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

Science.gov (United States)

Roshan, Aditya; Zhang, Yun

2014-06-01

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

Stimulus-response correspondence in go-nogo and choice tasks: Are reactions altered by the presence of an irrelevant salient object?

Science.gov (United States)

Lien, Mei-Ching; Pedersen, Logan; Proctor, Robert W

2016-11-01

In 2-choice tasks, responses are faster when stimulus location corresponds to response location, even when stimulus location is irrelevant. Dolk et al. (J Exp Psychol Hum Percept Perform 39:1248-1260, 2013a) found this stimulus-response correspondence effect with a single response location in a go-nogo task when an irrelevant Japanese waving cat was present. They argued that salient objects trigger spatial coding of the response relative to that object. We examined this claim using both behavioral and lateralized readiness potential (LRP) measures. In Experiment 1 participants determined the pitch of a left- or right-positioned tone, whereas in Experiment 2 they determined the color of a dot within a centrally located hand pointing left, right, or straight ahead. In both experiments, participants performed a go-nogo task with the right-index finger and a 2-choice task with both index fingers, with a left-positioned Japanese waving cat present or absent. For the go-nogo task, the cat induced a correspondence effect on response times (RT) to the tones (Experiment 1) but not the visual stimuli (Experiment 2). For the 2-choice task, a correspondence effect was evident in all conditions in both experiments. Cat's presence/absence did not significantly modulate the effect for right and left responses, although there was a trend toward increased RT and LRP for right responses in Experiment 1. The results imply that a salient, irrelevant object could provide a reference frame for response coding when attention is available to process it, as is likely in an auditory task (Experiment 1) but not a visual task (Experiment 2).

Global contrast based salient region detection

KAUST Repository

Cheng, Ming-Ming

2011-08-25

Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing.

Global contrast based salient region detection

KAUST Repository

Cheng, Ming-Ming; Zhang, Guo-Xin; Mitra, Niloy J.; Huang, Xiaolei; Hu, Shi-Min

2011-01-01

Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks including image segmentation, object recognition, and adaptive compression. We propose a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence. The proposed algorithm is simple, efficient, and yields full resolution saliency maps. Our algorithm consistently outperformed existing saliency detection methods, yielding higher precision and better recall rates, when evaluated using one of the largest publicly available data sets. We also demonstrate how the extracted saliency map can be used to create high quality segmentation masks for subsequent image processing.

The Effect of Resolution on Detecting Visually Salient Preattentive Features

Science.gov (United States)

2015-06-01

resolutions in descending order (a–e). The plot compiles the areas of interest displayed in the images and each symbol represents 1 of the images. Data...to particular regions in a scene by highly salient 2 features, for example, the color of the flower discussed in the previous example. These...descending order (a–e). The plot compiles the areas of interest displayed in the images and each symbol represents 1 of the images. Data clusters

Red to green or fast to slow? Infants' visual working memory for "just salient differences".

Science.gov (United States)

Kaldy, Zsuzsa; Blaser, Erik

2013-01-01

In this study, 6-month-old infants' visual working memory for a static feature (color) and a dynamic feature (rotational motion) was compared. Comparing infants' use of different features can only be done properly if experimental manipulations to those features are equally salient (Kaldy & Blaser, 2009; Kaldy, Blaser, & Leslie, 2006). The interdimensional salience mapping method was used to find two objects that each were one Just Salient Difference from a common baseline object (N = 16). These calibrated stimuli were then used in a subsequent two-alternative forced-choice preferential looking memory test (N = 28). Results showed that infants noted the color change, but not the equally salient change in rotation speed. © 2013 The Authors. Child Development © 2013 Society for Research in Child Development, Inc.

Minimum Delay Moving Object Detection

KAUST Repository

Lao, Dong

2017-05-14

This thesis presents a general framework and method for detection of an object in a video based on apparent motion. The object moves, at some unknown time, differently than the “background” motion, which can be induced from camera motion. The goal of proposed method is to detect and segment the object as soon it moves in an online manner. Since motion estimation can be unreliable between frames, more than two frames are needed to reliably detect the object. Observing more frames before declaring a detection may lead to a more accurate detection and segmentation, since more motion may be observed leading to a stronger motion cue. However, this leads to greater delay. The proposed method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms, defined as declarations of detection before the object moves or incorrect or inaccurate segmentation at the detection time. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.

