karcher mean is used to build a model library. Three kinds of moments - Hu moment invariants, affine moment invariants, and Zernike moment invariants based on support vector machines (SVMs) - are applied to evaluate this approach. The experiment results show that no matter what the azimuth angles of the testing samples are, this approach is capable of achieving a high recognition rate using only 3 model samples with different carrier to noise ratios (CNR); the performance of this approach is much better than that of three kinds of moments based on SVM, especially under high noise conditions.

Liu, Zheng-Jun; Li, Qi; Wang, Qi

2014-10-01

6

MBE based HgCdTe APDs and 3D LADAR sensors  

Science.gov (United States)

Raytheon is developing HgCdTe APD arrays and sensor chip assemblies (SCAs) for scanning and staring LADAR systems. The nonlinear characteristics of APDs operating in moderate gain mode place severe requirements on layer thickness and doping uniformity as well as defect density. MBE based HgCdTe APD arrays, engineered for high performance, meet the stringent requirements of low defects, excellent uniformity and reproducibility. In situ controls for alloy composition and substrate temperature have been implemented at HRL, LLC and Raytheon Vision Systems and enable consistent run to run results. The novel epitaxial designed using separate absorption-multiplication (SAM) architectures enables the realization of the unique advantages of HgCdTe including: tunable wavelength, low-noise, high-fill factor, low-crosstalk, and ambient operation. Focal planes built by integrating MBE detectors arrays processed in a 2 x 128 format have been integrated with 2 x 128 scanning ROIC designed. The ROIC reports both range and intensity and can detect multiple laser returns with each pixel autonomously reporting the return. FPAs show exceptionally good bias uniformity <1% at an average gain of 10. Recent breakthrough in device design has resulted in APDs operating at 300K with essentially no excess noise to gains in excess of 100, low NEP <1nW and GHz bandwidth. 3D LADAR sensors utilizing these FPAs have been integrated and demonstrated both at Raytheon Missile Systems and Naval Air Warfare Center Weapons Division at China Lake. Excellent spatial and range resolution has been achieved with 3D imagery demonstrated both at short range and long range. Ongoing development under an Air Force Sponsored MANTECH program of high performance HgCdTe MBE APDs grown on large silicon wafers promise significant FPA cost reduction both by increasing the number of arrays on a given wafer and enabling automated processing.

Jack, Michael; Asbrock, Jim; Bailey, Steven; Baley, Diane; Chapman, George; Crawford, Gina; Drafahl, Betsy; Herrin, Eileen; Kvaas, Robert; McKeag, William; Randall, Valerie; De Lyon, Terry; Hunter, Andy; Jensen, John; Roberts, Tom; Trotta, Patrick; Cook, T. Dean

2007-04-01

7

Research of range image on non-scanning LADAR based on APD arrays  

Science.gov (United States)

Compared to scanner imaging ladar, non-scanning LADAR plays a more prominent role in the militarily imaging scenarios. Non-scanning LADAR has many advantages, such as structure simplicity, high reliability, imaging efficiency and etc. However the range accuracy is low. This paper proposes a technique to use a designed delay line module in the APD array LADAR systems, which could significantly improve the range accuracy in all channels. A semiconductor laser is used as light source. A 5×5 APD array detector is adopted as the sensitive unit. A 25 channel parallel amplifier circuit is designed to process the signal with bandwidth 240 MHz . Field Programmable Gate Array (FPGA) is used to process these 25 signals paralleled, with a delay line module designed, to significant improve the ranging accuracy .The clock frequency of FPGA is 400MHz with accuracy 2.5ns. The delay lines module are used to measure part of pulse flying time, which is shorter than the clock cycle and could not be directly measured by the clock, and that is the cause of the ranging accuracy. Every delay cell is 46picoseconds , total timing accuracy is less than 500picoseconds. By using above technique, a short distance imaging experiment is presented and get the 5 ×5 pixels range image. The result is analyzed together with the factors, which influence the accuracy of ranging image, it shows the ranging accuracy of each pixel is 10cm. And some advanced methods are proposed to improve the accuracy of the system in the future.

Li, Baowei; Han, Shaokun; Xia, Wenze; Kang, Yanyan

2014-10-01

8

Echo signal modeling of imaging LADAR target simulator  

Science.gov (United States)

LADAR guidance technology is one of the most promising precision guidance technologies. In the aim of simulating the return waveform of the target, a 3D geometrical model of a target is built and mathematical model of target echo signal for imaging LADAR target simulator is established by using the coordinate transformation, radar equation and ranging equation. First, the 3D geometrical data of the object model is obtained by 3D geometrical modeling. Then, target coordinate system and viewpoint coordinate system are created respectively. 3D geometrical model is built in the target coordinate system. The 3D geometrical model is transformed to the viewpoint coordinate system based on the derived relationship between the two coordinate systems. Furthermore, the range information of the target could be obtained under viewpoint coordinate system. Thus, the data of the target echo signal can be obtained by using radar equation and ranging equation. Finally, the echo signal can be exported through corresponding data interface. In order to validate the method proposed in this paper, the echo signal generated by a typical target is computed and compared with the theory solutions. The signals can be applied to drive target simulator to generate a physical target LADAR image.

Xu, Rui; Shi, Rui; Wang, Xin; Li, Zhuo

2014-11-01

9

Detection and recognition in ladar using invariants and covariants  

Science.gov (United States)

Object-image relations (O-IRs) provide a powerful approach to performing detection and recognition with laser radar (LADAR) sensors. This paper presents the basics of O-I relations and shows how they are derived from invariants. It also explains and shows results of a computationally efficient approach applying covariants to 3-D LADAR data. The approach is especially appealing because the detection and segmentation processes are integrated with recognition into a robust algorithm. Finally, the method provides a straightforward approach to handling articulation and multi-scale decomposition.

Arnold, Gregory D.; Sturtz, Kirk; Weiss, Isaac

2001-10-01

10

Synthetic aperture ladar concept for infrastructure monitoring  

Science.gov (United States)

Long range surveillance of infrastructure is a critical need in numerous security applications, both civilian and military. Synthetic aperture radar (SAR) continues to provide high resolution radar images in all weather conditions from remote distances. As well, Interferometric SAR (InSAR) and Differential Interferometric SAR (D-InSAR) have become powerful tools adding high resolution elevation and change detection measurements. State of the art SAR systems based on dual-use satellites are capable of providing ground resolutions of one meter; while their airborne counterparts obtain resolutions of 10 cm. D-InSAR products based on these systems could produce cm-scale vertical resolution image products. Deformation monitoring of railways, roads, buildings, cellular antennas, power structures (i.e., power lines, wind turbines, dams, or nuclear plants) would benefit from improved resolution, both in the ground plane and vertical direction. The ultimate limitation to the achievable resolution of any imaging system is its wavelength. State-of-the art SAR systems are approaching this limit. The natural extension to improve resolution is to thus decrease the wavelength, i.e. design a synthetic aperture system in a different wavelength regime. One such system offering the potential for vastly improved resolution is Synthetic Aperture Ladar (SAL). This system operates at infrared wavelengths, ten thousand times smaller than radar wavelengths. This paper presents a laboratory demonstration of a scaled-down infrastructure deformation monitoring with an Interferometric Synthetic Aperture Ladar (IFSAL) system operating at 1.5 ?m. Results show sub-millimeter precision on the deformation applied to the target.

Turbide, Simon; Marchese, Linda; Terroux, Marc; Bergeron, Alain

2014-10-01

11

Multiscale target manifold characterization for 3D imaging ladar  

Science.gov (United States)

Manifold extraction techniques, such as ISOMAP, are capable of projecting nonlinear, high-dimensional data to a lower-dimensional subspace while retaining discriminatory information. In this investigation, ISOMAP is applied to 3D LADAR range imagery. Selected man-made objects are reduced to sets of spin-image feature vectors that describe object surface geometries. At various spin-image support scales, we use the distribution-free Henze-Penrose statistic test to quantify differences between man-made objects in both the high-dimensional spin-image vector representation and in the low-dimensional spin-image manifold extracted using ISOMAP.

Whittenberger, Estille; Waagen, Donald; Shah, Nitesh; Hulsey, Donald

2008-04-01

12

Parametric edge detection for ladar intensity image with different carrier-noise-ratio  

Science.gov (United States)

Parametric edge detector has been reported to be successfully applied in actual coherent ladar intensity images corrupted by speckle. Parametric edge detector derived from an exponential model for the speckle which has been described as a multiplicative noise corrupting intensity images. The performance of the noises is not the same when the intensity images are gained in different distance. So the application scope of this algorithm is of great value in actual ladar. In the paper, parametric edge detector, morphological filter based on parametric edge detector and Canny detector are used to detect coherent ladar simulated intensity images. The edge detection results are obtained and compared, when the values of carrier-noise-ratio (CNR) are changed. From the simulation results, the best application scope of parametric edge detection is described.

Zhou, Ming; Li, Qi; Wang, Qi

2009-07-01

13

32 × 32 Geiger-mode ladar camera  

Science.gov (United States)

For the wide applications of LAser Detection and Ranging (LADAR) imaging with large format Geiger-mode (GM) avalanche photodiode (APD) arrays, it is critical and challenging to develop a LADAR camera suitable to volume production with enough component tolerance and stable performance. Recently Spectrolab and Black Forest Engineering developed a new 32x32 Read-Out Integrated Circuit (ROIC) for LADAR applications. With a specially designed high voltage input protection circuit, the ROIC can work properly even with more than 1 % of pixels shorted in the APD array; this feature will greatly improve the camera long-term stability and manufacturing throughput. The Non-uniform Bias circuit provides bias voltage tunability over a 2.5 V range individually for each pixel and greatly reduces the impact of the non-uniformity of an APD array. A SMIA high speed serial digital interface streamlines data download and supports frame rates up to 30 kHz. The ROIC can operate with a 0.5 ns time resolution without vernier bits; 14 bits of dynamic range provides 8 ?s of range gate width. At the meeting we will demonstrate more performance of this newly developed 32x32 Geiger-mode LADAR camera.

Yuan, Ping; Sudharsanan, Rengarajan; Bai, Xiaogang; Boisvert, Joseph; McDonald, Paul; Labios, Eduardo; Salisbury, Michael S.; Stuart, Gary M.; Danny, Harrison; Portillo, Angel A.; Roybal, Alric B.; Van Duyne, Stephen; Pauls, Greg; Gaalema, Steve

2010-04-01

14

Doublet Pulse Coherent Laser Radar for Orbital Debris Tracking of Resident Space Objects  

Science.gov (United States)

In this paper, the development of a long range ladar system known as ExoSPEAR at NASA Langley Research Center for tracking rapidly moving resident space objects is discussed. Based on 100 W, nanosecond class, near-IR laser, this ladar system with coherent detection technique is currently being investigated for short dwell time measurements of resident space objects (RSOs) in LEO and beyond for space surveillance applications. This unique ladar architecture is configured using a continuously agile doublet-pulse waveform scheme coupled to a closed-loop tracking and control loop approach to simultaneously achieve mm class range precision and mm/s velocity precision and hence obtain unprecedented track accuracies. Salient features of the design architecture followed by performance simulations illustrating the dependence of range and velocity precision in LEO orbits on ladar power aperture product will be presented. Estimated limits on detectable optical cross sections of RSOs in LEO orbits will be analyzed. The suitability of this ladar for precision orbit determination will be discussed.

Prasad, N.; Rudd, V.,; DiMarcantonio, A.; Sandford, S.

2014-09-01

15

Characterization of articulated vehicles using ladar seekers  

Science.gov (United States)

Many vehicle targets of interest to military automatic target recognition (ATR) possess articulating components: that is, they have components that change position relative to the main body. Many vehicles also have multiple configurations wherein one or more devices or objects may be added to enhance specific military or logistical capabilities. As the expected target set for military ATR becomes more comprehensive, many additional articulations and optional components must be handled. Mobile air defense units often include moving radar antennae as well as turreted guns and missile launchers. Surface-to-surface missile launchers may be encountered with or without missiles, and with the launch rails raised or lowered. Engineers and countermine vehicles have a tremendous number of possible configurations and even conventional battle tanks may very items such as external reactive armor, long- range tanks, turret azimuth, and gun elevation. These changes pose a significant barrier to the target identification process since they greatly increase the range of possible target signatures. When combined with variations already encountered due to target aspect changes, an extremely large number of possible signatures is formed. Conventional algorithms cannot process so many possibilities effectively, so in response, the matching process is often made less selective. This degrades identification performance, increase false alarm rates, and increases data requirements for algorithm testing and training. By explicitly involving articulation in the detection and identification stages of an ATR algorithm, more precise matching constraints can be applied, and better selectivity can be achieve. Additional benefits include the measurement of the position and orientation of articulated components, which often has tactical significance. In this paper, the result of a study investigating the impact of target articulation in ATR for military vehicles are presented. 3D ladar signature data is used. An algorithmic solution is proposed and directions for further research are noted.

Wellfare, Michael R.; Norris-Zachery, Karen

1997-08-01

16

Worldwide uncertainty assessments of ladar and radar signal-to-noise ratio performance for diverse low altitude atmospheric environments  

Science.gov (United States)

In this study of atmospheric effects on laser ranging and detection (ladar) and radar systems, the parameter space is explored using the Air Force Institute of Technology Center for Directed Energy's (AFIT/CDE) High Energy Laser End-to-End Operational Simulation (HELEEOS) parametric one-on-one engagement level model. The expected performance of ladar systems is assessed at a representative wavelength of 1.557 µm at a number of widely dispersed land and maritime locations worldwide. Radar system performance is assessed at 95 GHz and 250 GHz. Scenarios evaluated include both down looking oblique and vertical engagement geometries over ranges up to 3000 meters in which clear air aerosols and thin layers of fog, locally heavy rain, and low stratus cloud types are expected to occur. Seasonal and boundary layer variations are considered to determine optimum employment techniques to exploit or defeat the environmental conditions. Each atmospheric particulate/obscurant/hydrometeor is evaluated based on its wavelength-dependent forward and off-axis scattering characteristics and absorption effects on system interrogation. Results are presented in the form of worldwide plots of notional signal to noise ratio. The ladar and 95 GHz system types exhibit similar SNR performance for forward oblique clear air operation. 1.557 µm ladar performs well for vertical geometries in the presence of ground fog, but has no near-horizontal performance under such meteorological conditions. It also has no performance if low altitude stratus is present. 95 GHz performs well for both the fog and stratus layer cases, for both vertical and forward oblique geometries. The 250 GHz radar system is heavily impacted by water vapor absorption in all scenarios studied; however it is not as strongly affected by clouds and fog as the 1.557 µm ladar. Locally heavy rain will severely limit ladar system performance at these wavelengths. However, under heavy rain conditions ladar outperforms both radar systems.

Fiorino, Steven T.; Bartell, Richard J.; Krizo, Matthew J.; Caylor, Gregory; Moore, Kenneth P.; Harris, Thomas R.; Cusumano, Salvatore J.

2010-06-01

17

Upgrades to DELTAS NRC (Defense Laser Target Signatures Code) for the evaluation of advanced ladar technologies  

Science.gov (United States)

DELTASNRCTM is a laser radar signature modeling and simulation code that was developed to evaluate potential LADAR systems for defense applications. DELTASNRCTM strength is in its flexibility. The user can create scenarios with realistic targets (the target model is based on constructive solid geometry) and real materials, and model coherent or direct detection LADAR systems at any wavelength (limited only to availability of material reflectance information). This paper focuses on recent improvements to the direct and coherent detection imaging capabilities of the code. These improvements were mandated by the BMDO Discriminating Interceptor Technology Program (DITP) which requires LADAR system models for the next generation of LADAR imaging systems on an interceptor platform. We introduce upgrades to the range-Doppler imaging algorithms that improve the fidelity of the laser cross section calculation and the range-Doppler image. We describe a model that introduces ambiguity in direct detection angle- angle-range images as a result of pulse duration and detector bandwidth. A direct detection receiver model has also been implemented. This model introduces optical modulation transfer functions and receiver noise to the direct detection images. The result of these improvements is an end-to-end LADAR simulation which can be used as a stand alone code or as part of a suite of sensor models capable of generating signatures which can be used for discrimination algorithm development, system analysis, etc. Demonstrations of these improvements as applied to DITP are presented, and a discussion of current applications of DELTASNRCTM in simulations such as Synthetic Scene Generation Model is included.

Lyons, Biff; Timmins, Michael

1998-09-01

18

A Comparative Study between Frequency-Modulated Continous Wave LADAR and Linear LiDAR  

Science.gov (United States)

Topographic Light Detection and Ranging (LiDAR) technology has advanced greatly in the past decade. Pulse repetition rates of terrestrial and airborne systems havemultiplied thus vastly increasing data acquisition rates. Geiger-mode and FLASH LiDAR have also become far more mature technologies. However, a new and relatively unknown technology is maturing rapidly: Frequency-Modulated Continuous Wave Laser Detection and Ranging (FMCW-LADAR). Possessing attributes more akin to modern radar systems, FMCWLADAR has the ability to more finely resolve objects separated by very small ranges. For tactical military applications (as described here), this can be a real advantage over single frequency, direct-detect systems. In fact, FMCW-LADAR can range resolve objects at 10-7 to 10-6 meter scales. FMCW-LADAR can also detect objects at greater range with less power. In this study, a FMCWLADAR instrument and traditional LiDAR instrument are compared. The co-located terrestrial scanning instruments were set up to perform simultaneous 3-D measurements of the given scene. Several targets were placed in the scene to expose the difference in the range resolution capabilities of the two instruments. The scans were performed at or nearly the same horizontal and vertical angular resolutions. It is demonstrated that the FMCW-LADAR surpasses the perfomance of the linear mode LiDAR scanner in terms of range resolution. Some results showing the maximum range acquisition are discussed but this was not studied in detail as the scanners' laser powers differed by a small amount. Applications and implications of this technology are also discussed.

Massaro, R. D.; Anderson, J. E.; Nelson, J. D.; Edwards, J. D.

2014-11-01

19

Segmentation, classification, and pose estimation of maritime targets in flash-ladar imagery  

OpenAIRE

The paper presents new techniques for automatic segmentation, classification, and generic pose estimation of ships and boats in laser radar imagery. Segmentation, which primarily involves elimination of water reflections, is based on modeling surface waves and comparing the expected water reflection signature to the ladar intensity image. Shape classification matches a parametric shape representation of a generic ship hull with parameters extracted from the range image. The extracted paramete...

Armbruster, Walter; Hammer, Marcus

2012-01-01

20

3D flash ladar at Raytheon  

Science.gov (United States)

Raytheon has recently been funded by DARPA to develop an FPA for single shot eyesafe ladar operation. The goal of the program is to develop new high speed imaging rays to rapidly acquire high resolution, 3D images of tactical targets at ranges as long as 7 to 10 kilometers. This would provide precision strike, target identification from rapidly moving platforms, such as air-to-ground seekers, which would enhance counter-counter measure performance and the ability to lock-on after launch. Also a goal is to demonstrate the acquisition of hidden, camouflaged and partially obscured targets. Raytheon's approach consists of using HgCdTe APD arrays which offer unique advantages for high performance eyesafe LADAR sensors. These include: eyesafe operation at room temperature, low excess noise, high gain to overcome thermal and preamp noise, Ghz bandwidth and high packing density. The detector array will be coupled with a Readout Integrated Circuit, that will capture all the information required for accurate range determination. The two components encompass a hybrid imaging array consisting of two IC circuit chips vertically integrated via an array of indium metal 'bumps'. The chip containing the PAD detector array and the silicon signal processing readout chip are independently optimized to provide the highest possible performance for each function.

Halmos, Maurice J.; Jack, Michael D.; Asbrock, James F.; Anderson, C.; Bailey, Steven L.; Chapman, George; Gordon, E.; Herning, P. E.; Kalisher, Murray H.; Klaras, Louis F.; Kosai, Kim; Liquori, V.; Pines, Mike; Randall, Valerie; Reeder, Robin; Rosbeck, Joseph P.; Sen, Sanghamitra; Trotta, Patrick A.; Wetzel, P.; Hunter, Andrew T.; Jensen, John E.; DeLyon, T. J.; Trussell, Charlie W.; Hutchinson, James A.; Balcerak, Raymond S.

2001-09-01

21

Sparsity based Single Object Tracking  

Directory of Open Access Journals (Sweden)

Full Text Available Object tracking has importance in various video processing applications like video surveillance, perceptual user interface driver assistance, tracking etc. This paper deals with a new tracking technique that combines the dictionary based background subtraction along with sparsity based tracking. The speed and performance challenges faced during the sparsity based tracking alone are addressed, as it is based on a background subtraction preprocessing and local compressive tracking. It also overcomes the challenges faced by the traditional techniques due to illumination variation, pose and shape change of the object. Output of the proposed technique is compared with that of compressive tracking technique.

Glincy Abraham

2013-07-01

22

Large format geiger-mode avalanche photodiode LADAR camera  

Science.gov (United States)

Recently Spectrolab has successfully demonstrated a compact 32x32 Laser Detection and Range (LADAR) camera with single photo-level sensitivity with small size, weight, and power (SWAP) budget for threedimensional (3D) topographic imaging at 1064 nm on various platforms. With 20-kHz frame rate and 500- ps timing uncertainty, this LADAR system provides coverage down to inch-level fidelity and allows for effective wide-area terrain mapping. At a 10 mph forward speed and 1000 feet above ground level (AGL), it covers 0.5 square-mile per hour with a resolution of 25 in2/pixel after data averaging. In order to increase the forward speed to fit for more platforms and survey a large area more effectively, Spectrolab is developing 32x128 Geiger-mode LADAR camera with 43 frame rate. With the increase in both frame rate and array size, the data collection rate is improved by 10 times. With a programmable bin size from 0.3 ps to 0.5 ns and 14-bit timing dynamic range, LADAR developers will have more freedom in system integration for various applications. Most of the special features of Spectrolab 32x32 LADAR camera, such as non-uniform bias correction, variable range gate width, windowing for smaller arrays, and short pixel protection, are implemented in this camera.

Yuan, Ping; Sudharsanan, Rengarajan; Bai, Xiaogang; Labios, Eduardo; Morris, Bryan; Nicholson, John P.; Stuart, Gary M.; Danny, Harrison

2013-05-01

23

Single-photon sensitive Geiger-mode LADAR cameras  

Science.gov (United States)

Three-dimensional (3D) imaging with Short wavelength infrared (SWIR) Laser Detection and Range (LADAR) systems have been successfully demonstrated on various platforms. It has been quickly adopted in many military and civilian applications. In order to minimize the LADAR system size, weight, and power (SWAP), it is highly desirable to maximize the camera sensitivity. Recently Spectrolab has demonstrated a compact 32x32 LADAR camera with single photo-level sensitivity at 1064. This camera has many special features such as non-uniform bias correction, variable range gate width from 2 microseconds to 6 microseconds, windowing for smaller arrays, and short pixel protection. Boeing integrated this camera with a 1.06 ?m pulse laser on various platforms and demonstrated 3D imaging. The features and recent test results of the 32x128 camera under development will be introduced.

Yuan, Ping; Sudharsanan, Rengarajan; Bai, Xiaogang; McDonald, Paul; Labios, Eduardo; Morris, Bryan; Nicholson, John P.; Stuart, Gary M.; Danny, Harrison

2012-10-01

24

Crossmodal Object-Based Attention: Auditory Objects Affect Visual Processing  

Science.gov (United States)

According to the object-based view, visual attention can be deployed to ''objects'' or perceptual units, regardless of spatial locations. Recently, however, the notion of object has also been extended to the auditory domain, with some authors suggesting possible interactions between visual and auditory objects. Here we show that task-irrelevant…

Turatto, M.; Mazza, V.; Umilta, C.

2005-01-01

25

Object-Based Image Compression  

Science.gov (United States)

Image compression frequently supports reduced storage requirement in a computer system, as well as enhancement of effective channel bandwidth in a communication system, by decreasing the source bit rate through reduction of source redundancy. The majority of image compression techniques emphasize pixel-level operations, such as matching rectangular or elliptical sampling blocks taken from the source data stream, with exemplars stored in a database (e.g., a codebook in vector quantization or VQ). Alternatively, one can represent a source block via transformation, coefficient quantization, and selection of coefficients deemed significant for source content approximation in the decompressed image. This approach, called transform coding (TC), has predominated for several decades in the signal and image processing communities. A further technique that has been employed is the deduction of affine relationships from source properties such as local self-similarity, which supports the construction of adaptive codebooks in a self-VQ paradigm that has been called iterated function systems (IFS). Although VQ, TC, and IFS based compression algorithms have enjoyed varying levels of success for different types of applications, bit rate requirements, and image quality constraints, few of these algorithms examine the higher-level spatial structure of an image, and fewer still exploit this structure to enhance compression ratio. In this paper, we discuss a fourth type of compression algorithm, called object-based compression, which is based on research in joint segmentaton and compression, as well as previous research in the extraction of sketch-like representations from digital imagery. Here, large image regions that correspond to contiguous recognizeable objects or parts of objects are segmented from the source, then represented compactly in the compressed image. Segmentation is facilitated by source properties such as size, shape, texture, statistical properties, and spectral signature. In particular, discussion addresses issues such as efficient boundary representation, variance assessment and representation, as well as a texture classification and replacement algorithms that can decrease compression overhead and increase reconstruction fidelity in the decompressed image. Contextual extraction of motion patterns in digital video sequences, using a frequency-domain pattern recognition technique based on interframe correlation, is described in a companion paper. This technique can also be extended to multidimensional image domains, to support joint spectral, spatial, and temporal compression.

Schmalz, Mark S.

2003-01-01

26

Noise filtering techniques for photon-counting ladar data  

Science.gov (United States)

Many of the recent small, low power ladar systems provide detection sensitivities on the photon(s) level for altimetry applications. These "photon-counting" instruments, many times, are the operational solution to high altitude or space based platforms where low signal strength and size limitations must be accommodated. Despite the many existing algorithms for lidar data product generation, there remains a void in techniques available for handling the increased noise level in the photon-counting measurements as the larger analog systems do not exhibit such low SNR. Solar background noise poses a significant challenge to accurately extract surface features from the data. Thus, filtering is required prior to implementation of other post-processing efforts. This paper presents several methodologies for noise filtering photoncounting data. Techniques include modified Canny Edge Detection, PDF-based signal extraction, and localized statistical analysis. The Canny Edge detection identifies features in a rasterized data product using a Gaussian filter and gradient calculation to extract signal photons. PDF-based analysis matches local probability density functions with the aggregate, thereby extracting probable signal points. The localized statistical method assigns thresholding values based on a weighted local mean of angular variances. These approaches have demonstrated the ability to remove noise and subsequently provide accurate surface (ground/canopy) determination. The results presented here are based on analysis of multiple data sets acquired with the high altitude NASA MABEL system and photon-counting data supplied by Sigma Space Inc. configured to simulate the NASA upcoming ICESat-2 mission instrument expected data product.

Magruder, Lori A.; Wharton, Michael E., III; Stout, Kevin D.; Neuenschwander, Amy L.

2012-06-01

27

Simulation of a Geiger-mode imaging LADAR system for performance assessment.  

Science.gov (United States)

As LADAR systems applications gradually become more diverse, new types of systems are being developed. When developing new systems, simulation studies are an essential prerequisite. A simulator enables performance predictions and optimal system parameters at the design level, as well as providing sample data for developing and validating application algorithms. The purpose of the study is to propose a method for simulating a Geiger-mode imaging LADAR system. We develop simulation software to assess system performance and generate sample data for the applications. The simulation is based on three aspects of modeling-the geometry, radiometry and detection. The geometric model computes the ranges to the reflection points of the laser pulses. The radiometric model generates the return signals, including the noises. The detection model determines the flight times of the laser pulses based on the nature of the Geiger-mode detector. We generated sample data using the simulator with the system parameters and analyzed the detection performance by comparing the simulated points to the reference points. The proportion of the outliers in the simulated points reached 25.53%, indicating the need for efficient outlier elimination algorithms. In addition, the false alarm rate and dropout rate of the designed system were computed as 1.76% and 1.06%, respectively. PMID:23823970

Kim, Seongjoon; Lee, Impyeong; Kwon, Yong Joon

2013-01-01

28

Advances in HgCdTe APDs and LADAR Receivers  

Science.gov (United States)

Raytheon is developing NIR sensor chip assemblies (SCAs) for scanning and staring 3D LADAR systems. High sensitivity is obtained by integrating high performance detectors with gain i.e. APDs with very low noise Readout Integrated Circuits. Unique aspects of these designs include: independent acquisition (non-gated) of pulse returns, multiple pulse returns with both time and intensity reported to enable full 3D reconstruction of the image. Recent breakthrough in device design has resulted in HgCdTe APDs operating at 300K with essentially no excess noise to gains in excess of 100, low NEP <1nW and GHz bandwidths and have demonstrated linear mode photon counting. SCAs utilizing these high performance APDs have been integrated and demonstrated excellent spatial and range resolution enabling detailed 3D imagery both at short range and long ranges. In this presentation we will review progress in high resolution scanning, staring and ultra-high sensitivity photon counting LADAR sensors.

Bailey, Steven; McKeag, William; Wang, Jinxue; Jack, Michael; Amzajerdian, Farzin

2010-01-01

29

3D-LZ helicopter ladar imaging system  

Science.gov (United States)

A joint-service team led by the Air Force Research Laboratory's Munitions and Sensors Directorates completed a successful flight test demonstration of the 3D-LZ Helicopter LADAR Imaging System. This was a milestone demonstration in the development of technology solutions for a problem known as "helicopter brownout", the loss of situational awareness caused by swirling sand during approach and landing. The 3D-LZ LADAR was developed by H.N. Burns Engineering and integrated with the US Army Aeroflightdynamics Directorate's Brown-Out Symbology System aircraft state symbology aboard a US Army EH-60 Black Hawk helicopter. The combination of these systems provided an integrated degraded visual environment landing solution with landing zone situational awareness as well as aircraft guidance and obstacle avoidance information. Pilots from the U.S. Army, Air Force, Navy, and Marine Corps achieved a 77% landing rate in full brownout conditions at a test range at Yuma Proving Ground, Arizona. This paper will focus on the LADAR technology used in 3D-LZ and the results of this milestone demonstration.

Savage, James; Harrington, Walter; McKinley, R. Andrew; Burns, H. N.; Braddom, Steven; Szoboszlay, Zoltan

2010-04-01

30

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

Science.gov (United States)

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. PMID:23128699

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

2012-11-01

31

Processing 3D flash LADAR point-clouds in real-time for flight applications  

Science.gov (United States)

Ball Aerospace & Technologies Corp. has demonstrated real-time processing of 3D imaging LADAR point-cloud data to produce the industry's first time-of-flight (TOF) 3D video capability. This capability is uniquely suited to the rigorous demands of space and airborne flight applications and holds great promise in the area of autonomous navigation. It will provide long-range, three dimensional video information to autonomous flight software or pilots for immediate use in rendezvous and docking, proximity operations, landing, surface vision systems, and automatic target recognition and tracking. This is enabled by our new generation of FPGA based "pixel-tube" processors, coprocessors and their associated algorithms which have led to a number of advancements in high-speed wavefront processing along with additional advances in dynamic camera control, and space laser designs based on Ball's CALIPSO LIDAR. This evolution in LADAR is made possible by moving the mechanical complexity required for a scanning system into the electronics, where production, integration, testing and life-cycle costs can be significantly reduced. This technique requires a state of the art TOF read-out integrated circuit (ROIC) attached to a sensor array to collect high resolution temporal data, which is then processed through FPGAs. The number of calculations required to process the data is greatly reduced thanks to the fact that all points are captured at the same time and thus correlated. This correlation allows extremely efficient FPGA processing. This capability has been demonstrated in prototype form at both Marshal Space Flight Center and Langley Research Center on targets that represent docking and landing scenarios. This report outlines many aspects of this work as well as aspects of our recent testing at Marshall's Flight Robotics Laboratory.

Craig, R.; Gravseth, I.; Earhart, R. P.; Bladt, J.; Barnhill, S.; Ruppert, L.; Centamore, C.

2007-04-01

32

Signal processing on waveform data from the eyesafe ladar testbed (ELT)  

OpenAIRE

The Eyesafe Ladar Test-bed (ELT) is a raster scanning, single-beam, energy-detection ladar with the capability of digitizing and recording the return pulse waveform at 2 GHz in the field for off-line 3D point cloud formation research in the laboratory. The ELT serves as a prime tool in understanding the behavior of ladar waveforms. Signal processing techniques have been applied to the ELT waveform in an effort to exploit the signal with respect to noise reduction, range resolution improvement...

Neilsen, K. D.; Budge, S. E.; Pack, R. T.

2014-01-01

33

Retratamento de LASIK com fotoablação personalizada versus fotoablação convencional utilizando o LADAR: Alcon / LASIK retreatment with customized versus conventional photo-ablation using LADAR: Alcon  

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: Portuguese Abstract in portuguese OBJETIVO:Avaliar os resultados do retratamento convencional (LADAR, Alcon) e do retratamento personalizado(LADARWave, Alcon) em olhos submetidos a LASIK primário convencional. MÉTODOS: Estudo retrospectivo de revisão de prontuários consecutivos, de 38 olhos em 38 pacientes, submetidos a retratamento [...] de LASIK para correção de miopia e astigmatismo. Os olhos operados foram divididos em dois grupos iguais. No primeiro grupo foi realizado o retratamento personalizado e, no outro, o retratamento convencional. As seguintes variáveis foram comparadas: acuidade visual de alto contraste e refração manifesta. A qualidade visual foi estimada e comparada através de inquérito subjetivo proposto aos pacientes. RESULTADOS: Não houve diferença estatística entre os grupos comparando-se as variáveis estudadas. O equivalente esférico pós-retratamento foi de 0,36 no grupo convencional e de 0,47 no personalizado (p=0,079). A acuidade visual de Snellen foi de 0,91 e 0,87, respectivamente, com p=0,07. O total de aberrações pré-operatório foi maior do que o pós-operatório no grupo personalizado (p Abstract in english OBJECTIVE: To evaluate the results of conventional (Ladar, Alcon) and customized (LADARWave, Alcon) retreatment ineyes undergoing conventional primary LASIK. METHODS: Retrospective revision of consecutive clinical report forms of 38 eyes of 38 patients who underwent LASIK retreatment for myopia and [...] astigmatism. The operated eyes were divided into two equal groups. In the first was performed customized retreatment and, in the other, conventional retreatment. The following variables were compared: high contrast visual acuity and manifest refraction. The visual quality was estimated and compared using subjective survey offered to patients. RESULTS: There was no statistical difference between the groups when comparing the variables studied. The spherical equivalent after retreatment was 0.36 in the conventional group and 0.47 in the custom (p = 0.079). Snelen visual acuity was 0.91 and 0.87, respectively (p = 0.07). The preoperative total aberrations was higher than the postoperative period in custom group (p

Lucas Monferrari Monteiro, Vianna; Heloisa Moraes do, Nascimento; Mauro, Campos.

2011-06-01

34

Advances in LADAR Components and Subsystems at Raytheon  

Science.gov (United States)

Raytheon is developing NIR sensor chip assemblies (SCAs) for scanning and staring 3D LADAR systems. High sensitivity is obtained by integrating high performance detectors with gain, i.e., APDs with very low noise Readout Integrated Circuits (ROICs). Unique aspects of these designs include: independent acquisition (non-gated) of pulse returns, multiple pulse returns with both time and intensity reported to enable full 3D reconstruction of the image. Recent breakthrough in device design has resulted in HgCdTe APDs operating at 300K with essentially no excess noise to gains in excess of 100, low NEP <1nW and GHz bandwidths and have demonstrated linear mode photon counting. SCAs utilizing these high performance APDs have been integrated and demonstrated excellent spatial and range resolution enabling detailed 3D imagery both at short range and long ranges. In the following we will review progress in real-time 3D LADAR imaging receiver products in three areas: (1) scanning 256 x 4 configuration for the Multi-Mode Sensor Seeker (MMSS) program and (2) staring 256 x 256 configuration for the Autonomous Landing and Hazard Avoidance Technology (ALHAT) lunar landing mission and (3) Photon-Counting SCAs which have demonstrated a dramatic reduction in dark count rate due to improved design, operation and processing.

Jack, Michael; Chapman, George; Edwards, John; McKeag, William; Veeder, Tricia; Wehner, Justin; Roberts, Tom; Robinson, Tom; Neisz, James; Andressen, Cliff; Rinker, Robert; Hall, Donald N. B.; Jacobson, Shane M.; Amzajerdian, Farzin; Cook, T. Dean

2012-01-01

35

Object-based sound source modeling  

OpenAIRE

This thesis proposes techniques for object-based audio and music. The work can be divided into two parts corresponding to model-based synthesis of musical instruments and computational analysis of audio and music. The contributions of this work are in signal and auditory analysis, sound object modeling, and sound signal synthesis. The first half of the thesis considers linear and particularly nonlinear discrete-time modeling of plucked string instruments. The nonlinear tension modulation ...

Tolonen, Tero

2000-01-01

36

High-resolution ladar for multidimension detection: design, modeling, and simulation  

Science.gov (United States)

As the development of application requirements, to obtain more information of the object is an important direction in many research fields. Not only 2-D image, but also range and velocity information are required. Hence, the imaging laser radar technology, which can obtain 3-D or even 4-D information, has been paid more attention. Because of the high range resolution character and direct Doppler frequency shift measurement function, frequency modulation / continuous wave (FM/CW) imaging laser radar can be called a 4-D imaging system. The modulated laser floodlights a moving object, and then the reflecting light is received by optical system and captured by a focal plane array. After obtaining a series of images, the range-Doppler processing algorithm is performed on the pixels with the same position in all the images to calculate the object's range and velocity information, and then a 4-D image (angle - angle - range - velocity) with high accuracy is obtained. This paper describes the FM/CW ladar system's principle, and presents an improved process algorithm to solve the problem of the traditional range-Doppler algorithm's limit used for high velocity object. The simulation results in typical object situation prove that the improved process algorithm could increase the velocity measurement range effectively.

Wei, Yu-fei; Fei, Jin-dong; Mi, Qiang; Gao, Yang

2009-07-01

37

Rapid and scalable 3D object recognition using LIDAR data  

Science.gov (United States)

This paper describes a model-based 3D object recognition system, which makes use of 3D data acquired by LIDAR sensors. The system is based on a coarse-to-fine scheme for object indexing and verification to achieve high efficiency and accuracy. The system employs rotationally invariant semi-local spin image features for object representation and formulates the recognition process as a feature searching through a database, followed by a matching process guided by putative candidates between the query features and the model features. To achieve recognition efficiency with sublinear dependency on the size of the model database, an approximate nearest-neighbor method, the locality-sensitivity-hashing (LSH), is used for feature search. Geometrically constrained scene-model correspondences are used to generate alignment hypotheses that are refined during a matching and verification process for achieving high recognition accuracy. A large model database of commercial and military vehicles is used for experiments. Results on real data acquired with commercial LADAR sensor systems, mounted on either high-lift or airborne platforms are presented. Our results indicate that 3D object recognition on LADAR data is matured to the point that it is ready for real large-scale applications.

Matei, Bogdan C.; Tan, Yi; Sawhney, Harpreet S.; Kumar, Rakesh

2006-05-01

38

Object detection based on LHOG feature matching  

Science.gov (United States)

This paper address the problem of detecting visual objects in images which is a fundamental problem in computer vision. We proposed a method based on matching a sample of object with all sub-windows in the testing images to solve this problem instead of training a classifier to determine the location of visual objects. Local histogram of gradient(LHOG) feature are extracted from the sample image and testing images respectively to describe patterns in the images. Integral image technique are employed to accelerate the process of calculating LHOG feature. Then, we apply PCA to reduce the dimensionality of LHOG feature. Distance between sample image and sub-windows are measured by using cosine angle. Adaptive strategy is used to distinguish the object sub-window from non-object sub-window.

Huang, Xiaoyuan; Luo, Yupin

2013-07-01

39

Spatio-activity based object detection  

CERN Document Server

We present the SAMMI lightweight object detection method which has a high level of accuracy and robustness, and which is able to operate in an environment with a large number of cameras. Background modeling is based on DCT coefficients provided by cameras. Foreground detection uses similarity in temporal characteristics of adjacent blocks of pixels, which is a computationally inexpensive way to make use of object coherence. Scene model updating uses the approximated median method for improved performance. Evaluation at pixel level and application level shows that SAMMI object detection performs better and faster than the conventional Mixture of Gaussians method.

Springett, Jarrad

2008-01-01

40

Theoretical for astigmatism Fourier transform-based imaging processor  

Science.gov (United States)

We propose a new step imaging method based on astigmatism Fourier transform for synthetic-aperture ladar imaging processor, which is mainly used in optical imaging processing system of synthetic-aperture imaging ladar. The time-domain data is translated into spatial coordinate expression suitable for space optical conversion. The Fourier transform is realized by astigmatism principle. It can simultaneously achieve radar goals focusing both on distance and azimuth. Processor scale is effectively reduced. The process of target echo confocal imaging data is simplified. The requirements of ladar imaging processing system are reduced. It has a great advantage in the synthetic-aperture imaging ladar target echo confocal imaging data processing.

Hou, Peipei; Sun, Jianfeng; Zhi, Ya'nan; Liu, Liren

2014-09-01

41

Object based image analysis for remote sensing  

Science.gov (United States)

Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of 'grey' literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.

Blaschke, T.

42

Coding Transparency in Object-Based Video  

DEFF Research Database (Denmark)

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 exploited. Coding results show the superior efficiency of the proposed scheme compared with MPEG-4

Aghito, Shankar Manuel; Forchhammer, SØren

2006-01-01

43

Outward atmospheric scintillation effects and inward atmospheric scintillation effects comparisons for direct detection ladar applications  

Science.gov (United States)

Atmospheric turbulence produces intensity modulation or "scintillation" effects on both on the outward laser-mode path and on the return backscattered radiation path. These both degrade laser radar (ladar) target acquisition, ranging, imaging, and feature estimation. However, the finite sized objects create scintillation averaging on the outgoing path and the finite sized telescope apertures produce scintillation averaging on the return path. We expand on previous papers going to moderate to strong turbulence cases by starting from a 20kft altitude platform and propagating at 0° elevation (with respect to the local vertical) for 100km range to a 1 m diameter diffuse sphere. The outward scintillation and inward scintillation effects, as measured at the focal plane detector array of the receiving aperture, will be compared. To eliminate hard-body surface speckle effects in order to study scintillation, Goodman's M-parameter is set to 106 in the analytical equations and the non-coherent imaging algorithm is employed in Monte Carlo realizations. The analytical equations of the signal-to-noise ratio (SNRp), or mean squared signal over a variance, for a given focal plane array pixel window of interest will be summarized and compared to Monte Carlo realizations of a 1m diffuse sphere.