Adobe Boxes: Locating Object Proposals Using Object Adobes.

Science.gov (United States)

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

2016-09-01

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

Distance-dependent pattern blending can camouflage salient aposematic signals.

Science.gov (United States)

Barnett, James B; Cuthill, Innes C; Scott-Samuel, Nicholas E

2017-07-12

The effect of viewing distance on the perception of visual texture is well known: spatial frequencies higher than the resolution limit of an observer's visual system will be summed and perceived as a single combined colour. In animal defensive colour patterns, distance-dependent pattern blending may allow aposematic patterns, salient at close range, to match the background to distant observers. Indeed, recent research has indicated that reducing the distance from which a salient signal can be detected can increase survival over camouflage or conspicuous aposematism alone. We investigated whether the spatial frequency of conspicuous and cryptically coloured stripes affects the rate of avian predation. Our results are consistent with pattern blending acting to camouflage salient aposematic signals effectively at a distance. Experiments into the relative rate of avian predation on edible model caterpillars found that increasing spatial frequency (thinner stripes) increased survival. Similarly, visual modelling of avian predators showed that pattern blending increased the similarity between caterpillar and background. These results show how a colour pattern can be tuned to reveal or conceal different information at different distances, and produce tangible survival benefits. © 2017 The Author(s).

Minimum Delay Moving Object Detection

KAUST Repository

Lao, Dong

2017-11-09

We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.

Minimum Delay Moving Object Detection

KAUST Repository

Lao, Dong

2017-01-08

We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.

Minimum Delay Moving Object Detection

KAUST Repository

Lao, Dong; Sundaramoorthi, Ganesh

2017-01-01

We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon it moves in an online manner. Due to unreliability of motion between frames, more than two frames are needed to reliably detect the object. Our method is designed to detect the object(s) with minimum delay, i.e., frames after the object moves, constraining the false alarms. Experiments on a new extensive dataset for moving object detection show that our method achieves less delay for all false alarm constraints than existing state-of-the-art.

A review of salient elements defining team collaboration in paediatric rehabilitation

NARCIS (Netherlands)

Nijhuis, B. J. G.; Reinders-Messelink, H. A.; de Blecourt, A. C. E.; Olijve, W. G.; Groothoff, J. W.; Nakken, H.; Postema, K.; Postuma, K.

Objective: To explicate the complex process of team collaboration and identify salient elements of team collaboration in paediatric rehabilitation. Data sources: After an initial search to define key features of team collaboration a systematic search on team collaboration and the key features was

Salience of the lambs: a test of the saliency map hypothesis with pictures of emotive objects.

Science.gov (United States)

Humphrey, Katherine; Underwood, Geoffrey; Lambert, Tony

2012-01-25

Humans have an ability to rapidly detect emotive stimuli. However, many emotional objects in a scene are also highly visually salient, which raises the question of how dependent the effects of emotionality are on visual saliency and whether the presence of an emotional object changes the power of a more visually salient object in attracting attention. Participants were shown a set of positive, negative, and neutral pictures and completed recall and recognition memory tests. Eye movement data revealed that visual saliency does influence eye movements, but the effect is reliably reduced when an emotional object is present. Pictures containing negative objects were recognized more accurately and recalled in greater detail, and participants fixated more on negative objects than positive or neutral ones. Initial fixations were more likely to be on emotional objects than more visually salient neutral ones, suggesting that the processing of emotional features occurs at a very early stage of perception.

Seeing Objects as Faces Enhances Object Detection.

Science.gov (United States)

Takahashi, Kohske; Watanabe, Katsumi

2015-10-01

The face is a special visual stimulus. Both bottom-up processes for low-level facial features and top-down modulation by face expectations contribute to the advantages of face perception. However, it is hard to dissociate the top-down factors from the bottom-up processes, since facial stimuli mandatorily lead to face awareness. In the present study, using the face pareidolia phenomenon, we demonstrated that face awareness, namely seeing an object as a face, enhances object detection performance. In face pareidolia, some people see a visual stimulus, for example, three dots arranged in V shape, as a face, while others do not. This phenomenon allows us to investigate the effect of face awareness leaving the stimulus per se unchanged. Participants were asked to detect a face target or a triangle target. While target per se was identical between the two tasks, the detection sensitivity was higher when the participants recognized the target as a face. This was the case irrespective of the stimulus eccentricity or the vertical orientation of the stimulus. These results demonstrate that seeing an object as a face facilitates object detection via top-down modulation. The advantages of face perception are, therefore, at least partly, due to face awareness.