Youmans, Douglas G.

2014-06-01

44

Low-cost compact MEMS scanning ladar system for robotic applications  

Science.gov (United States)

Future robots and autonomous vehicles require compact low-cost Laser Detection and Ranging (LADAR) systems for autonomous navigation. Army Research Laboratory (ARL) had recently demonstrated a brass-board short-range eye-safe MEMS scanning LADAR system for robotic applications. Boeing Spectrolab is doing a tech-transfer (CRADA) of this system and has built a compact MEMS scanning LADAR system with additional improvements in receiver sensitivity, laser system, and data processing system. Improved system sensitivity, low-cost, miniaturization, and low power consumption are the main goals for the commercialization of this LADAR system. The receiver sensitivity has been improved by 2x using large-area InGaAs PIN detectors with low-noise amplifiers. The FPGA code has been updated to extend the range to 50 meters and detect up to 3 targets per pixel. Range accuracy has been improved through the implementation of an optical T-Zero input line. A compact commercially available erbium fiber laser operating at 1550 nm wavelength is used as a transmitter, thus reducing the size of the LADAR system considerably from the ARL brassboard system. The computer interface has been consolidated to allow image data and configuration data (configuration settings and system status) to pass through a single Ethernet port. In this presentation we will discuss the system architecture and future improvements to receiver sensitivity using avalanche photodiodes.

Moss, Robert; Yuan, Ping; Bai, Xiaogang; Quesada, Emilio; Sudharsanan, Rengarajan; Stann, Barry L.; Dammann, John F.; Giza, Mark M.; Lawler, William B.

2012-06-01

45

Top-down facilitation of visual object recognition: object-based and context-based contributions.  

Science.gov (United States)

The neural mechanisms subserving visual recognition are traditionally described in terms of bottom-up analysis, whereby increasingly complex aspects of the visual input are processed along a hierarchical progression of cortical regions. However, the importance of top-down facilitation in successful recognition has been emphasized in recent models and research findings. Here we consider evidence for top-down facilitation of recognition that is triggered by early information about an object, as well as by contextual associations between an object and other objects with which it typically appears. The object-based mechanism is proposed to trigger top-down facilitation of visual recognition rapidly, using a partially analyzed version of the input image (i.e., a blurred image) that is projected from early visual areas directly to the prefrontal cortex (PFC). This coarse representation activates in the PFC information that is back-projected as "initial guesses" to the temporal cortex where it presensitizes the most likely interpretations of the input object. In addition to this object-based facilitation, a context-based mechanism is proposed to trigger top-down facilitation through contextual associations between objects in scenes. These contextual associations activate predictive information about which objects are likely to appear together, and can influence the "initial guesses" about an object's identity. We have shown that contextual associations are analyzed by a network that includes the parahippocampal cortex and the retrosplenial complex. The integrated proposal described here is that object- and context-based top-down influences operate together, promoting efficient recognition by framing early information about an object within the constraints provided by a lifetime of experience with contextual associations. PMID:17027376

Fenske, Mark J; Aminoff, Elissa; Gronau, Nurit; Bar, Moshe

2006-01-01

46

Phase error suppression by low-pass filtering for synthetic aperture imaging ladar  

Science.gov (United States)

Compared to synthetic aperture radar (SAR), synthetic aperture imaging ladar (SAIL) is more sensitive to the phase errors induced by atmospheric turbulence, undesirable line-of-sight translation-vibration and waveform phase error, because the light wavelength is about 3-6 orders of magnitude less than that of the radio frequency. This phase errors will deteriorate the imaging results. In this paper, an algorithm based on low-pass filtering to suppress the phase error is proposed. In this algorithm, the azimuth quadratic phase history with phase error is compensated, then the fast Fourier transform (FFT) is performed in azimuth direction, after the low-pass filtering, the inverse FFT is performed, then the image is reconstructed simultaneously in the range and azimuth direction by the two-dimensional (2D) FFT. The highfrequency phase error can be effectively eliminated hence the imaging results can be optimized by this algorithm. The mathematical analysis by virtue of data-collection equation of side-looking SAIL is presented. The theoretical modeling results are also given. In addition, based on this algorithm, a principle scheme of optical processor is proposed. The verified experiment is performed employing the data obtained from a SAIL demonstrator.

Sun, Zhiwei; Hou, Peipei; Zhi, Ya'nan; Sun, Jianfeng; Zhou, Yu; Xu, Qian; Lu, Zhiyong; Liu, Liren

2014-09-01

47

Three-dimensional imaging with 1.06?m Geiger-mode ladar camera  

Science.gov (United States)

Three-dimensional (3D) topographic imaging using Short wavelength infrared (SWIR) Laser Detection and Range (LADAR) systems have been successfully demonstrated on various platforms. LADAR imaging provides coverage down to inch-level fidelity and allows for effective wide-area terrain mapping. Recently Spectrolab has demonstrated a compact 32×32 LADAR camera with single photon-level sensitivity with small size, weight, and power (SWAP) budget. This camera has many special features such as non-uniform bias correction, variable range gate width from 2 microseconds to 6 microseconds, windowing for smaller arrays, and shorted pixel protection. Boeing integrated this camera with a 1.06 ?m pulse laser on various platforms and had demonstrated 3D imaging. In this presentation, the operation details of this camera and 3D imaging demonstration using this camera on various platforms will be presented.

Yuan, Ping; Sudharsanan, Rengarajan; Bai, Xiaogang; McDonald, Paul; Labios, Eduardo; Morris, Bryan; Nicholson, John P.; Stuart, Gary M.; Danny, Harrison; Van Duyne, Stephen; Pauls, Greg; Gaalema, Stephen

2012-06-01

48

Multi-function coherent ladar 3D imaging with S3  

Science.gov (United States)

The Super-resolution Sensor System (S 3) program is an ambitious effort to exploit the maximum information a laser-based sensor can obtain. At Lockheed Martin Coherent Technologies (LMCT), we are developing methods of incorporating multi-function operation (3D imaging, vibrometry, polarimetry, aperture synthesis, etc.) into a single device. The waveforms are matched to the requirements of both hardware (e.g., optical amplifiers, modulators) and the targets being imaged. The first successful demonstrations of this program have produced high-resolution, three-dimensional images at intermediate stand-off ranges. In addition, heavy camouflage penetration has been successfully demonstrated. The resolution of a ladar sensor scales with the bandwidth as dR = c/(2B), with a corresponding scaling of the range precision. Therefore, the ability to achieve large bandwidths is crucial to developing a high-resolution sensor. While there are many methods of achieving the benefit of large bandwidths while using lower bandwidth electronics (e.g., an FMCW implementation), the S 3 system produces and detects the full waveform bandwidth, enabling a large set of adaptive waveforms for applications requiring large range search intervals (RSI) and short duration waveforms. This paper highlights the combined three-dimensional imaging and vibrometry demos.

Buck, Joseph; Malm, Andrew; Zakel, Andrew; Krause, Brian; Tiemann, Bruce

2007-10-01

49

Ultra-Compact, High-Resolution LADAR System for 3D Imaging  

Science.gov (United States)

An eye-safe LADAR system weighs under 500 grams and has range resolution of 1 mm at 10 m. This laser uses an adjustable, tiny microelectromechanical system (MEMS) mirror that was made in SiWave to sweep laser frequency. The size of the laser device is small (70x50x13 mm). The LADAR uses all the mature fiber-optic telecommunication technologies in the system, making this innovation an efficient performer. The tiny size and light weight makes the system useful for commercial and industrial applications including surface damage inspections, range measurements, and 3D imaging.

Xu, Jing; Gutierrez, Roman

2009-01-01

50

Influence of space-time speckle effect on the image quality in a synthetic aperture imaging ladar  

Science.gov (United States)

Temporally and spatially varying speckle effect arises as a consequence of the frequency modulation chirped laser signal employed in synthetic aperture imaging ladar (SAIL).A variety of reconstructed images degraded by laser speckle effect have been reported. In this paper, space-time speckle effects and their influence on imaging based on the SAIL system in a far-field diffraction region are systematically studied. The first half of this paper provides the theoretical analyses of the 2D data acquisition with speckle effect in SAIL. Numerical simulations of the temporally varying speckle pattern, the integrated speckle field over a receiving antenna and their influence to the image quality for SAIL are obtained in the remaining of the paper. Our results will be valuable for further studies on the suppression of speckle effect in SAILs.

Xu, Qian; Zhou, Yu; Sun, Jianfeng; Sun, Zhiwei; Ma, Xiaoping; Liu, Liren

2014-12-01

51

Research progress on a focal plane array ladar system using a laser diode transmitter and FM/cw radar principles  

Science.gov (United States)

The Army Research Laboratory is developing scannerless ladar systems for smart munition and reconnaissance applications. Here we report on progress attained over the past year related to the construction of a 32x32 pixel ladar. The 32x32 pixel architecture achieves ranging based on a frequency modulation/continuous wave (FM/cw) technique implemented by directly amplitude modulating a near-IR diode laser transmitter with a radio frequency (rf) subcarrier that is linearly frequency modulated. The diode's output is collected and projected to form an illumination field in the downrange image area. The returned signal is focused onto an array of metal-semiconductor-metal (MSM) detectors where it is detected and mixed with a delayed replica of the laser modulation signal that modulates the responsivity of each detector. The output of each detector is an intermediate frequency (IF) signal (a product of the mixing process) whose frequency is proportional to the target range. This IF signal is continuously sampled over each period of the rf modulation. Following this, a N channel signal processor based-on field-programmable gate arrays calculates the discrete Fourier transform over the IF waveform in each pixel to establish the ranges to all the scatterers and their respective amplitudes. Over the past year, we have built one and two-dimensional self-mixing MSM detector arrays at .8 and 1.55 micrometers , designed and built circuit boards for reading data out of a 32x32 pixel array, and designed an N channel FPGA signal processor for high-speed formation of range gates. In this paper we report on the development and performance of these components and the results of system tests conducted in the laboratory.

Stann, Barry L.; Abou-Auf, Ahmed; Aliberti, Keith; Giza, Mark M.; Ovrebo, Greg; Ruff, William C.; Simon, Deborah R.; Stead, Michael R.

2002-07-01

52

Water Detection Based on Object Reflections  

Science.gov (United States)

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.

Rankin, Arturo L.; Matthies, Larry H.

2012-01-01

53

Line imaging ladar using a laser-diode transmitter and FM/cw radar principles for submunition applications  

Science.gov (United States)

We describe the technical approach, component development, and test results of a line imager laser radar (ladar) being developed at the Army Research Laboratory (ARL) for smart munition applications. We obtain range information using a frequency modulation/continuous wave (FM/cw) technique implemented by directly amplitude modulating a near-IR diode laser transmitter with a radio frequency (rf) subcarrier that is linearly frequency modulated. The diode's output is collimated and projected to form a line illumination in the downrange image area. The returned signal is focused onto a line array of metal-semiconductor-metal (MSM) detectors where it is detected and mixed with a delayed replica of the laser modulation signal that modulates the responsivity of each detector. The output of each detector is an intermediate frequency (IF) signal (a product of the mixing process) whose frequency is proportional to the target range. This IF signal is continuously sampled over each period of the rf modulation. Following this, a N-channel signal processor based on field- programmable gate arrays (FPGA) calculates the discrete Fourier transform over the IF waveform in each pixel to establish the ranges to all the scatterers and their respective amplitudes. Over the past year, we constructed the fundamental building blocks of this ladar, which include a 3.5-W line illuminator, a wideband linear FM chirp modulator, a N-pixel MSM detector line array, and a N-channel FPGA signal processor. In this paper we report on the development and performance of each building block and the results of system tests conducted in the laboratory.

Stann, Barry L.; Abou-Auf, Ahmed; Ruff, William C.; Robinson, Dale; Liss, Brian; Potter, William; Sarama, Scott D.; Giza, Mark M.; Simon, Deborah R.; Frankel, Scott; Sztankay, Zoltan G.

2000-09-01

54

Object Based Video Retrieval Using SIFT  

OpenAIRE

A method for retrieving video containing a particular object, a single image of the object is given as a query. The local invariant features are obtained for all frames in a sequence and tracked throughout the shot to extract stable features. In Video Retrieval system, each video that is stored in the database has its features extracted and compared to the features of the query image. Proposed work is to retrieve video from the database by giving query as an object. Video is firstly converted...

Neetesh Gupta, Shiv K. Sahu; Shradha Gupta

2011-01-01

55

Near real-time extraction of planar features from 3D flash-ladar video frames  

Science.gov (United States)

This paper describes a novel method used to extract planar surfaces from a stream of 3D images in near real-time. The method currently operates on 3D images acquired from a MESA SwissRanger SR-3000 infrared time of flight camera, which operates in a manner similar to flash-ladar sensors; the camera provides the user with range and intensity value for each pixel in the 176 by 144 image frame. After application of the camera calibration the range measurement associated with each pixel can be converted to a Cartesian coordinate. First, the proposed method splits the focal image plane into sub-images or sub-windows. The method then operates in the 3D parameter space to find an estimate of the planar equation best describing the point cloud associated with the window pixels and to compute a metric that defines how well the sub-window points fit to the planar estimate. The best fit sub-window is then used as an initialization to one of two investigated methods: a parameter based search technique and cluster validation using histogram thresholding to extract the entire plane from the 3D image frame. Once a plane is extracted, a feature vector describing that plane along with their describing statistics can be generated. These feature vectors can then be used to enable feature-based navigation. The paper will fully describe the feature extraction method and will provide application results of this method to extract features from indoor 3D video data obtained with the MESA SwissRanger SR-3000. Also provided is a brief overview of the generation of feature statistics and their importance.

Venable, Don; Uijt de Haag, Maarten

2008-03-01

56

University collections and object-based pedagogies  

Directory of Open Access Journals (Sweden)

Full Text Available Engagement with objects, either directly or through digital media, has long been recognized as a viable, constructivist pedagogy, capable of mediating significant meaning and context. The increasing uptake of digital technologies in university learning and teaching programs provides a timely opportunity for integrating museum and collection data and metadata in these programs. This project looked at the use of university museum and collection objects in teaching programs through a controlled experiment. A group of students were exposed directly to collection objects while another group was exposed to their digital surrogate. Students were then tested at later stages concerning their recall of didactic information. Results clearly show that students exposed to the original object had far better didactic recall over a longer time period than students exposed to their digital surrogates. This has implications for the development and rapid expansion of online education delivery in the tertiary education sector and elsewhere and the role university collections can play.

Andrew Simpson

2012-10-01

57

University collections and object-based pedagogies  

OpenAIRE

Engagement with objects, either directly or through digital media, has long been recognized as a viable, constructivist pedagogy, capable of mediating significant meaning and context. The increasing uptake of digital technologies in university learning and teaching programs provides a timely opportunity for integrating museum and collection data and metadata in these programs. This project looked at the use of university museum and collection objects in teaching programs through a controlled...

Andrew Simpson; Gina Hammond

2012-01-01

58

Object Recognition Based on Dual Tree Complex Wavelet Transform  

Directory of Open Access Journals (Sweden)

Full Text Available Automated recognition of objects from images plays an important role in many computer vision systems such as robot navigation, object manipulation and content based image retrieval. In this study, an approach for object recognition based on Dual Tree Complex Wavelet Transform (DTCWT is proposed. The proposed approach attempts to extract the detailed information of objects from the multi scale representation by DTCWT. The proposed system is tested on Columbia Object Image Library (COIL-100. All the objects are considered for the classification based on nearest neighbor classifier. The results show that the maximum recognition accuracy achieved by the proposed approach is 97.03%.

S. Elakkiya

2014-05-01

59

Object -based learning method in civil engineering  

OpenAIRE

Many researchers have developed learning methods that can be used in different situations and conditions, which has led to numerous theories and associated applications. Traditionally, most instruction occurs in a formal classroom setting; however, various types of computer-based instruction that can be delivered anytime, anywhere, and at a lower cost, are possible these days with the help of technology. In addition, as industry moves from traditional craft-based operations to more sophistica...

Lee, Joo Hyoung

2004-01-01

60

Co-Training Based Segmentation of Merged Moving Objects  

OpenAIRE

Object detection and tracking are basic tasks in video surveillance and have been an active research area. Using a standard Gaussian Mixture Model (GMM) based method, nearby objects could be merged into a single foreground object. This causes difficulties in foreground segmentation, especially when objects in the foreground have similar in color, texture and shape. This paper proposes a novel method for segmenting merged objects into individual ones. First, an unsupervised co-training framewo...

Zhang, Tianzhu; Li, Stan Z.; Xiang, Shiming; Zhang, Lun; Liu, Si

2008-01-01

61

THE ADMINISTRATOR OBJECT PATTERN FOR ROLE-BASED ACCESS CONTROL  

OpenAIRE

The Object-Oriented paradigm approaches the software development by representing real world entities into classes of software objects. Object oriented design patterns facilitate small scale and large scale design reuse. This paper presents an object oriented design pattern, Administrator Object, to address the User-Role assignment problem in Role Based Access Control (RBAC). Two alternative solutions are proposed. The pattern is presented according to the Gang of Four template.

Kodituwakku, S. R.

2010-01-01

62

Optical image reconstruction using an astigmatic lens for synthetic-aperture imaging ladar  

Science.gov (United States)

An optical processor for synthetic-aperture imaging ladar (SAIL) utilizing one astigmatic lens is proposed. The processor comprises two structures of transmitting and reflecting. The imaging process is mathematically analyzed using the unified data-collection equation of side-looking and down-looking SAILs. Results show that the astigmatic lens can be replaced with a cylindrical lens on certain conditions. To verify this concept, laboratory experiment is conducted, the imaging result of data collected from one SAIL demonstrator is given.

Sun, Zhiwei; Hou, Peipei; Zhi, Yanan; Sun, Jianfeng; Zhou, Yu; Xu, Qian; Liu, Liren

2014-11-01

63

Agents as objects with knowledge base state  

CERN Document Server

Advances in computer technology in general and computer networks in particular have significantly affected the requirements of modern applications, where the need to operate in decentralised environments is of primary importance. The conceptual models of the applications are also becoming complex and semantically rich.A promising technology towards the design and development of systems of such domains is agent based systems. Agents, having a knowledge component, act and interact with other agents and information sources in order to achieve some goals. Platforms intended for supporting the deve

Skarmeas, Nikolaos

1999-01-01

64

Application of Moving Object Tracking Based on Kalman Filter Algorithm  

OpenAIRE

The moving object module matching method base on Kalman Filter (KF) algorithm which proposed to solve the problem of traditional moving object matching method’s, that fault of huge searching range and weakness in real-time processing. Relative to traditional module matching method, the method mentioned here effectively improved the speed and the accuracy of object tracking. This method has tripled the object matching speed of traditional tracking method.

Xiao Zhansheng

2013-01-01

65

A semantic description of learning objects based on an ontology  

OpenAIRE

The number of learning objects available on the Internet has significantly grown these last years and the problem of indexing and searching these learning objects is becoming crucial. Standards and norms of educative metadata such as LOM and SCORM have been proposed to handle this problem but in our opinion these proposals are not a satisfactory solution. In this paper, we propose to extend these standards with a semantic description of learning objects based on an ontology. A learning object...

Duitama, John-freddy; Defude, Bruno; Bouzeghoub, Amel; Lecocq, Claire

2005-01-01

66

A semantic description of learning objects based on an ontology  

Directory of Open Access Journals (Sweden)

Full Text Available The number of learning objects available on the Internet has significantly grown these last years and the problem of indexing and searching these learning objects is becoming crucial. Standards and norms of educative metadata such as LOM and SCORM have been proposed to handle this problem but in our opinion these proposals are not a satisfactory solution. In this paper, we propose to extend these standards with a semantic description of learning objects based on an ontology. A learning object is described by prerequisites, a content and an acquisition function. This allows defining powerful search tools and improves reusing. For reusing, we propose to define new learning objects by assembling existing objects. Assembling is specified by a composition graph composed by learning objects and composition operators (sequence, parallel, and alternative. In order to improve flexibility, we have introduced intentional objects. An intentional object is defined by a composition graph where (at least one object has been replaced by an intentional query on the learning object repository. This model is used during the adaptive process of a learning object for a specific learner. We define a notion of quality on learning objects which mainly reflects their ability to reuse. This quality may be evaluated a posteriori using metrics on objects or controlled a priori using a type system. Our model has been implemented with Sesame, a RDF database which support SeRQL a powerful query language for RDF and RDFS.

John-Freddy DUITAMA

2005-01-01

67

Solid State Disk Object-Based Storage with Trim Commands  

OpenAIRE

This paper presents a model of NAND flash SSD utilization and write amplification when the ATA/ATAPI SSD Trim command is incorporated into object-based storage under a variety of user workloads, including a uniform random workload with objects of fixed size and a uniform random workload with objects of varying sizes. We first summarize the existing models for write amplification in SSDs for workloads with and without the Trim command, then propose an alteration of the models...

Frankie, Tasha; Hughes, Gordon; Kreutz-delgado, Ken

2012-01-01

68

Context based Coding of Quantized Alpha Planes for Video Objects  

DEFF Research Database (Denmark)

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.

Aghito, Shankar Manuel; Forchhammer, SØren

2002-01-01

69

Context based Coding of Quantized Alpha Planes for Video Objects  

OpenAIRE

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.

Aghito, Shankar Manuel; Forchhammer, Søren

2006-01-01

70

Object detection of speckle image base on curvelet transform  

Directory of Open Access Journals (Sweden)

Full Text Available The speckle degrades quality of the image and makes interpretations, segmentation of objects harder. In this paper, we present a method for object detection of speckle image base on curvelet transform. The approximate properties and the high directional sensitivity of the curvelet transform make the new method for object detection of speckle image. We construct a method segmentation that provides a sparse expansion for typical images having smooth contours.

Nguyen Thanh Binh

2007-06-01

71

Appearance-based object reacquisition for mobile manipulation  

OpenAIRE

This paper describes an algorithm enabling a human supervisor to convey task-level information to a robot by using stylus gestures to circle one or more objects within the field of view of a robot-mounted camera. These gestures serve to segment the unknown objects from the environment. Our method's main novelty lies in its use of appearance-based object “reacquisition” to reconstitute the supervisory gestures (and corresponding segmentation hints), even for robot viewpoints spatially and/...

Walter, Matthew R.; Friedman, Yuli; Antone, Matthew; Teller, Seth

2010-01-01

72

ParObj: an object-based parallel system kernel  

OpenAIRE

The objective of this thesis is to defined the functionalities of a virtual machine called ParObj, that support the notion of concurrent objects, and which is suitable for scalable parallelism. This work is in keeping with the general pattern of the PARX project within the "Massively Parallel System" team of the LGI, which aims at defining and implementing an operating system for parallel machines. By analysing of some known Object-based Distributed System, we extract the basic mechanisms tha...

Menneteau, Francois

1993-01-01

73

RFID and IP Based Object Identification in Ubiquitous Networking  

Directory of Open Access Journals (Sweden)

Full Text Available Ubiquitous networking is an integrated part of future networking technology that can provide capabilities for connecting all of objects (computers, human, PDAs, cell phones etc. in future network. It has to meet the challenge of seamless connection for communication between human and objects in internet infrastructure. Unique object identification is very much important to make the communication between objects possible. RFID tag can be used as unique identifier to identify a physical object. Radio Frequency Identification (RFID is a technology used for object identification of system. Internet Protocol (IPaddress is used to provide logical identity to find the location of object for communication. In this paper, IP based RFID architecture for unique identification and tracking of object by considering mobility isproposed. RFID Agent (RA is used to generate IP address based on RFID tags. In proposed solution, RFID-IP mapping is used to identify and track the location of object as RFID Tag ID can be used to generate unique identifier and IP can be used to find the location. RFID deployment is cost effective and IP is being used as current internet structure. In this way RFID-IP mapping provides better solution for object identification in ubiquitous networking environment.

Nisha Vaghela

2012-10-01

74

Content-Based Object Movie Retrieval and Relevance Feedbacks  

Directory of Open Access Journals (Sweden)

Full Text Available Object movie refers to a set of images captured from different perspectives around a 3D object. Object movie provides a good representation of a physical object because it can provide 3D interactive viewing effect, but does not require 3D model reconstruction. In this paper, we propose an efficient approach for content-based object movie retrieval. In order to retrieve the desired object movie from the database, we first map an object movie into the sampling of a manifold in the feature space. Two different layers of feature descriptors, dense and condensed, are designed to sample the manifold for representing object movies. Based on these descriptors, we define the dissimilarity measure between the query and the target in the object movie database. The query we considered can be either an entire object movie or simply a subset of views. We further design a relevance feedback approach to improving retrieved results. Finally, some experimental results are presented to show the efficacy of our approach.

Lee Greg C

2007-01-01

75

The IUE data base: Homogenizing the IUE object nomenclature  

Science.gov (United States)

The IUE project started to homogenize the object nomenclature in the IUE data base. Due to the absence of an official IAU nomenclature hierarchy and in view of the increasing confusion in IUE (and, in general, astronomical) object identifications, the IUE project adopted its own nomenclature hierarchy. The scheme and problems encountered in establishing it are described.

Barylak, Michael; Wamsteker, Willem; Schmitz, Marion

1988-01-01

76

A Method of Object-based De-duplication  

Directory of Open Access Journals (Sweden)

Full Text Available Today, the world is increasingly awash in more and more unstructured data, not only because of the Internet, but also because data that used to be collected on paper or media such as film, DVDs and compact discs has moved online [1]. Most of this data is unstructured and in diverse formats such as e-mail, documents, graphics, images, and videos. In managing unstructured data complexity and scalability, object storage has a clear advantage. Object-based data de-duplication is the current most advanced method and is the effective solution for detecting duplicate data. It can detect common embedded data for the first backup across completely unrelated files and even when physical block layout changes. However, almost all of the current researches on data de-duplication do not consider the content of different file types, and they do not have any knowledge of the backup data format. It has been proven that such method cannot achieve optimal performance for compound files.In our proposed system, we will first extract objects from files, Object_IDs are then obtained by applying hash function to the objects. The resulted Object_IDs are used to build as indexing keys in B+ tree like index structure, thus, we avoid the need for a full object index, the searching time for the duplicate objects reduces to O(log n.We introduce a new concept of a duplicate object resolver. The object resolver mediates access to all the objects and is a central point for managing all the metadata and indexes for all the objects. All objects are addressable by their IDs which is unique in the universe. The resolver stores metadata with triple format. This improved metadata management strategy allows us to set, add and resolve object properties with high flexibility, and allows the repeated use of the same metadata among duplicate object.

Fang Yan

2011-12-01

77

Autocorrelation based reconstruction of two-dimensional binary objects  

International Nuclear Information System (INIS)

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)

78

A Secure and Robust Object-Based Video Authentication System  

Directory of Open Access Journals (Sweden)

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.

Dajun He

2004-10-01

79

Stereovision-Based Object Segmentation for Automotive Applications  

Directory of Open Access Journals (Sweden)

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.

Fu Shan

2005-01-01

80

Study on living object identification based on genetic algorithms  

Science.gov (United States)

Fast and effectual salvage can reduce accident loss, ensure people's lives and belongings safely when shipwreck occurs. It is very important that discovering objects should be timely and exactly to insure the salvage going on wheels. This text puts forward an object identification arithmetic based on Genetic Algorithms, which makes use of Genetic Algorithms to search living objects in the sea based on different infrared radiation characteristics between living objects and background, uses single point crossover method and simple mutation method with adaptive probability, ensures the global and local searching ability of Genetic Algorithms. Thus GA can accomplish searching course of optimization quickly and exactly with favorable searching ability. From identification test aiming at standard infrared image, it is seen that the image is strengthened by Genetic Algorithms, and the living objects can be identified exactly.

Wang, Yao; Xiong, Mu-di; Jia, Si-nan

2007-12-01

81

Object-based mapping of drumlins from DTMs  

Science.gov (United States)

Until recently, landforms such as drumlins have only been manually delineated due to the difficulty in integrating contextual and semantic landform information in per cell classification approaches. Therefore, in most cases the results of per cell classifications presented basic landform elements or broad-scale physiographic regions that were only thematically defined. In contrast, object-based analysis provides spatially configured landform objects that are generated by terrain segmentation, the process of merging DTM cells to meaningful terrain objects at multiple scales. Such terrain objects should be favoured for landform modelling due to the following reasons: Firstly, their outlines potentially better correspond to the spatial limits of landforms as conceptualised by geoscientists; secondly, spatially aware objects enable the integration of semantic descriptions in the classification process. We present a multi-scale object-based study on automated delineation and classification of drumlins for a small test area in Bavaria, Germany. The multi-resolution segmentation algorithm is applied to create statistically meaningful objects patterns of selected DTMs, which are derived from a 5 m LiDAR DEM. For the subsequent classification of drumlins a semantics-based approach, which uses the principles of semantic modelling, is employed: initially, a geomorphological concept of the landform type drumlin is developed. The drumlin concept should ideally comprise verbal descriptions of the fundamental morphometric, morphological, hierarchical and contextual properties. Subsequently, the semantic model is built by structuring the conceptualised knowledge facts, and by associating those facts with object and class-related features, which are available in commonly used object-based software products for the development of classification rules. For the accuracy assessment we plan an integrated approach, which combines a statistical comparison to field maps and a qualitative evaluation based on expert consultation. The study on drumlins should demonstrate the applicability of the object-based approach for the extraction of specific landforms from DTMs in a multi-scale framework. The provision of meaningful spatial modelling units and the straightforward way for the integration of semantics make object-based analysis superior to field-based methods. However, an explicit representation of geomorphological knowledge - as for example in the form of a semantic model - prior to landform classification is a prerequisite for effective mapping. Such an approach allows the user to delineate and map drumlins in a way that is close to the human cognition of landforms. Once most of the drumlins are recognized by the developed classification system, those objects can further be investigated with respect to their morphometry and morphology in order to improve the understanding of glacial processes.

Eisank, C.; Dragut, L.; Blaschke, T.

2012-04-01

82

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

International Nuclear Information System (INIS)

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

83

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

International Nuclear Information System (INIS)

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.

84

Robust Object-Based Watermarking Using Feature Matching  

Science.gov (United States)

We present a robust object-based watermarking algorithm using the scale-invariant feature transform (SIFT) in conjunction with a data embedding method based on Discrete Cosine Transform (DCT). The message is embedded in the DCT domain of randomly generated blocks in the selected object region. To recognize the object region after being distorted, its SIFT features are registered in advance. In the detection scheme, we extract SIFT features from the distorted image and match them with the registered ones. Then we recover the distorted object region based on the transformation parameters obtained from the matching result using SIFT, and the watermarked message can be detected. Experimental results demonstrated that our proposed algorithm is very robust to distortions such as JPEG compression, scaling, rotation, shearing, aspect ratio change, and image filtering.

Pham, Viet-Quoc; Miyaki, Takashi; Yamasaki, Toshihiko; Aizawa, Kiyoharu

85

Research on moving object detection based on frog's eyes  

Science.gov (United States)

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.

Fu, Hongwei; Li, Dongguang; Zhang, Xinyuan

2008-12-01

86

Model-Based Software Testing for Object-Oriented Software  

Science.gov (United States)

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…

Biju, Soly Mathew

2008-01-01

87

A Wavelet - Based Object Watermarking System for MPEG4 Video  

Directory of Open Access Journals (Sweden)

Full Text Available Efficient storage, transmission and use of video information are key requirements in many multimedia applications currently being addressed by MPEG-4. To fulfill these requirements, a new approach for representing video information which relies on an object-based representation, has been adopted. Therefore, object-based watermarking schemes are needed for copyright protection. This paper presents a novel object based watermarking solution for MPEG4 video authentication using the shape adaptive-discrete wavelet transform (SA-DWT. In order to make the watermark robust and transparent, the watermark is embedded in the average of wavelet blocks using the visual model based on the human visual system. Wavelet coefficients n least significant bits (LSBs are adjusted in concert with the average. Simulation results shows that the proposed watermarking scheme is perceptually invisible and robust against many attacks such as lossy compression (e.g. MPEG1 and MPEG2,MPEG-4,H264

F.Regragui

2010-01-01

88

Edge-based approach to moving object location  

Science.gov (United States)

This paper presents an edge-based background subtraction approach to moving object location from video frames. First, a background frame without moving objects is obtained. For each subsequent frame with objects, edge detection with thresholding is performed on both the background frame and the current frame. The difference of the two edge images is computed. To emphasize the areas containing edge pixels belonging to the moving object, a selective dilation operation is performed. Connected component analysis is applied to remove insignificant candidate regions. An erosion operation is applied to compensate for earlier dilation. Finally, unwanted areas formed by the shadows of the moving object are removed. Experimental results with road scenes to locate vehicles approaching toll booths on a highway entrance demonstrate the effectiveness of the proposed approach. The extracted vehicles are input to a classification module for classifying vehicles into useful categories.

Soh, Jung; Chun, Byung-Tae; Wang, Min

1994-03-01

89

Visual statistical learning can drive object-based attentional selection.  

Science.gov (United States)

Recent work on statistical learning has demonstrated that environmental regularities can influence aspects of perception, such as familiarity judgments. Here, we ask if statistical co-occurrences accumulated from visual statistical learning could form objects that serve as the units of attention (i.e., object-based attention). Experiment 1 demonstrated that, after observers first viewed pairs of shapes that co-occurred in particular spatial relationships, they were able to recognize the co-occurring pairs, and were faster to discriminate two targets when they appeared within a learned pair ("object") than when the targets appeared between learned pairs, demonstrating an equivalent of an object-based attention effect. Experiment 2 replicated the results of Experiment 1 using a different set of shape pairs, and revealed a negative association between the attention effect and familiarity judgments of the co-occurred pairs. Experiment 3 reports three control experiments that validated the task procedure and ruled out alternative accounts. PMID:24935806

Zhao, Libo; Cosman, Joshua D; Vatterott, Daniel B; Gupta, Prahlad; Vecera, Shaun P

2014-11-01

90

Nanoscale synthesis and characterization of graphene-based objects  

OpenAIRE

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 (ST...

Daisuke Fujita

2011-01-01

91

Automatic Recognition of Object Use Based on Wireless Motion Sensors  

OpenAIRE

In this paper, we present a method for automatic, online detection of a user’s interaction with objects. This represents an essential building block for improving the performance of distributed activity recognition systems. Our method is based on correlating features extracted from motion sensors worn by the user and attached to objects. We present a complete implementation of the idea, using miniaturized wireless sensor nodes equipped with motion sensors. We achieve a recognition accura...

Bosch, Stephan; Marin-perianu, Raluca; Marin-perianu, Mihai; Horst, Arie; Vasilescu, Andrei; Havinga, Paul

2010-01-01

92

CONTENT BASED IMAGE RETRIEVAL - EXTRACTION BY OBJECTS OF USER INTEREST  

OpenAIRE

Content-based image retrieval (CBIR) systems normally return the retrieval results according to the similarity between features extracted from the query and candidate images. In certain circumstances, however users are most qualified to specify the query “content” or objects(e.g., Eiffel Tower) of their interest , not the computer and only wish to retrieve images containing relevant objects, while ignoring irrelevant image areas (such as the background). Previous work on this normally req...

Kishore Kumar, D.; E.Usha sree,; K.Suneera,; Chaitanya Kumar, P. V.

2011-01-01

93

Geophysics-based method of locating a stationary earth object  

Science.gov (United States)

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.

Daily, Michael R. (Albuquerque, NM); Rohde, Steven B. (Corrales, NM); Novak, James L. (Albuquerque, NM)

2008-05-20

94

An object-based methodology for knowledge representation in SGML  

Energy Technology Data Exchange (ETDEWEB)

An object-based methodology for knowledge representation and its Standard Generalized Markup Language (SGML) implementation is presented. The methodology includes class, perspective domain, and event constructs for representing knowledge within an object paradigm. The perspective construct allows for representation of knowledge from multiple and varying viewpoints. The event construct allows actual use of knowledge to be represented. The SGML implementation of the methodology facilitates usability, structured, yet flexible knowledge design, and sharing and reuse of knowledge class libraries.

Kelsey, R.L. [Los Alamos National Lab., NM (United States)|New Mexico State Univ., Las Cruces, NM (United States); Hartley, R.T. [New Mexico State Univ., Las Cruces, NM (United States); Webster, R.B. [Los Alamos National Lab., NM (United States)

1997-11-01

95

BOSD: Business Object Based Flexible Software Development for Enterprises  

OpenAIRE

The enterprise software need adapt to new requirements from the continuous change management. The recent development methods have increased the flexibility of software. However, previous studies have ignored the stability of business object and the particular business relationships to support the software development. In this paper, a coarse-grained business object based software development, BOSD, is presented to resolve this problem. By analyzing the characteristics of variable requirement,...

Xiaofei Xu; Jindan Feng; Dechen Zhan; Lanshun Nie

2010-01-01

96

A New Approach to Object Based Fuzzy Database Modeling  

OpenAIRE

The requirements in diversified application domains like Engineering, Scientific technology, Multimedia, Knowledge management in expert systems etc shift the momentum of current trends in designing database models to an innovative concept of Object Based fuzzy Database Model. The ongoing research concentrates on representing the imprecise information by taking object modelling methodology and fuzzy techniques through different levels of class hierarchy and abstractions. Still, a formal defini...

Debasis Dwibedy,; Dr. Laxman Sahoo; Sujoy Dutta,

2013-01-01

97

Robust Object Tracking Based on Adaptive Feature Selection  

OpenAIRE

In order to solve the video-based object tracking problem in complex dynamic scenes, a robust tracking algorithm based on adaptive feature selection was proposed in this paper. To address the poor robustness of candidates in the feature pool in the online Adaboost algorithm and the drift problem caused by the template updating, a new feature pool was built based on both color features and histogram of pyramidal gradient features. An occlusion detector is added after the tracking in the curren...

Chen Dong-Yue; Qi Yuan-Chen

2013-01-01

98

Segmentation of object-based video of gaze communication  

DEFF Research Database (Denmark)

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.

Aghito, Shankar Manuel; Stegmann, Mikkel Bille

2005-01-01

99

Novel Scheme for Object-based Embedded Image Coding  

Directory of Open Access Journals (Sweden)

Full Text Available Conventional EBCOT algorithm encodes an image with the same block-based coding strategy and it is difficult to acquire perceptually consistent results in practical applications. This paper presents a new scheme for object-based embedded coding aiming to enhance the flexibility and the coding efficiency. In our scheme, an original image is firstly segmented into objects, which are taken as basic coding units and are encoded independently. Then the compressed streams of all objects are further truncated and reassembled based on rate-distortion optimization principle according to given bit-rate. In our scheme, each object can be encoded independently with different strategy according to its visual interest at the encoder. At the decoder, it can be decoded and reconstructed independently and progressively. As a whole, the new scheme is more flexible and may enhance the overall subjective quality of reconstructed images. Experiments are conducted and the results show the proposed scheme can implement object-based image coding and rate control effectively.

Yuer Wang

2012-11-01

100

Inverse treatment planning using volume-based objective functions  

International Nuclear Information System (INIS)

The results of optimization of inverse treatment plans depend on a choice of the objective function. Even when the optimal solution for a given cost function can be obtained, a better solution may exist for a given clinical scenario and it could be obtained with a revised objective function. In the approach presented in this work mixed integer programming was used to introduce a new volume-based objective function, which allowed for minimization of the number of under- or overdosed voxels in selected structures. By selecting and prioritizing components of this function the user could drive the computations towards the desired solution. This optimization approach was tested using cases of patients treated for prostate and oropharyngeal cancer. Initial solutions were obtained based on minimization/maximization of the dose to critical structures and targets. Subsequently, the volume-based objective functions were used to locate solutions, which satisfied better clinical objectives particular to each of the cases. For prostate cases, these additional solutions offered further improvements in sparing of the rectum or the bladder. For oropharyngeal cases, families of solutions were obtained satisfying an intensity modulated radiation therapy protocol for this disease site, while offering significant improvement in the sparing of selected critical structures, e.g., parotid glands. An additional advantage of the present approach was in providing a convenient mechanism to testn providing a convenient mechanism to test the feasibility of the dose-volume histogram constraints

101

Model-based objects recognition in man-made environments  

OpenAIRE

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some ...

Marti? I Bonmati?, Joan; Casals, Ali?cia

1996-01-01

102

Improved Brain Tumor Detection Using Object Based Segmentation  

OpenAIRE

This paper has focused on the brain tumor detection techniques. The brain tumor detection is a very important vision application in the medical field. This work has firstly presented a review on various well known techniques for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main objective of the work is to explore various techniques to detect...

Harneet Kaur.; Sukhwinder Kaur

2014-01-01

103

Object based data access at the D0 experiment  

International Nuclear Information System (INIS)

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

104

Distributed Shared Memory Consistency Object-based Model  

Directory of Open Access Journals (Sweden)

Full Text Available A novel model that describes consistency in shared memory was developed, presented and discussed. The new object-based model handles errors of inaccuracy and misrepresentation in distributed shared memory process. The issue of misalignment was also covered.

Abdelfatah A. Yahya

2007-01-01

105

[Psychotherapy based on object relation with object and psychodynamic attitude to treatment of eating disorders].  

Science.gov (United States)

The article presents a proposal of integrating the basic concepts of object relations theory with the psychodramatic techniques by Moreno in treatment of eating disorders (anorexia and bulimia nervosa). The author pays attention to the common elements and also on the differences in psychopathology of eating disorder from a medical perspective, psychoanalytic and psychodynamic paradigm and moreover psychodrama by Moreno. Moreover she points to common elements and the possibility of applying psychodramatic and psychodynamic therapeutic techniques in individual and group psychotherapy in persons with eating disorders. The presented attempt of integrating psychotherapy based on relation with object and psychodramatic techniques by Moreno can enlarge the repertoir of therapeutic techniques which intensify the recovery process in the group of persons with eating disorders (anorexia and bulimia nervosa). PMID:21452503

Izydorczyk, Bernadetta

2010-01-01

106

Robust Object Tracking Based on Adaptive Feature Selection  

Directory of Open Access Journals (Sweden)

Full Text Available In order to solve the video-based object tracking problem in complex dynamic scenes, a robust tracking algorithm based on adaptive feature selection was proposed in this paper. To address the poor robustness of candidates in the feature pool in the online Adaboost algorithm and the drift problem caused by the template updating, a new feature pool was built based on both color features and histogram of pyramidal gradient features. An occlusion detector is added after the tracking in the current frame to improve the reliability of the realtime updated template. Experimental results showed that the proposed algorithm has better performance against object deformation, pose transformation, illumination variance and occlusion.

Chen Dong-Yue

2013-01-01

107

SPANISH TOURIST BEHAVIOUR. A SPECIFIC OBJECTIVE BASED SEGMENTATION  

Directory of Open Access Journals (Sweden)

Full Text Available 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 analysis. Afterwards, identified segments are evaluated to analyse their tourist profiles with a view to examining sociological perspectives of tourist behaviour.

Oscar Molina Molina

2009-11-01

108

Nanoscale synthesis and characterization of graphene-based objects  

Energy Technology Data Exchange (ETDEWEB)

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. Moire patterns and one-dimensional reconstruction were observed on multilayer graphite terraces. As a useful functionality, application to repairable high-resolution STM probes is demonstrated.

Fujita, Daisuke, E-mail: FUJITA.Daisuke@nims.go.jp [International Center for Materials Nanoarchitectonics (WPI-MANA), National Institute for Materials Science 1-2-1 Sengen, Tsukuba 305-0047 (Japan)

2011-08-15

109

Research on object detection based on circular polarization property  

Science.gov (United States)

It is an important subject in information scout, battlefield surveillance and automatic target recognition to detect interesting objects from complicated background. Compared with intensity detection, polarization detection has its advantage in identifying some camouflage targets. Usually, in the studies of target polarization detection, circular polarization property is usually neglected because of its small value. But in particular conditions, the circular polarization property of target will be used to accomplish object detection with their obviously different value. In this study, a single reflectance model of Mueller matrix is established, and based on Fresnel's law, circular polarization property of object is analyzed which is obvious while linear polarization property is obscure in particular condition. It is available to use the circular polarization component to detect target.

Wu, Yun-zhi; Zeng, Xian-fang; Yin, Cheng-liang; Luo, Xiao-lin

2013-09-01

110

Nanoscale synthesis and characterization of graphene-based objects  

Directory of Open Access Journals (Sweden)

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.

Daisuke Fujita

2011-01-01

111

Likelihood-based CT reconstruction of objects containing known components  

International Nuclear Information System (INIS)

There are many situations in medical imaging where there are known components within the imaging volume. Such is the case in diagnostic X-ray CT imaging of patients with implants, in intraoperative CT imaging where there may be surgical tools in the field, or in situations where the patient support (table or frame) or other devices are outside the (truncated) reconstruction FOV. In such scenarios it is often of great interest to image the relation between the known component and the surrounding anatomy, or to provide high-quality images at the boundary of these objects, or simply to minimize artifacts arising from such components. We propose a framework for simultaneously estimating the position and orientation of a known component and the surrounding volume. Toward this end, we adopt a likelihood-based objective function with an image volume jointly parameterized by a known object, or objects, with unknown registration parameters and an unknown background attenuation volume. The objective is solved iteratively using an alternating minimization approach between the two parameter types. Because this model integrates a substantial amount of prior knowledge about the overall volume, we expect a number of advantages including the reduction of metal artifacts, potential for more sparse data acquisition (decreased time and dose), and/or improved image quality. We illustrate this approach using simulated spine CT data that contains pedicle screws placed in a vertebra, and deedicle screws placed in a vertebra, and demonstrate improved performance over traditional filtered-backprojection and penalized-likelihood reconstruction techniques. (orig.)

112

Improved Brain Tumor Detection Using Object Based Segmentation  

Directory of Open Access Journals (Sweden)

Full Text Available This paper has focused on the brain tumor detection techniques. The brain tumor detection is a very important vision application in the medical field. This work has firstly presented a review on various well known techniques for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main objective of the work is to explore various techniques to detect brain tumor in an efficient way. It has been found that the most of existing methods has ignored the poor quality images like images with noise or poor brightness. Also the most of the existing work on tumor detection has neglected the use of object based segmentation. So to overcome the limitations of earlier work a new technique has been proposed in this research work. The technique has shown quite effective results than neural based tumor detection technique. The design and implementation of the proposed algorithm is done in MATLAB using image processing toolbox. The comparison has shown that the proposed technique has achieved up to 94 % accuracy which was 78 % in neural based technique. Also for high corrupted noisy images the proposed technique has shown quite effective results than the neural based tumor detection.

Harneet Kaur

2014-07-01

113

A Learning Object Approach To Evidence based learning  

Directory of Open Access Journals (Sweden)

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 implement a range of teaching and learning strategies that would accommodate the flexibility required by such a scheme. At the same time the specific requirements of individual programmes are satisfied. The body of elements provide an integrated path taking students through the range of operational, tactical and strategic issues involved in Web Based Learning, sustained by learning object abstract framework and Agent technology, within a distant learning context.

Zabin Visram

2005-06-01

114

Geographic Object-Based Image Analysis – Towards a new paradigm  

OpenAIRE

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)...

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

2014-01-01

115

Automated object-based classification of topography from SRTM data  

OpenAIRE

We introduce an object-based method to automatically classify topography from SRTM data. The new method relies on the concept of decomposing land-surface complexity into more homogeneous domains. An elevation layer is automatically segmented and classified at three scale levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation is performed at these appropriate s...

Dra?gut?, Lucian; Eisank, Clemens

2012-01-01

116

Modal Logics for Reasoning about Object-based Component Composition  

OpenAIRE

Component-oriented development of software supports the adaptability and maintainability of large systems, in particular if requirements change over time and parts of a system have to be modified or replaced. The software architecture in such systems can be described by components and their composition. In order to describe larger architectures, the composition concept becomes crucial. We will present a formal framework for component composition for object-based software development. The d...

Pahl, Claus

2000-01-01

117

An object-based approach to medical process automation.  

OpenAIRE

The medical events of providers rendering services for patient care are necessarily interrelated. A clinical information system must reliably record these events and relate the information about their inter-dependency. The quality of clinical information therefore depends crucially on the proper coordination and tracking of these events according to established protocols. We introduce an object-based approach to define medical processes for their automation. For each medical process, we captu...

Gangopadhyay, D.; Wu, P. Y.

1993-01-01

118

Efficient Model-Based 3D Tracking of Deformable Objects  

OpenAIRE

Efficient incremental image alignment is a topic of renewed interest in the computer vision community because of its applications in model fitting and model-based object tracking. Successful compositional procedures for aligning 2D and 3D models under weak-perspective imaging conditions have already been proposed. Here we present a mixed compositional and additive algorithm which is applicable to the full projective camera case.

Mun?oz, Enrique; Buenaposada Biencinto, Jose? Miguel; Baumela Molina, Luis

2005-01-01

119

Geographic Object-Based Image Analysis – Towards a new paradigm  

Science.gov (United States)

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

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

120

Graph - Based High Resolution Satellite Image Segmentation for Object Recognition  

Science.gov (United States)

Object based image processing and analysis is challenging research in very high resolution satellite utilisation. Commonly ei ther pixel based classification or visual interpretation is used to recognize and delineate land cover categories. The pixel based classification techniques use rich spectral content of satellite images and fail to utilise spatial relations. To overcome th is drawback, traditional time consuming visual interpretation methods are being used operational ly for preparation of thematic maps. This paper addresses computational vision principles to object level image segmentation. In this study, computer vision algorithms are developed to define the boundary between two object regions and segmentation by representing image as graph. Image is represented as a graph G (V, E), where nodes belong to pixels and, edges (E) connect nodes belonging to neighbouring pixels. The transformed Mahalanobis distance has been used to define a weight function for partition of graph into components such that each component represents the region of land category. This implies that edges between two vertices in the same component have relatively low weights and edges between vertices in different components should have higher weights. The derived segments are categorised to different land cover using supervised classification. The paper presents the experimental results on real world multi-spectral remote sensing images of different landscapes such as Urban, agriculture and mixed land cover. Graph construction done in C program and list the run time for both graph construction and segmentation calculation on dual core Intel i7 system with 16 GB RAM, running 64bit window 7.

Ravali, K.; Kumar, M. V. Ravi; Venugopala Rao, K.

2014-11-01

121

Object-oriented programming of PLC based on IEC 1131  

OpenAIRE

The software development for programmable logical controllers is usually based on low-level languages such as the instruction list or the ladder diagram. At the same time, the programmer looks at a machine or an assembly system in a bit-oriented way: he translates the operational sequences into logical and/or time based combinations of binary signals described by the means of boolean algebra. A machine, however, does not only consist of binary signals but of technical components, i.e. objects...

Weule, Hartmut; Spath, Dieter; Schelberg, Hans-joachim

1994-01-01

122

Salient object detection: manifold-based similarity adaptation approach  

Science.gov (United States)

A saliency detection algorithm based on manifold-based similarity adaptation is proposed. The proposed algorithm is divided into three steps. First, we segment an input image into superpixels, which are represented as the nodes in a graph. Second, a new similarity measurement is used in the proposed algorithm. The weight matrix of the graph, which indicates the similarities between the nodes, uses a similarity-based method. It also captures the manifold structure of the image patches, in which the graph edges are determined in a data adaptive manner in terms of both similarity and manifold structure. Then, we use local reconstruction method as a diffusion method to obtain the saliency maps. The objective function in the proposed method is based on local reconstruction, with which estimated weights capture the manifold structure. Experiments on four bench-mark databases demonstrate the accuracy and robustness of the proposed method.

Zhou, Jingbo; Ren, Yongfeng; Yan, Yunyang; Gao, Shangbing

2014-11-01

123

A mobile agent-based moving objects indexing algorithm in location based service  

Science.gov (United States)

This paper will extends the advantages of location based service, specifically using their ability to management and indexing the positions of moving object, Moreover with this objective in mind, a mobile agent-based moving objects indexing algorithm is proposed in this paper to efficiently process indexing request and acclimatize itself to limitation of location based service environment. The prominent feature of this structure is viewing moving object's behavior as the mobile agent's span, the unique mapping between the geographical position of moving objects and span point of mobile agent is built to maintain the close relationship of them, and is significant clue for mobile agent-based moving objects indexing to tracking moving objects.

Fang, Zhixiang; Li, Qingquan; Xu, Hong

2006-10-01

124

CONTENT BASED IMAGE RETRIEVAL - EXTRACTION BY OBJECTS OF USER INTEREST  

Directory of Open Access Journals (Sweden)

Full Text Available Content-based image retrieval (CBIR systems normally return the retrieval results according to the similarity between features extracted from the query and candidate images. In certain circumstances, however users are most qualified to specify the query “content” or objects(e.g., Eiffel Tower of their interest , not the computer and only wish to retrieve images containing relevant objects, while ignoring irrelevant image areas (such as the background. Previous work on this normally requires complicated segmentation of the object from the background. In this paper, the user can select “object of user’s interest” of different shapes, non homogenous texture containing different colors regardless of manyobjects present in the same image using varied tools like polygonal, rectangle, circle selector tools. A twostate procedure is used to query the image from the Image database. First, we integrate global color and texture feature vectors to narrow down the search space and in next state we Process using local features. We use color moments and subband statistics of wavelet decomposition as color and texture features respectively. The shape features, generated by mathematical morphology operators, are further employed to produce the final retrieval results.

Mr. D. Kishore Kumar

2011-03-01

125

A detection method of moving object based on hybrid difference  

Science.gov (United States)

The detection method is based on background subtraction and inter-frame difference. To use statistical model of RGB color histograms to extracting background. In this way, the initial background image could be extracted without noise effect to a great extent. To get difference image of moving object according to the results of background subtraction and three frames difference. To get binary Image A which difference from Frame k-1 and Frame k, to get Image B which difference from Frame k and Frame k+1. Let Image A and Image B do LOR operation to get Image C for obtaining more information of the moving object. Finally, let binary image of background subtraction and Image C do LAND operation to get outline of moving object. To use self-adaption method updates background image to promise the instantaneity. If a pixel of the current frame is estimated as moving target, we set the corresponding pixel of current background image to instead of the pixel in background image, else set the corresponding pixel of current frame to update the corresponding pixel of background. To use background updating factor ? to control update rate. Moving object can be detected more accurately by mathematical morphology. This method can improve the shortcomings of background subtraction and inter-frame difference.

Wang, Chuncai; Yan, Lei; Li, Yingtao

2014-11-01

126

Filling-Based Techniques Applied to Object Projection Feature Estimation  

CERN Document Server

3D motion tracking is a critical task in many computer vision applications. Unsupervised markerless 3D motion tracking systems determine the most relevant object in the screen and then track it by continuously estimating its projection features (center and area) from the edge image and a point inside the relevant object projection (namely, inner point), until the tracking fails. Existing object projection feature estimation techniques are based on ray-casting from the inner point. These techniques present three main drawbacks: when the inner point is surrounded by edges, rays may not reach other relevant areas; as a consequence of that issue, the estimated features may greatly vary depending on the position of the inner point relative to the object projection; and finally, increasing the number of rays being casted and the ray-casting iterations (which would make the results more accurate and stable) increases the processing time to the point the tracking cannot be performed on the fly. In this paper, we anal...

Quesada, Luis

2012-01-01

127

Automated object-based classification of topography from SRTM data  

Science.gov (United States)

We introduce an object-based method to automatically classify topography from SRTM data. The new method relies on the concept of decomposing land-surface complexity into more homogeneous domains. An elevation layer is automatically segmented and classified at three scale levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation is performed at these appropriate scales. Objects resulting from segmentation are partitioned into sub-domains based on thresholds given by the mean values of elevation and standard deviation of elevation respectively. Results resemble reasonably patterns of existing global and regional classifications, displaying a level of detail close to manually drawn maps. Statistical evaluation indicates that most of classes satisfy the regionalization requirements of maximizing internal homogeneity while minimizing external homogeneity. Most objects have boundaries matching natural discontinuities at regional level. The method is simple and fully automated. The input data consist of only one layer, which does not need any pre-processing. Both segmentation and classification rely on only two parameters: elevation and standard deviation of elevation. The methodology is implemented as a customized process for the eCognition® software, available as online download. The results are embedded in a web application with functionalities of visualization and download.

Dr?gu?, Lucian; Eisank, Clemens

2012-03-01

128

Object detection based on multiscale discrete points sampling and grouping  

Science.gov (United States)

The topology information of a grey picture can be carried by a series of discrete points with a different number of points representing different dimensions of information. Our human brain can easily group these points and perceive information with different dimensions. In this paper, we present how to use multi-scale discrete points to segment, extrude and detect objects. Those points are divided into yin points and yang points, according to their position in a digital picture. We also use a method based on peripheral direction contributions to group these points for the purpose of round building detection and road detection. Primary experimental results show that our ways and means can be used in several representative applications of object detection with a laudable performance.

Zhu, Zongxiao; Wang, Guoyou; Liu, Jianguo

2009-10-01

129

Multiview-Based Cooperative Tracking of Multiple Human Objects  

Directory of Open Access Journals (Sweden)

Full Text Available Abstract Human tracking is a popular research topic in computer vision. However, occlusion problem often complicates the tracking process. This paper presents the so-called multiview-based cooperative tracking of multiple human objects based on the homographic relation between different views. This cooperative tracking applies two hidden Markov processes (tracking and occlusion processes for each target in each view. The tracking process locates the moving target in each view, whereas the occlusion process represents the possible visibility of the specific target in that designated view. Based on the occlusion process, the cooperative tracking process may reallocate tracking resources for different trackers in different views. Experimental results show the efficiency of the proposed method.

Lien Kuo-Chin

2008-01-01

130

A Quaternionic Wavelet Transform-based Approach for Object Recognition  

Directory of Open Access Journals (Sweden)

Full Text Available Recognizing the objects in complex natural scenes is the challenging task as the object may be occluded, may vary in shape, position and in size. In this paper a method to recognize objects from different categories of images using quaternionic wavelet transform (QWT is presented. This transform separates the information contained in the image better than a traditional Discrete wavelet transform and provides a multiscale image analysis whose coefficients are 2D analytic, with one near-shift invariant magnitude and three phases. The two phases encode local image shifts and the third one contains texture information. In the domain of object recognition, it is often to classify objects from images that make only limited part of the image. Hence to identify local features and certain region of images, patches are extracted over the interest points detected from the original image using Wavelet based interest point detector. Here QWT magnitude and phase features are computed for every patch. Then these features are trained, tested and classified using SVM classifier in order to have supervised learning model. In order to compare the performance of local feature with global feature, the transform is applied to the entire image and the global features are derived. The performance of QWT is compared with discrete wavelet transform (DWT and dual tree discrete wavelet transform (DTDWT. Observations revealed that QWT outperforms the DWT and shift invariant DTDWT with lesser equal error rate. The experimental evaluation is done using the complex Graz databases.Defence Science Journal, Vol. 64, No. 4, July 2014, pp. 350-357, DOI:http://dx.doi.org/10.14429/dsj.64.4503

R. Ahila Priyadharshini

2014-07-01

131

Analysis of manufacturing based on object oriented discrete event simulation  

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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.

Eirik Borgen

1990-01-01

132

Cloud Aggregation and Bursting for Object Based Sharable Environment  

Directory of Open Access Journals (Sweden)

Full Text Available Cloud computing promises innate scalability and high availability at low cost. So far cloud storage deployments were subject to big companies but an increasing amount of available open-source systems allow also smaller private cloud installations. In this paper we discuss cloud aggregation and cloud bursting with their empirical review. Based on the review we map class and object in the sharable small clouds for making clouds more efficient. We also consider some of the security concern for the cloud computing for authorized data sharing between clouds.

Mr. Pradeep Kumar Tripathi

2011-09-01

133

Adaptive color correction based on object color classification  

Science.gov (United States)

An adaptive color management strategy depending on the image contents is proposed. Pictorial color image is classified into different object areas with clustered color distribution. Euclidian or Mahalanobis color distance measures, and maximum likelihood method based on Bayesian decision rule, are introduced to the classification. After the classification process, each clustered pixels are projected onto principal component space by Hotelling transform and the color corrections are performed for the principal components to be matched each other in between the individual clustered color areas of original and printed images.

Kotera, Hiroaki; Morimoto, Tetsuro; Yasue, Nobuyuki; Saito, Ryoichi

1998-09-01

134

Cloud Aggregation and Bursting for Object based Sharable Environment  

Directory of Open Access Journals (Sweden)

Full Text Available Cloud computing promises innate scalability and high availability at low cost. So far cloud storage deployments were subject to big companies but an increasing amount of available open-source systems allow also smaller private cloud installations. In this paper we discuss cloud aggregation and cloud bursting with their empirical review. Based on the review we map class and object in the sharable small clouds for making clouds more efficient. We also consider some of the security concern for the cloud computing for authorized data sharing between clouds.

Pradeep Kumar Tripathi

2011-09-01

135

An Object-oriented Design and Push Web Server based Framework for Physical Object Interactions and Services  

Directory of Open Access Journals (Sweden)

Full Text Available One of the substantial issues in ubiquitous computing is the automatic processing of information from real world objects and enabling their interactions in the background. This scenario requires a framework on which such information processing and object interaction can be supported. This article presents our research progress in developing a framework based on the object-oriented design approach and the use of a push web server. With the concept of object abstraction, an object can hide its internal structure from the outside world, which can make the object secure. Moreover, object interaction can be conducted via message exchanges, which makes the interface simple and standardized and the heterogeneous objects can be easily handled as well. Instead of using the traditional pull web server, a push web server, i.e., Comet, which runs on top of HTTP protocol is used to exchange messages. To this, object interactions can be operated smoothly and seamlessly in real time with shorter delay.

Runhe Huang

2008-11-01

136

Interactive object modelling based on piecewise planar surface patches.  

Science.gov (United States)

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

Prankl, Johann; Zillich, Michael; Vincze, Markus

2013-06-01

137

Interactive object modelling based on piecewise planar surface patches?  

Science.gov (United States)

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

Prankl, Johann; Zillich, Michael; Vincze, Markus

2013-01-01

138

Toward an efficient objective metric based on perceptual criteria  

Science.gov (United States)

Quality assessment is a very challenging problem and will still as is since it is difficult to define universal tools. So, subjective assessment is one adapted way but it is tedious, time consuming and needs normalized room. Objective metrics can be with reference, with reduced reference and with no-reference. This paper presents a study carried out for the development of a no-reference objective metric dedicated to the quality evaluation of display devices. Initially, a subjective study has been devoted to this problem by asking a representative panel (15 male and 15 female; 10 young adults, 10 adults and 10 seniors) to answer questions regarding their perception of several criteria for quality assessment. These quality factors were hue, saturation, contrast and texture. This aims to define the importance of perceptual criteria in the human judgment of quality. Following the study, the factors that impact the quality evaluation of display devices have been proposed. The development of a no-reference objective metric has been performed by using statistical tools allowing to separate the important axes. This no-reference metric based on perceptual criteria by integrating some specificities of the human visual system (HVS) has a high correlation with the subjective data.

Quintard, Ludovic; Larabi, Mohamed-Chaker; Fernandez-Maloigne, Christine

2008-01-01

139

OBJECT BASED SEGMENTATION TECHNIQUES FOR CLASSIFICATION OF SATELLITE IMAGE  

Directory of Open Access Journals (Sweden)

Full Text Available Automatic feature extraction of features such as building, roads, vegetation etc., which also includes collapsed, height-changed and removed buildings, require the use of high resolution images. This research is helpful to develop a technique that can detect changes in urban buildings after natural disasters such as earthquakes, typhoons or tsunamis. This is also helpful in providing an accurate information about the changes for urban planning and updating Geo-spatial information system (GIS.It is necessary that the remote sensing imagery has to be converted into some meaning information, which in turn requires some segmentation methods followed by classification. Earlier pixel based approaches was followed which requires more computation on these high resolution images .This paper proposes few object based method (K-means , KFCM , Moving KFCMfor classification that segments the image followed by classification.

B.Ankayarkanni

2014-07-01

140

Objective Mixed and Manually Controlled Data Base OMG  

Science.gov (United States)

Many customers of Deutscher Wetterdienst get forecast products (different deterministic meteorological parameters) as so-called Point-Time-Prognoses (PTPs). There's the need for data of high resolution in space (several kilometers, cities) and time (hourly up to forecast-day 7, updated each hour). Those interpretations of the numerical models have been designed by different methods, called DMO, Model Output Statistics (MOS), Kalman filtering. Because of different model results and different methods there are between five and ten different PTPs for the same location. While a forecaster, based on his experience, can choose between that different interpretations, an automatic customers supply can't do so. Because updates of those interpretations are only available for each new model run, there's also the need for more actual forecast data, using nowcasting methods as well as controlling and modifying mechanisms for the forecasters. A process "Objective Optimization” has been implemented which automatically combines the available PTP data, nowcasting products and observations at all required locations. A frequent update of this optimization process every 30 minutes ensures that the OOG is based on the latest available model, nowcasting, and observational information. The resulting so-called "Objectively Optimized Guidance” (OOG) passes an interactive quality control step to produce a "Man Modified output” (MMO). The corresponding tool "MMO-Editor” provides point editing functions together with automatic quality checks and corrections. The combination of automatic data merging (OOG) and quality check with optional manual corrections (MMO) aims at providing a single quality ensured data base for subsequent automatic customer supply. The presentation describes the methods, the "status quo” and the plans of implementation a data base called OMG.

Kratzsch, T.; Rohn, M.

2009-09-01

141

Model-based reconstruction of objects with inexactly known components  

Science.gov (United States)

Because tomographic reconstructions are ill-conditioned, algorithms that incorporate additional knowledge about the imaging volume generally have improved image quality. This is particularly true when measurements are noisy or have missing data. This paper presents a general framework for inclusion of the attenuation contributions of specific component objects known to be in the field-of-view as part of the reconstruction. Components such as surgical devices and tools may be modeled explicitly as being part of the attenuating volume but are inexactly known with respect to their locations poses, and possible deformations. The proposed reconstruction framework, referred to as Known-Component Reconstruction (KCR), is based on this novel parameterization of the object, a likelihood-based objective function, and alternating optimizations between registration and image parameters to jointly estimate the both the underlying attenuation and unknown registrations. A deformable KCR (dKCR) approach is introduced that adopts a control pointbased warping operator to accommodate shape mismatches between the component model and the physical component, thereby allowing for a more general class of inexactly known components. The KCR and dKCR approaches are applied to low-dose cone-beam CT data with spine fixation hardware present in the imaging volume. Such data is particularly challenging due to photon starvation effects in projection data behind the metallic components. The proposed algorithms are compared with traditional filtered-backprojection and penalized-likelihood reconstructions and found to provide substantially improved image quality. Whereas traditional approaches exhibit significant artifacts that complicate detection of breaches or fractures near metal, the KCR framework tends to provide good visualization of anatomy right up to the boundary of surgical devices.

Stayman, J. W.; Otake, Y.; Schafer, S.; Khanna, A. J.; Prince, J. L.; Siewerdsen, J. H.

2012-03-01

142

Detection of windthrow areas by object based image segmentation  

Science.gov (United States)

In high resolution aerial images, areas that are uniform from the view of the application are not represented by an average spectral pattern, but are resolved into their components. While this enhanced information content offers the possibility of a more differentiating and correct classification, the classical spectral classification of single pixels comes up against its limits. Image analysis methods that take into account local neighborhood characteristics (edges, textures) can help to some extent, but deliver crumbled information that needs additional treatment. The new method of object based multispectral image segmentation (software "eCognition") promises a sulution. In a first step, the image is segmented into areas that are "looking" uniform, with respect to spectral, textural and shape properties. For each area, some characteristic values are calculated. In the second step, the segments are classified according to these attributes. The classification can be refined by giving training areas and previous knowledge (fuzzy class membership functions). In a third step, the classification can be improved by iterative application of neighbourhood criteria. In this work, the object based segmentation approach is applied to the detection of windthrow areas in multispectral images gained by an airborne survey with a digital line scanner. The characteristic pattern of lying trees, that is obvious to the human observer, can be detected in this way. Additionally, foreground objects (clouds) and settelement areas, which must be excluded, can be found. The derivated damage pattern can be used for an analysis of orographical influence on storm damage to forests in mountain areas (contribution of J. Schmoeckel and Ch. Kottmeier).

Schmoeckel, J.; Kauffmann, M.

2003-04-01

143

Power System Information Delivering System Based on Distributed Object  

Science.gov (United States)

In recent years, improvement in computer performance and development of computer network technology or the distributed information processing technology has a remarkable thing. Moreover, the deregulation is starting and will be spreading in the electric power industry in Japan. Consequently, power suppliers are required to supply low cost power with high quality services to customers. Corresponding to these movements the authors have been proposed SCOPE (System Configuration Of PowEr control system) architecture for distributed EMS/SCADA (Energy Management Systems / Supervisory Control and Data Acquisition) system based on distributed object technology, which offers the flexibility and expandability adapting those movements. In this paper, the authors introduce a prototype of the power system information delivering system, which was developed based on SCOPE architecture. This paper describes the architecture and the evaluation results of this prototype system. The power system information delivering system supplies useful power systems information such as electric power failures to the customers using Internet and distributed object technology. This system is new type of SCADA system which monitors failure of power transmission system and power distribution system with geographic information integrated way.

Tanaka, Tatsuji; Tsuchiya, Takehiko; Tamura, Setsuo; Seki, Tomomichi; Kubota, Kenji

144

Multi-objective reliability-based optimization with stochastic metamodels.  

Science.gov (United States)

This paper addresses continuous optimization problems with multiple objectives and parameter uncertainty defined by probability distributions. First, a reliability-based formulation is proposed, defining the nondeterministic Pareto set as the minimal solutions such that user-defined probabilities of nondominance and constraint satisfaction are guaranteed. The formulation can be incorporated with minor modifications in a multiobjective evolutionary algorithm (here: the nondominated sorting genetic algorithm-II). Then, in the perspective of applying the method to large-scale structural engineering problems--for which the computational effort devoted to the optimization algorithm itself is negligible in comparison with the simulation--the second part of the study is concerned with the need to reduce the number of function evaluations while avoiding modification of the simulation code. Therefore, nonintrusive stochastic metamodels are developed in two steps. First, for a given sampling of the deterministic variables, a preliminary decomposition of the random responses (objectives and constraints) is performed through polynomial chaos expansion (PCE), allowing a representation of the responses by a limited set of coefficients. Then, a metamodel is carried out by kriging interpolation of the PCE coefficients with respect to the deterministic variables. The method has been tested successfully on seven analytical test cases and on the 10-bar truss benchmark, demonstrating the potential of the proposed approach to provide reliability-based Pareto solutions at a reasonable computational cost. PMID:21281119

Coelho, Rajan Filomeno; Bouillard, Philippe

2011-01-01

145

Locally accurate motion estimation for object-based video coding  

Science.gov (United States)

We describe new motion coding algorithms that develop fixed size block matching (FSBM) into variable sized block matching (VSBM) and a modified approach (MVSBM) which can exploit irregularly shaped areas of uniform motion. New generations of video coding standards handle arbitrary shaped visual objects as well as frame based input. We explain how MVSBM strategies work when combining shaped data and block based algorithms. Locally accurate motion information is produced by the combination of otherwise ambiguous estimates produced by the small area matching required to detect locally diverse movements. The success of various prediction strategies indicates that the motion information is well behaved and thus likely to be accurate, given complex natural image sequence source material. MVSBM is evaluated by forcing it to perform with the same quality prediction as FSBM, then comparing the number of bits required by each technique. FSBM vector coding methods are taken from H.263 and MPEG-4 for comparison with those developed for MVSBM, extra compression phases are developed for MVSBM by utilizing the greater structure of the representation. Results are presented for three MPEG-4 test sequences, 'Container', 'weather' and 'Stefan'. We show bit savings of 67 percent for 'container', and 13 percent for 'Stefan' with more complex object activity.

Steliaros, Michael K.; Martin, Graham R.; Packwood, Roger A.

1998-01-01

146

A new two-level efficient technique for 2D image patches registration via optimized cross correlation, wavelet transform, and moments of inertia with application to ladar imaging  

Science.gov (United States)

A new fast feature-based approach for efficient and accurate automated image registration with applications to multiple-views or Multi-sensor LADAR imaging is presented. As it is known, highly accurate and efficient Image registration is highly needed and desired in ground or Airborne LADAR imaging. The proposed approach is two-fold: First, direct comparison of sub-image patches of the overlapping images is performed applying the normalized cross-correlation technique. A pre-specified window sub-image patch size is used to speed up the matching process. In particular, a 65x65 window is defined in the right 50% of the left image (reference image) then, a matching window in the left 50% of the right image (unregistered image) is searched. The beauty of this approach is that the original images are reduced to small and similar sub-image patches of size 65x65, reducing tremendously the computation time of the matching point pairs search process, which as we show, speeds up tremendously the derivation of the matching points pairs described in the next phase. Second, Wavelet transform is applied then to the small and similar sub-image patches to extract a number of matching feature points. Each feature point is an edge point whose edge response is the maximum within a neighborhood. The normalized cross correlation technique is applied again this time to find the matching pairs between the feature points. From the matching pairs, the moments of inertia are applied to estimate the rigid transformation parameters between the overlapping images. In general, the overlapping images can have an arbitrarily large orientation difference. Therefore, this angle must be found first to correct the unregistered image. In order to estimate the rotation angle, we show how a so-called "angle histogram" is derived and calculated. The rotation angle selected is the one that corresponds to the maximum peak in the angle histogram. We show how the proposed approach is of an order of magnitude faster than the existing methods, on a single-processor computer. We show also that the proposed approach is automatic, robust, and can work with any partially overlapping images rotated from each other. Experimental results using rotated and non-rotated images are presented.

Bejar, Carlos; Megherbi, Dalila B.

2005-08-01

147

Object Persistence: A Framework Based On Design Patterns  

OpenAIRE

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.

Kienzle, Jo?rg; Romanovsky, Alexander

2000-01-01

148

Implementation and Comparison of Kernel and Silhouette Based Object Tracking  

Directory of Open Access Journals (Sweden)

Full Text Available Object tracking in video sequences is one of the important ongoing exploration areas in the field of computer vision. Computer vision is an arena that comprises methods for acquiring, processing, analyzing images and also covers the essential technology of automatic image analysis which is used in various fields. The aim of object tracking is to find the trajectory of the target objects through a number of frames from an image sequence. Object Tracking is identification of interesting object, especially on tracking of walkers or moving vehicles. Tracking is an interesting problem owing to, object occlusion, varying of illumination, unexpected object motion and camera motion. Normally many algorithms were developed for successful tracking. Object Tracking is mainly classified of three stages: object extraction, object recognition and tracking, and decisions about activities. In this paper we have implemented some algorithms and comparison table are analyzed.

Mr. Joshan Athanesious J , Mr. Suresh P

2013-04-01

149

RFID and IP Based Object Identification in Ubiquitous Networking  

OpenAIRE

Ubiquitous networking is an integrated part of future networking technology that can provide capabilities for connecting all of objects (computers, human, PDAs, cell phones etc.) in future network. It has to meet the challenge of seamless connection for communication between human and objects in internet infrastructure. Unique object identification is very much important to make the communication between objects possible. RFID tag can be used as unique identifier to identify a physical object...

Nisha Vaghela; Parikshit Mahalle

2012-01-01

150

Underwater object detection technology based on polarization image fusion  

Science.gov (United States)

The performance of the traditional underwater optical imaging systems is ultimately limited by the absorption and scattering properties of the water substance. Polarimetric imaging can be used to remove degradation effects, and can be applied to high-level vision tasks, such as object classification and recognition and camouflage identification, etc. The method of improving contrast was presented by polarization imaging. The polarization images when the angle of polarization are 0°, 45°, 90° and 135°, the gray levels of the images are calculated by program. There are much complementary and redundancy information among the polarization images. According to the character of parameter I of stokes vector, degree of linear polarization (DoLP) and the angle of polarization (AoP), A RGB false color based polarimetric images fusion are given to enhance the contrast.

Li, Yongguo; Wang, Shiming

2010-10-01

151

On Objective Estimation Algorithm Based on PCA Image Sparse  

Directory of Open Access Journals (Sweden)

Full Text Available Since it is usually unknown the over-sampling and under-sampling of image sparse in image compression, thereby can not verify the merits of sampling and reconstruction algorithm. Therefore it studies the signal sparse K estimation method of compressive sensing and proposes the objective estimation algorithm based on PCA image sparse which combined forward projection transformation theory and principal component transformation (PCA method. In this paper, it creates image sparse and a linear relationship between the variance of coefficient function through the elaboration of compressive sensing theory to PCA and under the assumption that the principal component transform coefficient is approximately normal function. Experimental results show that: the proposed algorithm possess advantages of fast, low complexity and so on

Ran Yu

2014-03-01

152

Model-Based Object Tracking in Cluttered Scenes with Occlusions  

OpenAIRE

We propose an efficient method for tracking 3D modelled objects in cluttered scenes. Rather than tracking objects in the image, our approach relies on the object recognition aspect of tracking. Candidate matches between image and model features define volumes in the space of transformations. The volumes of the pose space satisfying the maximum number of correspondences are those that best align the model with the image. Object motion defines a trajectory in the pose space. We give some result...

Jurie, Fre?de?ric

1997-01-01

153

Resultados a curto prazo de ceratotomia lamelar pediculada (LASIK para correção de hipermetropia com o sistema Ladar Vision de excimer laser Short-term results of hyperopic laser in situ keratomileusis (LASIK with the Ladar Vision excimer laser system  

Directory of Open Access Journals (Sweden)

Full Text Available OBJETIVO: Avaliar a eficácia e a segurança do LASIK (ceratotomia lamelar pediculada hipermetrópico utilizando-se o sistema Ladar Vision de excimer laser. MÉTODOS: Foram analisados, retrospectivamente, 28 olhos de 17 pacientes com hipermetropia de +1,00 a +3,00 D (grupo 1 e 29 olhos de 18 pacientes com hipermetropia de + 3,25 a + 6,00 D (grupo 2, submetidos à cirurgia de LASIK, com o Sistema Ladar Vision de excimer laser. Acuidade visual sem correção, melhor acuidade visual corrigida e refração sob cicloplegia foram avaliadas em um, três e seis meses de pós-operatório. RESULTADOS: No grupo 1, o equivalente esférico médio pré-operatório, sob cicloplegia, era de + 2,14 ± 0,64 D, passando para + 0,44 ± 0,38 D no sexto mês de pós-operatório. No grupo 2, o equivalente esférico médio pré-operatório era de +4,26 ± 0,75 D, diminuindo para +1,14 ± 0,63 D no sexto mês de pós-operatório. 3,4% dos olhos do grupo 2 perderam três linhas de visão no primeiro mês de pós-operatório. No grupo 1, não houve perda de duas ou mais linhas de visão. CONCLUSÕES: O LASIK hipermetrópico com o sistema Ladar Vision mostrou-se procedimento eficaz e seguro. Pacientes do grupo 2 parecem estar sob maior risco de perda de linhas de melhor acuidade visual corrigida no pós-operatório.PURPOSE: To analyze the efficacy and safety of hyperopic laser in situ keratomileusis using the Ladar Vision excimer laser system. METHODS: Twenty-eight eyes of 17 patients with hyperopia from +1.00 to +3.00 D (group 1, and 29 eyes of 18 patients with hyperopia from +3.25 to +6.00 D (group 2 that had LASIK for hyperopia with the Ladar Vision, were retrospectively analyzed. Uncorrected visual acuity, best spectacle-corrected visual acuity and cycloplegic refraction were evaluated 1 , 3 and 6 months after surgery. RESULTS: In group 1, the mean preoperative cycloplegic spherical equivalent (SE was +2.14 ± 0.64 D and 6-month postoperative SE was +0.44 ± 0.38 D. In group 2, the mean preoperative SE was +4.26 ± 0.75 D and the 6-month postoperative SE was +1.14 ± 0.63 D. 3.4% of the eyes in group 2 and none of the eyes in group 1 lost 2 or more lines of best spectacle-corrected visual acuity in the first postoperative month. CONCLUSIONS: LASIK with the Ladar Vision excimer laser system is an effective and safe procedure to correct hyperopia. Patients in group 2 appear to be at greater risk for loss of lines of best spectacle-corrected visual acuity.

Larissa Madeira Nunes

2004-02-01

154

Advances in linear and area HgCdTe APD arrays for eyesafe LADAR sensors  

Science.gov (United States)

HgCdTe APDs and APD arrays offer unique advantages for high-performance eyesafe LADAR sensors. These include: operation at room temperature, low-excess noise, high gain, high-quantum efficiency at eyesafe wavelengths, GHz bandwidth, and high-packing density. The utility of these benefits for systems are being demonstrated for both linear and area array sensors. Raytheon has fabricated 32 element linear APD arrays utilizing liquid phase epitaxy (LPE), and packaged and integrating these arrays with low-noise amplifiers. Typical better APDs configured as 50-micron square pixels and fabricated utilizing RIE, have demonstrated high fill factors, low crosstalk, excellent uniformity, low dark currents, and noise equivalent power (NEP) from 1-2 nW. Two units have been delivered to NVESD, assembled with range extraction electronics, and integrated into the CELRAP laser radar system. Tests on these sensors in July and October 2000 have demonstrated excellent functionality, detection of 1-cm wires, and range imaging. Work is presently underway under DARPA's 3-D imaging Sensor Program to extend this excellent performance to area arrays. High-density arrays have been fabricated using LPE and molecular beam epitaxy (MBE). HgCdTe APD arrays have been made in 5 X 5, 10 X 10 and larger formats. Initial data shows excellent typical better APD performance with unmultiplied dark current < 10 nA; and NEP < 2.0 nW at a gain of 10.

Jack, Michael D.; Asbrock, James F.; Anderson, C.; Bailey, Steven L.; Chapman, George; Gordon, E.; Herning, P. E.; Kalisher, Murray H.; Kosai, Kim; Liquori, V.; Randall, Valerie; Rosbeck, Joseph P.; Sen, Sanghamitra; Wetzel, P.; Halmos, Maurice J.; Trotta, Patrick A.; Hunter, Andrew T.; Jensen, John E.; de Lyon, Terence J.; Johnson, W.; Walker, B.; Trussel, Ward; Hutchinson, Andy; Balcerak, Raymond S.

2001-11-01

155

Workbench for 3D target detection and recognition from airborne motion stereo and ladar imagery  

Science.gov (United States)

3D imagery has a well-known potential for improving situational awareness and battlespace visualization by providing enhanced knowledge of uncooperative targets. This potential arises from the numerous advantages that 3D imagery has to offer over traditional 2D imagery, thereby increasing the accuracy of automatic target detection (ATD) and recognition (ATR). Despite advancements in both 3D sensing and 3D data exploitation, 3D imagery has yet to demonstrate a true operational gain, partly due to the processing burden of the massive dataloads generated by modern sensors. In this context, this paper describes the current status of a workbench designed for the study of 3D ATD/ATR. Among the project goals is the comparative assessment of algorithms and 3D sensing technologies given various scenarios. The workbench is comprised of three components: a database, a toolbox, and a simulation environment. The database stores, manages, and edits input data of various types such as point clouds, video, still imagery frames, CAD models and metadata. The toolbox features data processing modules, including range data manipulation, surface mesh generation, texture mapping, and a shape-from-motion module to extract a 3D target representation from video frames or from a sequence of still imagery. The simulation environment includes synthetic point cloud generation, 3D ATD/ATR algorithm prototyping environment and performance metrics for comparative assessment. In this paper, the workbench components are described and preliminary results are presented. Ladar, video and still imagery datasets collected during airborne trials are also detailed.

Roy, Simon; Se, Stephen; Kotamraju, Vinay; Maheux, Jean; Nadeau, Christian; Larochelle, Vincent; Fournier, Jonathan

2010-04-01

156

Distributed object computing: DEVS-based modeling and simulation  

Science.gov (United States)

This research examines an approach to modeling and simulating distributed object computer systems in terms of distributed software components mapped onto a set of interconnected network nodes. The overall model of a distributed object computing system has clearly separated hardware and software components enabling co-design engineering. The software component modules form a distributed cooperative object (DCO) model to represent interacting software objects. The hardware component models forma loosely coupled network (LCN) model of processing nodes, network gates, and communication links interconnecting them. The software objects of the DCO are then `distributed' across the processors of the LCN to form a distributed object computing system model. This approach facilitates design analysis of each of these components separately as well as the combined systems behavior. The Discrete Event System Specification formalism is used to implement dynamic models of the DCO components, LCN components, and experimental frames to analyze system behavior.