Seeing Objects as Faces Enhances Object Detection

Directory of Open Access Journals (Sweden)

Kohske Takahashi

2015-09-01

Full Text Available The face is a special visual stimulus. Both bottom-up processes for low-level facial features and top-down modulation by face expectations contribute to the advantages of face perception. However, it is hard to dissociate the top-down factors from the bottom-up processes, since facial stimuli mandatorily lead to face awareness. In the present study, using the face pareidolia phenomenon, we demonstrated that face awareness, namely seeing an object as a face, enhances object detection performance. In face pareidolia, some people see a visual stimulus, for example, three dots arranged in V shape, as a face, while others do not. This phenomenon allows us to investigate the effect of face awareness leaving the stimulus per se unchanged. Participants were asked to detect a face target or a triangle target. While target per se was identical between the two tasks, the detection sensitivity was higher when the participants recognized the target as a face. This was the case irrespective of the stimulus eccentricity or the vertical orientation of the stimulus. These results demonstrate that seeing an object as a face facilitates object detection via top-down modulation. The advantages of face perception are, therefore, at least partly, due to face awareness.

A view not to be missed: Salient scene content interferes with cognitive restoration

Science.gov (United States)

Van der Jagt, Alexander P. N.; Craig, Tony; Brewer, Mark J.; Pearson, David G.

2017-01-01

Attention Restoration Theory (ART) states that built scenes place greater load on attentional resources than natural scenes. This is explained in terms of "hard" and "soft" fascination of built and natural scenes. Given a lack of direct empirical evidence for this assumption we propose that perceptual saliency of scene content can function as an empirically derived indicator of fascination. Saliency levels were established by measuring speed of scene category detection using a Go/No-Go detection paradigm. Experiment 1 shows that built scenes are more salient than natural scenes. Experiment 2 replicates these findings using greyscale images, ruling out a colour-based response strategy, and additionally shows that built objects in natural scenes affect saliency to a greater extent than the reverse. Experiment 3 demonstrates that the saliency of scene content is directly linked to cognitive restoration using an established restoration paradigm. Overall, these findings demonstrate an important link between the saliency of scene content and related cognitive restoration. PMID:28723975

A view not to be missed: Salient scene content interferes with cognitive restoration.

Directory of Open Access Journals (Sweden)

Alexander P N Van der Jagt

Full Text Available Attention Restoration Theory (ART states that built scenes place greater load on attentional resources than natural scenes. This is explained in terms of "hard" and "soft" fascination of built and natural scenes. Given a lack of direct empirical evidence for this assumption we propose that perceptual saliency of scene content can function as an empirically derived indicator of fascination. Saliency levels were established by measuring speed of scene category detection using a Go/No-Go detection paradigm. Experiment 1 shows that built scenes are more salient than natural scenes. Experiment 2 replicates these findings using greyscale images, ruling out a colour-based response strategy, and additionally shows that built objects in natural scenes affect saliency to a greater extent than the reverse. Experiment 3 demonstrates that the saliency of scene content is directly linked to cognitive restoration using an established restoration paradigm. Overall, these findings demonstrate an important link between the saliency of scene content and related cognitive restoration.

Minimum Delay Moving Object Detection

KAUST Repository

Lao, Dong; Sundaramoorthi, Ganesh

2017-01-01

We present a general framework and method for detection of an object in a video based on apparent motion. The object moves relative to background motion at some unknown time in the video, and the goal is to detect and segment the object as soon

Decoupling Object Detection and Categorization

Science.gov (United States)

Mack, Michael L.; Palmeri, Thomas J.

2010-01-01

We investigated whether there exists a behavioral dependency between object detection and categorization. Previous work (Grill-Spector & Kanwisher, 2005) suggests that object detection and basic-level categorization may be the very same perceptual mechanism: As objects are parsed from the background they are categorized at the basic level. In…

Imaging, object detection, and change detection with a polarized multistatic GPR array

Science.gov (United States)

Beer, N. Reginald; Paglieroni, David W.