Hild, Daryl; Sarjoughian, Hessam S.; Zeigler, Bernard P.

1999-06-01

157

Robust object detection based on local similar structure statistical matching  

Science.gov (United States)

We present a robust object detection method to detect generic objects with incompact, complex and changeable shapes without training. First, we build a composite template set, which contains changeful shapes, scales and viewpoints of an interested object class, extract the local structure features from the composite template set and simplify them to construct a non-similar local structure feature set of the object class. Then, we propose a matching method of local similar structure statistical matching (LSSSM) to obtain the similarity image from a test image to the local structure feature set. Finally, we use the method of non-maxima suppression in the similarity image to extract the object position and mark the object in the test image. The experimental results demonstrate that our approach performs effectively on the face and infrared human body detection.

Luo, Feiyang; Han, Jing; Qi, Wei; Zhang, Yi; Bai, Lianfa

2015-01-01

158

Object Recognition Algorithm Utilizing Graph Cuts Based Image Segmentation  

Directory of Open Access Journals (Sweden)

Full Text Available This paper concentrates on designing an object recognition algorithm utilizing image segmentation. The main innovations of this paper lie in that we convert the image segmentation problem into graph cut problem, and then the graph cut results can be obtained by calculating the probability of intensity for a given pixel which is belonged to the object and the background intensity. After the graph cut process, the pixels in a same component are similar, and the pixels in different components are dissimilar. To detect the objects in the test image, the visual similarity between the segments of the testing images and the object types deduced from the training images is estimated. Finally, a series of experiments are conducted to make performance evaluation. Experimental results illustrate that compared with existing methods, the proposed scheme can effectively detect the salient objects. Particularly, we testify that, in our scheme, the precision of object recognition is proportional to image segmentation accuracy

Zhaofeng Li

2014-02-01

159

Objective Based Flexible Business Process Management Using the Map Model  

OpenAIRE

In the proposal, a flexible business process management axed on the objective concept and for the process lifecycle is presented. The main feature of this approach is that the map model is used as the key element to drive the construction and execution of flexible business processes. An analysis phase starts with a model which fully considers the objective and sub-objectives of the business process, when defining it. A design phase uses the map model for specifying and representing the possib...

Bentellis, A.; Boufaida, Z.

2009-01-01

160

Partial Evaluation for Class-Based Object-Oriented Languages  

DEFF Research Database (Denmark)

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.

Schultz, Ulrik Pagh

2001-01-01

161

Patch-based models for visual object classes  

OpenAIRE

This thesis concerns models for visual object classes that exhibit a reasonable amount of regularity, such as faces, pedestrians, cells and human brains. Such models are useful for making “within-object” inferences such as determining their individual characteristics and establishing their identity. For example, the model could be used to predict the identity of a face, the pose of a pedestrian or the phenotype of a cell and segment parts of a human brain. Existing object m...

Aghajanian, J.

2011-01-01

162

Automatic modelling image represented objects using a statistic based approach  

OpenAIRE

This paper presents new methodologies to automatically extract significant points, from an object represented in images, useful to construct Point Distribution Models. Each model consists of a flexible shape template, describing how significant points of the object can vary, and a statistical model of the expected grey levels in regions around each model point. This information can be used to search objects in new images: Active Shape and Active Appearance Models. Both use PDMs for image anal...

Maria João Medeiros de Vasconcelos; João Manuel Ribeiro da Silva Tavares

2005-01-01

163

Model Based Fault Isolation for Object-Oriented Control Systems  

OpenAIRE

This report addresses the problem of fault propagation between software modules in a large industrial control system with anobject oriented architecture. There exists a conflict between object-oriented design goals such as encapsulation and modularity, and the possibility to suppress propagating error conditions. When an object detects an error condition, it is not desirable toperform the extensive querying of other objects that would be necessary to decide how close to the real fault the obj...

Larsson, Magnus; Klein, Inger; Lawesson, Dan; Nilsson, Ulf

1999-01-01

164

Wandering: A Web-Based Platform for the Creation of Location-Based Interactive Learning Objects  

Science.gov (United States)

Wandering is an innovative web-based platform that was designed to facilitate outdoor, authentic, and interactive learning via the creation of location-based interactive learning objects (LILOs). Wandering was integrated as part of a novel environmental education program among middle school students. This paper describes the Wandering platform's…

Barak, Miri; Ziv, Shani

2013-01-01

165

Feature based recognition of submerged objects in holographic imagery  

Science.gov (United States)

The ability to autonomously sense and characterize underwater objects in situ is desirable in applications of unmanned underwater vehicles (UUVs). In this work, underwater object recognition was explored using a digital holographic system. Two experiments were performed in which several objects of varying size, shape, and material were submerged in a 43,000 gallon test tank. Holograms were collected from each object at multiple distances and orientations, with the imager located either outside the tank (looking through a porthole) or submerged (looking downward). The resultant imagery from these holograms was preprocessed to improve dynamic range, mitigate speckle, and segment out the image of the object. A collection of feature descriptors were then extracted from the imagery to characterize various object properties (e.g., shape, reflectivity, texture). The features extracted from images of multiple objects, collected at different imaging geometries, were then used to train statistical models for object recognition tasks. The resulting classification models were used to perform object classification as well as estimation of various parameters of the imaging geometry. This information can then be used to inform the design of autonomous sensing algorithms for UUVs employing holographic imagers.

Ratto, Christopher R.; Beagley, Nathaniel; Baldwin, Kevin C.; Shipley, Kara R.; Sternberger, Wayne I.

2014-05-01

166

The Emergence of Kind-Based Object Individuation in Infancy  

Science.gov (United States)

Four experiments investigated whether 12-month-old infants use perceptual property information in a complex object individuation task, using the violation-of-expectancy looking time method (Xu, 2002; Xu & Carey, 1996). Infants were shown two objects with different properties emerge and return behind an occluder, one at a time. The occluder was…

Xu, Fei; Carey, Susan; Quint, Nina

2004-01-01

167

Multimedia Learning Systems Based on IEEE Learning Object Metadata (LOM).  

Science.gov (United States)

One of the "hottest" topics in recent information systems and computer science is metadata. Learning Object Metadata (LOM) appears to be a very powerful mechanism for representing metadata, because of the great variety of LOM Objects. This is on of the reasons why the LOM standard is repeatedly cited in projects in the field of eLearning Systems.…

Holzinger, Andreas; Kleinberger, Thomas; Muller, Paul

168

Video Object Matching Based on SIFT and Rotation Invariant LBP  

Directory of Open Access Journals (Sweden)

Full Text Available Object detection and tracking is an essential preliminary task in event analysis systems (e.g. Visual surveillance.Typically objects are extracted and tagged, forming representative tracks of their activity. Tagging is usually performed by probabilistic data association. However, as data may have been collected at different times or in different locations, it is often impossible to establish such associations in systems capturing disjoint areas. In this case, appearance matching is a valuable aid. This paper proposes a object matching method for multi-camera by combining HOG and block LBP, and computes accuracy rate by SVM. Using independent tracks of 30 different persons, we show that the proposed representation effectively discriminates visual object and that it presents high resilience to incorrect object segmentation and illumination. Experimental results show that the average accuracy

Deng Yi

2013-10-01

169

Adaptability Evaluation of Enterprise Information Systems Based on Object-based Knowledge Mesh  

OpenAIRE

To apply enterprise information systems more widely, it is necessary to evaluate their adaptability. In this paper, firstly, an index set of adaptability evaluation system based on object-based knowledge mesh (OKM) is proposed and then, according to goal-question-metrics (GQM) for enterprise information systems, and the quantitative measurement is given. Second, based on similarity to ideal solution (TOPSIS), the evaluation model is built and the adaptability evaluation (AE) algorithm is prop...

Chaogai Xue

2013-01-01

170

Exploiting database technology for object based event storage and retrieval  

International Nuclear Information System (INIS)

This paper discusses the storage and retrieval of experimental data on relational databases. Physics experiments carried out using reactors and particle accelerators, generate huge amount of data. Also, most of the data analysis and simulation programs are developed using object oriented programming concepts. Hence, one of the most important design features of an experiment related software framework is the way object persistency is handled. We intend to discuss these issues in the light of the module developed by us for storing C++ objects in relational databases like Oracle. This module was developed under the POOL persistency framework being developed for LHC, CERN grid. (author)

171

Moving Object Tracking Based on EKF and Mean Shift  

Directory of Open Access Journals (Sweden)

Full Text Available Kalman filter is a traditional method of optimal estimation which is appropriate for linear and Gaussian model. But in practical application, there are many nonlinear and non-Gaussian models, and Extend Kalman filter is mainly used for nonlinear model. In this paper, Extend Kalman filter and Mean shift are combined to track the moving object. Firstly Extend Kalman filter is used to predict the next possible position of the object at target center. Secondly, mean shift is adapted to search moving target later. Experiment results show that this method reduces the time for searching object, thus it improves the speed of tracking target.

Liwei Chen

2012-04-01

172

Fuzzy-Rule-Based Object Identification Methodology for NAVI System  

OpenAIRE

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...

Porle, Rosalyn R.; Sazali Yaacob 2; Sainarayanan, G.; Nagarajan, R.

2005-01-01

173

Drifting Recovery Base Concept for GEO Derelict Object Capture  

Science.gov (United States)

Over 250 objects hover within 6 m/sec of perfect geostationary orbit. Over half of these objects lie within 0.1 m/sec of the GEO velocity. Such items have 62% of the total velocity required to achieve Earth gravitational escape. A conceptual architecture is proposed to clean this orbit area of derelict objects while providing a demonstration mission for many facets of future asteroid mining operations. These near-GEO objects average nearly 2000kg each, consisting of (typically functioning) power systems, batteries, and large quantities of components and raw aerospace-grade refined materials. Such a demonstration collection system could capture, collect and remove all GEO derelict objects in an international effort to create a depot of components and of aerospace-grade raw materials--with a total mass greater than that of the International Space Station--as a space scrap depot ready for transfer to lunar or Mars orbit, using only two heavy-lift launches and 2-3 years of on-orbit operations.

Bacon, John B.

2009-01-01

174

LBP-based edge-texture features for object recognition.  

Science.gov (United States)

This paper proposes two sets of novel edge-texture features, Discriminative Robust Local Binary Pattern (DRLBP) and Ternary Pattern (DRLTP), for object recognition. By investigating the limitations of Local Binary Pattern (LBP), Local Ternary Pattern (LTP) and Robust LBP (RLBP), DRLBP and DRLTP are proposed as new features. They solve the problem of discrimination between a bright object against a dark background and vice-versa inherent in LBP and LTP. DRLBP also resolves the problem of RLBP whereby LBP codes and their complements in the same block are mapped to the same code. Furthermore, the proposed features retain contrast information necessary for proper representation of object contours that LBP, LTP, and RLBP discard. Our proposed features are tested on seven challenging data sets: INRIA Human, Caltech Pedestrian, UIUC Car, Caltech 101, Caltech 256, Brodatz, and KTH-TIPS2-a. Results demonstrate that the proposed features outperform the compared approaches on most data sets. PMID:24690574

Satpathy, Amit; Jiang, Xudong; Eng, How-Lung

2014-05-01

175

MOPSO-based multi-objective TSO planning considering uncertainties  

DEFF Research Database (Denmark)

The concerns of sustainability and climate change have posed a significant growth of renewable energy associated with smart grid technologies. Various uncertainties are the major problems need to be handled by transmission system operator (TSO) planning. This paper mainly focuses on three uncertain 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 and the improved MOPSO have been verified by the 77-bus system.

Wang, Qi; Zhang, Chunyu

2014-01-01

176

Aircraft Simulator Designing based on Object Oriented Methodologies  

Directory of Open Access Journals (Sweden)

Full Text Available Object plays a very important role in comparing softwareentities. But the role of object when it comes to simulationfield is still undefined. Several standards have been createdwith the purpose to provide a design solution for the projectof Simulators. The design solution reported in this paperaggregates the principles of both software and simulatorarchitectures. The objective is to invert the top-downstrategy of model-driven development with the SimulationModel Portability (SMP standard into a bottom-updevelopment process of a SMP framework. The paper alsoprovides solution for two development lines of two differentframeworks: the first is the SMP framework that changesaccording to the design models, and the second is aframework designed to support the development ofreusable behavior implementations

Rahul Kosarwal

2012-03-01

177

Man-made Object Detection Based on Latent Dirichlet Allocation  

Directory of Open Access Journals (Sweden)

Full Text Available With the rapid development of multimedia technologies, man-made object detection is one of the important applications. An improved LDA approach was used to learn and recognize man-made and natural scene categories. It represent the image of a scene by a collection of local regions, denoted as codewords, each region is represented as part of a “theme”. It learns the theme distributions as well as the codewords distribution over the themes. At last Support Vector Machine (SVM classifier was used to image database for the man-made object detection. We report satisfactory categorization performances on a large set of image database.

Xiaojun Xu

2013-01-01

178

Objective, Way and Method of Faculty Management Based on Ergonomics  

Science.gov (United States)

The core problem that influences educational quality of talents in colleges and universities is the faculty management. Without advanced faculty, it is difficult to cultivate excellent talents. With regard to some problems in present faculty construction of colleges and universities, this paper puts forward the new objectives, ways and methods of…

WANG, Hong-bin; Liu, Yu-hua

2008-01-01

179

Proto-object based rate control for JPEG2000: an approach to content-based scalability.  

Science.gov (United States)

The JPEG2000 system provides scalability with respect to quality, resolution and color component in the transfer of images. However, scalability with respect to semantic content is still lacking. We propose a biologically plausible salient region based bit allocation mechanism within the JPEG2000 codec for the purpose of augmenting scalability with respect to semantic content. First, an input image is segmented into several salient proto-objects (a region that possibly contains a semantically meaningful physical object) and background regions (a region that contains no object of interest) by modeling visual focus of attention on salient proto-objects. Then, a novel rate control scheme distributes a target bit rate to each individual region according to its saliency, and constructs quality layers of proto-objects for the purpose of more precise truncation comparable to original quality layers in the standard. Empirical results show that the suggested approach adds to the JPEG2000 system scalability with respect to content as well as the functionality of selectively encoding, decoding, and manipulation of each individual proto-object in the image, with only some slightly trivial modifications to the JPEG2000 standard. Furthermore, the proposed rate control approach efficiently reduces the computational complexity and memory usage, as well as maintains the high quality of the image to a level comparable to the conventional post-compression rate distortion (PCRD) optimum truncation algorithm for JPEG2000. PMID:20858580

Xue, Jianru; Li, Ce; Zheng, Nanning

2011-04-01

180

Object Based Real Time Lossless Video Compression – A REVIEW  

Directory of Open Access Journals (Sweden)

Full Text Available This paper describes video compression in real time.The aim is to achieve higher compression ratio in losslesscompression. Efficient compression is achieved by separating themoving objects from stationary background and compactlyrepresenting their shape, motion, and the content. Videocompression techniques are used to make efficient use of theavailable bandwidth. Lossless means that the output from thedecompressor is bit-for-bit identical with the original input to thecompressor. The decompressed video should be completelyidentical to original. In addition to providing improved codingefficiency in real time the technique provides the ability toselectively encode, decode, and manipulate individual objects in avideo stream. The technique used results in video coding that ahigh compression ratio can be obtained without any loss in data inreal time.

Preeti Markan

2012-08-01

181

Hierarchical Object Category Recognition Technique for Image Based Search System  

Science.gov (United States)

In this paper, we present an object category recognition method for an information search system which is queried by camera of mobile phone or by servers of internet services. In such a system, processing speed is an important requirement. To improve processing speed, the hierarchical object category recognition technique proposed by Serre(23) is modified using Haar-Like features, vector quantization of feature models, and reduction of processing area. In addition, by retaining the information of each feature's position, it compensates the accuracy which is a little reduced in exchange of processing speed. We implemented this method to web server, and proved this system can work in practical processing time. Through the experiment for Caltech-101 image database and natural scene category images, we also confirm the accuracy of our approach.

Minagawa, Takuya; Saito, Hideo

182

Adaptability Evaluation of Enterprise Information Systems Based on Object-based Knowledge Mesh  

Directory of Open Access Journals (Sweden)

Full Text Available To apply enterprise information systems more widely, it is necessary to evaluate their adaptability. In this paper, firstly, an index set of adaptability evaluation system based on object-based knowledge mesh (OKM is proposed and then, according to goal-question-metrics (GQM for enterprise information systems, and the quantitative measurement is given. Second, based on similarity to ideal solution (TOPSIS, the evaluation model is built and the adaptability evaluation (AE algorithm is proposed to evaluate enterprise information systems’ adaptability. Finally, the application of the evaluation system and AE algorithm is verified through an example, which provides quantitative references for evaluating and optimizing enterprise information systems’ adaptability.

Chaogai Xue

2013-05-01

183

A fuzzy rule base system for object-based feature extraction and classification  

Science.gov (United States)

In this paper, we present a fuzzy rule base system for object-based feature extraction and classification on remote sensing imagery. First, the object primitives are generated from the segmentation step. Object primitives are defined as individual regions with a set of attributes computed on the regions. The attributes computed include spectral, texture and shape measurements. Crisp rules are very intuitive to the users. They are usually represented as "GT (greater than)", "LT (less than)" and "IB (In Between)" with numerical values. The features can be manually generated by querying on the attributes using these crisp rules and monitoring the resulting selected object primitives. However, the attributes of different features are usually overlapping. The information is inexact and not suitable for traditional digital on/off decisions. Here a fuzzy rule base system is built to better model the uncertainty inherent in the data and vague human knowledge. Rather than representing attributes in linguistic terms like "Small", "Medium", "Large", we proposed a new method for automatic fuzzification of the traditional crisp concepts "GT", "LT" and "IB". Two sets of membership functions are defined to model those concepts. One is based on the piecewise linear functions, the other is based on S-type membership functions. A novel concept "fuzzy tolerance" is proposed to control the degree of fuzziness of each rule. The experimental results on classification and extracting features such as water, buildings, trees, fields and urban areas have shown that this newly designed fuzzy rule base system is intuitive and allows users to easily generate fuzzy rules.

Jin, Xiaoying; Paswaters, Scott

2007-04-01

184

Object Based Real Time Lossless Video Compression – A REVIEW  

OpenAIRE

This paper describes video compression in real time.The aim is to achieve higher compression ratio in losslesscompression. Efficient compression is achieved by separating themoving objects from stationary background and compactlyrepresenting their shape, motion, and the content. Videocompression techniques are used to make efficient use of theavailable bandwidth. Lossless means that the output from thedecompressor is bit-for-bit identical with the original input to thecompressor. The decompre...

Preeti Markan; Balwinder Singh

2012-01-01

185

Application of Object-Based Industrial Controls for Cryogenics  

CERN Document Server

The first application of the CERN Unified Industrial Control system (UNICOS) has been developed for the 1.8 K refrigerator at point 1.8 in mid-2001. This paper presents the engineering methods used for application development, in order to reach the objectives of maintainability and reusability, in the context of a development done by an external consortium of engineering firms. It will also review the lessons learned during this first development and the improvements planned for the next applications.

Casas-Cubillos, J; Gomes, P; Pezzetti, M; Sicard, Claude Henri; Varas, F J

2002-01-01

186

Real-time Object Detection Based on ARM9  

Directory of Open Access Journals (Sweden)

Full Text Available Object detection applications are associated with real-time performance constraints that originate from the embedded system that they are often deployed in. Our Embedded system using ARM 32 bit Microcontroller has the feature of image/video processing by using various features and classification algorithms have been proposed for object detection. It overcomes the performance in terms of sensors and hardware cost is also very high. So, our design Embedded system that detects partially visible pedestrians with low false alarm rate and high speed wherever they enter the camera view. This system takes captured image by means of web camera connected to ARM microcontroller through USB and the image is processed by using image processing technique. Image processing is a signal processing for which the input is an image, whether it is a photograph or a video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. The captured image undergoes spatio temporal reference samples in terms of both back ground and fore ground estimation, evaluation and spatial Gaussian kernel to provide high quality image of object detection that detected image is continuously displayed on display unit and the data is stored in pen drive connected to it.

M.Vijay babu

2013-09-01

187

A Knowledge-Based Approach to Describe and Adapt Learning Objects  

Science.gov (United States)

Our claim is that semantic metadata are required to allow a real reusing and assembling of learning objects. Our system is based on three models used to describe the domain, learners, and learning objects. The learning object model is inspired from knowledge representation proposals. A learning object can be reused directly or can be combined with…

Bouzeghoub, Amel; Defude, Bruno; Duitama, John Freddy; Lecocq, Claire

2006-01-01

188

Integration of an object knowledge base into a medical workstation.  

OpenAIRE

A simple, yet powerful, knowledge base and its development environment is described that can act as a "knowledge server", integrated into a medical workstation. In many areas, such an integration of a knowledge base with other modules and systems is required, but difficult or impossible to achieve with existing commercial development shells. Three applications of the knowledge base are described: a controlled vocabulary for the classification of Congenital Heart Diseases, an extended data mod...

Timmers, T.; Mulligen, E. M.; Den Heuvel, F.

1991-01-01

189

Finger Readjustment Algorithm for Object Manipulation Based on Tactile Information  

Directory of Open Access Journals (Sweden)

Full Text Available This paper presents a novel algorithm which registers pressure information from tactile sensors installed over the fingers of a robotic hand in order to perform manipulation tasks with objects. This algorithm receives as an input the joint trajectories of the fingers which have to be executed and adapts it to the real contact pressure of each finger in order to guarantee that undesired slippage or contact?breaking is avoided during the execution of the manipulation task. This algorithm has been applied not only for the manipulation of normal rigid bodies but also for bodies whose centre of mass can be changed during the execution of the manipulation task.

Juan Antonio Corrales Ramo?n

2013-01-01

190

Attentional spreading to task-irrelevant object features: Experimental support and a 3-step model of attention for object-based selection and feature-based processing modulation  

Directory of Open Access Journals (Sweden)

Full Text Available Directing attention to a specific feature of an object has been linked to different forms of attentional modulation. Object-based attention theory founds on the finding that even task-irrelevant features at the selected object are subject to attentional modulation, while feature-based attention theory proposes a global processing benefit for the selected feature even at other objects. Most studies investigated either the one or the other form of attention, leaving open the possibility that both object- and feature-specific attentional effects do occur at the same time and may just represent two sides of a single attention system. We here investigate this issue by testing attentional spreading within and across objects, using reaction time measurements to changes of attended and unattended features on both attended and unattended objects. We asked subjects to report color and speed changes occurring on one of two overlapping random dot patterns, presented at the center of gaze. The key property of the stimulation was that only one of the features (e.g. motion direction was unique for each object, whereas the other feature (e.g. color was shared by both. The results of two experiments show that co-selection of unattended features even occurs when those features have no means for selecting the object. At the same time, they demonstrate that this processing benefit is not restricted to the selected object but spreads to the task-irrelevant one. We conceptualize these findings by a 3-step model of attention that assumes a task-dependent top-down gain, object-specific feature selection based on task- and binding characteristics, and a global feature-specific processing enhancement. The model allows for the unification of a vast amount of experimental results into a single model, and makes various experimentally testable predictions for the interaction of object- and feature-specific processes.

Detlef Wegener

2014-06-01

191

A survey – Quality based Object Oriented Software Fault Prediction  

Directory of Open Access Journals (Sweden)

Full Text Available Software fault prediction is the most efficient methodology to improve the quality of the products. To improve the quality it is essential to find the error or fault as quick as possible. To determine and advance the development of product there are different prediction approaches are available like correction cost prediction, test effort prediction, software fault prediction (SFP, security prediction and so on. This paper survey the variety of methods and metrics used to improve the quality of the object oriented software. We compared different prediction models and proposed the methodology with the datasets they used for easy understanding. Software fault prediction using different techniques to improve the qualityand the error free software delivery. This paper gives overview about the prediction models for software in design and implementation phase and also it discuss trends which are currently used software faultprediction.

R.Sathyaraj

2013-06-01

192

Android Based Robot Implementation For Pick and Retain of Objects  

Directory of Open Access Journals (Sweden)

Full Text Available Now-a-days it is complicated about terrorists and their bomb attacks. Even though we found a bomb it is much more complicated to remove the bomb safely. Many lives are depending on the bomb diffusion. Our project helps in diffusion of bombs with safe distance from the bomb. Bomb diffusion is controlled with the help of wireless communication using android phones. By our project we can diffuse the bomb from safe distance and it can save more lives. We can send the few commands to the robot situated at the bomb. We can control two motors situated at the wheels for direction control and other two motors at robot hand. With these four motors we can control all the directions of the robot and at the same time we can pick any object at any direction.

Ranjith Kumar Goud

2014-10-01

193

An image retrieval system based on the feature of color differences among the edges of objects  

OpenAIRE

This paper focuses on color differences among the edges of objects in an image. The variations of colors among the objects in the image can depict the directions and simple geometric shapes of most objects in the image regardless of the influence from the shift variant of objects and scale variant in the image. Based on the feature of color differences among the edges of objects, this paper constructs an image retrieval system. Experimental results show that this system can effectively and qu...

Chan, Yung-kuan; Chen, Chih-ya

2005-01-01

194

Line fitting based feature extraction for object recognition  

Science.gov (United States)

Image feature extraction plays a significant role in image based pattern applications. In this paper, we propose a new approach to generate hierarchical features. This new approach applies line fitting to adaptively divide regions based upon the amount of information and creates line fitting features for each subsequent region. It overcomes the feature wasting drawback of the wavelet based approach and demonstrates high performance in real applications. For gray scale images, we propose a diffusion equation approach to map information-rich pixels (pixels near edges and ridge pixels) into high values, and pixels in homogeneous regions into small values near zero that form energy map images. After the energy map images are generated, we propose a line fitting approach to divide regions recursively and create features for each region simultaneously. This new feature extraction approach is similar to wavelet based hierarchical feature extraction in which high layer features represent global characteristics and low layer features represent local characteristics. However, the new approach uses line fitting to adaptively focus on information-rich regions so that we avoid the feature waste problems of the wavelet approach in homogeneous regions. Finally, the experiments for handwriting word recognition show that the new method provides higher performance than the regular handwriting word recognition approach.

Li, Bing

2014-06-01

195

Object-based change detection for landslide monitoring based on SPOT imagery  

Science.gov (United States)

The steadily increasing availability of Earth observation (EO) data from a wide range of sensors facilitates the long-time monitoring of mass movements and retrospective analysis. Pixel-based approaches are most commonly used for detecting changes based on optical remote sensing data. However, single pixels are not suitable for depicting natural phenomena such as landslides in their full complexity and their transformation over time. By applying semi-automated object-based change detection limitations inherent to pixel-based methods can be overcome to a certain extent. For instance, the problem of variant spectral reflectance for the same pixel location in images from different points in time can be minimized. Therefore, atmospheric and radiometric correction of input data sets - although highly recommended - seems to be not that important for developing a straightforward change detection approach based on object-based image analysis (OBIA). The object-based change detection approach was developed for a subset of the Baichi catchment, which is located in the Shihmen Reservoir watershed in northern Taiwan. The study area is characterized by mountainous terrain with steep slopes and is regularly affected by severe landslides and debris flows. Several optical satellite images, i.e. SPOT images from different years and seasons with a spatial resolution ranging from 2.5 to 6.25 m, have been used for monitoring the past evolution of landslides and landslide affected areas. A digital elevation model (DEM) with 5 m spatial resolution was integrated in the analysis for supporting the differentiation of landslides and debris flows. The landslide changes were identified by comparing feature values of segmentation-derived image objects between two subsequent images in eCognition (Trimble) software. To increase the robustness and transferability of the approach we identified changes by using the relative difference in values of band-specific relational features, spectral indices and texture instead of by applying absolute spectral thresholds. Especially the Normalized Differenced Vegetation Index (NDVI) turned out to be useful as indicator for change. In this course, recent landslides can be differentiated from already existing mass movements. Furthermore, old landslides, which are already overgrown by vegetation, can be identified as well as reactivated ones. The presented approach can be applied for the regular update of existing landslide inventory maps or for the identification of areas that are potentially susceptible to landslides by analyzing the frequency of landslide events in the past. This might be of interest for decision makers and local stakeholders, as this kind of information can serve as useful input for disaster prevention and risk analysis.

Hölbling, Daniel; Friedl, Barbara; Eisank, Clemens

2014-05-01

196

A Moving Object Detection Algorithm Based On Multiple Judgments  

Directory of Open Access Journals (Sweden)

Full Text Available In the field of moving object detection, the traditional background subtraction method is used broadly, which seems more sensitive to light and shows poor anti-interference performance. Background modeling is the key step of background subtraction method. The Local Binary Pattern (LBP algorithm is considered to put texture information into the background model, combining color and texture information and an improved background subtraction method proposed. In addition, a new method is proposed combining the inter-frame difference method with improved background subtraction method in this paper. It can overcome traditional methods only using the pixel gray value changes for moving targets detection. The method makes use of dual-threshold to detect moving targets and makes multiple judgments. It not only uses the change of pixel gray value to detect moving targets, but also takes advantage of the number of changed pixels to detect moving targets which we are interested in. The experiments show that the algorithm proposed is adopted to detect the moving target accurately and can resist interferences brought about by the slow slight movements in the scene with better robustness.

Mengxin Li

2013-10-01

197

Relational and Object-Oriented Methodology in Data Bases Systems  

Directory of Open Access Journals (Sweden)

Full Text Available Database programming languages integrate concepts of databases and programming languages to provide both implementation tools for data-intensive applications and high-level user interfaces to databases. Frequently, database programs contain a large amount of application knowledge which is hidden in the procedural code and thus difficult to maintain with changing data and user views. This paper presents a first attempt to improve the situation by supporting the integrated definition and management of data and rules based on a setoriented and predicative approach. The use of database technology for integrated fact and rule base management is shown to have some important advantages in terms of fact and rule integrity, question-answering, and explanation of results.

Marian Pompiliu CRISTESCU

2006-01-01

198

Multiple object image segmentation algorithm based on wavelet theory  

Science.gov (United States)

The colony characteristics are used for evaluating the quality of water and food. Auto-detecting colony in an image is a hard task. This paper proposes a new multi-scale segmentation technique based on wavelet decompositions and watersheds. Firstly, we dispose the tiny colonies by using a wavelet domain median filter. Secondly, wavelet transform is used to create multi-resolution images. Then watershed segmentation algorithm is applied to segment the lowestresolution image and obtain the initial watershed segmentation result. Finally, we do segmentation on the high-resolution image based on the low-resolution image. Experiments results show that the colony images can be well segmented by using the new algorithm.

Wang, W.; Wang, Z.

2009-06-01

199

A Machine Learning based Efficient Software Reusability Prediction Model for Java Based Object Oriented Software  

Directory of Open Access Journals (Sweden)

Full Text Available Software reuse refers to the development of new software systems with the likelihood of completely or partially using existing components or resources with or without modification. Reusability is the measure of the ease with which previously acquired concepts and objects can be used in new contexts. It is a promising strategy for improvements in software quality, productivity and maintainability as it provides for cost effective, reliable (with the consideration that prior testing and use has eliminated bugs and accelerated (reduced time to market development of the software products. In this paper we present an efficient automation model for the identification and evaluation of reusable software components to measure the reusability levels (high, medium or low of procedure oriented Java based (object oriented software systems. The presented model uses a metric framework for the functional analysis of the Object oriented software components that target essential attributes of reusability analysis also taking into consideration Maintainability Index to account for partial reuse. Further machine learning algorithm LMNN is explored to establish relationships between the functional attributes. The model works at functional level rather than at structural level. The system is implemented as a tool in Java and the performance of the automation tool developed is recorded using criteria like precision, recall, accuracy and error rate. The results gathered indicate that the model can be effectively used as an efficient, accurate, fast and economic model for the identification of procedure based reusable components from the existing inventory of software resources.

Surbhi Maggo

2014-01-01

200

Incremental Test Selection for Specification-Based Unit Testing of Object-Oriented Software Based on Formal Specifications  

OpenAIRE

We propose a test case selection methodology for object-oriented software. This methodology is based on formal specifications and is aimed at verifying the correctness of method interaction of an object or a cluster of objects. The essence of this methodology is to select test cases by a reduction process. The exhaustive test set, the reference for correctness, is transformed into a practicable test set by applying hypotheses to the objects. We present a set of hypotheses specific to object-o...

Barbey, Ste?phane; Buchs, Didier; Pe?raire, Ce?cile; Strohmeier, Alfred

1998-01-01

201

Image restoration technique for motion-compensated frame averaged data collected by 3D flash ladar imaging system  

Science.gov (United States)

A new image restoration algorithm is proposed to remove the effect of atmospheric turbulence on motion-compensated frame averaged data collected by a three dimensional FLASH Laser Radar (LADAR) imaging system. The algorithm simultaneously arrives at an enhanced image as well as Fried's seeing parameter through an Expectation Maximization (EM) technique. Unlike blind deconvolution algorithms that operate only on two dimensional images, this technique accounts for both the spatial and temporal mixing that is caused by the atmosphere through which the system is imaging. Additionally, due to the over-determined nature of this problem, the point-spread function parameterized by Fried's seeing parameter can be deduced without the requirement for additional assumptions or constraints. The utility of the approach lies in its application to laser illuminated imaging where processing time is important.

Neff, Brian J.; Cain, Stephen C.

2012-10-01

202

A Review of Computer Vision based Algorithms for accurate and efficient Object Detection  

OpenAIRE

In this paper two vision-based algorithms are adopted to locate and identify the objects and obstacles from the environment. In recent days, robot vision and navigation are emerging as essential services especially in hazardous environments. In this work, two vision based techniques such as color based thresholding and template matching – both correlation based similarity measure and FFT (Fast Fourier Transform) based have been adopted and used for object identification and classification u...

Aneissha Chebolu; Manvi Malik

2013-01-01

203

A Review of Computer Vision based Algorithms for accurate and efficient Object Detection  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper two vision-based algorithms are adopted to locate and identify the objects and obstacles from the environment. In recent days, robot vision and navigation are emerging as essential services especially in hazardous environments. In this work, two vision based techniques such as color based thresholding and template matching – both correlation based similarity measure and FFT (Fast Fourier Transform based have been adopted and used for object identification and classification using the image captured in CCD camera attached to the robotic arm. Then, the robotic arm manipulator is integrated with the computer for futher manipulation of the objects based on the application.

Aneissha Chebolu

2013-05-01

204

Ontological representation based on semantic descriptors applied to geographic objects  

Directory of Open Access Journals (Sweden)

Full Text Available En esta tesis se presenta la metodología GEONTO - MET, orientada a formalizar la conceptualización del dominio geográfico, considerando las especificaciones del Instituto Nacional de Estadística, Geografía e Informática (INEGI. El espíritu de esta metodología es proporcionar un conjunto de descripciones semánticas que reflejen las propiedades y relaciones que describen entre sí el comportamiento de los objetos geográficos, tomando estos elementos directamente de la ontología de dominio geográfico diseñada para este fin. Esta tesis sienta las bases para una futura interpretación automática de regiones geográficas, orientado al soporte de toma de decisiones, haciendo uso de la conceptualización del dominio geográfico; con el objeto de generar nuevo conocimiento en forma automática y realizar un análisis más profundo del entorno geoespacial. El hecho de contar con una descripción semántica de un entorno geográfico, permite establecer los requerimientos necesarios hacia un razonamiento espacial; es decir, el hecho de conocer el comportamiento que presentan los objetos geográficos inmersos en una partición geográfica es de suma importancia para entender e interpretar la semántica que representan y explotar la inferencia implícita que posee la conceptualización. En consecuencia, este trabajo aporta la metodología que permite integrar y compartir información geoespacial. GEONTO - MET proporciona soluciones viables hacia estos tópicos y otros, como el hecho de compactar datos mediante estructuras alternas a modelos tradicionales y evitar la ambigüedad de términos al utilizar una conceptualización del dominio.

Miguel Jes\\u00FAs Torres-Ruiz

2009-01-01

205

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

International Nuclear Information System (INIS)

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)

206

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

Science.gov (United States)

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.

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

207

Memory-based multiagent coevolution modeling for robust moving object tracking.  

Science.gov (United States)

The three-stage human brain memory model is incorporated into a multiagent coevolutionary process for finding the best match of the appearance of an object, and a memory-based multiagent coevolution algorithm for robust tracking the moving objects is presented in this paper. Each agent can remember, retrieve, or forget the appearance of the object through its own memory system by its own experience. A number of such memory-based agents are randomly distributed nearby the located object region and then mapped onto a 2D lattice-like environment for predicting the new location of the object by their coevolutionary behaviors, such as competition, recombination, and migration. Experimental results show that the proposed method can deal with large appearance changes and heavy occlusions when tracking a moving object. It can locate the correct object after the appearance changed or the occlusion recovered and outperforms the traditional particle filter-based tracking methods. PMID:23843739

Wang, Yanjiang; Qi, Yujuan; Li, Yongping

2013-01-01

208

Bidirectional reflectance distribution function effects in ladar-based reflection tomography.  

Science.gov (United States)

Light reflection from a surface is described by the bidirectional reflectance distribution function (BRDF). In this paper, BRDF effects in reflection tomography are studied using modeled range-resolved reflection from well-characterized geometrical surfaces. It is demonstrated that BRDF effects can cause a darkening at the interior boundary of the reconstructed surface analogous to the well-known beam hardening artifact in x-ray transmission computed tomography (CT). This artifact arises from reduced reflection at glancing incidence angles to the surface. It is shown that a purely Lambertian surface without shadowed components is perfectly reconstructed from range-resolved measurements. This result is relevant to newly fabricated carbon nanotube materials. Shadowing is shown to cause crossed streak artifacts similar to limited-angle effects in CT reconstruction. In tomographic reconstruction, these effects can overwhelm highly diffuse components in proximity to specularly reflecting elements. Diffuse components can be recovered by specialized processing, such as reducing glints via thresholded measurements. PMID:19623233

Jin, Xuemin; Levine, Robert Y

2009-07-20

209

Object-based surveillance video retrieval system with real-time indexing methodology  

OpenAIRE

This paper presents a novel surveillance video indexing and retrieval system based on object features similarity measurement. The system firstly extracts moving objects from the videos by an efficient motion segmentation method. The fundamental features of each moving object are then extracted and indexed into the database. During retrieval, the system matches the query with the features indexed in the database without re-processing the videos. Video clips which contain the objects with suffi...

Wong, Kyk; Chung, Rhy; Chow, Kp; Chin, Fyl; Yuk, Jsc; Tsang, Ksh

2007-01-01

210

Parallel WiSARD object tracker: a ram-based tracking system  

OpenAIRE

This paper proposes the Parallel WiSARD Object Tracker (PWOT), a new object tracker based on the WiSARD weightless neural network that is robust against quantization errors. Object tracking in video is an important and challenging task in many applications. Difficulties can arise due to weather conditions, target trajectory and appearance, occlusions, lighting conditions and noise. Tracking is a high-level application and requires the object location frame by frame in real t...

Moreira, Rodrigo Da Silva; Ebecken, Nelson Francisco Favilla

2014-01-01

211

Object oriented image analysis based on multi-agent recognition system  

Science.gov (United States)

In this paper, the capabilities of multi-agent systems are used in order to solve object recognition difficulties in complex urban areas based on the characteristics of WorldView-2 satellite imagery and digital surface model (DSM). The proposed methodology has three main steps: pre-processing of dataset, object based image analysis and multi-agent object recognition. Classified regions obtained from object based image analysis are used as input datasets in the proposed multi-agent system in order to modify/improve results. In the first operational level of the proposed multi-agent system, various kinds of object recognition agents modify initial classified regions based on their spectral, textural and 3D structural knowledge. Then, in the second operational level, 2D structural knowledge and contextual relations are used by agents for reasoning and modification. Evaluation of the capabilities of the proposed object recognition methodology is performed on WorldView-2 imagery over Rio de Janeiro (Brazil) which has been collected in January 2010. According to the obtained results of the object based image analysis process, contextual relations and structural descriptors have high potential to modify general difficulties of object recognition. Using knowledge based reasoning and cooperative capabilities of agents in the proposed multi-agent system in this paper, most of the remaining difficulties are decreased and the accuracy of object based image analysis results is improved for about three percent.

Tabib Mahmoudi, Fatemeh; Samadzadegan, Farhad; Reinartz, Peter

2013-04-01

212

A Topology-based Object Representation for Clasping, Latching and Hooking  

OpenAIRE

We present a loop-based topological object representation for objects with holes. The representation is used to model object parts suitable for grasping, e.g. handles, and it incorporates local volume information about these. Furthermore, we present a grasp synthesis framework that utilizes this representation for synthesizing caging grasps that are robust under measurement noise. The approach is complementary to a local contact-based force-closure analysis as it depends on global topological...

Stork, Johannes A.; Pokorny, Florian T.; Kragic, Danica

2013-01-01

213

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

OpenAIRE

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...

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

2010-01-01

214

Introducing AN Agent-Based Object Recognition Operator for Proximity Analysis  

Science.gov (United States)

Object selection is a basic procedure in a Geographic Information System (GIS). Most current methods for doing so, select objects in two phases: create a simple distance-bounded geometric buffer; and intersect it with available features. This paper introduces a novel and intelligent selection operator based on the autonomy of the agent-based approach. The proposed operator recognizes objects around one object only in one step. In the proposed approach, each point object acts as an agent-automata object. It then senses its vicinity and identifies the surrounding objects. To assess the proposed model, the operator is designed, implemented, and evaluated in a case study. Finally, the results are evaluated and presented in details in the paper.

Behzadi, S.; Ali. Alesheikh, A.

2013-09-01

215

Object Classification based Context Management for Identity Management in Internet of Things  

DEFF Research Database (Denmark)

As computing technology is becoming more tightly coupled into dynamic and mobile world of the Internet of Things (IoT), security mechanism is more stringent, flexible and less intrusive. Scalability issue in IoT makes identity management (IdM) of ubiquitous objects more challenging, and there is a need of context-aware access control solution for IdM. Confronting uncertainty of different types of objects in IoT is not easy. This paper presents the logical framework for object classification in context aware IoT, as richer contextual information creates an impact on the access control. This paper proposes decision theory based object classification to provide contextual information and context management. Simulation results show that the proposed object classification is useful to improve network lifetime. Results also give motivation of object classification in terms of energy consumption. This paper also presents proof of concept and time analysis of the proposed decision theory based object classification.

Mahalle, Parikshit N.; Prasad, Neeli R.

2013-01-01

216

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

Science.gov (United States)

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. PMID:23624577

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

2013-10-01

217

Shifting attention in viewer- and object-based reference frames after unilateral brain injury.  

Science.gov (United States)

The aims of the present study were to investigate the respective roles that object- and viewer-based reference frames play in reorienting visual attention, and to assess their influence after unilateral brain injury. To do so, we studied 16 right hemisphere injured (RHI) and 13 left hemisphere injured (LHI) patients. We used a cueing design that manipulates the location of cues and targets relative to a display comprised of two rectangles (i.e., objects). Unlike previous studies with patients, we presented all cues at midline rather than in the left or right visual fields. Thus, in the critical conditions in which targets were presented laterally, reorienting of attention was always from a midline cue. Performance was measured for lateralized target detection as a function of viewer-based (contra- and ipsilesional sides) and object-based (requiring reorienting within or between objects) reference frames. As expected, contralesional detection was slower than ipsilesional detection for the patients. More importantly, objects influenced target detection differently in the contralesional and ipsilesional fields. Contralesionally, reorienting to a target within the cued object took longer than reorienting to a target in the same location but in the uncued object. This finding is consistent with object-based neglect. Ipsilesionally, the means were in the opposite direction. Furthermore, no significant difference was found in object-based influences between the patient groups (RHI vs. LHI). These findings are discussed in the context of reference frames used in reorienting attention for target detection. PMID:21504751

List, Alexandra; Landau, Ayelet N; Brooks, Joseph L; Flevaris, Anastasia V; Fortenbaugh, Francesca C; Esterman, Michael; Van Vleet, Thomas M; Albrecht, Alice R; Alvarez, Bryan D; Robertson, Lynn C; Schendel, Krista

2011-06-01

218

Shifting Attention in Viewer and Object-Based Reference Frames after Unilateral Brain Injury  

Science.gov (United States)

The aims of the present study were to investigate the respective roles that object- and viewer-based reference frames play in reorienting visual attention, and to assess their influence after unilateral brain injury. To do so, we studied 16 right hemisphere injured (RHI) and 13 left hemisphere injured (LHI) patients. We used a cueing design that manipulates the location of cues and targets relative to a display comprised of two rectangles (i.e., objects). Unlike previous studies with patients, we presented all cues at midline rather than in the left or right visual fields. Thus, in the critical conditions in which targets were presented laterally, reorienting of attention was always from a midline cue. Performance was measured for lateralized target detection as a function of viewer-based (contra- and ipsilesional sides) and object-based (requiring reorienting within or between objects) reference frames. As expected, contralesional detection was slower than ipsilesional detection for all patients. More importantly, objects influenced target detection differently in the contralesional and ipsilesional fields. Contralesionally, reorienting to a target within the cued object took longer than reorienting to a target in the same location but in the uncued object. This finding is consistent with object-based neglect. Ipsilesionally, the means were in the opposite direction. Furthermore, no significant difference was found in object-based influences between the patient groups (RHI vs. LHI). These findings are discussed in the context of reference frames used in reorienting attention for target detection. PMID:21504751

List, Alexandra; Landau, Ayelet N.; Brooks, Joseph L.; Flevaris, Anastasia; Fortenbaugh, Francesca; Esterman, Michael; VanVleet, Thomas M.; Albrecht, Alice R.; Alvarez, Bryan; Robertson, Lynn C.; Schendel, Krista

2011-01-01

219

Video Image Object Tracking Algorithm based on Improved Principal Component Analysis  

Directory of Open Access Journals (Sweden)

Full Text Available Since the existing object tracking algorithms are very difficult to adapt the object appearance changes caused by illumination changes, large pose variations, and partial or full occlusions, an object tracking algorithm based on two-dimensional principal component analysis (2DPCA and sparse-representation is proposed in this paper. The tracking algorithm adopts 2DPCA and sparse-representation to establish object appearance model. In order to reduce dimension of object template, incremental subspace updating algorithm is introduced to online update the object template, reduce the requirement of memory space and enhance the precision of object appearance description. Experimental results show the proposed algorithm is robust for image illumination variance and object partial occlusion.

Liping Wang

2014-05-01

220

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

International Nuclear Information System (INIS)

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

221

Moving Object Detection Based on the Histograms of Oriented Gradients and Cloud Model  

Directory of Open Access Journals (Sweden)

Full Text Available We present a Histograms of Oriented Gradients (HOG features and the cloud model approach for the moving object detection from video sequences. Our model is based on the HOG features for the moving object and then uses the cloud model to find the moving object. First the HOG features are described from the image and then a HOG map is used by the cloud model to detect the moving object. The experiment shows our method is relative effect and has advantage in the detecting moving object.

Yuyong Cui

2012-08-01

222

A Novel And Fast Feature Based Motion estimation ALgorithm Through extraction Of Background And Object  

OpenAIRE

This paper presents a novel and fast Feature Based Motion Estimation algorithm which is developed for typical video-phone scenario. In essence it combines the technique of object extraction with traditional block based motion estimation methods by estimating the background and extracting the moving object continuously in the first stage, then performs a block based motion estimation on the extracted. Simulation of the algorithm with full search as core shows that the estimation time can be re...

Mok, Wh; Yung, Nhc

1998-01-01

223

Method for object motion characteristic estimation based on wavelet Multi-Resolution Analysis: MRA  

OpenAIRE

Method for object motion characteristic estimation based on wavelet Multi-Resolution Analysis: MRA is proposed. With moving pictures, the motion characteristics, direction of translation, roll/pitch/yaw rotations can be estimated by MRA with an appropriate support length of the base function of wavelet. Through simulation study, method for determination of the appropriate support length of Daubechies base function is clarified. Also it is found that the proposed method for object motion chara...

Kohei Arai

2013-01-01

224

Multiple Moving Object Recognitions in video based on Log Gabor-PCA Approach  

OpenAIRE

Object recognition in the video sequence or images is one of the sub-field of computer vision. Moving object recognition from a video sequence is an appealing topic with applications in various areas such as airport safety, intrusion surveillance, video monitoring, intelligent highway, etc. Moving object recognition is the most challenging task in intelligent video surveillance system. In this regard, many techniques have been proposed based on different methods. Despite of ...

Gopalakrishna, M. T.; Ravishankar, M.; Rameshbabu, D. R.

2014-01-01

225

Semantic Predicate Types and Approximation for Class-based Object Oriented Programming  

OpenAIRE

We apply the principles of the intersection type discipline to the study of class-based object oriented programs and; our work follows from a similar approach (in the context of Abadi and Cardelli's Varsigma-object calculus) taken by van Bakel and de'Liguoro. We define an extension of Featherweight Java, FJc and present a predicate system which we show to be sound and expressive. We also show that our system provides a semantic underpinning for the object oriented paradigm b...

Bakel, Steffen; Rowe, Reuben N. S.

2011-01-01

226

Towards time-critical collision detection for deformable objects based on reduced models  

OpenAIRE

Topics of physically based modeling of deformable objects and collision detection have been extensively researched. Nowadays, the combination of GPU techniques and multiresolution physical models allows interactive simulations of complex deformable objects with a large number of polygons. To achieve this, the geometry of the object is separated from the deformable model in order to represent the latter at different levels of resolutions (reduced models). Recently...

Mendoza, Cesar; O Sullivan, Carol Ann

2005-01-01

227

Web Services Based Learning Objects Generator for Device-Independent M-Learning  

OpenAIRE

Learning objects, which are the base component of m-learning system, are usually target to modifications in contexts and formats. The device- dependent applications of hand-held devices have proven to be ineffective for creating m-learning courseware. Learning Objects Metadata (LOM) is the most popular standard specification for learning objects but lacks the ability to facilitate platforms descriptions. This paper outlines various aspects of design and implementation of Web Services Oriente...

Akram Moh. Alkouz

2006-01-01

228

Reusability Evaluation of Learning Objects Stored in Open Repositories Based on Their Metadata  

Science.gov (United States)

Reusability is considered to be the key property of learning objects residing in open repositories. In consecuence, measurement instruments for learning object reusability should be developed. In this preliminary research we propose to evaluate the reusability of learning objects by a priori reusability analysis based on their metadata records. A set of reusability metrics extracted from metadata records are defined and a quality assessment of the metadata application profiles defined in repositories eLera and Merlot is exposed.

Sanz, Javier; Sánchez-Alonso, Salvador; Dodero, Juan Manuel

229

Technique of identifying speedy hyperspectral images object based on spectrum repository  

International Nuclear Information System (INIS)

Aimed at the imported airborne hyperspectral data collection system (CASI/SASI), the authors first introduced the knowledge based spectral repository for multi ground objects, the study on the technique approaches to identifying object at speed with hyperspectral images, and then expound the principle of object identification, the flow of data processing and the programming procedure with open code, finally make a brief analysis for the results and its application. (authors)

230

Principal Objects Detection Using Graph-Based Segmentation and Normalized Histogram  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, we introduce a new method to distinguish the principal objects in image datasets using graph-based segmentation and normalized histogram (PODSH. Unlike the usual object detection systems which require the input objects, we propose a new approach to recognize objects one might focus on when taking images. Motivated by the habit of taking picture, we suppose that the position of a main object is located near the image centre and this object always holds a large area. The normalized histogram is added to increase the effect of our system. In the experiment, we used images which consist of objects to test the precision of PODSH. Our system is implemented by Matlab.

Pham The Bao

2012-01-01

231

Attention-Guided Organized Perception and Learning of Object Categories Based on Probabilistic Latent Variable Models  

Directory of Open Access Journals (Sweden)

Full Text Available This paper proposes a probabilistic model of object category learning in conjunction with attention-guided organized perception. This model consists of a model of attention-guided organized perception of object segments on Markov random fields and a model of learning object categories based on a probabilistic latent component analysis. In attention guided organized perception, concurrent figure-ground segmentation is performed on dynamically-formed Markov random fields around salient preattentive points and co-occurring segments are grouped in the neighborhood of selective attended segments. In object category learning, a set of classes of each object category is obtained based on the probabilistic latent component analysis with the variable number of classes from bags of features of segments extracted from images which contain the categorical objects in context and an object category is represented by a composite of object classes. Through experiments using two image data sets, it is shown that the model learns a probabilistic structure of intra-categorical composition and inter-categorical difference of object categories and achieves high performance in object category recognition.

Masayasu Atsumi

2013-05-01

232

Reference Point Based TR-PSO for Multi-Objective Environmental/Economic Dispatch  

OpenAIRE

A reference point based multi-objective optimization using a combination between trust region (TR) algorithm and particle swarm optimization (PSO) to solve the multi-objective environmental/economic dispatch (EED) problem is presented in this paper. The EED problem is handled by Reference Point Interactive Approach. One of the main advantages of th...

El-shorbagy, Mohamed A.; Zeinab Mohamed Hendawy; Ahmed Ahmed El-Sawy

2013-01-01

233

An automated object-based approach for the multiscale image segmentation of forest scenes  

Science.gov (United States)

Over the last decade the analysis of Earth observation data has evolved from what were predominantly per-pixel multispectral-based approaches, to the development and application of multiscale object-based methods. To empower users with these emerging object-based approaches, methods need to be intuitive, easy to use, require little user intervention, and provide results closely matching those generated by human interpreters. In an attempt to facilitate this, we present multiscale object-specific segmentation (MOSS) as an integrative object-based approach for automatically delineating image-objects (i.e., segments) at multiple scales from a high-spatial resolution remotely sensed forest scene. We further illustrate that these segments cognitively correspond to individual tree crowns, ranging up to forest stands, and describe how such a tool may be used in computer-assisted forest inventory mapping. MOSS is composed of three primary components: object-specific analysis (OSA), object-specific upscaling (OSU), and a new segmentation algorithm referred to as size constrained region merging (SCRM). The rationale for integrating these methods is that the first two provide the third with object-size parameters that otherwise would need to be specified by a user. Analysis is performed on an IKONOS-2 panchromatic image that represents a highly fragmented forested landscape in the Sooke Watershed on southern Vancouver Island, BC, Canada.

Hay, Geoffrey J.; Castilla, Guillermo; Wulder, Michael A.; Ruiz, Jose R.

2005-12-01

234

An object-based method for mapping and change analysis in mangrove ecosystems  

Science.gov (United States)

Object-based methods for image analysis have the advantage of incorporating spatial context and mutual relationships between objects. Few studies have explored the application of object-based approaches to mangrove mapping. This research applied an object-based method to SPOT XS data to map the land cover in the mangrove ecosystem of Low Casamance, Senegal. In parallel, the object-based method was tested to analyse the changes in the mangrove area between 1986 and 2006. The object-based method for mangrove mapping applied a multi-resolution segmentation and implemented class-specific rules that incorporate spectral properties and relationships between image objects at different hierarchical levels. The object-based approach for change analysis conducted the segmentation on the multi-date composite of the 1986 and 2006 images and applied a nearest neighbour classifier. The object-based method clearly discriminated the different land cover classes within the mangrove ecosystem. The overall accuracy of the land cover classification was 86%, the overall kappa value was 0.83 and the user's accuracy of the 'mangroves' class was higher than 97%. The estimated area of mangroves was 76,550 hectares in 2006. This result is an important update reference for mangrove studies in Senegal and the proposed method may represent a valid instrument for similar exercises in other regions. The image-to-image, object-based approach to change analysis clearly captured the fragmented and scattered pattern of change that prevails in the study area. The user's accuracy of the increase and decrease classes of transition produced results better than 85%. The overall accuracy, however, is lower due to the method's difficulties in detecting the small areas of change. To have conclusive evidence for the suitability of this method for change analysis of mangrove forest, this object-based approach should be tested in mangrove ecosystems where changes have different spatial patterns and modifications are more evident. Between 1986 and 2006, a small increase in the mangrove area was observed in Low Casamance. This was probably due to improved rainfall conditions after the droughts of the 1970s and 1980s. The pattern of change detected with the object-based approach corresponds to natural transitions and suggests that anthropogenic influence was limited.

Conchedda, Giulia; Durieux, Laurent; Mayaux, Philippe

235

Object-based learning in higher education: The pedagogical power of museums  

Directory of Open Access Journals (Sweden)

Full Text Available Following a special conference focused on object-based learning in higher education at University College London (UCL, this paper provides the overview for a series of subsequent papers which explore the value of object-based learning, including the pedagogical framework for museum learning in the university classroom and practice led examples from a range of disciplines. Object-based learning in higher education draws on many of the learning strategies already known to inform students, including active learning and experiential learning; this collection of papers draws together examples of object-based learning pioneered at UCL and seeks to encourage enhanced use of university collections in new, pedagogically powerful, modes.

Helen J. Chatterjee

2010-01-01

236

A simple and efficient object detection method based on saliency measure for infrared radiation image  

Science.gov (United States)

Detection of visually salient objects plays an important role in applications such as object segmentation, adaptive compression, object recognition, etc. A simple and computationally efficient method is presented in this paper for detecting visually salient objects in Infrared Radiation images. The proposed method can be divided into three steps. Firstly, the infrared image is pre-processed to increase the contrast between objects and background. Secondly, the spectral residual of the pre-processed image is extracted in the log spectrum, then via corresponding inverse transform and threshold segmentation we can get the rough regions of the salient objects. Finally, we apply a sliding window to acquire the explicit position of the salient objects using the probabilistic interpretation of the semi-local feature contrast which is estimated by comparing the gray level distribution of the object and the surrounding area in the original image. And as we change the size of the sliding window, different size of objects can be found out. In our proposed method, the first two steps combined together to play a role in narrowing the searching region and thus accelerating computation. The third procedure is applied to extract the salient objects. We test our method on abundant amount of Infrared Radiation images, and the results show that our saliency detection based object detection method is effective and robust.

Sun, Zhaolei; Hui, Bin

2014-11-01

237

An Ada-based preprocessor language for concurrent object oriented programming  

International Nuclear Information System (INIS)

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)

238

Image-based approaches for photo-realistic rendering of complex objects  

OpenAIRE

One principal intention of computer graphics is the achievement of photorealism. With physically-based methods, achieving photorealism is still computationally demanding. This dissertation proposes new approaches for image-based visualization of complex objects, concentrating on clothes. The developed methods use real images as appearance examples to guide complex animation or texture modification processes, combining the photorealism of images with the ability to animate or modify an object....

Hilsmann, Anna

2014-01-01

239

A LOW INDEXED CONTENT BASED NEURAL NETWORK APPROACH FOR NATURAL OBJECTS RECOGNITION  

OpenAIRE

In this paper, an approach to integral color texture invariant information with a neural networkapproach to object recognition is proposed. A color-texture context for image retrieval system based onthe integral information of an image is represented as one compact representation base on colorhistogram approach. A general and efficient design approach using a neural classifier to cope with smalltraining sets of high dimension, which is a problem frequency encountered in object recognition, is...

Shyama Chandra Prasad, G.; Govardhan, Dr A.; Rao, Dr T. V.

2009-01-01

240

Object classification methods for application in FPGA based vehicle video detector  

OpenAIRE

The paper presents a discussion of properties of object classification methods utilized in processing video streams from a camera. Methods based on feature extraction, model fitting and invariant determination are evaluated. Petri nets are used for modelling the processing flow. Data objects and transitions are defined which are suitable for efficient implementation in FPGA circuits. Processing characteristics and problems of the implementations are shown. An invariant based method is assesse...

Pamu?a, Wies?aw

2009-01-01

241

Study on geographic ontology based on object-oriented remote sensing analysis  

Science.gov (United States)

In the preprocessing course of spatial data, different departments always have diverse naming methods when describing the same geographical entity, due to different backgrounds and views of angle. There is also great difference among the feature sets which are used to describe concepts of geo-ontology, making it difficult to conduct semantic interoperation based on the theory of concepts reasoning in the information science. Consequently, this paper takes green land system for example and presents a reasoning method of geo-ontology based on object-oriented remote sensing analysis. We firstly establish an image hierarchical network system by using the object-oriented multi-scale segmentation technology. Then, the mapping from domain ontologies to image objects is realized by the maximum area method. Finally, through analyzing the features of image objects, the reasoning principles are built up, realizing the semantic interoperation between concepts of ontologies and image objects.

Cui, Wei; Gao, Liping; Le, Wang; Li, Deren

2008-12-01

242

An Efficient Lagrangean Relaxation-based Object Tracking Algorithm in Wireless Sensor Networks  

OpenAIRE

In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs). Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based) heuristic algorithm is p...

Frank Yeong-Sung Lin; Cheng-Ta Lee

2010-01-01

243

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

Energy Technology Data Exchange (ETDEWEB)

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.

Fan, W J; Lu, Y [College of Metrological Technology and Engineering, China Jiliang University, Hangzhou (China)

2006-10-15

244

Object density-based image segmentation and its applications in biomedical image analysis.  

Science.gov (United States)

In many applications of medical image analysis, the density of an object is the most important feature for isolating an area of interest (image segmentation). In this research, an object density-based image segmentation methodology is developed, which incorporates intensity-based, edge-based and texture-based segmentation techniques. The proposed method consists of three main stages: preprocessing, object segmentation and final segmentation. Image enhancement, noise reduction and layer-of-interest extraction are several subtasks of preprocessing. Object segmentation utilizes a marker-controlled watershed technique to identify each object of interest (OI) from the background. A marker estimation method is proposed to minimize over-segmentation resulting from the watershed algorithm. Object segmentation provides an accurate density estimation of OI which is used to guide the subsequent segmentation steps. The final stage converts the distribution of OI into textural energy by using fractal dimension analysis. An energy-driven active contour procedure is designed to delineate the area with desired object density. Experimental results show that the proposed method is 98% accurate in segmenting synthetic images. Segmentation of microscopic images and ultrasound images shows the potential utility of the proposed method in different applications of medical image processing. PMID:19473717

Yu, Jinhua; Tan, Jinglu

2009-12-01

245

An Automatic Algorithm for Object Recognition and Detection Based on Asift Keypoints  

Directory of Open Access Journals (Sweden)

Full Text Available Object recognition is an important task in image processing and computer vision. This paper presents aperfect method for object recognition with full boundary detection by combining affine scale invariantfeature transform (ASIFT and a region merging algorithm. ASIFT is a fully affine invariant algorithm that means features are invariant to six affine parameters namely translation (2 parameters, zoom, rotation and two camera axis orientations. The features are very reliable and give us strong keypoints that can be used for matching between different images of an object. We trained an object in several images with different aspects for finding best keypoints of it. Then, a robust region merging algorithm is used torecognize and detect the object with full boundary in the other images based on ASIFT keypoints and a similarity measure for merging regions in the image. Experimental results show that the presented method is very efficient and powerful to recognize the object and detect it with high accuracy.

Reza Oji

2012-11-01

246

Modeling and Analysis for Obstacle Avoidance of a Behavior-Based Robot with Objected Oriented Methods  

Directory of Open Access Journals (Sweden)

Full Text Available Object Modeling Technique is widely applied in the field of software engineering; and in this paper we applied this technique to model a mobile robot including its behaviors and interactions with environment. The paper first describes key background knowledge about object oriented analysis in software engineering, behavior based robotics and their similarities. Then, based on these similarities, the paper uses object oriented methods of software engineering, such as unified modeling language (UML, to analyze and model the architecture; and to design behaviors for a behavior-based robot, which is expected to wander with autonomous obstacle avoidance in unknown environment. Object oriented methods permit a translation from conceptual behavior models to computer programming representations, and separate concrete control algorithms from robot modeling. With this approach, the paper implements a fuzzy algorithm for obstacle avoidance behavior of the constructed behavior models in a physical robot, and made experiments in the given indoor environment.

Qian Zhang

2009-04-01

247

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

Directory of Open Access Journals (Sweden)

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.

L. DJEROU,

2012-01-01

248

Object and rule based approach for classification of high spatial resolution data over urban areas  

Science.gov (United States)

Using the inherent features of high resolution data, such as the shape and the texture, this paper proposed an object and rule based fuzzy classification method. First, multi-scale segmentations were used to obtain homogeneous objects at different scales. According to fuzzy classification ideas, these segmented objects were further classified by using the corresponding spectral, shape, texture, topology and other object-related characteristics. This method not only overcomes the limitations of pixel based classifications, but also takes advantage of the inherent features of high resolution data. To fully compare and analyze the proposed classification method, an IKONOS image of urban areas was selected as test data. According to four main classification steps, this data was classified as houses, roads, vegetation, and bare land. The classification results showed that the proposed method enhances the accuracy of classification and is of great advantages compared with the traditional pixel based classification methods.

Ni, Li

2010-09-01

249

Study on tracking technology of the moving object based on computer vision  

Science.gov (United States)

The tracking technology of the moving object has been an active topic of the visual tracking system. In this paper, the tracking algorithms are classified into four classes: correlation-based methods, boundary-based methods, model-based methods and multifunctional methods. Based on the analysis of the advantages and disadvantages of all these algorithms, a new tracking algorithm, integrating SSDA and advanced Camshift algorithm, is put forward here.

Xu, Ranran; Xu, Zhenying; Li, Boquan

2010-10-01

250

A Framework based on concurrent Object-oriented programming for Building Behavior-based Control Systems for Mobile Robots  

Scientific Electronic Library Online (English)

Full Text Available SciELO Brazil | Language: English Abstract in english An approach based on concurrent object oriented programming (COOP) to build a control system for a mobile robot is presented. A behavior-based control system is decomposed in intercommunicating concurrent objects named Agents. These agents belong to five categories: Primitive Sensor, Virtual Sensor, [...] Behavior, Primitive Actuator and Virtual Actuator. Based on this approach, a C++ tool is developed, where the categories above are implemented as C++ classes, in which built-in communication mechanisms are included. Each class has a standard interface and functionality. It is possible, then, to develop a complex control system by deriving new classes from the base classes and by instantiating objects. These objects are interconnected in a dynamic manner and thus building a control system with different behavior levels, which is able to react to environment changes.

José Eduardo Mendonça, Xavier; Hansjörg Andreas, Schneebeli.

1998-04-01

251

An Object-Level High-Order Contextual Descriptor Based on Semantic, Spatial, and Scale Cues.  

Science.gov (United States)

Context has been playing an increasingly important role in areas such as object detection, scene understanding, and image segmentation. Although many different types of contextual cues have been successfully explored, most of them only consider the pair-wise relationship between objects or parts. Several models utilize the high-order relationship for encoding contextual information. However, they mainly use a single contextual cue. In this paper, we present a novel high-order contextual descriptor (HOOD) to measure the strength of interactions among objects within an image. Heterogeneous contextual cues like semantic, spatial, and scale contexts are jointly integrated into HOOD to define the high-order interactions. The strength of these interactions are inferred by applying Bayes' rule on the pure dependence of the involved objects. As a result, an object-level graph is constructed to represent the contextually consistent interactions. Moreover, we propose a HOOD based object localization framework to verify the effectiveness of HOOD. Experimental results on two benchmark datasets including SUN09 and PASCAL2007 show that our framework outperforms the state-of-the-art context based object localization methods. Finally, we apply HOOD on two multimedia applications: structured image retrieval and out-of-context object detection, which demonstrates the potential usages of HOOD. PMID:25204006

Cao, Xiaochun; Wei, Xingxing; Han, Yahong; Chen, Xiaowu

2014-09-01

252

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

Directory of Open Access Journals (Sweden)

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.

Seoksoo Kim

2010-11-01

253

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

Science.gov (United States)

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. PMID:22163444

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

2010-01-01

254

A generic discriminative part-based model for geospatial object detection in optical remote sensing images  

Science.gov (United States)

Detecting geospatial objects with complex structure has been explored for years and it is still a challenging task in high resolution optical remote sensing images (RSI) interpretation. In this paper, we mainly focus on the problem of rotation variance in detecting geospatial objects and propose a generic discriminative part-based model (GDPBM) to build a practical object detection framework. In our model, a geospatial object with arbitrary orientation is divided into several parts and represented via three terms: the appearance features, the spatial deformation features and the rotation deformation features. The appearance features characterize the local patch appearance of the object and parts, and we propose a new kind of rotation invariant feature to represent the appearance using the local intensity gradients. The spatial deformation features capture the geometric deformation of parts by representing the relative displacements among parts. The rotation deformation features define the pose variances of the parts relative to the objects based on their dominant orientations. In generating the two deformation features, we introduce the statistic methods to encode the features in the category level. Concatenating the three terms of the features, a classifier based on the support vector machine is learned to detect geospatial objects. In the experiments, two datasets in optical RSI are used to evaluate the performance of our model and the results demonstrate its robustness and effectiveness.

Zhang, Wanceng; Sun, Xian; Wang, Hongqi; Fu, Kun

2015-01-01

255

Mining moving object trajectories in location-based services for spatio-temporal database update  

Science.gov (United States)

Advances in wireless transmission and mobile technology applied to LBS (Location-based Services) flood us with amounts of moving objects data. Vast amounts of gathered data from position sensors of mobile phones, PDAs, or vehicles hide interesting and valuable knowledge and describe the behavior of moving objects. The correlation between temporal moving patterns of moving objects and geo-feature spatio-temporal attribute was ignored, and the value of spatio-temporal trajectory data was not fully exploited too. Urban expanding or frequent town plan change bring about a large amount of outdated or imprecise data in spatial database of LBS, and they cannot be updated timely and efficiently by manual processing. In this paper we introduce a data mining approach to movement pattern extraction of moving objects, build a model to describe the relationship between movement patterns of LBS mobile objects and their environment, and put up with a spatio-temporal database update strategy in LBS database based on trajectories spatiotemporal mining. Experimental evaluation reveals excellent performance of the proposed model and strategy. Our original contribution include formulation of model of interaction between trajectory and its environment, design of spatio-temporal database update strategy based on moving objects data mining, and the experimental application of spatio-temporal database update by mining moving objects trajectories.

Guo, Danhuai; Cui, Weihong

2008-10-01

256

Foreground object detection using top-down information based on EM framework.  

Science.gov (United States)

In this paper, we present a novel foreground object detection scheme that integrates the top-down information based on the expectation maximization (EM) framework. In this generalized EM framework, the top-down information is incorporated in an object model. Based on the object model and the state of each target, a foreground model is constructed. This foreground model can augment the foreground detection for the camouflage problem. Thus, an object's state-specific Markov random field (MRF) model is constructed for detection based on the foreground model and the background model. This MRF model depends on the latent variables that describe each object's state. The maximization of the MRF model is the M-step in the EM framework. Besides fusing spatial information, this MRF model can also adjust the contribution of the top-down information for detection. To obtain detection result using this MRF model, sampling importance resampling is used to sample the latent variable and the EM framework refines the detection iteratively. Besides the proposed generalized EM framework, our method does not need any prior information of the moving object, because we use the detection result of moving object to incorporate the domain knowledge of the object shapes into the construction of top-down information. Moreover, in our method, a kernel density estimation (KDE)-Gaussian mixture model (GMM) hybrid model is proposed to construct the probability density function of background and moving object model. For the background model, it has some advantages over GMM- and KDE-based methods. Experimental results demonstrate the capability of our method, particularly in handling the camouflage problem. PMID:22645266

Liu, Zhou; Huang, Kaiqi; Tan, Tieniu

2012-09-01

257

On-line object tracking method based on co-training  

Science.gov (United States)

The tracking method based on Co-Training framework considers the object tracking as a semi-supervised learning problem. This paper proposes a new on-line tracking method based on Co-Training framework. The method fuses two features to describe the object and do randomizing affine deformation with positive examples to increase the number of positive examples. Experimental results demonstrate, the on-line tracking method based on Co-training framework can work robustly in long-term tracking and the drift of tracking can be effectively avoided.

Lai, Jianhong; Peng, Zhenming; Yang, Yong

2012-11-01

258

A genetic algorithm based multi-objective shape optimization scheme for cementless femoral implant.  

Science.gov (United States)

The shape and geometry of femoral implant influence implant-induced periprosthetic bone resorption and implant-bone interface stresses, which are potential causes of aseptic loosening in cementless total hip arthroplasty (THA). Development of a shape optimization scheme is necessary to achieve a trade-off between these two conflicting objectives. The objective of this study was to develop a novel multi-objective custom-based shape optimization scheme for cementless femoral implant by integrating finite element (FE) analysis and a multi-objective genetic algorithm (GA). The FE model of a proximal femur was based on a subject-specific CT-scan dataset. Eighteen parameters describing the nature of four key sections of the implant were identified as design variables. Two objective functions, one based on implant-bone interface failure criterion, and the other based on resorbed proximal bone mass fraction (BMF), were formulated. The results predicted by the two objective functions were found to be contradictory; a reduction in the proximal bone resorption was accompanied by a greater chance of interface failure. The resorbed proximal BMF was found to be between 23% and 27% for the trade-off geometries as compared to ?39% for a generic implant. Moreover, the overall chances of interface failure have been minimized for the optimal designs, compared to the generic implant. The adaptive bone remodeling was also found to be minimal for the optimally designed implants and, further with remodeling, the chances of interface debonding increased only marginally. PMID:25392855

Chanda, Souptick; Gupta, Sanjay; Kumar Pratihar, Dilip

2015-03-01

259

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

Science.gov (United States)

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.

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

2014-03-01

260

A Fast Object Tracking Approach Based on the Motion Vector in a Compressed Domain  

Directory of Open Access Journals (Sweden)

Full Text Available Particle set sampling and weighting are both at the core of particle filter?based object tracking methods. Aiming to optimally represent the object?s motion state, a large amount of particles ? in the classical particle method ? is a prerequisite. The high?cost calculation of these particles significantly slows down the convergence of the algorithm. To this problem, a prior approach which originated from the process of video compressing and uncompressing is introduced to optimize the phase of particle sampling, making the collected particles centre on and cover the object region in the current image. This advantage dramatically reduces the number of particles required by the regularized particle sampling method, solving the problem of the high computational cost for tracking objects, while the performance of the algorithm is stable.

Hui-bin Wang

2013-01-01

261

An object oriented framework of EPICS for MicroTCA based control system  

International Nuclear Information System (INIS)

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)

262

Brazilian Proposal for Agent-Based Learning Objects Metadata Standard - OBAA  

Science.gov (United States)

This paper presents the Agent Based Learning Objects - OBAA standard proposal. The main goal of the research was to establish a standardized specification of the technical and functional requirements of interoperable learning objects. In our context, interoperability regards the operation of the content inside Web, Digital TV and mobile environments, supporting accessibility and pedagogical issues. In this proposal it has been explored the convergence among the multi-agent systems, learning object and ubiquitous computing technologies, allowing the authoring, storage and recovery of learning object in varied contexts and through different digital platforms. The result of this research was the definition of the OBAA proposal containing the requirements, specifications and architectures that will compose the Brazilian standard for the management, transmission, storage, search, editing and use of interoperable learning object.

Vicari, Rosa Maria; Ribeiro, Alexandre; da Silva, Júlia Marques Carvalho; Santos, Elder Rizzon; Primo, Tiago; Bez, Marta

263

Key Object Discovery and Tracking Based on Context-Aware Saliency  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, we propose an online key object discovery and tracking system based on visual saliency. We formulate the problem as a temporally consistent binary labelling task on a conditional random field and solve it by using a particle filter. We also propose a context?aware saliency measurement, which can be used to improve the accuracy of any static or dynamic saliency maps. Our refined saliency maps provide clearer indications as to where the key object lies. Based on good saliency cues, we can further segment the key object inside the resulting bounding box, considering the spatial and temporal context. We tested our system extensively on different video clips. The results show that our method has significantly improved the saliency maps and tracks the key object accurately.

Geng Zhang

2013-01-01

264

???????????Pareto???????? Pareto Multi-Objective Distribution Network Reconfiguration Based on Improved Niche Particle Swarm Optimization Algorithm  

Directory of Open Access Journals (Sweden)

Full Text Available ????????????????????????????????????????????????????????????????????5???“????????????Pareto????????”?????????????????????????????????????????????????????????????????????????Pareto??????????????????????????????????????Pareto??????????????????????????????????????????????????????????????????????????????????????????????????????????????????Distribution network reconfiguration can improve the operation security, economy and power qua- lity of distribution network, for the current national construction and application of distribution automation system it has great significance. This paper presents a multi-objective distribution network optimal reconfi- guration of the particle swarm algorithm which based on a niche technology, the introduction of the concept of Pareto optimal to achieve a true sense of the multi-objective optimization; apply the particle swarm algori- thm to achieve the search of the Pareto optimal solution set of multi-objective reconfiguration, using niche technology and mutation operators to maintain the population diversity and dispersion, improved particle swarm algorithm global convergence reliability and convergence speed. Theoretical analysis and numerical results show that: distribution network reconfiguration based on niche particle swarm optimization meet the requirements in the speed and accuracy, and have more practical significance than the single-objective op- timization.

???

2011-12-01

265

An Efficient Lagrangean Relaxation-based Object Tracking Algorithm in Wireless Sensor Networks  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs. Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based heuristic algorithm is proposed for solving the optimization problem. Experimental results showed that the proposed algorithm achieves near optimization in energy-efficient object tracking. Furthermore, the algorithm is very efficient and scalable in terms of the solution time.

Frank Yeong-Sung Lin

2010-08-01

266

3D-modeling of deformed halite hopper crystals by Object Based Image Analysis  

Science.gov (United States)

Object Based Image Analysis (OBIA) is an established method for analyzing multiscale and multidimensional imagery in a range of disciplines. In the present study this method was used for the 3D reconstruction of halite hopper crystals in a mudrock sample, based on Computed Tomography data. To quantitatively assess the reliability of OBIA results, they were benchmarked against a corresponding "gold standard", a reference 3D model of the halite crystals that was derived by manual expert digitization of the CT images. For accuracy assessment, classical per-scene statistics were extended to per-object statistics. The strength of OBIA was to recognize all objects similar to halite hopper crystals and in particular to eliminate cracks. Using a support vector machine (SVM) classifier on top of OBIA, unsuitable objects like halite crystal clusters, polyhalite-coated crystals and spherical halite crystals were effectively dismissed, but simultaneously the number of well-shaped halites was reduced.

Leitner, Christoph; Hofmann, Peter; Marschallinger, Robert

2014-12-01

267

A weight space-based approach to fuzzy multiple-objective linear programming  

OpenAIRE

In this paper, the effects of uncertainty on multiple-objective linear programming models are studied using the concepts of fuzzy set theory. The proposed interactive decision support system is based on the interactive exploration of the weight space. The comparative analysis of indifference regions on the various weight spaces (which vary according to intervals of values of the satisfaction degree of objective functions and constraints) enables to study the stability and evolution of the bas...

Borges, Ana Rosa; Antunes, Carlos Henggeler

2003-01-01

268

A theorem prover-based analysis tool for object-oriented databases  

OpenAIRE

We present a theorem-prover based analysis tool for object-oriented database systems with integrity constraints. Object-oriented database specifications are mapped to higher-order logic (HOL). This allows us to reason about the semantics of database operations using a mechanical theorem prover such as Isabelle or PVS. The tool can be used to verify various semantics requirements of the schema (such as transaction safety, compensation, and commutativity) to support the advanced transaction mod...

Spelt, D.; Even, S. J.

1999-01-01

269

Shifting Attention in Viewer and Object-Based Reference Frames after Unilateral Brain Injury  

OpenAIRE

The aims of the present study were to investigate the respective roles that object- and viewer-based reference frames play in reorienting visual attention, and to assess their influence after unilateral brain injury. To do so, we studied 16 right hemisphere injured (RHI) and 13 left hemisphere injured (LHI) patients. We used a cueing design that manipulates the location of cues and targets relative to a display comprised of two rectangles (i.e., objects). Unlike previous studies with patients...

List, Alexandra; Landau, Ayelet N.; Brooks, Joseph L.; Flevaris, Anastasia; Fortenbaugh, Francesca; Esterman, Michael; Vanvleet, Thomas M.; Albrecht, Alice R.; Alvarez, Bryan; Robertson, Lynn C.; Schendel, Krista

2011-01-01

270

Real world object based access to architecture learning material - the MACE experience  

OpenAIRE

The MACE project aims to support architecture students while searching for learning materials by offering advanced graphical metadata-based access to learning resources in architecture across repository boundaries. Therefore, the MACE system uses real world object representations which serve as connection between learning materials. This enables the students to explore new and more complete learning paths. In this paper we outline the generation and usage of real world object representations ...

Niemann, K.; Wolpers, M.

2010-01-01

271

An object-oriented, knowledge-based system for cardiovascular rehabilitation--phase II.  

OpenAIRE

The Heart Monitor is an object-oriented, knowledge-based system designed to support the clinical activities of cardiovascular (CV) rehabilitation. The original concept was developed as part of graduate research completed in 1992. This paper describes the second generation system which is being implemented in collaboration with a local heart rehabilitation program. The PC UNIX-based system supports an extensive patient database organized by clinical areas. In addition, a knowledge base is empl...

Ryder, R. M.; Inamdar, B.

1995-01-01

272

MULTI OBJECTIVE OPTIMIZATION OF VEHICLE ACTIVE SUSPENSION SYSTEM USING DEBBO BASED PID CONTROLLER  

OpenAIRE

This paper proposes the Multi Objective Optimization (MOO) of Vehicle Active Suspension System (VASS) with a hybrid Differential Evolution (DE) based Biogeography-Based Optimization (BBO) (DEBBO) for the parameter tuning of Proportional Integral Derivative (PID) controller. Initially a conventional PID controller, secondly a BBO, an rising nature enthused global optimization procedure based on the study of the ecological distribution of biological organisms and a hybridized DEBBO algorithm wh...

Kalaivani Rajagopal; Lakshmi Ponnusamy

2014-01-01

273

A Methodology to Evaluate Object oriented Software Systems Using Change Requirement Traceability Based on Impact Analysis  

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Full Text Available It is a well known fact that software maintenance plays a major role and finds importance in software development life cycle. As object - oriented programming has become the standard, it is very important to understand the problems of maintaining object -oriented software systems. This paper aims at evaluating object - oriented software system through change requirement traceability – based impact analysis methodology for non functional requirements using functional requirements . The major issues have been related to change impact algorithms and inheritance of functionality.

Sunil T. D

2014-06-01

274

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

Science.gov (United States)

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.

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

2007-06-01

275

Estimating Selectivity for Current Query of Moving Objects Using Index-Based Histogram  

Science.gov (United States)

Selectivity estimation is one of the query optimization techniques. It is difficult for the previous selectivity estimation techniques for moving objects to apply the location change of moving objects to synopsis. Therefore, they result in much error when estimating selectivity for queries, because they are based on the extended spatial synopsis which does not consider the property of the moving objects. In order to reduce the estimation error, the existing techniques should often rebuild the synopsis. Consequently problem occurs, that is, the whole database should be read frequently. In this paper, we proposed a moving object histogram method based on quadtree to develop a selectivity estimation technique for moving object queries. We then analyzed the performance of the proposed method through the implementation and evaluation of the proposed method. Our method can be used in various location management systems such as vehicle location tracking systems, location based services, telematics services, emergency rescue service, etc in which the location information of moving objects changes over time.

Chi, Jeong Hee; Kim, Sang Ho

276

Region-Based Object Recognition by Color Segmentation Using a Simplified PCNN.  