2015-07-21

A polarized detection system performs imaging, object detection, and change detection factoring in the orientation of an object relative to the orientation of transceivers. The polarized detection system may operate on one of several modes of operation based on whether the imaging, object detection, or change detection is performed separately for each transceiver orientation. In combined change mode, the polarized detection system performs imaging, object detection, and change detection separately for each transceiver orientation, and then combines changes across polarizations. In combined object mode, the polarized detection system performs imaging and object detection separately for each transceiver orientation, and then combines objects across polarizations and performs change detection on the result. In combined image mode, the polarized detection system performs imaging separately for each transceiver orientation, and then combines images across polarizations and performs object detection followed by change detection on the result.

Ferromagnetic Objects Magnetovision Detection System.

Science.gov (United States)

Nowicki, Michał; Szewczyk, Roman

2013-12-02

This paper presents the application of a weak magnetic fields magnetovision scanning system for detection of dangerous ferromagnetic objects. A measurement system was developed and built to study the magnetic field vector distributions. The measurements of the Earth's field distortions caused by various ferromagnetic objects were carried out. The ability for passive detection of hidden or buried dangerous objects and the determination of their location was demonstrated.

A Local Texture-Based Superpixel Feature Coding for Saliency Detection Combined with Global Saliency

Directory of Open Access Journals (Sweden)

Bingfei Nan

2015-12-01

Full Text Available Because saliency can be used as the prior knowledge of image content, saliency detection has been an active research area in image segmentation, object detection, image semantic understanding and other relevant image-based applications. In the case of saliency detection from cluster scenes, the salient object/region detected needs to not only be distinguished clearly from the background, but, preferably, to also be informative in terms of complete contour and local texture details to facilitate the successive processing. In this paper, a Local Texture-based Region Sparse Histogram (LTRSH model is proposed for saliency detection from cluster scenes. This model uses a combination of local texture patterns and color distribution as well as contour information to encode the superpixels to characterize the local feature of image for region contrast computing. Combining the region contrast as computed with the global saliency probability, a full-resolution salient map, in which the salient object/region detected adheres more closely to its inherent feature, is obtained on the bases of the corresponding high-level saliency spatial distribution as well as on the pixel-level saliency enhancement. Quantitative comparisons with five state-of-the-art saliency detection methods on benchmark datasets are carried out, and the comparative results show that the method we propose improves the detection performance in terms of corresponding measurements.

Learning to Detect Human-Object Interactions

KAUST Repository

Chao, Yu-Wei; Liu, Yunfan; Liu, Xieyang; Zeng, Huayi; Deng, Jia

2017-01-01

In this paper we study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. We propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN), a novel DNN-based framework for HOI detection. At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. We validate the effectiveness of our HO-RCNN using HICO-DET. Experiments demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.

Learning to Detect Human-Object Interactions

KAUST Repository

Chao, Yu-Wei

2017-02-17

In this paper we study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. We propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN), a novel DNN-based framework for HOI detection. At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. We validate the effectiveness of our HO-RCNN using HICO-DET. Experiments demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.

Ferromagnetic Objects Magnetovision Detection System

Directory of Open Access Journals (Sweden)

Michał Nowicki

2013-12-01

Full Text Available This paper presents the application of a weak magnetic fields magnetovision scanning system for detection of dangerous ferromagnetic objects. A measurement system was developed and built to study the magnetic field vector distributions. The measurements of the Earth’s field distortions caused by various ferromagnetic objects were carried out. The ability for passive detection of hidden or buried dangerous objects and the determination of their location was demonstrated.

Dementias show differential physiological responses to salient sounds.

Science.gov (United States)

Fletcher, Phillip D; Nicholas, Jennifer M; Shakespeare, Timothy J; Downey, Laura E; Golden, Hannah L; Agustus, Jennifer L; Clark, Camilla N; Mummery, Catherine J; Schott, Jonathan M; Crutch, Sebastian J; Warren, Jason D