Science.gov (United States)

In this paper, we propose a region-based object recognition (RBOR) method to identify objects from complex real-world scenes. First, the proposed method performs color image segmentation by a simplified pulse-coupled neural network (SPCNN) for the object model image and test image, and then conducts a region-based matching between them. Hence, we name it as RBOR with SPCNN (SPCNN-RBOR). Hereinto, the values of SPCNN parameters are automatically set by our previously proposed method in terms of each object model. In order to reduce various light intensity effects and take advantage of SPCNN high resolution on low intensities for achieving optimized color segmentation, a transformation integrating normalized Red Green Blue (RGB) with opponent color spaces is introduced. A novel image segmentation strategy is suggested to group the pixels firing synchronously throughout all the transformed channels of an image. Based on the segmentation results, a series of adaptive thresholds, which is adjustable according to the specific object model is employed to remove outlier region blobs, form potential clusters, and refine the clusters in test images. The proposed SPCNN-RBOR method overcomes the drawback of feature-based methods that inevitably includes background information into local invariant feature descriptors when keypoints locate near object boundaries. A large number of experiments have proved that the proposed SPCNN-RBOR method is robust for diverse complex variations, even under partial occlusion and highly cluttered environments. In addition, the SPCNN-RBOR method works well in not only identifying textured objects, but also in less-textured ones, which significantly outperforms the current feature-based methods. PMID:25494514

Chen, Yuli; Ma, Yide; Kim, Dong Hwan; Park, Sung-Kee

2014-12-01

277

Object shape-based optical sensing methodology and system for condition monitoring of contaminated engine lubricants  

Science.gov (United States)

Presence of contaminants, such as gasoline, moisture, and coolant in the engine lubricant indicates mechanical failure within the engine and significantly reduces lubricant quality. This paper describes a novel sensing system, its methodology and experimental verifications for analysis of the presence of contaminants in the engine lubricants. The sensing methodology is based on the statistical shape analysis methodology utilizing optical analysis of the distortion effect when an object image is obtained through a thin random optical medium. The novelty of the proposed sensing system lies within the employed methodology which an object with a known periodic shape is introduced behind a thin film of the contaminated lubricant. In this case, an acquired image represents a combined lubricant-object optical appearance, where an a priori known periodical structure of the object is distorted by a contaminated lubricant. The object, e.g. a stainless steel woven wire cloth with a mesh size of 65×65 µm2 and a circular wire diameter of 33 µm was placed behind a microfluidic channel, containing engine lubricant and optical images of flowing lubricant with stationary object were acquired and analyzed. Several parameters of acquired optical images, such as, color of lubricant and object, object shape width at object and lubricant levels, object relative color, and object width non-uniformity coefficient, were proposed. Measured on-line parameters were used for optical analysis of fresh and contaminated lubricants. Estimation of contaminant presence and lubricant condition was performed by comparison of parameters for fresh and contaminated lubricants. Developed methodology was verified experimentally showing ability to distinguish lubricants with 1%, 4%, 7%, and 10% coolant, gasoline and water contamination individually and in a combination form of coolant (0%-5%) and gasoline (0%-5%).

Bordatchev, Evgueni; Aghayan, Hamid; Yang, Jun

2014-03-01

278

A Moving Object Extraction Method for Video Based on Color Kernel Histogram  

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Full Text Available The traditional moving object extraction method based on Gaussian model has such defects as poor anti-noise performance, bad real-time performance. Considering these shortcomings, this paper proposed a new moving object extraction method for color video image based on regional kernel histogram. The method first proposed the idea of kernel histogram description theory which utilizing the kernel histogram to describe the area of video image. Then a new metric function for measuring the kernel histogram model is proposed. According to the features of measurement values, using the Gaussian mixture model and the metric of kernel histogram model to build the model. At last, based on this model, the moving object of video images is extracted. The experimental results show that the algorithm have a better segmentation result and have the better anti-noise and real-time performance compared with the traditional Gaussian mixture model algorithm.

Cao Yujie

2013-01-01

279

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

Directory of Open Access Journals (Sweden)

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.

Muhammad Kamal

2011-10-01

280

Efficient Spam Filtering System Based on Smart Cooperative Subjective and Objective Methods  

Directory of Open Access Journals (Sweden)

Full Text Available Most of the spam filtering techniques are based on objective methods such as the content filtering and DNS/reverse DNS checks. Recently, some cooperative subjective spam filtering techniques are proposed. Objective methods suffer from the false positive and false negative classification. Objective methods based on the content filtering are time consuming and resource demanding. They are inaccurate and require continuous update to cope with newly invented spammer’s tricks. On the other side, the existing subjective proposals have some drawbacks like the attacks from malicious users that make them unreliable and the privacy. In this paper, we propose an efficient spam filtering system that is based on a smart cooperative subjective technique for content filtering in addition to the fastest and the most reliable non-content-based objective methods. The system combines several applications. The first is a web-based system that we have developed based on the proposed technique. A server application having extra features suitable for the enterprises and closed work groups is a second part of the system. Another part is a set of standard web services that allow any existing email server or email client to interact with the system. It allows the email servers to query the system for email filtering. They can also allow the users via the mail user agents to participate in the subjective spam filtering problem.

Samir A. Elsagheer Mohamed

2013-02-01

281

Semi-Automatic Objects Recognition in Urban Areas Based on Fuzzy Logic  

Directory of Open Access Journals (Sweden)

Full Text Available Three dimensional object extraction and recognition (OER from geographic data has been definitely one of more important topic in photogrammetry for quite a long time. Today, the capability of rapid generating high-density DSM increases the supply of geographic information but the discrete nature of the measuring makes more difficult to recognize correctly and to extract 3D objects from these surface. The proposed methodology wants to semi-automate some geographic objects clustering operations, in order to perform the recognition process. The clustering is a subjective process; the same set of data items often needs to be partitioned differently based on the application. Fuzzy logic gives the possibility to use in a mathematical process the uncertain information typical of human reasoning. The concept at the base of our proposal is to use the information contained in Image Matching or LiDAR DSM, and typically understood by the human operator, in a fuzzy recognition process able to combine the different input in order to perform the classification. So the object recognition approach proposed in our workflow integrates 3D structural descriptive components of objects, extracted from DSM, into a fuzzy reasoning process in order to exploit more fully all available information, which can contribute to the extraction and recognition process and, to handling the object’s vagueness. The recognition algorithm has been tested with to different data set and different objectives. An important issue is to apply the typical human process which allows to recognize objects in a range image in a fuzzy reasoning process. The investigations presented here have given a first demonstration of the capability of this approach.

Federico Prandi

2010-05-01

282

Moving object segmentation algorithm based on cellular neural networks in the H.264 compressed domain  

Science.gov (United States)

A cellular neural network (CNN)-based moving object segmentation algorithm in the H.264 compressed domain is proposed. This algorithm mainly utilizes motion vectors directly extracted from H.264 bitstreams. To improve the robustness of the motion vector information, the intramodes in I-frames are used for smooth and nonsmooth region classification, and the residual coefficient energy of P-frames is used to update the classification results first. Then, an adaptive motion vector filter is used according to interpartition modes. Finally, many CNN models are applied to implement moving object segmentation based on motion vector fields. Experiment results are presented to verify the efficiency and the robustness of this algorithm.

Feng, Jie; Chen, Yaowu; Tian, Xiang

2009-07-01

283

A LOW INDEXED CONTENT BASED NEURAL NETWORK APPROACH FOR NATURAL OBJECTS RECOGNITION  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, an approach to integral color texture invariant information with a neural networkapproach to object recognition is proposed. A color-texture context for image retrieval system based onthe integral information of an image is represented as one compact representation base on colorhistogram approach. A general and efficient design approach using a neural classifier to cope with smalltraining sets of high dimension, which is a problem frequency encountered in object recognition, isfocused in this paper for general images. The proposed system is tested for various colored imagesamples and the recognition accuracy is evaluated.

G.Shyama Chandra Prasad

2009-11-01

284

Key Object Discovery and Tracking Based on Context-Aware Saliency  

OpenAIRE

In this paper, we propose an online key object discovery and tracking system based on visual saliency. We formulate the problem as a temporally consistent binary labelling task on a conditional random field and solve it by using a particle filter. We also propose a context?aware saliency measurement, which can be used to improve the accuracy of any static or dynamic saliency maps. Our refined saliency maps provide clearer indications as to where the key object lies. Based on good saliency c...

Geng Zhang; Zejian Yuan; Nanning Zheng

2013-01-01

285

Goal-directed attention alters the tuning of object-based representations in extrastriate cortex  

Directory of Open Access Journals (Sweden)

Full Text Available Humans survive in environments that contain a vast quantity and variety of visual information. All items of perceived visual information must be represented within a limited number of brain networks. The human brain requires mechanisms for selecting only a relevant fraction of perceived information for more in-depth processing, where neural representations of that information may be actively maintained and utilized for goal-directed behavior. Object-based attention is crucial for goal-directed behavior and yet remains poorly understood. Thus, in the study we investigate how neural representations of visual object information are guided by selective attention. The magnitude of activation in human extrastriate cortex has been shown to be modulated by attention; however object-based attention is not likely to be fully explained by a localized gain mechanism. Thus, we measured information coded in spatially distributed patterns of brain activity with fMRI while human participants performed a task requiring selective processing of a relevant visual object category that differed across conditions. Using pattern classification and spatial correlation techniques, we found that the direction of selective attention is implemented as a shift in the tuning of object-based information representations within extrastriate cortex. In contrast, we found that representations within lateral prefrontal cortex coded for the attention condition rather than the concrete representations of object category. In sum, our findings are consistent with a model of object-based selective attention in which representations coded within extrastriate cortex are tuned to favor the representation of goal-relevant information, guided by more abstract representations within lateral prefrontal cortex.

AnthonyJ.-W.Chen

2012-06-01

286

Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches  

Directory of Open Access Journals (Sweden)

Full Text Available We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in the field. The six most common species, collectively representing 43% of the total trees sampled, were selected for mapping using pixel-based and object-based approaches. All pairs of species were separable based on spectral reflectance values in at least one band, with Peltophorum pterocarpum being most distinct from other species. Object-based approaches were consistently superior to pixel-based methods, which were particularly low in accuracy for tree species with small canopy sizes, such as Cocos nucifera and Roystonea regia. There was a strong and significant correlation between the number of trees determined on the ground and from object-based classification. Overall, object-based approaches appear capable of discriminating the six most common species in a challenging urban environment, with substantial heterogeneity of tree canopy sizes.

Harini Nagendra

2013-03-01

287

Review of holographic-based three-dimensional object recognition techniques [invited].  

Science.gov (United States)

With the advancement of computing and optical technologies, it is now possible to capture digital holograms of real-life object scenes. Theoretically, through the analysis of a hologram, the three-dimensional (3D) objects coded on the hologram can be identified. However, being different from an optical image, a hologram is composed of complicated fringes that cannot be analyzed easily with traditional computer vision methods. Over the years, numerous important research investigations have been attempted to provide viable solutions to this problem. The aim of this work is three-fold. First, we provide a quick walkthrough on the overall development of holographic-based 3D object recognition (H3DOR) in the past five decades, from film-based approaches to digital-based innovation. Second, we describe in more detail a number of selected H3DOR methods that are introduced at different timelines, starting from the late sixties and then from the seventies, where viable digital holographic-based 3D recognition methods began to emerge. Finally, we present our work on digital holographic, pose-invariant 3D object recognition that is based on a recently introduced virtual diffraction plane framework. As our method has not been reported elsewhere, we have included some experimental results to demonstrate the feasibility of the approach. PMID:25322141

Tsang, P W M; Poon, T-C; Liu, J-P; Situ, W C

2014-09-20

288

Learning cascaded shared-boost classifiers for part-based object detection.  

Science.gov (United States)

This paper focuses on the problem of detecting a number of different class objects in images. We present a novel part-based model for object detection with cascaded classifiers. The coarse root and fine part classifiers are combined into the model. Different from the existing methods which learn root and part classifiers independently, we propose a shared-Boost algorithm to jointly train multiple classifiers. This paper is distinguished by two key contributions. The first is to introduce a new definition of shared features for similar pattern representation among multiple classifiers. Based on this, a shared-Boost algorithm which jointly learns multiple classifiers by reusing the shared feature information is proposed. The second contribution is a method for constructing a discriminatively trained part-based model, which fuses the outputs of cascaded shared-Boost classifiers as high-level features. The proposed shared-Boost-based part model is applied for both rigid and deformable object detection experiments. Compared with the state-of-the-art method, the proposed model can achieve higher or comparable performance. In particular, it can lift up the detection rates in low-resolution images. Also the proposed procedure provides a systematic framework for information reusing among multiple classifiers for part-based object detection. PMID:24808352

Yali Li; Shengjin Wang; Qi Tian; Xiaoqing Ding

2014-04-01

289

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

Directory of Open Access Journals (Sweden)

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.

Kompatsiaris Ioannis

2004-01-01

290

Incrementally learning objects by touch: online discriminative and generative models for tactile-based recognition.  

Science.gov (United States)

Human beings not only possess the remarkable ability to distinguish objects through tactile feedback but are further able to improve upon recognition competence through experience. In this work, we explore tactile-based object recognition with learners capable of incremental learning. Using the sparse online infinite Echo-State Gaussian process (OIESGP), we propose and compare two novel discriminative and generative tactile learners that produce probability distributions over objects during object grasping/palpation. To enable iterative improvement, our online methods incorporate training samples as they become available. We also describe incremental unsupervised learning mechanisms, based on novelty scores and extreme value theory, when teacher labels are not available. We present experimental results for both supervised and unsupervised learning tasks using the iCub humanoid, with tactile sensors on its five-fingered anthropomorphic hand, and 10 different object classes. Our classifiers perform comparably to state-of-the-art methods (C4.5 and SVM classifiers) and findings indicate that tactile signals are highly relevant for making accurate object classifications. We also show that accurate "early" classifications are possible using only 20-30 percent of the grasp sequence. For unsupervised learning, our methods generate high quality clusterings relative to the widely-used sequential k-means and self-organising map (SOM), and we present analyses into the differences between the approaches. PMID:25532151

Soh, Harold; Demiris, Yiannis

2014-01-01

291

An approach for autonomous space object identification based on normalized AMI and illumination invariant MSA  

Science.gov (United States)

The space environment is becoming more and more severe and crowded because of the rapid growth of space objects, which reveals an urgent demand to protect active satellites and other space assets. To accomplish such missions, e.g. the collision warning, the identification of space objects is important. In this paper, a three-stage approach for autonomous space object identification based on optical images is proposed. Firstly, on the basis of the approximate perspective imaging model, a scale and illumination invariant descriptor, composed of the normalized affine moment invariants (AMI) and the illumination invariant multiscale autoconvolution (MSA) transform, is developed to characterize the space object. Secondly, a multi-view modeling method is applied to construct multi-view databases of space objects for handling the viewpoint change. Finally, considering the extensibility of the databases, a K-nearest neighbor classifier is employed, and a K-means clustering is adopted to boost the search speed. Furthermore, to test the performance, a novel system based on the proposed approach is built and evaluated. The experimental evidence suggests that the system is stable and works well when the scale of a space object, the phase angle and the viewpoint change.

Ding, Hao; Li, Xudong; Zhao, Huijie

2013-03-01

292

Track-Before-Detect Algorithm for Faint Moving Objects based on Random Sampling and Consensus  

Science.gov (United States)

There are many algorithms developed for tracking and detecting faint moving objects in congested backgrounds. One obvious application is detection of targets in images where each pixel corresponds to the received power in a particular location. In our application, a visible imager operated in stare mode observes geostationary objects as fixed, stars as moving and non-geostationary objects as drifting in the field of view. We would like to achieve high sensitivity detection of the drifters. The ability to improve SNR with track-before-detect (TBD) processing, where target information is collected and collated before the detection decision is made, allows respectable performance against dim moving objects. Generally, a TBD algorithm consists of a pre-processing stage that highlights potential targets and a temporal filtering stage. However, the algorithms that have been successfully demonstrated, e.g. Viterbi-based and Bayesian-based, demand formidable processing power and memory. We propose an algorithm that exploits the quasi constant velocity of objects, the predictability of the stellar clutter and the intrinsically low false alarm rate of detecting signature candidates in 3-D, based on an iterative method called "RANdom SAmple Consensus” and one that can run real-time on a typical PC. The technique is tailored for searching objects with small telescopes in stare mode. Our RANSAC-MT (Moving Target) algorithm estimates parameters of a mathematical model (e.g., linear motion) from a set of observed data which contains a significant number of outliers while identifying inliers. In the pre-processing phase, candidate blobs were selected based on morphology and an intensity threshold that would normally generate unacceptable level of false alarms. The RANSAC sampling rejects candidates that conform to the predictable motion of the stars. Data collected with a 17 inch telescope by AFRL/RH and a COTS lens/EM-CCD sensor by the AFRL/RD Satellite Assessment Center is used to assess the performance of the algorithm. In the second application, a visible imager operated in sidereal mode observes geostationary objects as moving, stars as fixed except for field rotation, and non-geostationary objects as drifting. RANSAC-MT is used to detect the drifter. In this set of data, the drifting space object was detected at a distance of 13800 km. The AFRL/RH set of data, collected in the stare mode, contained the signature of two geostationary satellites. The signature of a moving object was simulated and added to the sequence of frames to determine the sensitivity in magnitude. The performance compares well with the more intensive TBD algorithms reported in the literature.

Dao, P.; Rast, R.; Schlaegel, W.; Schmidt, V.; Dentamaro, A.

2014-09-01

293

Research on digital holographic interferometry based on EALCD in three dimensional deformation measurements of objects  

Science.gov (United States)

In recent years, with the occurrence of photoelectric sensitive devices such as charge-coupled device (CCD) and electrical addressed liquid crystal display (EALCD), particularly with the development in the computer technology, digital holography interferometry has been developing rapidly. Digital holography was one of the important techniques in the field of three-dimensional non-contact testing. In order to realize the digitalization and real-time of the holographic interferometry, the digital holographic interferometry system based on EALCD and CCD for displacement and three dimensional deformation measurement of objects is established. The hologram and reconstructed hologram are recorded by CCD. The hologram is reconstructed by EALCD instead of holographic plate. The interference fringes which caused by micro displacement and deformation has been realized by this system. The reconstruction images of objects and holographic interference fringes are all preferable. In this paper, the holographic reconstruction and interference fringe has been enhanced and denoised by compiling program based on MATLAB and C++ in digital image processing. The single algorithm cannot meet the requirement of the experiment, so various algorithms were used in combination in order to enhance image quality and make interpretation and calculation more convenient. The straight fringes reflect the micro displacement of the object, the bending fringes represent the deformation of the object. Based many advantages of CCD and EALCD, the actual holographic interferogram with high contrast can be obtained quickly in our experiments, which the precise measurement of micro displacement and three-dimensional deformation of objects can be acquired

Li, Quan-yong; Mu, Da; Guo, Jun; Sun, Jie; Wang, Wen-sheng

2012-10-01

294

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

International Nuclear Information System (INIS)

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

295

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

International Nuclear Information System (INIS)

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)

296

An object-based conceptual model of a nuclear physics experiments database  

International Nuclear Information System (INIS)

Nuclear physics experiments to be conducted at future accelerators will result in the accumulation of vast quantities of data. This paper briefly discusses an object-oriented database (OODB) approach for effectively managing this data and, more importantly, presents an object-based conceptual model of a nuclear physics experiments database as an initial by-product of applying this approach. The database model is provided by an object-relationship diagram (ORD). The concepts and conventions related to an ORD are explained, and the ORDs and associated terminology of the proposed model are given. Two prototypes that are currently being developed based on the model are also briefly discussed. The nuclear physics experiments database model and prototypes result from software research and development efforts associated with the Continuous Electron Beam Accelerator Facility (CEBAF). (orig.)

297

MODEL OF STORAGE XML DATABASE BASED ON THE RELATIONAL-OBJECT MODEL  

Directory of Open Access Journals (Sweden)

Full Text Available The objective of this work is to define a model of storage represented strictly in XML, this model is based on the structure of the relational model using the types of the model object. We suggest then creating our database according to this model by requests SQL3. We thus realize a framework of management and administration ofdatabases XML based on requests object relational SQL3. We analyze at first the mapping since a request SQL towards a structure of a document XML. We shall describe the mapping by using the language XML SCHEMA because the data must be validated in every operation on the database. The database structure, types and tables definitions will be transformed in an equivalent XML SCHEMA which will be used to store the valid XML data.

Laila Alami Kasri

2010-11-01

298

Costs and Advantages of Object-Based Image Coding with Shape-Adaptive Wavelet Transform  

Directory of Open Access Journals (Sweden)

Full Text Available Object-based image coding is drawing a great attention for the many opportunities it offers to high-level applications. In terms of rate-distortion performance, however, its value is still uncertain, because the gains provided by an accurate image segmentation are balanced by the inefficiency of coding objects of arbitrary shape, with losses that depend on both the coding scheme and the object geometry. This work aims at measuring rate-distortion costs and gains for a wavelet-based shape-adaptive encoder similar to the shape-adaptive texture coder adopted in MPEG-4. The analysis of the rate-distortion curves obtained in several experiments provides insight about what performance gains and losses can be expected in various operative conditions and shows the potential of such an approach for image coding.

Cagnazzo Marco

2007-01-01

299

Refreshment need metrics for improved shape and texture object-based resilient video coding.  

Science.gov (United States)

Video encoders may use several techniques to improve error resilience. In particular, for video encoders that rely on predictive (inter) coding to remove temporal redundancy, intra coding refreshment is especially useful to stop error propagation when errors occur in the transmission or storage of the coded streams, which can cause the decoded quality to decay very rapidly. In the context of object-based video coding, the video encoder can apply intra coding refreshment to both the shape and the texture data. In this paper, shape refreshment need and texture refreshment need metrics are proposed which can be used by object-based video encoders, notably MPEG-4 video encoders, to determine when the shape and the texture of the various video objects in the scene should be refreshed in order to improve the decoded video quality, e.g., for a given bitrate. PMID:18237912

Soares, Luis Ducla; Pereira, Fernando

2003-01-01

300

Object Hierarchy-based Supervised Characterisation ofSynthetic Aperture Radar Sensor Images  

Directory of Open Access Journals (Sweden)

Full Text Available A method of supervised characterisation of synthetic aperture radar (SAR satellite imageshas been discussed in which simple object shape features of satellite images have been usedto classify and describe the terrain types. This scheme is based on a multilevel approach inwhich objects of interest are first segmented out from the image and subsequently characterisedbased on their shape features. Once all objects have been characterised, the entire image canbe characterised. Emphasis has been laid on the hierarchical information extraction from theimage which enables greater flexibility in characterising the image and is not restricted to mereclassification. The paper also describes a method for giving relative importance among features,i.e., to give more weights to those features that are better than others in distinguishing betweencompeting classes. A method of comparing two SAR sensor images based on terrain elementspresent in the images has also been described here.

Ish Rishabh

2008-01-01

301

Model-based objects recognition in industrial environments for autonomous vehicles control  

OpenAIRE

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from ...

Marti? I Bonmati?, Joan; Batlle I Grabulosa, Joan; Casals, Ali?cia

1997-01-01

302

A component-based, distributed object services architecture for a clinical workstation.  

OpenAIRE

Attention to an architectural framework in the development of clinical applications can promote reusability of both legacy systems as well as newly designed software. We describe one approach to an architecture for a clinical workstation application which is based on a critical middle tier of distributed object-oriented services. This tier of network-based services provides flexibility in the creation of both the user interface and the database tiers. We developed a clinical workstation for a...

Chueh, H. C.; Raila, W. F.; Pappas, J. J.; Ford, M.; Zatsman, P.; Tu, J.; Barnett, G. O.

1996-01-01

303

Competency-Based Training: Objective Structured Clinical Exercises (OSCE) in Marriage and Family Therapy  

Science.gov (United States)

The field of marriage and family therapy (MFT) has recently engaged in the process of defining core competencies for the profession. Many MFT training programs are adapting their curriculum to develop more competency-based training strategies. The Objective Structured Clinical "Examination" (OSCE) is widely used in the medical profession to assess…

Miller, John K.

2010-01-01

304

Object-based analysis of hyperspectral and thermal infrared satellite imagery  

International Nuclear Information System (INIS)

The given paper proposes an object-based procedure for the combined analysis of high-resolution optical, thermal infrared and hyperspectral satellite imagery for different nuclear safeguards-related tasks. Some case studies using Hyperion, Landsat, QuickBird and Ikonos data will demonstrate the advantages of this approach. (author)

305

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

Science.gov (United States)

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…

Marshall, Neil; Buteau, Chantal

2014-01-01

306

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

Science.gov (United States)

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. PMID:24457503

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

2014-03-01

307

Ground-based follow-up of Solar System objects detected by Gaia  

Science.gov (United States)

In the frame of the DPAC consortium preparing the Gaia mission, a specific follow-up activity has been set up in order to ensure best scientific return related to solar-system-object (SSO) science. This activity encompasses a system of alerts for newly detected objects provided by CNES, the French data center in charge of the Solar System data processing, and IMCCE, to organize and publish the alerts, and to retrieve the objects astrometry and feed the Minor Planet Center database. We are expecting in particular the detection of new near-Earth objects (NEO) at low solar elongation, or even inner-Earth objects. Owing to its observing mode, the satellite will not be able to monitor these objects after discovery and they could be lost. It is thus important to consolidate and improve their orbital parameters. This is the objective of the SSO ground-based follow-up. Once the objective is reached, it is possible to update the auxiliary database of orbital elements used within the Gaia data reduction pipeline for identifying the known SSOs and to allow Gaia to subsequently identify these objects properly during its mission. In order to reach these goals we have carried out two main activities: -- We have developed a pipeline for processing the Gaia raw data that will be received, and for disseminating only the topocentric data useful for observers in an automatized way -- We have set up a worldwide network of observing stations, the Gaia-FUN-SSO network (shortly described at https://www.imcce.fr/gaia-fun-sso/). At this date, 55 observing sites have registered and many participants have already contributed to several training campaigns for NEO observations. We will describe both activities and we will give preliminary results regarding the Gaia Solar System alerts, depending on the status of the triggering system during this early stage of the mission.

Thuillot, W.; Carry, B.; Berthier, J.; David, P.; Devillepoix, H.; Hestroffer, D.

2014-07-01

308

Adaptive Morphological Feature-Based Object Classifier for a Color Imaging System  

Science.gov (United States)

Utilizing a Compact Color Microscope Imaging System (CCMIS), a unique algorithm has been developed that combines human intelligence along with machine vision techniques to produce an autonomous microscope tool for biomedical, industrial, and space applications. This technique is based on an adaptive, morphological, feature-based mapping function comprising 24 mutually inclusive feature metrics that are used to determine the metrics for complex cell/objects derived from color image analysis. Some of the features include: Area (total numbers of non-background pixels inside and including the perimeter), Bounding Box (smallest rectangle that bounds and object), centerX (x-coordinate of intensity-weighted, center-of-mass of an entire object or multi-object blob), centerY (y-coordinate of intensity-weighted, center-of-mass, of an entire object or multi-object blob), Circumference (a measure of circumference that takes into account whether neighboring pixels are diagonal, which is a longer distance than horizontally or vertically joined pixels), . Elongation (measure of particle elongation given as a number between 0 and 1. If equal to 1, the particle bounding box is square. As the elongation decreases from 1, the particle becomes more elongated), . Ext_vector (extremal vector), . Major Axis (the length of a major axis of a smallest ellipse encompassing an object), . Minor Axis (the length of a minor axis of a smallest ellipse encompassing an object), . Partial (indicates if the particle extends beyond the field of view), . Perimeter Points (points that make up a particle perimeter), . Roundness [(4(pi) x area)/perimeter(squared)) the result is a measure of object roundness, or compactness, given as a value between 0 and 1. The greater the ratio, the rounder the object.], . Thin in center (determines if an object becomes thin in the center, (figure-eight-shaped), . Theta (orientation of the major axis), . Smoothness and color metrics for each component (red, green, blue) the minimum, maximum, average, and standard deviation within the particle are tracked. These metrics can be used for autonomous analysis of color images from a microscope, video camera, or digital, still image. It can also automatically identify tumor morphology of stained images and has been used to detect stained cell phenomena (see figure).

McDowell, Mark; Gray, Elizabeth

2009-01-01

309

Space-Based but not Object-Based Inhibition of Return is Impaired in Parkinson's Disease  

OpenAIRE

Impairments in certain aspects of attention have frequently been reported in Parkinson's disease (PD), including reduced inhibition of return (IOR). Recent evidence suggests that IOR can occur when attention is directed at objects or locations, but previous investigations of IOR in PD have not systematically compared these two frames of reference. The present study compared the performance of 18 nondemented patients with PD and 18 normal controls on an IOR task with two conditions. In the “...

Possin, Katherine L.; Filoteo, J. Vincent; Song, David D.; Salmon, David P.

2009-01-01

310

Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography  

Science.gov (United States)

Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1 m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural Imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. Image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land-cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time.

Li, Xiaoxiao; Myint, Soe W.; Zhang, Yujia; Galletti, Chritopher; Zhang, Xiaoxiang; Turner, Billie L.

2014-12-01

311

Autonomic Management of Object Replication for FT-CORBA Based Intelligent Transportation Systems  

Science.gov (United States)

Intelligent Transportation Systems (ITS) comprises the electronics, communications or information processing used singly or integrated to improve the efficiency or safety of surface transportation. Accordingly, the ITS has to perform collection, management, and provision of real time transport information reliably. It can be deployed based on the Common Object Request Broker Architecture (CORBA) of the Object Management Group (OMG) because it consists of many interconnected heterogeneous systems deployed by independent organizations. Fault Tolerant CORBA (FT-CORBA) supports real time requirement of transport information stably through redundancy by replication of server objects. However, object replication, management, and related protocols of FT-CORBA require extra system resources of CPU and memory, and can degrade the system performance both locally and as a whole. This paper proposes an architecture to enhance performance of FT-CORBA based ITS in terms of CPU and memory by managing object replication adaptively during system operation with an agent. The application of the agent is expected to support fault tolerance of real ITS efficiently.

Suh, Woonsuk; Lee, Eunseok

312

A Generalized Definition Language for Implementing the Object Based Fuzzy Class Model  

Directory of Open Access Journals (Sweden)

Full Text Available The emerging application domains in Engineering, Scientific Technology, Multimedia, GIS, Knowledge management, Expert system design etc require advanced data models to represent and manipulate the data values, because the information resides in these domains are often vague or imprecise in nature & difficult to represent while implementing the application software. In order to fulfill the requirements of such application demands, researchers have put the innovative concept of object based fuzzy database system by extending the object oriented system and adding fuzzy techniques to handle complex object and imprecise data together. Some extensions of the OODMS have been proposed in the literature, but what is still lacking a unifying & systematic formalization of these dedicated concepts. This paper is the consequence research of our previous work, in which we proposed an effective & formal Fuzzy class model to represent all type of fuzzy attributes & objects those can be confined to fuzzy class. Here, we introduce a generalized definition language for the fuzzy class which can efficiently define the proposed fuzzy class model along with all possible fuzzy data type to describe the structure of the database & thus serve as data definition language for the object based fuzzy database system.

Debasis Dwibedy, Dr. Laxman Sahoo, Sujoy Dutta

2013-04-01

313

Web Services Based Learning Objects Generator for Device-Independent M-Learning  

Directory of Open Access Journals (Sweden)

Full Text Available Learning objects, which are the base component of m-learning system, are usually target to modifications in contexts and formats. The device- dependent applications of hand-held devices have proven to be ineffective for creating m-learning courseware. Learning Objects Metadata (LOM is the most popular standard specification for learning objects but lacks the ability to facilitate platforms descriptions. This paper outlines various aspects of design and implementation of Web Services Oriented Rendering Architecture (WSORA which combines LOM Editor with any available published web services. This arrangement is devised in order to make a device-independent m-learning gateway between different mobile devices, such as cell phones, PDA’s, palmtops, and laptops and the vast learning objects available on the World Wide Web. The key technologies behind WSORA are extending the IEEE LOM base scheme structure, LOM Editor, device-independent LO generator, and web services. The major advantage of WSORA is thus achieved to give mobile devices of different types clean and quick access to learning objects customarily designed for desktop browsers.

Akram Moh. Alkouz

2006-06-01

314

Multi-class geospatial object detection and geographic image classification based on collection of part detectors  

Science.gov (United States)

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.

Cheng, Gong; Han, Junwei; Zhou, Peicheng; Guo, Lei

2014-12-01

315

Object-based image analysis through nonlinear scale-space filtering  

Science.gov (United States)

In this research, an object-oriented image classification framework was developed which incorporates nonlinear scale-space filtering into the multi-scale segmentation and classification procedures. Morphological levelings, which possess a number of desired spatial and spectral properties, were associated with anisotropically diffused markers towards the construction of nonlinear scale spaces. Image objects were computed at various scales and were connected to a kernel-based learning machine for the classification of various earth-observation data from both active and passive remote sensing sensors. Unlike previous object-based image analysis approaches, the scale hierarchy is implicitly derived from scale-space representation properties. The developed approach does not require the tuning of any parameter—of those which control the multi-scale segmentation and object extraction procedure, like shape, color, texture, etc. The developed object-oriented image classification framework was applied on a number of remote sensing data from different airborne and spaceborne sensors including SAR images, high and very high resolution panchromatic and multispectral aerial and satellite datasets. The very promising experimental results along with the performed qualitative and quantitative evaluation demonstrate the potential of the proposed approach.

Tzotsos, Angelos; Karantzalos, Konstantinos; Argialas, Demetre

316

Multi-object segmentation algorithm based on improved Chan-Vese model  

Science.gov (United States)

Because Chan and Vese(C-V) model using one level set function can only represent one object and one background, it cannot represent multiple junctions of multiple objects. In this paper, an improved multi-object segment algorithm is proposed based on C-V model of single level set. First, the given image resolution is deduced by wavelet transform. Since the low resolution approximate image contains less noise and pixels, it can speed up the active contour evolution. Secondly, an improved C-V model of a single level set is introduced to obtain the multi-objects' approximate contour, which can make use of topology split information of the contour effectively. Thirdly, the inverse discrete wavelet transform is used to the resulted image and level set of the coarse scale image, which can get the approximation contour on the original image. Lastly, the approximation contour is taken as an initial level set function and the second active contour evolution is performed on the original image to get the real multi-objects contour. Experimental results show that the proposed algorithm can realize the multi-object segmentation effectively and quickly.

Fu, Xiaowei; Ding, Mingyue; Zhou, Chengping; Cai, Chao

2007-11-01

317

Object Search for the Internet of Things Using Tag-based Location Signatures  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, an object search solution for the Internet of Things (IoT is proposed. This study first differentiates localization and searching. Localization is to calculate an object’s current location. Searching is to return a set of locations where a target object could be. It is possible that the locations of the returned set are not contiguous. Searching accuracy can be improved if the number of the returned locations is small. Even though localization technique is applicable to searching applications, a simpler and easier solution will attract more enterprise users. In this paper, based on a concept called location signature, defined by a set of reference tags, an object searching method named Location Signature Search (LSS is proposed. The study of LSS shows that the searching accuracy can be very high if a location signature is not shared by too many locations. Since location signatures are affected by the deployment of the reference tags, trade-off between searching accuracy and implementation cost is achievable. A real world experiment is conducted in this research. The results show that LSS indeed is a practical method for object searching applications.

Jung-Sing Jwo

2012-12-01

318

Thermodynamic modeling and multi-objective evolutionary-based optimization of a new multigeneration energy system  

International Nuclear Information System (INIS)

Highlights: • Comprehensive thermodynamic modeling of a multi-generation system is reported. • New multi-generation systems are proposed for more environmentally benign applications. • Multi-objective optimization technique is applied based on a code developed in the Matlab software. • A sensitivity analysis to determine the effect of design parameters on objective functions is conducted. - Abstract: A comprehensive thermodynamic modeling and multi-objective optimization is reported of a multigeneration energy system, based on a micro gas turbine, a dual pressure heat recovery steam generator, an absorption chiller, an ejector refrigeration cycle, a domestic water heater and a proton exchange membrane electrolyzer, that produces multiple commodities: power, heating, cooling, hot water and hydrogen. Energy and exergy analyses and an environmental impact assessment are included. A multi-objective optimization method based on a fast and elitist non-dominated sorting genetic algorithm (NSGA-II) is applied to determine the best design parameters for the system. The two objective functions utilized in the optimization study are the total cost rate of the system, which is the cost associated with fuel, component purchasing and environmental impact, and the system exergy efficiency. The total cost rate of the system is minimized while the cycle exergy efficiency is maximized using an evolutionary algorithm. To provide insight, the Pareto frontier is shown for a multi-objective optimization. In addition, a closed form equation for the relationship between exergy efficiency and total cost rate is derived. A sensitivity analysis is performed to assess the effects of several design parameters on the system total exergy destruction rate, CO2 emission and exergy efficiency

319

A component-based, distributed object services architecture for a clinical workstation.  

Science.gov (United States)

Attention to an architectural framework in the development of clinical applications can promote reusability of both legacy systems as well as newly designed software. We describe one approach to an architecture for a clinical workstation application which is based on a critical middle tier of distributed object-oriented services. This tier of network-based services provides flexibility in the creation of both the user interface and the database tiers. We developed a clinical workstation for ambulatory care using this architecture, defining a number of core services including those for vocabulary, patient index, documents, charting, security, and encounter management. These services can be implemented through proprietary or more standard distributed object interfaces such as CORBA and OLE. Services are accessed over the network by a collection of user interface components which can be mixed and matched to form a variety of interface styles. These services have also been reused with several applications based on World Wide Web browser interfaces. PMID:8947744

Chueh, H C; Raila, W F; Pappas, J J; Ford, M; Zatsman, P; Tu, J; Barnett, G O

1996-01-01

320

Modelica-based object-orient modeling of rotor system with multi-faults  

Science.gov (United States)

Modelica-based object-orient method is proved to be rapid, accurate and easy to modify, which is suitable for prototype modeling and simulation of rotor system, whose parameters need to be modified frequently. Classical non-object-orient method appears to be inefficient because the code is difficult to modify and reuse. An adequate library for object-orient modeling of rotor system with multi-faults is established, a comparison with non-object-orient method on Jeffcott rotor system and a case study on turbo expander with multi-faults are implemented. The relative tolerance between object-orient method and non-object-orient is less than 0.03%, which proves that these two methods are as accurate as each other. Object-orient modeling and simulation is implemented on turbo expander with crack, rub-impact, pedestal looseness and multi-faults simultaneously. It can be conclude from the case study that when acting on compress side of turbo expander separately, expand wheel is not influenced greatly by crack fault, the existence of rub-impact fault forces expand wheel into quasi-periodic motion and the orbit of expand wheel is deformed and enhanced almost 1.5 times due to pedestal looseness. When acting simultaneously, multi-faults cannot be totally decomposed but can be diagnosed from the feature of vibration. Object-orient method can enhance the efficiency of modeling and simulation of rotor system with multi-faults, which provides an efficient method on prototype modeling and simulation.

Li, Ming; Wang, Yu; Li, Fucai; Li, Hongguang; Meng, Guang

2013-11-01

321

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

DEFF Research Database (Denmark)

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. This scenario is characterized by large volumes of updates, for which reason location update technologies become important. A setting is assumed in which a central database stores a representation of each moving object's current position. This position is to be maintained so that it deviates from the user's real position by at most a given threshold. To do so, each moving object stores locally the central representation of its position. Then an object updates the database whenever the deviation between its actual position (as obtained from a GPS device) and the database position exceeds the threshold. 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-data and a corresponding real road network, the paper offers empirical evaluations and comparisons that include three existing approaches and all the proposed approaches. Udgivelsesdato: May

Civilis, A.; Jensen, Christian SØndergaard

2005-01-01

322

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

DEFF Research Database (Denmark)

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. This scenario is characterized by large volumes of updates, for which reason location update technologies become important. A setting is assumed in which a central database stores a representation of each moving object's current position. This position is to be maintained so that it deviates from the user's real position by at most a given threshold. To do so, each moving object stores locally the central representation of its position. Then an object updates the database whenever the deviation between its actual position (as obtained from a GPS device) and the database position exceeds the threshold. The main issue considered is how to represent the location of a moving object in a database so that tracking can be donewith 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-data and a corresponding real road network, the paper offers empirical evaluations and comparisons that include three existing approaches and all the proposed approaches.

Civilis, Alminas; Jensen, Christian SØndergaard

2005-01-01

323

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

International Nuclear Information System (INIS)

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

324

On Decomposing Object Appearance using PCA and Wavelet bases with Applications to Image Segmentation  

DEFF Research Database (Denmark)

Generative models capable of synthesising complete object images have over the past few years proven their worth when interpreting images. Due to the recent development of computational machinery it has become feasible to model the variation of image intensities and landmark positions over the complete object surface using principal component analysis. This typically involves matrices with a few thousands and up to 100.000+ rows. This paper demonstrates applications of such models applied on colour images of human faces and cardiac magnetic resonance images. Further, we devise methods for alleviating the obvious computational and storage requirements of these large models by means of truncated wavelet bases.

Stegmann, Mikkel Bille; Forchhammer, SØren

2002-01-01

325

Interactive 2D and 3D object definition in medical images based on multiresolution image descriptions  

International Nuclear Information System (INIS)

The authors present means of interactive definition of anatomic objects in medical images via a description of the image in terms of visually sensible regions. The description is produced by computing structures capturing image geometry and following them through the image simplification of Gaussian blurring. In particular, the authors suggest that the structure made from intensity ''ridge'' and ''course'' curves defined by the locus of intensity level curve vertices, augmented by the pile of internal and external symmetric axes of these level curves, satisfies desirable criteria for a structure on which to base such object definition

326

Implementation of intelligent nuclear material diagnosis module based on the component object model  

International Nuclear Information System (INIS)

In this paper, the implementation techniques of intelligent nuclear material surveillance system based on the COM (Component Object Model) and SOM (Self Organized Mapping) was described. The surveillance system that is to be developed is consist of CCD cameras, neutron monitors, and PC for data acquisition. To develop the system, the properties of the COM based software development technology was investigated, and the characteristics of related platform APIs was summarized. This report could be used for the developers who want to develop the intelligent surveillance system for various experimental environments based on the DVR and sensors using Borland C++ Builder

327

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

OpenAIRE

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 ...

Muhammad Kamal; Stuart Phinn

2011-01-01

328

Model-based system engineering in support of the eGY data lifecycle objectives  

Science.gov (United States)

The objectives of the Electronic Geophysical Year (eGY) address the challenges and opportunities associated with modern digital technology for the creation and delivery of earth science data. In particular, eGY focuses on issues of access, sharing, archiving and interoperability of this science data and their associated enabling tools. System engineering supported by modelbased tools and techniques has a pivotal role to play in this endeavor since for most space missions the first formal representation of eGY related data results from system engineering activities and the extent to which these activities and the resulting representations (or models) make data life cycle issues prominent is the extent to which eGY objectives are satisfied or supported by the particular missions. The paper will review ongoing system engineering standardization efforts addressing the data life cycle and in particular will review how relatively new model-based system engineering methodologies contribute to eGY objectives.