2015-01-01

Abnormal responsiveness to salient sensory signals is often a prominent feature of dementia diseases, particularly the frontotemporal lobar degenerations, but has been little studied. Here we assessed processing of one important class of salient signals, looming sounds, in canonical dementia syndromes. We manipulated tones using intensity cues to create percepts of salient approaching ("looming") or less salient withdrawing sounds. Pupil dilatation responses and behavioral rating responses to these stimuli were compared in patients fulfilling consensus criteria for dementia syndromes (semantic dementia, n = 10; behavioral variant frontotemporal dementia, n = 16, progressive nonfluent aphasia, n = 12; amnestic Alzheimer's disease, n = 10) and a cohort of 26 healthy age-matched individuals. Approaching sounds were rated as more salient than withdrawing sounds by healthy older individuals but this behavioral response to salience did not differentiate healthy individuals from patients with dementia syndromes. Pupil responses to approaching sounds were greater than responses to withdrawing sounds in healthy older individuals and in patients with semantic dementia: this differential pupil response was reduced in patients with progressive nonfluent aphasia and Alzheimer's disease relative both to the healthy control and semantic dementia groups, and did not correlate with nonverbal auditory semantic function. Autonomic responses to auditory salience are differentially affected by dementias and may constitute a novel biomarker of these diseases.

ACTION RECOGNITION USING SALIENT NEIGHBORING HISTOGRAMS

DEFF Research Database (Denmark)

Ren, Huamin; Moeslund, Thomas B.

2013-01-01

Combining spatio-temporal interest points with Bag-of-Words models achieves state-of-the-art performance in action recognition. However, existing methods based on “bag-ofwords” models either are too local to capture the variance in space/time or fail to solve the ambiguity problem in spatial...... and temporal dimensions. Instead, we propose a salient vocabulary construction algorithm to select visual words from a global point of view, and form compact descriptors to represent discriminative histograms in the neighborhoods. Those salient neighboring histograms are then trained to model different actions...

The subjective experience of object recognition: comparing metacognition for object detection and object categorization.

Science.gov (United States)

Meuwese, Julia D I; van Loon, Anouk M; Lamme, Victor A F; Fahrenfort, Johannes J

2014-05-01

Perceptual decisions seem to be made automatically and almost instantly. Constructing a unitary subjective conscious experience takes more time. For example, when trying to avoid a collision with a car on a foggy road you brake or steer away in a reflex, before realizing you were in a near accident. This subjective aspect of object recognition has been given little attention. We used metacognition (assessed with confidence ratings) to measure subjective experience during object detection and object categorization for degraded and masked objects, while objective performance was matched. Metacognition was equal for degraded and masked objects, but categorization led to higher metacognition than did detection. This effect turned out to be driven by a difference in metacognition for correct rejection trials, which seemed to be caused by an asymmetry of the distractor stimulus: It does not contain object-related information in the detection task, whereas it does contain such information in the categorization task. Strikingly, this asymmetry selectively impacted metacognitive ability when objective performance was matched. This finding reveals a fundamental difference in how humans reflect versus act on information: When matching the amount of information required to perform two tasks at some objective level of accuracy (acting), metacognitive ability (reflecting) is still better in tasks that rely on positive evidence (categorization) than in tasks that rely more strongly on an absence of evidence (detection).

Dementias show differential physiological responses to salient sounds

Directory of Open Access Journals (Sweden)

Phillip David Fletcher

2015-03-01

Full Text Available Abnormal responsiveness to salient sensory signals is often a prominent feature of dementia diseases, particularly the frontotemporal lobar degenerations, but has been little studied. Here we assessed processing of one important class of salient signals, looming sounds, in canonical dementia syndromes. We manipulated tones using intensity cues to create percepts of salient approaching (‘looming’ or less salient withdrawing sounds. Pupil dilatation responses and behavioural rating responses to these stimuli were compared in patients fulfilling consensus criteria for dementia syndromes (semantic dementia, n=10; behavioural variant frontotemporal dementia, n=16, progressive non-fluent aphasia, n=12; amnestic Alzheimer’s disease, n=10 and a cohort of 26 healthy age-matched individuals. Approaching sounds were rated as more salient than withdrawing sounds by healthy older individuals but this behavioural response to salience did not differentiate healthy individuals from patients with dementia syndromes. Pupil responses to approaching sounds were greater than responses to withdrawing sounds in healthy older individuals and in patients with semantic dementia: this differential pupil response was reduced in patients with progressive nonfluent aphasia and Alzheimer’s disease relative both to the healthy control and semantic dementia groups, and did not correlate with nonverbal auditory semantic function. Autonomic responses to auditory salience are differentially affected by dementias and may constitute a novel biomarker of these diseases.

Dementias show differential physiological responses to salient sounds

Science.gov (United States)

Fletcher, Phillip D.; Nicholas, Jennifer M.; Shakespeare, Timothy J.; Downey, Laura E.; Golden, Hannah L.; Agustus, Jennifer L.; Clark, Camilla N.; Mummery, Catherine J.; Schott, Jonathan M.; Crutch, Sebastian J.; Warren, Jason D.