Hartman, Leo; Melanson, Philip; Piggott, Stephen

329

Kernel Based Approach toward Automatic object Detection and Tracking in Surveillance Systems  

Directory of Open Access Journals (Sweden)

Full Text Available A modified object-tracking algorithm that uses the flexible Metric Distance Transform kernel and multiple features for the Mean shift procedure is proposed and tested. The Faithful target separation based on RGB joint pdf of the target region and that of a neighborhood surrounding the object is obtained. The non-linear log-likelihood function maps the multimodal object/background distribution as positive values for colors associated with foreground, while negative values are marked for background. This replaces the more usual Epanechnikov kernel (E-kernel, improving target representation and localization without increasing the processing time, minimizing the similarity measure using the Bhattacharya coefficient. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.

Amir Aliabadian

2012-03-01

330

An object-based approach to hierarchical classification of the Earth's topography from SRTM data  

Science.gov (United States)

Digital classification of the Earth's surface has significantly benefited from the availability of global DEMs and recent advances in image processing techniques. Such an innovative approach is object-based analysis, which integrates multi-scale segmentation and rule-based classification. Since the classification is based on spatially configured objects and no longer on solely thematically defined cells, the resulting landforms or landform types are represented in a more realistic way. However, up to now, the object-based approach has not been adopted for broad-scale topographic modelling. Existing global to almost-global terrain classification systems have been implemented on per cell schemes, accepting disadvantages such as the speckled character of outputs and the non-consideration of space. We introduce the first object-based method to automatically classify the Earth's surface as represented by the SRTM into a three-level hierarchy of topographic regions. The new method relies on the concept of decomposing land-surface complexity into ever more homogeneous domains. The SRTM elevation layer is automatically segmented and classified at three levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation is performed at these recognised scales. Objects resulting from segmentation are partitioned into sub-domains based on thresholds given by the mean values of elevation and standard deviation of elevation respectively. Results resemble patterns of existing global and regional classifications, displaying a level of detail close to manually drawn maps. Statistical evaluation indicates that most of the classes satisfy the regionalisation requirements of maximising internal homogeneity while minimising external homogeneity. Most objects have boundaries matching natural discontinuities at the regional level. The method is simple and fully automated. The input data consist of only one layer, which does not need any pre-processing. Both segmentation and classification rely on only two parameters: elevation and standard deviation of elevation. The methodology is implemented as an eCognition® customised process, available as free online download. The results are embedded in a web application, where users can visualise and download the data of interest in GIS ready vector format. The method has originally been developed on the SRTM, but may be applied to any other DEM and regional area of interest. The tool allows for modifications in order to meet the requirements of individual research tasks. Both segmentation and class thresholds are relative to the extent and characteristics of the input DEM. Therefore, when applying the tool to regional or national scales, the results should be interpreted within the adequate context.

Eisank, C.; Dragut, L.

2012-04-01

331

An Index Structure for Fast Query Retrieval in Object Oriented Data Bases Using Signature Weight Declustering  

Directory of Open Access Journals (Sweden)

Full Text Available An important question in information retrieval is how to create a database index which can be searched efficiently for the data one seeks. One such technique called signature file based access method is preferred for its easy handling of insertion and update operations. Most of the proposed methods use either efficient search method or tree based intermediate data structure to filter data objects matching the query. Use of search techniques retrieves the objects by sequentially comparing the positions of 1s in it. Such methods take longer retrieval time. On the other hand tree based structures traverse multiple paths making comparison process tedious. This study describes a new indexing technique for object-oriented data bases using the dynamic balancing of B+ tree called SD (Signature Declustering tree. The SD-tree represents all 1s in signatures in a compact manner that results in saving of insertion and searching time. Analytical experiments have been conducted by varying the signature length and the distribution of signature weight. The study clearly indicates the advantage of fast retrieval time in a way quite different from the other methods suggested in the past.

I. Elizabeth Shanthi

2009-01-01

332

A New Object-Based System for Fractal Video Sequences Compression  

Directory of Open Access Journals (Sweden)

Full Text Available A novel object-based fractal monocular and stereo video compression scheme with quadtree-based motion and disparity compensation is proposed in this paper. Fractal coding is adopted and each object is encoded independently by a prior image segmentation alpha plane, which is defined exactly as in MPEG-4. The first n frames of right video sequence are encoded by using the Circular Prediction Mapping (CPM and the remaining frames are encoded by using the Non Contractive Interframe Mapping (NCIM. The CPM and NCIM methods accomplish the motion estimation/ compensation of right video sequence. According to the different coding or user requirements, the spatial correlations between the left and right frames can be explored by partial or full affine transformation quadtree-based disparity estimation/ compensation, or simply by applying CPM/NCIM on left video sequence. The testing results with the nature monocular and stereo video sequences provide promising performances at low bit rate coding. We believe it will be a powerful and efficient technique for the object-based monocular and stereo video sequences coding.

Kamel Belloulata

2007-06-01

333

Geometrically-correct projection-based texture mapping onto a deformable object.  

Science.gov (United States)

Projection-based Augmented Reality commonly employs a rigid substrate as the projection surface and does not support scenarios where the substrate can be reshaped. This investigation presents a projection-based AR system that supports deformable substrates that can be bent, twisted or folded. We demonstrate a new invisible marker embedded into a deformable substrate and an algorithm that identifies deformations to project geometrically correct textures onto the deformable object. The geometrically correct projection-based texture mapping onto a deformable marker is conducted using the measurement of the 3D shape through the detection of the retro-reflective marker on the surface. In order to achieve accurate texture mapping, we propose a marker pattern that can be partially recognized and can be registered to an object?s surface. The outcome of this work addresses a fundamental vision recognition challenge that allows the underlying material to change shape and be recognized by the system. Our evaluation demonstrated the system achieved geometrically correct projection under extreme deformation conditions. We envisage the techniques presented are useful for domains including prototype development, design, entertainment and information based AR systems. PMID:24650981

Fujimoto, Yuichiro; Smith, Ross T; Taketomi, Takafumi; Yamamoto, Goshiro; Miyazaki, Jun; Kato, Hirokazu; Thomas, Bruce H

2014-04-01

334

Transmission of object based fine-granular-scalability video over networks  

Science.gov (United States)

It is a hot focus of current researches in video standards that how to transmit video streams over Internet and wireless networks. One of the key methods is FGS(Fine-Granular-Scalability), which can always adapt to the network bandwidth varying but with some sacrifice of coding efficiency, is supported by MPEG-4. Object-based video coding algorithm has been firstly included in MPEG-4 standard that can be applied in interactive video. However, the real time segmentation of VOP(video object plan) is difficult that limit the application of MPEG-4 standard in interactive video. H.264/AVC is the up-to-date video-coding standard, which enhance compression performance and provision a network-friendly video representation. In this paper, we proposed a new Object Based FGS(OBFGS) coding algorithm embedded in H.264/AVC that is different from that in mpeg-4. After the algorithms optimization for the H.264 encoder, the FGS first finish the base-layer coding. Then extract moving VOP using the base-layer information of motion vectors and DCT coefficients. Sparse motion vector field of p-frame composed of 4*4 blocks, 4*8 blocks and 8*4 blocks in base-layer is interpolated. The DCT coefficient of I-frame is calculated by using information of spatial intra-prediction. After forward projecting each p-frame vector to the immediate adjacent I-frame, the method extracts moving VOPs (video object plan) using a recursion 4*4 block classification process. Only the blocks that belong to the moving VOP in 4*4 block-level accuracy is coded to produce enhancement-layer stream. Experimental results show that our proposed system can obtain high interested VOP quality at the cost of fewer coding efficiency.

Shi, Xu-li; Jin, Zhi-cheng; Teng, Guo-wei; Zhang, Zhao-yang; An, Ping; Xiao, Guang

2006-05-01

335

Delineating Parameters for Object-Based Urban Structure Mapping in Santiago de Chile Using Quickbird Data  

Science.gov (United States)

This work aims to parameterize the urban structure types (UST) in Santiago de Chile on statistical block level. In connotation of remote sensing UST are defined as land-use structure entities. Central input data for this object-oriented approach is spatially very high resolution panfused and atmospherically corrected Quickbird data. To analyse and assess the structural properties of urban land- cover objects within block level entities, basic and robust land-cover class descriptions are developed. For enhanced class descriptions several image object scales are created. Based on defined UST and additional field data a set of test areas is selected for four municipalities assigned to different socio-spatial clusters in Santiago de Chile. In all test areas the distribution of the basic land- cover classes is parameterized using complex sub-object and relational image object descriptions. The central features to characterise the UST in this study are percentage area and density of subscale land-cover class objects. To carry out this analysis, the expert knowledge on UST is valuable to choose specific reference objects within the statistical block level. After the concept is implemented at the smallest scale, the approach can successfully be applied to the whole municipality once specific structural information are aggregated. The work is linked to activities of the project Risk Habitat Megacity and developed in close cooperation with the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. Finally, the resulting land-use structure entities will be linked to socio-spatial characteristics in the above mentioned cluster with respect to urban vulnerability.

Huck, A.; Hese, S.; Banzhaf, E.

2011-09-01

336

Object Tracking with an Evolutionary Particle Filter Based on Self-Adaptive Multi-Features Fusion  

Directory of Open Access Journals (Sweden)

Full Text Available Particle filter algorithms are widely used for object tracking in video sequences, but the standard particle filter algorithm cannot solve the validity of particles ideally. To solve the problems of particle degeneration and sample impoverishment in a particle filter tracking algorithm, an improved object tracking algorithm is proposed, which combines a multi?feature fusion method and a genetic evolution mechanism. The algorithm dynamically computes the feature’s fusion weight by the discriminability of each vision feature and then constructs the important density function based on selecting a feature’s fusion method adaptively. Moreover, a self?adaptive genetic evolutionary mechanism is introduced into the particle resampling process and makes the particle become an agent with the ability of dynamic self?adaption. With self?adaptive crossover and mutation operators, the evolution system produces a large number of new particles, which can better approximate the true state of the tracking object. The experimental results show that the proposed object tracking algorithm surpasses the conventional particle filter on both robustness and accuracy, even though the tracking object is very challenging regarding illumination variation, structural deformation, the interference of similar targets and occlusion.

Zhang Xiaowei

2013-01-01

337

A Preliminary Correctness Evaluation Model of Object-Oriented Software Based on UML  

Directory of Open Access Journals (Sweden)

Full Text Available Concurrent engineering is a philosophy that attempts to take into account of all the activities of a product life cycle early in the design stage. In the manufacturing industry, approximately 70% of a product`s manufacting and assembly costs are determined during the design stage. Similar to the software industry system analysis and design has significant influence on later activities of the software development life cycle. Object-oriented approach has been the main stream for software development, and unified Modeling Language (UML integrates most of the object-oriented modeling methods and has become the standards. This paper incorporates the CE concept into the evaluation of object-oriented software development and proposes a Hierarchical Aggregation Model (HAM to early evaluate the object-oriented software design quality based on UML. There are three advantages of using this model. First, this model can help reduce the project risk, cost, and time span, and eventually improve the software quality early in the software development life cycle. Secondly, this model facilitates the use of the standards of object-oriented modeling language, UML, which makes proposed model more applicable to real software development. Thirdly, this model is easy to implement, that can potentially be imbedded in CASE tools to directly support the project manager`s decision making.

S. Wesley Changchien

2002-01-01

338

IMPLEMENTATION OF OBJECT TRACKING SYSTEM USING REGION FILTERING ALGORITHM BASED ON SIMULINK BLOCKSETS  

Directory of Open Access Journals (Sweden)

Full Text Available Video tracking is the process of locating a moving object in time that is visualized by camera and are widely used in surveillance, animation and robotics Tracking describes the process of recording movement and translating that movement onto a digital model. The set of constraints that produce the most accurate tracking is the one that describes better the action performed. The key difficulty in video tracking is to associate target locations in consecutive video frames, especially when the objects are moving fast relative to the frame rate. Here, a video tracking system is been employed in which motion model describes how the image of the target might change for different possible motions of the object to track. The role of the tracking algorithm adopted for this system is to analyze the video frames to estimate the motion parameters. These parameters characterize the location of the target. In this research, three features are been extracted from each moving objects such as centroid, area, average luminance. Finally the similarity function is applied to tracking and the attempt proves that the chosen method has good performance under dynamic circumstances for real time tracking. Simulink is integrated with MATLAB to build a model for object tracking and data transfer is easily handled between the programs. The Simulink based customizable framework is designed for rapid simulation, implementation, and verification of video and image processing algorithms and systems.

DR.P.SUBASHINI

2011-08-01

339

Bionics-Based Approach for Object Tracking to Implementin Robot Applications  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, an approach for object tracking that is inspired from human oculomotor system is proposed and verified experimentally. The developed approach divided into two phases, fast tracking or saccadic phase and smooth pursuit phase. In the first phase, the field of the view is segmented into four regions that are analogue to retinal periphery in the oculomotor system. When the object of interest is entering these regions, the developed vision system responds by changing the values of the pan and tilt angles to allow the object lies in the fovea area and then the second phase will activate. A fuzzy logic method is implemented in the saccadic phase as an intelligent decision maker to select the values of the pan and tilt angle based on suggested logical rules. In smooth pursuit phase, the object is kept in the center area of the field of the view by smooth adjusting the values of the pan and tilt angles where this stage is analogue to putting the observing object in the fovea centralis of the oculomotor system. The proposed approach was implemented by using (Camera-Pan / Tilt configuration and showed good results to improve the vision capability of the humanoid robots.

Hussam K. Abdul-Ameer

2010-01-01

340

Video objects segmentation based on spatio-temporal information and its realization in CNNUM  

Science.gov (United States)

In this paper, we propose a new segmentation method aimed at separating the moving objects from the background in a generic video sequence using Cellular Neural Networks (CNN). This task may be accomplished to support the functionalities foreseen by new multimedia scenarios, and in particular the content-based functionalities focused by the MPEG-4 activity. Extraction of motion information from video series is very power consuming, the proposed scheme extracts moving objects based on both motion and spatial information. Initially, a symmetrical inter-frame difference is performed on a group of gray image, so the approximate area of the video object was presented, then this area can be divided into some flat zones with uninterrupted grey scale information. Finally some zones are merged and forming the object according to a certain rule, others are discarded. It is the case of stationary background hereinbefore, in the case of moving, we will do some motion estimation at first. For the good of laborsaving, some work will be realized by CNN,. At the end of this paper, some typical results obtained on MPEG-4 sequences are here shown, in order to illustrate the segmentation algorithm performance using Aladdin V1.3 simulator system.

Chang, Qingli; Mo, Yulong; Lin, Xiaomei

2004-05-01

341

Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR, Structural Similarity Index (SSIM and the Visual Information Fidelity (VIF. The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided.

M. Cedillo-Hernandez

2013-12-01

342

Recovering Use Case Diagrams from Object Oriented Code: an MDA-based Approach  

Directory of Open Access Journals (Sweden)

Full Text Available Modernization of legacy systems requires the existence of technicalframeworks for information integration and tool interoperability that allow managing new platform technologies, design techniques and processes. MDA (Model Driven Architecture, adopted by the OMG (Object Management Group, is aligned with this requirement. Reverse engineering techniques play a crucial role in system modernization. In light of these issues, this article describes a framework to reverse engineering MDA models from object oriented code. This framework distinguishes three different abstraction levelslinked to models, metamodels and formal specifications. At model level,transformations are based on static and dynamic analysis. At metamodel level, transformations are specified as OCL (Object Constraint Languagecontracts between MOF (Meta Object Facility metamodels which control the consistency of these transformations. The level of formal specification includes algebraic specifications of MOF metamodels and metamodel-based transformations. This article shows how to reverse engineering use case diagrams from Java code in the MDA context focusing on transformations at model and metamodel levels. We validate our approach by using Eclipse Modeling Framework, Ecore metamodels and ATL (Atlas Transformation Language.

Claudia T. Pereira

2012-07-01

343

An automated object-based classification approach for updating CORINE land cover data  

Science.gov (United States)

In this paper, an object based classification approach for land cover and land use classes is presented, and first test results are shown. Recently, there is an increasing demand for information on actual land cover resp. land use from planning, administration and science institutions. Remote sensing provides timely information products in different geometric and thematic scales. The effort to manually classify land use data is still very high. Therefore a new approach is required to incorperate automated image classification to human image understanding. The proposed approach couples object-based clasification technique -a rather new trend in image classification - with machine learning capacities (Support Vector Classifier) depending on information levels. To ensure spatial and spectral transferability of the classification scheme, the data has to be passed through several generalisation levels. The segmentation generates homogeneous and contiguous image objects. The hierarchical rule type uses direct and derived spectral attributes combined with spatial features and information extracted from the metadata. The identified land cover objects can be converted into the current CORINE classes after classification.

Wehrmann, Thilo; Dech, Stefan; Glaser, Ruediger

2004-10-01

344

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

CERN Document Server

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...

Trunfio, Paolo

2014-01-01

345

Vineyard parcel identification from Worldview-2 images using object-based classification model  

Science.gov (United States)

Accurate identification of spatial distribution and characteristics of vineyard parcels is an important task for the effective management of vineyard areas, precision viticulture, and farmer registries. This study aimed to develop rule sets to be used in object-based classification of Worldview-2 satellite images to accurately delineate the boundaries of vineyards having different plantation styles. Multilevel segmentation was applied to Worldview-2 images to create different sizes of image objects representing different land cover categories with respect to scale parameter. Texture analysis and several new spectral indices were applied to objects at different segmentation levels to accurately classify land cover classes of forest, cultivated areas, harvested areas, impervious, bareland, and vineyards. A specific attention was given to vineyard class to identify vine areas at the parcel level considering their different plantation styles. The results illustrated that the combined usage of a newly developed decision tree and image segmentation during the object-based classification process could provide highly accurate results for the identification of vineyard parcels. Linearly planted vineyards could be classified with 100% producer's accuracy due to their regular textural characteristics, whereas regular gridwise and irregular gridwise (distributed) vineyard parcels could be classified with 94.87% producer's accuracy in this research.

Sertel, Elif; Yay, Irmak

2014-01-01

346

Model Parameter Adaption-Based Multi-Model Algorithm for Extended Object Tracking Using a Random Matrix  

OpenAIRE

Traditional object tracking technology usually regards the target as a point source object. However, this approximation is no longer appropriate for tracking extended objects such as large targets and closely spaced group objects. Bayesian extended object tracking (EOT) using a random symmetrical positive definite (SPD) matrix is a very effective method to jointly estimate the kinematic state and physical extension of the target. The key issue in the application of this random matrix-based EO...

Borui Li; Chundi Mu; Shuli Han; Tianming Bai

2014-01-01

347

A fusion algorithm for joins based on collections in Odra (Object Database for Rapid Application development)  

CERN Document Server

In this paper we present the functionality of a currently under development database programming methodology called ODRA (Object Database for Rapid Application development) which works fully on the object oriented principles. The database programming language is called SBQL (Stack based query language). We discuss some concepts in ODRA for e.g. the working of ODRA, how ODRA runtime environment operates, the interoperability of ODRA with .net and java .A view of ODRA's working with web services and xml. Currently the stages under development in ODRA are query optimization. So we present the prior work that is done in ODRA related to Query optimization and we also present a new fusion algorithm of how ODRA can deal with joins based on collections like set, lists, and arrays for query optimization.

Satish, Laika

2011-01-01

348

Job Scheduling Model for Cloud Computing Based on Multi-Objective Genetic Algorithm  

Directory of Open Access Journals (Sweden)

Full Text Available Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. To improve the overall performance of cloud computing, with the deadline constraint, a task scheduling model is established for reducing the system power consumption of cloud computing and improving the profit of service providers. For the scheduling model, a solving method based on multi-objective genetic algorithm (MO-GA is designed and the research is focused on encoding rules, crossover operators, selection operators and the method of sorting Pareto solutions. Based on open source cloud computing simulation platform CloudSim, compared to existing scheduling algorithms, the results show that the proposed algorithm can obtain a better solution, and it provides a balance for the performance of multiple objects.

Jing Liu

2013-01-01

349

A Layer-Based Object-Oriented Parallel Framework for Beam Dynamics Studies  

International Nuclear Information System (INIS)

A three-dimensional time-dependent parallel particle-in-cell framework has been developed to model complex accelerator systems. This framework has been designed based on object-oriented methodology using a layered structure. The layer-based object-oriented software design helps to encapsulate both the details of the physical application and its parallel implementation and gives the program good maintainability and extensibility. The new framework is currently being applied to the study of the LEDA beam halo experiment at the Los Alamos National Laboratory. Using the new framework running on a parallel supercomputer we can simulate, with high resolution, multiple bunches propagating and merging through the LEDA system, including the effects of interbunch and intrabunch 3D space-charge forces. Such high resolution multi-bunch simulation is beyond the capability of current serial beam dynamics codes.

350

Object-based benthic habitat mapping in the Florida Keys from hyperspectral imagery  

Science.gov (United States)

Accurate mapping of benthic habitats in the Florida Keys is essential in developing effective management strategies for this unique coastal ecosystem. In this study, we evaluated the applicability of hyperspectral imagery collected from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) for benthic habitat mapping in the Florida Keys. An overall accuracy of 84.3% and 86.7% was achieved respectively for a group-level (3-class) and code-level (12-class) classification by integrating object-based image analysis (OBIA), hyperspectral image processing methods, and machine learning algorithms. Accurate and informative object-based benthic habitat maps were produced. Three commonly used image correction procedures (atmospheric, sun-glint, and water-column corrections) were proved unnecessary for small area mapping in the Florida Keys. Inclusion of bathymetry data in the mapping procedure did not increase the classification accuracy. This study indicates that hyperspectral systems are promising in accurate benthic habitat mapping at a fine detail level.

Zhang, Caiyun; Selch, Donna; Xie, Zhixiao; Roberts, Charles; Cooper, Hannah; Chen, Ge

2013-12-01

351

Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches  

OpenAIRE

We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in the field. The six most common species, collectively representing 43% of the total trees sampled, were selected for mapping using pixel-based and object-based approaches. All pairs of species were separable based on spectral reflectance values i...

Harini Nagendra; Shivani Agarwal; Madhumitha Jaganmohan; Lionel Sujay Vailshery

2013-01-01

352

Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis  

OpenAIRE

Savanna ecosystems are an important component of dryland regions and yet are exceedingly difficult to study using satellite imagery. Savannas are composed are varying amounts of trees, shrubs and grasses and typically traditional classification schemes or vegetation indices cannot differentiate across class type. This research utilizes object based classification (OBC) for a region in Namibia, using IKONOS imagery, to help differentiate tree canopies and therefore woodland savanna, from shrub...

Jane Southworth; Youliang Qiu; Luke Rostant; Sanchayeeta Adhikari; Cerian Gibbes

2010-01-01

353

Evaluation of regional bulbar redness using an image-based objective method  

OpenAIRE

AIM: To develop an image-based objective method to precisely evaluate regional ocular bulbar injection.METHODS:Six healthy adult volunteers were photographed in four orientations (superior, inferior, nasal and temporal sides) with and without stimulating eye drops. Six line segments (covering 30°) were drawn 4mm away from the limbus on each image using ImageJ software. The graph peaks, which were derived from the areas under the line segments and corresponded to the cross-secti...

Wen-Juan Zhao; Fang Duan; Zhong-Ting Li; Hua-Jun Yang; Qiang Huang; Kai-Li Wu

2014-01-01

354

Creating telecommunication services based on object-oriented frameworks and SDL  

OpenAIRE

This paper describes the tools and techniques being applied in the TINA Open Service Creation Architecture (TOSCA) project to develop object-oriented models of distributed telecommunication services in SDL. The paper also describes the way in which Tree and Tabular Combined Notation (TTCN) test cases are derived from these models and subsequently executed against the CORBA-based implementations of these services through a TTCN/CORBA gateway.

Sinnott, R. O.; Kolberg, M.

1999-01-01

355

An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors  

OpenAIRE

In this study, the capability of geographic object-based image analysis (GEOBIA) in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images in order to accurately map burned areas in the Mediterranean island of Thasos. The developed GEOBIA ruleset ...

Thomas Katagis; Gitas, Ioannis Z.; Mitri, George H.

2014-01-01

356

The role of object-based learning in transferable skills development  

Directory of Open Access Journals (Sweden)

Full Text Available This paper considers how object-based learning (OBL can be used to complement reflective skills development systems, which are commonplace in UK universities. It describes how some UCL students had difficulty understanding the concept of such a system and in choosing skills to develop. We therefore began developing a series of OBL activities, which could be used to help students understand how the system should be used and to identify their skill strengths and weaknesses.

Jenny Marie

2010-01-01

357

Evolutionary Algorithm based on simulated annealing for the multi-objective optimization of combinatorial problems  

OpenAIRE

This paper states a novel hybrid-metaheuristic based on the Theory of Deterministic Swapping, Theory of Evolution and Simulated Annealing Metaheuristic for the multi-objective optimization of combinatorial problems. The proposed algorithm is named EMSA. It is an improvement of MODS algorithm. Unlike MODS, EMSA works using a search direction given through the assignation of weights to each function of the combinatorial problem to optimize. Also, in order to avoid local optimums, EMSA uses cr...

Elias David Nino Ruiz; Henry Nieto Parra; Anangelica Isabel Chinchilla Camargo

2013-01-01

358

Job Scheduling Model for Cloud Computing Based on Multi-Objective Genetic Algorithm  

OpenAIRE

Cloud computing is an emerging high performance computing environment with a large scale, heterogeneous collection of autonomous systems and flexible computational architecture. To improve the overall performance of cloud computing, with the deadline constraint, a task scheduling model is established for reducing the system power consumption of cloud computing and improving the profit of service providers. For the scheduling model, a solving method based on multi-objective genetic algorithm (...

Jing Liu; Xing-Guo Luo; Xing-Ming Zhang; Fan Zhang; Bai-Nan Li

2013-01-01

359

Semantic annotation of multilingual learning objects based on a domain ontology  

OpenAIRE

One of the important tasks in the use of learning resources in e-learning is the necessity to annotate learning objects with appropriate metadata. However, annotating resources by hand is time consuming and difficult. Here we explore the problem of automatic extraction of metadata for description of learning resources. First, theoretical constraints for gathering certain types of metadata important for e-learning systems are discussed. Our approach to annotation is then outlined. This is base...

Knoth, Petr

2009-01-01

360

Fourier-transform Ghost Imaging for pure phase object based on Compressive Sampling algorithm  

CERN Document Server

A special algorithm for the Fourier-transform Ghost Imaging (GI) scheme is discussed based on the Compressive Sampling (CS) theory. Though developed mostly in real space, CS algorithm could also be used for the Fourier spectrum reconstruction of pure phase object by setting a proper sensing matrix. This could find its application in diffraction imaging of X-ray, neutron and electron with higher efficiency and resolution. Simulation and experiment results are also presented to prove the feasibility.

Wang, Hui

2009-01-01

361

Bootstrapping a Compiler for an Equation-Based Object-Oriented Language  

OpenAIRE

What does it mean to bootstrap a compiler, and why do it? This paper reports on the first bootstrapping of a full-scale EOO (Equation-based Object-Oriented) modeling language such as Modelica. Bootstrapping means that the compiler of a language can compile itself. However, the usual application area for the Modelica is modeling and simulation of complex physical systems. Fortunately it turns out that with some minor extensions, the Modelica language is well suited for the modeling of language...

Sjo?lund, Martin; Fritzson, Peter; Pop, Adrian

2014-01-01

362

Safety, Security, and Semantic Aspects of Equation-Based Object-Oriented Languages and Environments  

OpenAIRE

During the last two decades, the interest for computer aided modeling and simulation of complex physical systems has witnessed a significant growth. The recent possibility to create acausal models, using components from different domains (e.g., electrical, mechanical, and hydraulic) enables new opportunities. Modelica is one of the most prominent equation-based object-oriented (EOO) languages that support such capabilities, including the ability to simulate both continuous- and discrete-time ...

Broman, David

2007-01-01

363

Parallel Code Generation in MathModelica / An Object Oriented Component Based Simulation Environment  

OpenAIRE

Modelica is an a-causal, equation based, object oriented modeling lan- guage for modeling and efficient simulation of large and complex multi domain systems. The Modelica language, with its strong software component model, makes it possible to use visual component programming, where large complex physical systems can be modeled and composed in a graphical way. One tool with support for both graphical modeling, textual programming and simulation is MathModelica. To deal with growing complexity...

Aronsson, Peter; Fritzson, Peter

2001-01-01

364

An IPM-APSO based hybrid method for multiple objective minimizations using TCPS  

OpenAIRE

This paper presents an Interior Pont Method (IPM) and variant of Particle Swarm Optimization (APSO) based hybrid method to solve optimal power flow in power system incorporating Flexible AC Transmission Systems (FACTS) such as Thyristor Controlled Phase Shifter (TCPS) for minimization of multiple objectives. The proposed IPM-APSO algorithm identifies the optimal values of generator active-power output and the adjustment of reactive power control devices. The proposed optimization process with...

Balasubba Reddy, M.; Obulesh, Dr Y. P.; Sivanaga Raju, Dr S.

2012-01-01

365

Validating an objective video-based dyskinesia severity score in Parkinson’s disease patients  

OpenAIRE

Dyskinesia is a common side effect of prolonged dopaminergic therapy in Parkinson’s disease patients. Assessing the severity of dyskinesia can help develop better pharmacological and surgical interventions. We have developed a semi-automatic video-based objective dyskinesia quantifying measure called the severity score (SVS) that was evaluated on 35 patient videos. We present a study to evaluate the utility of our severity score and compare its performance to clinical ratings of neurologist...

Rao, Anusha Sathyanarayanan; Dawant, Benoit M.; Bodenheimer, Robert E.; Li, Rui; Fang, John; Phibbs, Fenna; Hedera, Peter; Davis, Thomas

2012-01-01

366

Pixel-based and object-oriented change detection analysis using high-resolution imagery  

International Nuclear Information System (INIS)

The high spatial resolution of state-of-the-art commercial satellite imagery provides a good basis for recognising and monitoring even small-scale structural changes within nuclear facilities and for planning of routine and/or challenge inspections of nuclear sites. Despite the advantages of the improved spatial resolution some problems exist that may make the interpretation of the changes more difficult: Firstly, the results of the change analysis can be very complex and unclear at a glance. Secondly, shadow formation and off-nadir images due to different sensor and solar conditions at the acquisition times can cause false signals or overlap real changes. In view to the fast-growing amount of data from different sensor types there are then some requirements of an effective change detection procedure for safeguards purposes: i. The techniques involved should possess a certain amount of robustness in terms of small misregistration errors, different atmospheric conditions at the acquiring dates, off-nadir angles. ii. Given large multisensor data sets it would be necessary that the procedure operates as automatically as possible. Iii. The procedure has to imply techniques to initially pinpoint out those parts of a scene in which significant changes have taken place. iv. All image areas indicating changes then should be subject to a detailed classification and interpretation procedure. We have already investigated some pixel-based change detection for the routine nuclear change detection for the routine nuclear verification, based on recently published visualisation and change detection algorithms: canonical correlation analysis (MAD transformation) to enhance the change information in the difference images and bayesian techniques for the automatic determination of significant thresholds. Some steps have been taken by combining pixel- and object-oriented approaches, i.e. MAD transformation of the image data and object- oriented post-classification of the changes and object extraction for the image data or MAD transformation of the objects and post-classification of the change objects. Another approach implies a solely object-oriented change detection technique: Object extraction, semantic classification and post- classification comparison by means of a change matrix. The object-oriented change analysis procedures are carried out with a relatively new technology for image analysis, eCognition (http://www.definiens-imaging.com)

367

Pixel-based and object-oriented change detection analysis using high-resolution imagery  

Energy Technology Data Exchange (ETDEWEB)

The high spatial resolution of state-of-the-art commercial satellite imagery provides a good basis for recognising and monitoring even small-scale structural changes within nuclear facilities and for planning of routine and/or challenge inspections of nuclear sites. Despite the advantages of the improved spatial resolution some problems exist that may make the interpretation of the changes more difficult: Firstly, the results of the change analysis can be very complex and unclear at a glance. Secondly, shadow formation and off-nadir images due to different sensor and solar conditions at the acquisition times can cause false signals or overlap real changes. In view to the fast-growing amount of data from different sensor types there are then some requirements of an effective change detection procedure for safeguards purposes: i. The techniques involved should possess a certain amount of robustness in terms of small misregistration errors, different atmospheric conditions at the acquiring dates, off-nadir angles. ii. Given large multisensor data sets it would be necessary that the procedure operates as automatically as possible. Iii. The procedure has to imply techniques to initially pinpoint out those parts of a scene in which significant changes have taken place. iv. All image areas indicating changes then should be subject to a detailed classification and interpretation procedure. We have already investigated some pixel-based change detection for the routine nuclear verification, based on recently published visualisation and change detection algorithms: canonical correlation analysis (MAD transformation) to enhance the change information in the difference images and bayesian techniques for the automatic determination of significant thresholds. Some steps have been taken by combining pixel- and object-oriented approaches, i.e. MAD transformation of the image data and object- oriented post-classification of the changes and object extraction for the image data or MAD transformation of the objects and post-classification of the change objects. Another approach implies a solely object-oriented change detection technique: Object extraction, semantic classification and post- classification comparison by means of a change matrix. The object-oriented change analysis procedures are carried out with a relatively new technology for image analysis, eCognition (http://www.definiens-imaging.com)

Niemeyer, Irmgard [TU Bergakademie Freiberg (Germany). Inst. for Mine-Surveying and Geodesy; Canty, Morton J. [Forschungszentrum Juelich (Germany). Programme Group Systems Analysis and Technology Evaluation (STE)

2003-05-01

368

Compression scheme by use of object-segmented sub-image array transformed from computational elemental image array based on multiple objects in 3D integral imaging  

Science.gov (United States)

In this paper, we address a highly enhanced compression scheme in the condition of multiple objects in Integral Imaging (InIm) by use of sub-images (SIs) to segment each object and to remove the Motion Vector (MV) of residual image array transformed from Sub-Image Array (SIA). In the pick-up process, SIA is generated from EIA after the perspectives passing through virtual pinhole array is recorded as Elemental Image Array (EIA). The similarity enhancement among SIs expects compression efficiency to improve, but the compression efficiency of the EIA in the picked-up condition of multiple objects does not correspond to that of the picked-up condition of a simplified object. In the proposed scheme, the depth of objects is computed by two adaptive SIs located at horizontal left and right side from the reference SI positioned to the center of the SIA. A depth map image generated from two adaptive the SIs and a reference SI is applied to segment each object considering to the distance between those. Therefore, an adaptive objectsegmented SI is obtained and, which is motion-estimated from the original SIA based on MSE to generate the motioncompensated object-segmented SIA and which SIAs from each segmented object are finally combined as the motioncompensated SIA, and which based on multiple objects is transformed to residual SIA to minimize the spatial redundancy and which SIA is compressed by MPEG-4. The proposed algorithm shows the enhanced compression efficiency than that of the baseline JPEG and the conventional EIA compression scheme.

Lee, Hyoung-Woo; Lee, Ju-Han; Kang, Ho-Hyun; Kim, Eun-Soo

2012-10-01

369

Object-Based Image Classification of Summer Crops with Machine Learning Methods  

Directory of Open Access Journals (Sweden)

Full Text Available The strategic management of agricultural lands involves crop field monitoring each year. Crop discrimination via remote sensing is a complex task, especially if different crops have a similar spectral response and cropping pattern. In such cases, crop identification could be improved by combining object-based image analysis and advanced machine learning methods. In this investigation, we evaluated the C4.5 decision tree, logistic regression (LR, support vector machine (SVM and multilayer perceptron (MLP neural network methods, both as single classifiers and combined in a hierarchical classification, for the mapping of nine major summer crops (both woody and herbaceous from ASTER satellite images captured in two different dates. Each method was built with different combinations of spectral and textural features obtained after the segmentation of the remote images in an object-based framework. As single classifiers, MLP and SVM obtained maximum overall accuracy of 88%, slightly higher than LR (86% and notably higher than C4.5 (79%. The SVM+SVM classifier (best method improved these results to 89%. In most cases, the hierarchical classifiers considerably increased the accuracy of the most poorly classified class (minimum sensitivity. The SVM+SVM method offered a significant improvement in classification accuracy for all of the studied crops compared to the conventional decision tree classifier, ranging between 4% for safflower and 29% for corn, which suggests the application of object-based image analysis and advanced machine learning methods in complex crop classification tasks.

José M. Peña

2014-05-01

370

A preference-based multi-objective model for the optimization of best management practices  

Science.gov (United States)

The optimization of best management practices (BMPs) at the watershed scale is notably complex because of the social nature of decision process, which incorporates information that reflects the preferences of decision makers. In this study, a preference-based multi-objective model was designed by modifying the commonly-used Non-dominated Sorting Genetic Algorithm (NSGA-II). The reference points, achievement scalarizing functions and an indicator-based optimization principle were integrated for searching a set of preferred Pareto-optimality solutions. Pareto preference ordering was also used for reducing objective numbers in the final decision-making process. This proposed model was then tested in a typical watershed in the Three Gorges Region, China. The results indicated that more desirable solutions were generated, which reduced the burden of decision effort of watershed managers. Compare to traditional Genetic Algorithm (GA), those preferred solutions were concentrated in a narrow region close to the projection point instead of the entire Pareto-front. Based on Pareto preference ordering, the solutions with the best objective function values were often the more desirable solutions (i.e., the minimum cost solution and the minimum pollutant load solution). In the authors' view, this new model provides a useful tool for optimizing BMPs at watershed scale and is therefore of great benefit to watershed managers.

Chen, Lei; Qiu, Jiali; Wei, Guoyuan; Shen, Zhenyao

2015-01-01

371

Active shape model-based real-time tracking of deformable objects  

Science.gov (United States)

Tracking non-rigid objects such as people in video sequences is a daunting task due to computational complexity and unpredictable environment. The analysis and interpretation of video sequence containing moving, deformable objects have been an active research areas including video tracking, computer vision, and pattern recognition. In this paper we propose a robust, model-based, real-time system to cope with background clutter and occlusion. The proposed algorithm consists of following four steps: (i) localization of an object-of-interest by analyzing four directional motions, (ii) region tracker for tracking moving region detected by the motion detector, (iii) update of training sets using the Smart Snake Algorithm (SSA) without preprocessing, (iv) active shape model-based tracking in region information. The major contribution this work lies in the integration for a completed system, which covers from image processing to tracking algorithms. The approach of combining multiple algorithms succeeds in overcoming fundamental limitations of tracking and at the same time realizes real time implementation. Experimental results show that the proposed algorithm can track people under various environment in real-time. The proposed system has potential uses in the area of surveillance, sape analysis, and model-based coding, to name of few.

Kim, Sangjin; Kim, Daehee; Shin, Jeongho; Paik, Joonki

2005-10-01

372

An advanced object-based software framework for complex ecosystem modeling and simulation  

Energy Technology Data Exchange (ETDEWEB)

Military land managers and decision makers face an ever increasing challenge to balance maximum flexibility for the mission with a diverse set of multiple land use, social, political, and economic goals. In addition, these goals encompass environmental requirements for maintaining ecosystem health and sustainability over the long term. Spatiotemporal modeling and simulation in support of adaptive ecosystem management can be best accomplished through a dynamic, integrated, and flexible approach that incorporates scientific and technological components into a comprehensive ecosystem modeling framework. The Integrated Dynamic Landscape Analysis and Modeling System (IDLAMS) integrates ecological models and decision support techniques through a geographic information system (GIS)-based backbone. Recently, an object-oriented (OO) architectural framework was developed for IDLAMS (OO-IDLAMS). This OO-IDLAMS Prototype was built upon and leverages from the Dynamic Information Architecture System (DIAS) developed by Argonne National Laboratory. DIAS is an object-based architectural framework that affords a more integrated, dynamic, and flexible approach to comprehensive ecosystem modeling than was possible with the GIS-based integration approach of the original IDLAMS. The flexibility, dynamics, and interoperability demonstrated through this case study of an object-oriented approach have the potential to provide key technology solutions for many of the military's multiple-use goals and needs for integrated natural resource planning and ecosystem management.

Sydelko, P. J.; Dolph, J. E.; Majerus, K. A.; Taxon, T. N.

2000-06-29

373

Component-based software architecture recovery from an object oriented system. A search-based approach  

OpenAIRE

Software architecture modeling and representation are a main phase of the development process of complex systems. In fact, software architecture representation provides many advantages during all phases of software life cycle. Nevertheless, for many systems, like legacy or eroded ones, there is no available representation of their architectures. In order to benefit from this representation, we propose, in this thesis, an approach called ROMANTIC which focuses on recovering a component-based a...

Chardigny, Sylvain

2009-01-01

374

UAV-based urban structural damage assessment using object-based image analysis and semantic reasoning  

OpenAIRE

Structural damage assessment is critical after disasters but remains a challenge. Many studies have explored the potential of remote sensing data, but limitations of vertical data persist. Oblique imagery has been identified as more useful, though the multi-angle imagery also adds a new dimension of complexity. This paper addresses damage assessment based on multi-perspective, overlapping, very high resolution oblique images obtained with unmanned aerial vehicles (UAV...

Fernandez Galarreta, J.; Kerle, N.; Gerke, M.

2014-01-01

375

A graph-based model of object recognition self-learning  

Directory of Open Access Journals (Sweden)

Full Text Available In this paper, we study the object recognition self-learning for robots.In particular, we consider the self-learning during solution of typicaltasks. We propose a graph-based model for self-learning. This model isbased on the problem of monochromatic path for given set of weights.We prove that the problem is NP-complete. We consider an approachto solve the problem. This approach is based on an explicit reductionfrom the problem to the satisfiability problem.

A. Gorbenko

2013-01-01

376

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

OpenAIRE

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 Image Analysis (OBIA) and segmentation optimization using 1m LiDAR DTMs of 2002 and 2005. Based on the 2002 data, the scale parameter for each geomorphological feature was optimized by comparing ma...

Seijmonsbergen, A. C.; Anders, N. S.; Bouten, W.

2012-01-01

377

Fuzzy Concurrent Object Oriented Expert System for Fault Diagnosis in 8085 Microprocessor Based System Board  

Directory of Open Access Journals (Sweden)

Full Text Available With the acceptance of artificial intelligence paradigm, a number of successful artificial intelligence systems were created. Fault diagnosis in microprocessor based boards needs lot of empirical knowledge and expertise and is a true artificial intelligence problem. Research on fault diagnosis in microprocessor based system boards using new fuzzy-object oriented approach is presented in this paper. There are many uncertain situations observed during fault diagnosis. These uncertain situations were handled using fuzzy mathematics properties. Fuzzy inference mechanism is demonstrated using one case study. Some typical faults in 8085 microprocessor board and diagnostic procedures used is presented in this paper.

Mr.D. V. Kodavade

2014-09-01

378

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

Energy Technology Data Exchange (ETDEWEB)

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 {mu}m 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.

Zhu, Zhiwei; Zhou, Xiaoqin; Liu, Qiang; Zhao, Shaoxin [Jilin University, Changchun (China)

2011-12-15

379

An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images  

Directory of Open Access Journals (Sweden)

Full Text Available There have been increasing demands for automatically monitoring urban areas in very high detail, and the Unmanned Aerial Vehicle (UAV with auto-navigation (AUNA system offers such capability. This study proposes an object-based hierarchical method to detect changes from UAV images taken at different times. It consists of several steps. In the first step, an octocopter with AUNA capability is used to acquire images at different dates. These images are registered automatically, based on SIFT (Scale-Invariant Feature Transform feature points, via the general bundle adjustment framework. Thus, the Digital Surface Models (DSMs and orthophotos can be generated for raster-based change analysis. In the next step, a multi-primitive segmentation method combining the spectral and geometric information is proposed for object-based analysis. In the final step, a multi-criteria decision analysis is carried out concerning the height, spectral and geometric coherence, and shape regularity for change determination. Experiments based on UAV images with five-centimeter ground resolution demonstrate the effectiveness of the proposed method, leading to the conclusion that this method is practically applicable for frequent monitoring.

Rongjun Qin

2014-08-01

380

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

International Nuclear Information System (INIS)

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)

381

High Quality Prime Farmland extraction pattern based on object-oriented image analysis  

Science.gov (United States)

High Quality Prime Farmland (HQPF) is high, stable yields based on land consolidation of prime farmland, and has its important impact upon China's food security. To make clear the status-in-quo of the HQPF is important to its construction and management. However, it is difficult to get the spatial distribution information of the constructed HQPF enough rapidly in mountainous area using ground investigation, as well as hard to satisfy the requirements of large-scale promotion. A HQPF extraction framework based on object-oriented image analysis is discussed and applied to aerial imageries of Tonglu County. The approach can be divided into 3 steps: image segmentation, feature analysis & feature selection and extraction rules generation. In the image segmentation procedure, canny operator is used in edge detection, an edge growth algorithm is used to link discontinuous edge, and region labelling is carried out to generate image object. In the feature analysis & selection procedure, object-oriented feature analysis and feature selection methods are also discussed to construct a feature subset with fine divisibility for HQPF extraction. In the extraction rules generation procedure, the C4.5 algorithm is used to establish and trim the decision tree, then HQPF decision rules are generated from the decision tree. Compared with supervised classification (MLC classifier, ERDAS 8.7) and another object-oriented image analysis method (FNEA, e-Cognition4.0), the accuracy assessment shows that the extraction results by the object-oriented extraction patters have a high level of category consistency, size consistency and shape consistency.

Liu, Yong-xue; Li, Man-chun; Chen, Zhen-jie; Li, Fei-xue; Zhang, Yu; Zhao, Bo; Tan, Lu

2008-10-01

382

Highly accurate moving object detection in variable bit rate video-based traffic monitoring systems.  

Science.gov (United States)

Automated motion detection, which segments moving objects from video streams, is the key technology of intelligent transportation systems for traffic management. Traffic surveillance systems use video communication over real-world networks with limited bandwidth, which frequently suffers because of either network congestion or unstable bandwidth. Evidence supporting these problems abounds in publications about wireless video communication. Thus, to effectively perform the arduous task of motion detection over a network with unstable bandwidth, a process by which bit-rate is allocated to match the available network bandwidth is necessitated. This process is accomplished by the rate control scheme. This paper presents a new motion detection approach that is based on the cerebellar-model-articulation-controller (CMAC) through artificial neural networks to completely and accurately detect moving objects in both high and low bit-rate video streams. The proposed approach is consisted of a probabilistic background generation (PBG) module and a moving object detection (MOD) module. To ensure that the properties of variable bit-rate video streams are accommodated, the proposed PBG module effectively produces a probabilistic background model through an unsupervised learning process over variable bit-rate video streams. Next, the MOD module, which is based on the CMAC network, completely and accurately detects moving objects in both low and high bit-rate video streams by implementing two procedures: 1) a block selection procedure and 2) an object detection procedure. The detection results show that our proposed approach is capable of performing with higher efficacy when compared with the results produced by other state-of-the-art approaches in variable bit-rate video streams over real-world limited bandwidth networks. Both qualitative and quantitative evaluations support this claim; for instance, the proposed approach achieves Similarity and F1 accuracy rates that are 76.40% and 84.37% higher than those of existing approaches, respectively. PMID:24805212

Huang, Shih-Chia; Chen, Bo-Hao

2013-12-01

383

MULTI OBJECTIVE OPTIMIZATION OF VEHICLE ACTIVE SUSPENSION SYSTEM USING DEBBO BASED PID CONTROLLER  

Directory of Open Access Journals (Sweden)

Full Text Available This paper proposes the Multi Objective Optimization (MOO of Vehicle Active Suspension System (VASS with a hybrid Differential Evolution (DE based Biogeography-Based Optimization (BBO (DEBBO for the parameter tuning of Proportional Integral Derivative (PID controller. Initially a conventional PID controller, secondly a BBO, an rising nature enthused global optimization procedure based on the study of the ecological distribution of biological organisms and a hybridized DEBBO algorithm which inherits the behaviours of BBO and DE have been used to find the tuning parameters of the PID controller to improve the performance of VASS by considering a MOO function as the performance index. Simulations of passive system, active system having PID controller with and without optimizations have been performed by considering dual and triple bump kind of road disturbances in MATLAB/Simulink environment. The simulation results show the effectiveness of DEBBO based PID (DEBBOPID in achieving the goal.

Kalaivani Rajagopal

2014-03-01

384

Pixel VS Object-Based Image Classification Techniques for LIDAR Intensity Data  

Science.gov (United States)

Light Detection and Ranging (LiDAR) systems are remote sensing techniques used mainly for terrain surface modelling. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which provides high separability in the reflected energy by the target. This phenomenon is investigated to use the LiDAR intensity data for land-cover classification. The goal of this paper is to investigate and evaluates the use of different image classification techniques applied on LiDAR intensity data for land cover classification. The two techniques proposed are: a) Maximum likelihood classifier used as pixel- based classification technique; and b) Image segmentation used as object-based classification technique. A study area covers an urban district in Burnaby, British Colombia, Canada, is selected to test the different classification techniques for extracting four feature classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas, from the LiDAR intensity data. Generally, the results show that LiDAR intensity data can be used for land cover classification. An overall accuracy of 63.5% can be achieved using the pixel-based classification technique. The overall accuracy of the results is improved to 68% using the object- based classification technique. Further research is underway to investigate different criteria for segmentation process and to refine the design of the object-based classification algorithm.

El-Ashmawy, N.; Shaker, A.; Yan, W.

2011-09-01

385

Confidence-Level-Based New Adaptive Particle Filter for Nonlinear Object Tracking  

Directory of Open Access Journals (Sweden)

Full Text Available Nonlinear object tracking from noisy measurements is a basic skill and a challenging task of mobile robotics, especially under dynamic environments. The particle filter is a useful tool for nonlinear object tracking with non?Gaussian noise. Nonlinear object tracking needs the real?time processing capability of the particle filter. While the number in a traditional particle filter is fixed, that can lead to a lot of unnecessary computation. To address this issue, a confidence?level? based new adaptive particle filter (NAPF algorithm is proposed in this paper. In this algorithm the idea of confidence interval is utilized. The least number of particles for the next time instant is estimated according to the confidence level and the variance of the estimated state. Accordingly, an improved systematic re?sampling algorithm is utilized for the new improved particle filter. NAPF can effectively reduce the computation while ensuring the accuracy of nonlinear object tracking. The simulation results and the ball tracking results of the robot verify the effectiveness of the algorithm.

Xiaoyong Zhang

2012-11-01

386

Line-Based Object Recognition using Hausdorff Distance: From Range Images to Molecular Secondary Structure  

Energy Technology Data Exchange (ETDEWEB)

Object recognition algorithms are fundamental tools in automatic matching of geometric shapes within a background scene. Many approaches have been proposed in the past to solve the object recognition problem. Two of the key aspects that distinguish them in terms of their practical usability are: (i) the type of input model description and (ii) the comparison criteria used. In this paper we introduce a novel scheme for 3D object recognition based on line segment representation of the input shapes and comparison using the Hausdor distance. This choice of model representation provides the flexibility to apply the scheme in different application areas. We define several variants of the Hausdor distance to compare the models within the framework of well defined metric spaces. We present a matching algorithm that efficiently finds a pattern in a 3D scene. The algorithm approximates a minimization procedure of the Hausdor distance. The output error due to the approximation is guaranteed to be within a known constant bound. Practical results are presented for two classes of objects: (i) polyhedral shapes extracted from segmented range images and (ii) secondary structures of large molecules. In both cases the use of our approximate algorithm allows to match correctly the pattern in the background while achieving the efficiency necessary for practical use of the scheme. In particular the performance is improved substantially with minor degradation of the quality of the matching.

Guerra, C; Pascucci, V

2004-12-13

387

A Hybrid Simplex Multi-Objective Evolutionary Algorithm Based on A New Fitness Assignment Strategy  

Directory of Open Access Journals (Sweden)

Full Text Available In multi-objective evolutionary algorithms (MOEAs, the traditional fitness assignment strategy based on Pareto dominance is ineffective in sorting out the high-quality solutions when the number of the objective is large. Recently, many scholars have used preference order (PO ranking approach as an optimality criterion in the ranking stage of MOEAs. The experiment shows that the algorithms equipped with the PO ranking procedures can have a better convergence to the true Pareto surface, but are ineffective to maintain a set of well-distributed solutions over the Pareto surface. In order to overcome above shortcomings, a new algorithm is proposed which adopts a new fitness assignment strategy using the information of the individual preference order ranking and the individual density. In this way,  it is helpful to guide the individuals to more sparse areas in the Pareto Front. At the same time, the proposed algorithm effectively combines multi-objective evolutionary algorithm with the Nelder-Mead simplex search to get a balance between the exploration and exploitation abilities. In each generation, the algorithm adopts a parallel hybrid way to evolve two subsets simultaneously, and the population will be improved by both evolution algorithm and simplex search. The proposed algorithm has been compared with other MOEAs on some many-objective problems by experiments. The experimental results indicate that the proposed algorithm achieves a better performance in convergence and diversity.

Xiaofang Guo

2013-02-01

388

Laser imaging for the underwater object and image segmentation based on fractal  

Science.gov (United States)

Water is a kind of special natural background. The image acquired with laser imaging system for the underwater object always has some speckle noises caused by the backscattering of water and suspended particles, which gives birth to inconvenient to extract features of the image. In this paper, a set of laser underwater imaging system, which uses range-gated technique to avoid the backscatter and imaging distance up to above 20 meters, and its experimental results in boat-pool are introduced. According to the inherent mechanism of the underwater laser image, we propose a fractal-characters-based method for segmentation of the nature scene to find the artifact object from the image, which adopts region segmentation by Hausdroff dimension obtained by blanket covering method, and depends on the different distribution of the texture characteristic and multi-scale analysis to carry out the image segmentation. Experiments show the approach is suitable for texture segmentation and object finding in the image acquired b laser imaging laser system for the underwater object.

Chang, Yanjun; Peng, Fuyuan; Luo, Lin; Zhang, Ying

2003-09-01

389

Line-Based Object Recognition using Hausdorff Distance: From Range Images to Molecular Secondary Structure  

International Nuclear Information System (INIS)

Object recognition algorithms are fundamental tools in automatic matching of geometric shapes within a background scene. Many approaches have been proposed in the past to solve the object recognition problem. Two of the key aspects that distinguish them in terms of their practical usability are: (i) the type of input model description and (ii) the comparison criteria used. In this paper we introduce a novel scheme for 3D object recognition based on line segment representation of the input shapes and comparison using the Hausdor distance. This choice of model representation provides the flexibility to apply the scheme in different application areas. We define several variants of the Hausdor distance to compare the models within the framework of well defined metric spaces. We present a matching algorithm that efficiently finds a pattern in a 3D scene. The algorithm approximates a minimization procedure of the Hausdor distance. The output error due to the approximation is guaranteed to be within a known constant bound. Practical results are presented for two classes of objects: (i) polyhedral shapes extracted from segmented range images and (ii) secondary structures of large molecules. In both cases the use of our approximate algorithm allows to match correctly the pattern in the background while achieving the efficiency necessary for practical use of the scheme. In particular the performance is improved substantially with minor degradation of the quality of the matchingdegradation of the quality of the matching

390

Digital Image Watermarking for Arbitrarily Shaped Objects Based On SA-DWT  

Directory of Open Access Journals (Sweden)

Full Text Available Many image watermarking schemes have been proposed in recent years, but they usually involve embedding a watermark to the entire image without considering only a particular object in the image, which the image owner may be interested in. This paper proposes a watermarking scheme that can embed a watermark to an arbitrarily shaped object in an image. Before embedding, the image owner specifies an object of arbitrary shape that is of a concern to him. Then the object is transformed into the wavelet domain using in place lifting shape adaptive DWT(SADWT and a watermark is embedded by modifying the wavelet coefficients. In order to make the watermark robust and transparent, the watermark is embedded in the average of wavelet blocks using the visual model based on the human visual system. Wavelet coefficients n least significant bits (LSBs are adjusted in concert with the average. Simulation results shows that the proposed watermarking scheme is perceptually invisible and robust against many attacks such as lossy compression (e.g. JPEG, JPEG2000, scaling, adding noise, filtering, etc.

F. Regragui

2009-10-01

391

An effective docking strategy for virtual screening based on multi-objective optimization algorithm  

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Full Text Available Abstract Background Development of a fast and accurate scoring function in virtual screening remains a hot issue in current computer-aided drug research. Different scoring functions focus on diverse aspects of ligand binding, and no single scoring can satisfy the peculiarities of each target system. Therefore, the idea of a consensus score strategy was put forward. Integrating several scoring functions, consensus score re-assesses the docked conformations using a primary scoring function. However, it is not really robust and efficient from the perspective of optimization. Furthermore, to date, the majority of available methods are still based on single objective optimization design. Results In this paper, two multi-objective optimization methods, called MOSFOM, were developed for virtual screening, which simultaneously consider both the energy score and the contact score. Results suggest that MOSFOM can effectively enhance enrichment and performance compared with a single score. For three different kinds of binding sites, MOSFOM displays an excellent ability to differentiate active compounds through energy and shape complementarity. EFMOGA performed particularly well in the top 2% of database for all three cases, whereas MOEA_Nrg and MOEA_Cnt performed better than the corresponding individual scoring functions if the appropriate type of binding site was selected. Conclusion The multi-objective optimization method was successfully applied in virtual screening with two different scoring functions that can yield reasonable binding poses and can furthermore, be ranked with the potentially compromised conformations of each compound, abandoning those conformations that can not satisfy overall objective functions.

Kang Ling

2009-02-01

392

Joint source-channel coding for wireless object-based video communications utilizing data hiding.  

Science.gov (United States)

In recent years, joint source-channel coding for multimedia communications has gained increased popularity. However, very limited work has been conducted to address the problem of joint source-channel coding for object-based video. In this paper, we propose a data hiding scheme that improves the error resilience of object-based video by adaptively embedding the shape and motion information into the texture data. Within a rate-distortion theoretical framework, the source coding, channel coding, data embedding, and decoder error concealment are jointly optimized based on knowledge of the transmission channel conditions. Our goal is to achieve the best video quality as expressed by the minimum total expected distortion. The optimization problem is solved using Lagrangian relaxation and dynamic programming. The performance of the proposed scheme is tested using simulations of a Rayleigh-fading wireless channel, and the algorithm is implemented based on the MPEG-4 verification model. Experimental results indicate that the proposed hybrid source-channel coding scheme significantly outperforms methods without data hiding or unequal error protection. PMID:16900673

Wang, Haohong; Tsaftaris, Sotirios A; Katsaggelos, Aggelos K

2006-08-01

393

An Object Oriented Knowledge based System for Basic Problem solving in Science and Engineering .  

Directory of Open Access Journals (Sweden)

Full Text Available The databases systems and knowledge-based systems are inadequate to handle the knowledge required to solve computational problems in science and engineering. The conventional handbooks and textbooks of science and engineering contain data on scientific quantities in the form of tables and a large number of formulae relating these quantities in the form of algebraic equations. This paper describes a knowledge-based system, developed using object-oriented approach, to store and manipulate this kind of knowledge. The formulae are input in the same form as they are written, in terms of the well known symbols used by an engineer or a scientist, The system interprets the symbols as representing scientific quantities and links them with the underlying data and methods in the knowledge base. By linking data on scientific quantities with one or more appropriate formulae, it is shown that the knowledge base can be used for basic problem solving in science and engineering. The system has been developed on a Sun Sparc Station using object-oriented environment, objectworks C++.

M. V. Krishnamurthy

2013-04-01

394

Remote sensing and object-based techniques for mapping fine-scale industrial disturbances  

Science.gov (United States)

Remote sensing provides an important data source for the detection and monitoring of disturbances; however, using this data to recognize fine-spatial resolution industrial disturbances dispersed across extensive areas presents unique challenges (e.g., accurate delineation and identification) and deserves further investigation. In this study, we present and assess a geographic object-based image analysis (GEOBIA) approach with high-spatial resolution imagery (SPOT 5) to map industrial disturbances using the oil sands region of Alberta's northeastern boreal forest as a case study. Key components of this study were (i) the development of additional spectral, texture, and geometrical descriptors for characterizing image-objects (groups of alike pixels) and their contextual properties, and (ii) the introduction of decision trees with boosting to perform the object-based land cover classification. Results indicate that the approach achieved an overall accuracy of 88%, and that all descriptor groups provided relevant information for the classification. Despite challenges remaining (e.g., distinguishing between spectrally similar classes, or placing discrete boundaries), the approach was able to effectively delineate and classify fine-spatial resolution industrial disturbances.

Powers, Ryan P.; Hermosilla, Txomin; Coops, Nicholas C.; Chen, Gang

2015-02-01

395

Multi-objective scheduling in an agent based Holonic manufacturing system  

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Full Text Available The present paper is aimed at multi-objective scheduling in an agent based holonic manufacturing system to satisfy the goal of several communities namely the product, the resource, and the organization simultaneously. In this attempt, first a multi criteria based priority rule is developed following Simple Additive Weight (SAW method under Multi Criteria Decision Making (MCDM environment to rank the products. Accordingly, the products are allowed to select a particular resource for execution by negotiation considering minimum time as criterion. The interests of different communities are accomplished by allocating the ordered rank of products to the ordered rank of resources. Conflict, if arises between products and resources, are resolved by introducing the concept of Early Finish Time (EFT as criterion for task allocation. A scheduling algorithm is proposed for execution of the rule. In view of machine failure, a cooperation strategy is evolved that also optimizes reallocation of the incomplete task. It is concluded that the proposed scheduling algorithm together with the disturbance handling algorithm are poised to satisfy the agent’s local objective as well as organization’s global objective concurrently and are commensurable with multi agent paradigm.

T. K. Jana

2014-01-01

396

Object-based change detection: dimension of damage in residential areas of Abu Suruj, Sudan  

Science.gov (United States)

Given the importance of Change Detection, especially in the field of crisis management, this paper discusses the advantage of object-based Change Detection. This project and the used methods give an opportunity to coordinate relief actions strategically. The principal objective of this project was to develop an algorithm which allows to detect rapidly damaged and destroyed buildings in the area of Abu Suruj. This Sudanese village is located in West-Darfur and has become the victim of civil war. The software eCognition Developer was used to per-form an object-based Change Detection on two panchromatic Quickbird 2 images from two different time slots. The first image shows the area before, the second image shows the area after the massacres in this region. Seeking a classification for the huts of the Sudanese town Abu Suruj was reached by first segmenting the huts and then classifying them on the basis of geo-metrical and brightness-related values. The huts were classified as "new", "destroyed" and "preserved" with the help of a automated algorithm. Finally the results were presented in the form of a map which displays the different conditions of the huts. The accuracy of the project is validated by an accuracy assessment resulting in an Overall Classification Accuracy of 90.50 percent. These change detection results allow aid organizations to provide quick and efficient help where it is needed the most.

Demharter, Timo; Michel, Ulrich; Ehlers, Manfred; Reinartz, Peter

2011-11-01

397

Object-Based Analysis of Aerial Photogrammetric Point Cloud and Spectral Data for Land Cover Mapping  

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The acquisition of 3D point data with the use of both aerial laser scanning (ALS) and matching of aerial stereo images coupled with advances in image processing algorithms in the past years provide opportunities to map land cover types with better precision than before. The present study applies Object-Based Image Analysis (OBIA) to 3D point cloud data obtained from matching of stereo aerial images together with spectral data to map land cover types of the Nord-Trøndelag county of Norway. The multi-resolution segmentation algorithm of the Definiens eCognition™ software is used to segment the scenes into homogenous objects. The objects are then classified into different land cover types using rules created based on the definitions given for each land cover type by the Norwegian Forest and Landscape Institute. The quality of the land cover map was evaluated using data collected in the field as part of the Norwegian National Forest Inventory. The results show that the classification has an overall accuracy of about 80% and a kappa index of about 0.65. OBIA is found to be a suitable method for utilizing 3D remote sensing data for land cover mapping in an effort to replace manual delineation methods.

Debella-Gilo, M.; Bjørkelo, K.; Breidenbach, J.; Rahlf, J.

2013-04-01

398

Optimizing Object-Based Classification in Urban Environments Using Very High Resolution GEOEYE-1 Imagery  

Science.gov (United States)

The latest breed of very high resolution (VHR) commercial satellites opens new possibilities for cartographic and remote sensing applications. In fact, one of the most common applications of remote sensing images is the extraction of land cover information for digital image base maps by means of classification techniques. When VHR satellite images are used, an object-based classification strategy can potentially improve classification accuracy compared to pixel based classification. The aim of this work is to carry out an accuracy assessment test on the classification accuracy in urban environments using pansharpened and panchromatic GeoEye-1 orthoimages. In this work, the influence on object-based supervised classification accuracy is evaluated with regard to the sets of image object (IO) features used for classification of the land cover classes selected. For the classification phase the nearest neighbour classifier and the eCognition v. 8 software were used, using seven sets of IO features, including texture, geometry and the principal layer values features. The IOs were attained by eCognition using a multiresolution segmentation approach that is a bottom-up regionmerging technique starting with one-pixel. Four different sets or repetitions of training samples, always representing a 10% for each classes were extracted from IOs while the remaining objects were used for accuracy validation. A statistical test was carried out in order to strengthen the conclusions. An overall accuracy of 79.4% was attained with the panchromatic, red, blue, green and near infrared (NIR) bands from the panchromatic and pansharpened orthoimages, the brightness computed for the red, blue, green and infrared bands, the Maximum Difference, a mean of soil-adjusted vegetation index (SAVI), and, finally the normalized Digital Surface Model or Object Model (nDSM), computed from LiDAR data. For buildings classification, nDSM was the most important feature attaining producer and user accuracies of around 95%. On the other hand, for the class "vegetation", SAVI was the most significant feature, obtaining accuracies close to 90%.

Aguilar, M. A.; Vicente, R.; Aguilar, F. J.; Fernández, A.; Saldaña, M. M.

2012-07-01

399

A Novel Java Based Computation and Comparison Method (JBCCM for Differentiating Object Oriented Paradigm Using Coupling Measures  

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Full Text Available In this paper we propose a novel java based computation and comparison method (JBCCM. In this method we taking three type of object oriented files for showing the computation. Those three files belong to C++, Java and C#. We first compute class, Inheritance, Interface, object and Line of Codes (LOC. Then we assume three databases based on several properties of C++, java, C#. Then we compare three files Based on class (BOC, Based on Inheritance (BOI, Based on Interfaces (BOIN, Based on Object (BOO and Based on LOC (BOL. Then we deduce a comparative result for that particular object oriented file. The result approximate that the file is best suited on which platform. So we can deduce best platform and coupling measures for Object Oriented Paradigm.

Sandeep Singh

2011-12-01

400

Object composition  

OpenAIRE

an extension of Java with incomplete objects. This extension is based on the definition of specific annotations (thus the Java grammar is not extended). It provides a preprocessor that, given a program that uses our special annotations (for incomplete classes) and generates standard Java classes. Incomplete Objects The programmer, besides standard classes, can define incomplete classes whose instances are incomplete objects that can be composed in an object-based fashion. Hence, in our langua...

Bono, Viviana; Bettini, Lorenzo

2010-01-01

401

Detection and object-based classification of offshore oil slicks using ENVISAT-ASAR images.  

Science.gov (United States)

The aim of this study is to propose and test a multi-level methodology for detection of oil slicks in ENVISAT Advanced Synthetic Aperture Radar (ASAR) imagery, which can be used to support the identification of hydrocarbon seeps. We selected Andrusov Ridge in the Central Black Sea as the test study area where extensive hydrocarbon seepages were known to occur continuously. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface and form thin oil films called oil slicks. Microwave sensors like synthetic aperture radar (SAR) are very suitable for ocean remote sensing as they measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. The proposed and applied methodology includes three levels: visual interpretation, image filtering and object-based oil spill detection. Level I, after data preparation with visual interpretation, includes dark spots identification and subsets/scenes creation. After this process, the procedure continues with categorization of subsets/scenes into three cases based on contrast difference of dark spots to the surroundings. In level II, by image and morphological filtering, it includes preparation of subsets/scenes for segmentation. Level III includes segmentation and feature extraction which is followed by object-based classification. The object-based classification is applied with the fuzzy membership functions defined by extracted features of ASAR subsets/scenes, where the parameters of the detection algorithms are tuned specifically for each case group. As a result, oil slicks are discriminated from look-alikes with an overall classification accuracy of 83% for oil slicks and 77% for look-alikes obtained by averaging three different cases. PMID:21380923

Akar, Sertac; Süzen, Mehmet Lutfi; Kaymakci, Nuretdin

2011-12-01

402

Phantoms for diffuse optical imaging based on totally absorbing objects, part 2: experimental implementation  

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We present the experimental implementation and validation of a phantom for diffuse optical imaging based on totally absorbing objects for which, in the previous paper [J. Biomed. Opt. 18(6), 066014, (2013)], we have provided the basic theory. Totally absorbing objects have been manufactured as black polyvinyl chloride (PVC) cylinders and the phantom is a water dilution of intralipid-20% as the diffusive medium and India ink as the absorber, filled into a black scattering cell made of PVC. By means of time-domain measurements and of Monte Carlo simulations, we have shown the reliability, the accuracy, and the robustness of such a phantom in mimicking typical absorbing perturbations of diffuse optical imaging. In particular, we show that such a phantom can be used to generate any absorption perturbation by changing the volume and position of the totally absorbing inclusion.

Martelli, Fabrizio; Ninni, Paola Di; Zaccanti, Giovanni; Contini, Davide; Spinelli, Lorenzo; Torricelli, Alessandro; Cubeddu, Rinaldo; Wabnitz, Heidrun; Mazurenka, Mikhail; Macdonald, Rainer; Sassaroli, Angelo; Pifferi, Antonio

2014-07-01

403

Linear stereo vision based objects detection and tracking using spectral clustering  

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Objects detection and tracking is a key function for many applications like video surveillance, robotic, intelligent transportation systems,...etc. This problem is widely treated in the literature in terms of sensors (video cameras, laser range finder, Radar) and methodologies. This paper proposes a new approach for detecting and tracking objects using stereo vision with linear cameras. After the matching process applied to edge points extracted from the images, the reconstructed points in the scene are clustered using spectral analysis. The obtained clusters are then tracked throughout their center of gravity using a Kalman filter and a Nearest Neighbour (NN) based data association algorithm. The approach is tested and evaluated on real data to demonstrate its effectiveness for obstacle detection and tracking in front of a vehicle. This work is a part of a project that aims to develop advanced driving aid systems, supported by the CPER, STIC and Volubilis programs.

Moqqaddem, Safaa; Ruichek, Y.; Touahni, R.; Sbihi, A.

2011-01-01

404

Development of an image based system to objectively score the severity of phoriasis  

DEFF Research Database (Denmark)

The objective of this thesis is to provide a possible solution to one of the current problems in dermatology: the lack of suitable methods to objectively evaluate the severity of dermatological lesions. An image based system is developed with the goal of automatically obtaining summarization values that characterize the lesion and help to track the evolution of the disease. The thesis starts by analyzing an accurate type of equipment with which collect dermatological images. Later, a method to segment the different areas embedded in dermatological lesions is developed. Results of the segmentation task will be used to obtain values that characterize the lesion. The last part of the thesis considers the possibility of including more bands in the analysis in order to increase the accuracy of the proposed method.

Gomez, David Delgado

2005-01-01

405

Evolutionary Algorithm based on simulated annealing for the multi-objective optimization of combinatorial problems  

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Full Text Available This paper states a novel hybrid-metaheuristic based on the Theory of Deterministic Swapping, Theory of Evolution and Simulated Annealing Metaheuristic for the multi-objective optimization of combinatorial problems. The proposed algorithm is named EMSA. It is an improvement of MODS algorithm. Unlike MODS, EMSA works using a search direction given through the assignation of weights to each function of the combinatorial problem to optimize. Also, in order to avoid local optimums, EMSA uses crossover strategy of Genetic Algorithm. Lastly, EMSA is tested using well know instances of the Bi-Objective Traveling Salesman Problem (TSP from TSPLIB. Its results were compared with MODS Metaheuristic (its precessor. The comparison was made using metrics from the specialized literature such as Spacing, Generational Distance, Inverse Generational Distance and Non-Dominated Generation Vectors. In every case, the EMSA results on the metrics were always better and in some of those cases, the superiority was 100%.

Elias David Nino Ruiz

2013-05-01

406

Comparison of Planar Parallel Manipulator Architectures based on a Multi-objective Design Optimization Approach  

CERN Document Server

This paper deals with the comparison of planar parallel manipulator architectures based on a multi-objective design optimization approach. The manipulator architectures are compared with regard to their mass in motion and their regular workspace size, i.e., the objective functions. The optimization problem is subject to constraints on the manipulator dexterity and stiffness. For a given external wrench, the displacements of the moving platform have to be smaller than given values throughout the obtained maximum regular dexterous workspace. The contributions of the paper are highlighted with the study of 3-RPR, 3-RPR and 3-RPR planar parallel manipulator architectures, which are compared by means of their Pareto frontiers obtained with a genetic algorithm.

Chablat, Damien; Ur-Rehman, Raza; Wenger, Philippe

2010-01-01

407

Context based Coding of Binary Shapes by Object Boundary Straightness Analysis  

DEFF Research Database (Denmark)

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 more efficient than the state-of-the-art and more complex free tree coder for most of the binary shape and map test images.

Aghito, Shankar Manuel; Forchhammer, SØren

2004-01-01

408

Extraction of urban impervious surface information based on object-oriented technology  

Science.gov (United States)

Impervious surface is an important part of urban underlying surface, as well as an important monitoring index for city ecological system and environment changes. However, accurate impervious surface extraction is still a challenge. This paper uses the color, shape and overall heterogeneity features from the high spatial resolution remote sensing image to extract the impervious surface. An edge-based image segmentation algorithm is put forward to fuse heterogeneous objects which integrates edge features and multi-scale segmentation algorithm and uses the edge information to guide image objects generation. Results showed that this method can greatly improve the accuracy of image segmentation. Accuracy assessment indicated that the overall impervious surface classification accuracy and a Kappa coefficient yield 87% and 0.84, respectively.

Liu, Aixia; Zhao, Xiaojie; Wang, Jing; He, Ting

2013-10-01

409

Microlensing-Based Estimate of the Mass Fraction in Compact Objects in Lens  

CERN Document Server

We estimate the fraction of mass that is composed of compact objects in gravitational lens galaxies. This study is based on microlensing measurements (obtained from the literature) of a sample of 29 quasar image pairs seen through 20 lens galaxies. We determine the baseline for no microlensing magnification between two images from the ratios of emission line fluxes. Relative to this baseline, the ratio between the continua of the two images gives the difference in microlensing magnification. The histogram of observed microlensing events peaks close to no magnification and is concentrated below 0.6 magnitudes, although two events of high magnification, $\\Delta m \\sim 1.5$, are also present. We study the likelihood of the microlensing measurements using frequency distributions obtained from simulated microlensing magnification maps for different values of the fraction of mass in compact objects, $\\alpha$. The concentration of microlensing measurements close to $\\Delta m \\sim 0$ can be explained only by simulati...

Mediavilla, E; Falco, E; Motta, V; Guerras, E; Canovas, H; Jean, C; Oscoz, A; Mosquera, A M

2009-01-01

410

A Moving Objects Detection Algorithm Based on Three-Frame Difference and Sparse Optical Flow  

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Full Text Available Moving objects detection is an important research of computer vision. Optical flow method is a main way, but it is limited to use because of its complexity. A moving objects detection algorithm based on three-frame difference and optical flow is proposed. The calculation of optical flow is simplified. Harris corners are detected and then only the corners are selected to compute optical flow information, which reduce the algorithm’s complexity. Because the detected moving target area is not complete, three-frame difference method is introduced as a supplement. The experimental results show that the algorithm can achieve real-time and has better results than anyone of these two separate algorithms.

Guo-Wu Yuan

2014-01-01

411

ROCIT : a visual object recognition algorithm based on a rank-order coding scheme.  

Energy Technology Data Exchange (ETDEWEB)

This document describes ROCIT, a neural-inspired object recognition algorithm based on a rank-order coding scheme that uses a light-weight neuron model. ROCIT coarsely simulates a subset of the human ventral visual stream from the retina through the inferior temporal cortex. It was designed to provide an extensible baseline from which to improve the fidelity of the ventral stream model and explore the engineering potential of rank order coding with respect to object recognition. This report describes the baseline algorithm, the model's neural network architecture, the theoretical basis for the approach, and reviews the history of similar implementations. Illustrative results are used to clarify algorithm details. A formal benchmark to the 1998 FERET fafc test shows above average performance, which is encouraging. The report concludes with a brief review of potential algorithmic extensions for obtaining scale and rotational invariance.

Gonzales, Antonio Ignacio; Reeves, Paul C.; Jones, John J.; Farkas, Benjamin D.

2004-06-01

412

Multi-equipment condition based maintenance optimization by multi- objective genetic algorithm  

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Full Text Available Purpose: This paper deals with the optimization of the condition based maintenance (CBM applied on manufacturing multi-equipment system under cost and benefit criteria.Design/methodology/approach: The system is modeled using Discrete Event Simulation (DES and optimized by means of the application of a Multi-Objective Evolutionary Algorithm (MOEA.Findings: Solution for the joint optimization of the condition based maintenance model applied on several equipment has been obtained.Research limitations/implications: The developed approach has been successfully applied to the optimization of condition based maintenance activities of a hubcap production system composed by three plastic injection machines and a painting station, for management decision support.Originality/value: This paper provides a solution for the joint optimization of CBM strategies applied on several equipments

Š. Val?uha

2011-04-01

413

Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking  

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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.

Ming Xue

2014-02-01

414

Using Rule-Based Reasoning and Object-Oriented Methodologies to Diagnose Diabetes  

Directory of Open Access Journals (Sweden)

Full Text Available Problem statement: Diabetes mellitus or diabetes epidemic is one of the high prevalence diseases worldwide with increased number of disability, complications and death toll. An early diagnosis helps patients and medical practitioners to reduce the burden of diabetes. Approach: In this research, we propose a framework for a system using rule-based reasoning and object-oriented methodologies to diagnose both Type 1 and Type 2 diabetes. Results: Extensive literature reviews were carried out and questionnaires were distributed to medical practitioners to build the knowledge base. This knowledge base stores the rules needed to perform a diagnosis. Conclusion: This study only presents the proposed framework and not the system itself. We believe that great improvements can be provided to the medical practitioners and also the diabetics with the implementation of this system in future.

Vithyatheri Govindan

2012-01-01

415

Balancing Multiple Objectives Using a Classification-Based Forest Management System in Changbai Mountains, China  

Science.gov (United States)

Contemporary forest management often consists of multiple objectives, including restoration of human-impacted forested landscapes toward their range of natural variability (RNV) and sustainable levels of timber production. Balancing multiple management objectives is often challenging due to intrinsic conflicts between these objectives and a lack of reference conditions for evaluating the effectiveness of forest restoration efforts. We used a spatially explicit forest landscape model to assess how well a classification-based forest management (CFM) system could achieve multiple objectives in a Korean pine broadleaf mixed forest ecosystem at Changbai Mountain in Northeast China. The CFM system divided the forest landscape into three management areas (Commercial Forest, Special Ecological Welfare Forest, and General Ecological Welfare Forest), each with its own management objectives and prescriptions, but with an overall goal of increasing the ecological and economic sustainability of the entire landscape. The zoning approach adopted in the Chinese CFM system is very similar to the TRIAD approach that is being advocated for managing public forests in Canada. In this study, a natural disturbance scenario and seven harvest scenarios (one identical to the current harvest regime and six alternative scenarios) were simulated to examine how tree species composition, age structure, and timber production at the landscape level can be affected by different strategies under the CFM system. The results indicated that the current forest management regime would not only fail to reach the designated timber production level but also move the forest landscape far away from its RNV. In order to return the currently altered forest landscape to approach its RNV while providing a stable level of timber production over time, harvest intensities should be reduced to a level that is equivalent to the amount of timber removals that would occur under the natural disturbances; and the establishment of forest plantations is also required.

Zhao, Fuqiang; Yang, Jian; Liu, Zhihua; Dai, Limin; He, Hong S.

2011-12-01

416

Optimization of Enterprise Information System based on Object-based Knowledge Mesh and Binary Tree with Maximum User Satisfaction  

Directory of Open Access Journals (Sweden)

Full Text Available This paper deals with an approach to the optimization of enterprise information system (EIS based on the object-based knowledge mesh (OKM and binary tree. Firstly, to explore the optimization of EIS by the user’s function requirements, an OKM expression representation based on the user’s satisfaction and binary tree is proposed. Secondly, based on the definitions of the fuzzy function-satisfaction degree relationships on the OKM functions, the optimization model is constructed. Thirdly, the OKM multiple set operation expression is optimized by the immune genetic algorithm and binary tree, with the steps of the OKM optimization presented in detail as well. Finally, the optimization of EIS is illustrated by an example to verify the proposed approaches.

Haiwang Cao

2012-04-01

417

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

Directory of Open Access Journals (Sweden)

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 incorporating the crop height information into the hyperspectral extracted features provided a substantial increase in the classification accuracy. The combination of MNF and CHM produced higher classification accuracy than the combination of VHR and CHM, and the solely MNF-based classification results. The textural and geometric features in the object-based classification could significantly improve the accuracy of the crop species classification. By using the proposed object-based classification framework, a crop species classification result with an overall accuracy of 90.33% and a kappa of 0.89 was achieved in our study area.

Xiaolong Liu

2015-01-01

418

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

Science.gov (United States)

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 proper wages usage. Thus a more precise and unambiguous boundaries of segments (objects) were received. As a results of the classification 5 classes of land cover (buildings, water, high and low vegetation and others) were extracted. Both pixel-based image analysis and OBIA were conducted with a minimum mapping unit of 10m2. Results were validated on the basis on manual classification and random points (80 per test area), reference data set was manually interpreted using ortophotomaps and expert knowledge of the test site areas.

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

419

Evaluation of regional bulbar redness using an image-based objective method  

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Full Text Available AIM: To develop an image-based objective method to precisely evaluate regional ocular bulbar injection.METHODS:Six healthy adult volunteers were photographed in four orientations (superior, inferior, nasal and temporal sides with and without stimulating eye drops. Six line segments (covering 30° were drawn 4mm away from the limbus on each image using ImageJ software. The graph peaks, which were derived from the areas under the line segments and corresponded to the cross-sectional grey-level of the vessels, were analyzed to obtain peak area, peak height/width (PH/PW, and peak numbers. Different-sized areas were selected to calculate the pixels based on the edge-detection algorithm. Also, conjunctival and superficial scleral vessels were analyzed separately. RESULTS:This method had a smaller coefficient of variation, especially for PH/PW, in all four orientations. Hyperaemia parameters changed the least after challenging in the superior region. Moreover, 95% of the PH/PW ratios were greater than 0.87 in conjunctival vessels and less than 1.00 in superficial scleral vessels. PH/PW significantly increased in conjunctival vessels and changed less in superficial scleral vessels.CONCLUSION:A new method of objectively assessing bulbar injection based on ocular surface images was developed. This method can be used to quantify ocular regional injection and to distinguish the superficial scleral and conjunctival vessels.

Wen-Juan Zhao

2014-02-01

420

Evaluation of regional bulbar redness using an image-based objective method  

Science.gov (United States)

AIM To develop an image-based objective method to precisely evaluate regional ocular bulbar injection. METHODS Six healthy adult volunteers were photographed in four orientations (superior, inferior, nasal and temporal sides) with and without stimulating eye drops. Six line segments (covering 30°) were drawn 4mm away from the limbus on each image using ImageJ software. The graph peaks, which were derived from the areas under the line segments and corresponded to the cross-sectional grey-level of the vessels, were analyzed to obtain peak area, peak height/width (PH/PW), and peak numbers. Different-sized areas were selected to calculate the pixels based on the edge-detection algorithm. Also, conjunctival and superficial scleral vessels were analyzed separately. RESULTS This method had a smaller coefficient of variation, especially for PH/PW, in all four orientations. Hyperaemia parameters changed the least after challenging in the superior region. Moreover, 95% of the PH/PW ratios were greater than 0.87 in conjunctival vessels and less than 1.00 in superficial scleral vessels. PH/PW significantly increased in conjunctival vessels and changed less in superficial scleral vessels. CONCLUSION A new method of objectively assessing bulbar injection based on ocular surface images was developed. This method can be used to quantify ocular regional injection and to distinguish the superficial scleral and conjunctival vessels. PMID:24634867

Zhao, Wen-Juan; Duan, Fang; Li, Zhong-Ting; Yang, Hua-Jun; Huang, Qiang; Wu, Kai-Li

2014-01-01