2015-01-01

Abnormal responsiveness to salient sensory signals is often a prominent feature of dementia diseases, particularly the frontotemporal lobar degenerations, but has been little studied. Here we assessed processing of one important class of salient signals, looming sounds, in canonical dementia syndromes. We manipulated tones using intensity cues to create percepts of salient approaching (“looming”) or less salient withdrawing sounds. Pupil dilatation responses and behavioral rating responses to these stimuli were compared in patients fulfilling consensus criteria for dementia syndromes (semantic dementia, n = 10; behavioral variant frontotemporal dementia, n = 16, progressive nonfluent aphasia, n = 12; amnestic Alzheimer's disease, n = 10) and a cohort of 26 healthy age-matched individuals. Approaching sounds were rated as more salient than withdrawing sounds by healthy older individuals but this behavioral response to salience did not differentiate healthy individuals from patients with dementia syndromes. Pupil responses to approaching sounds were greater than responses to withdrawing sounds in healthy older individuals and in patients with semantic dementia: this differential pupil response was reduced in patients with progressive nonfluent aphasia and Alzheimer's disease relative both to the healthy control and semantic dementia groups, and did not correlate with nonverbal auditory semantic function. Autonomic responses to auditory salience are differentially affected by dementias and may constitute a novel biomarker of these diseases. PMID:25859194

Buried object detection in GPR images

Science.gov (United States)

Paglieroni, David W; Chambers, David H; Bond, Steven W; Beer, W. Reginald

2014-04-29

A method and system for detecting the presence of subsurface objects within a medium is provided. In some embodiments, the imaging and detection system operates in a multistatic mode to collect radar return signals generated by an array of transceiver antenna pairs that is positioned across the surface and that travels down the surface. The imaging and detection system pre-processes the return signal to suppress certain undesirable effects. The imaging and detection system then generates synthetic aperture radar images from real aperture radar images generated from the pre-processed return signal. The imaging and detection system then post-processes the synthetic aperture radar images to improve detection of subsurface objects. The imaging and detection system identifies peaks in the energy levels of the post-processed image frame, which indicates the presence of a subsurface object.

Memory detection 2.0: the first web-based memory detection test.

Science.gov (United States)

Kleinberg, Bennett; Verschuere, Bruno

2015-01-01

There is accumulating evidence that reaction times (RTs) can be used to detect recognition of critical (e.g., crime) information. A limitation of this research base is its reliance upon small samples (average n = 24), and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262) tried to hide 2 high salient (birthday, country of origin) and 2 low salient (favourite colour, favourite animal) autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research.

Memory detection 2.0: the first web-based memory detection test.

Directory of Open Access Journals (Sweden)

Bennett Kleinberg

Full Text Available There is accumulating evidence that reaction times (RTs can be used to detect recognition of critical (e.g., crime information. A limitation of this research base is its reliance upon small samples (average n = 24, and indications of publication bias. To advance RT-based memory detection, we report upon the development of the first web-based memory detection test. Participants in this research (Study1: n = 255; Study2: n = 262 tried to hide 2 high salient (birthday, country of origin and 2 low salient (favourite colour, favourite animal autobiographical details. RTs allowed to detect concealed autobiographical information, and this, as predicted, more successfully so than error rates, and for high salient than for low salient items. While much remains to be learned, memory detection 2.0 seems to offer an interesting new platform to efficiently and validly conduct RT-based memory detection research.

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

Science.gov (United States)

Paglieroni, David W.; Beer, Reginald N.

2016-01-19

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

Track-before-detect procedures for detection of extended object

Science.gov (United States)

Fan, Ling; Zhang, Xiaoling; Shi, Jun

2011-12-01

In this article, we present a particle filter (PF)-based track-before-detect (PF TBD) procedure for detection of extended objects whose shape is modeled by an ellipse. By incorporating of an existence variable and the target shape parameters into the state vector, the proposed algorithm performs joint estimation of the target presence/absence, trajectory and shape parameters under unknown nuisance parameters (target power and noise variance). Simulation results show that the proposed algorithm has good detection and tracking capabilities for extended objects.

Track-before-detect procedures for detection of extended object

Directory of Open Access Journals (Sweden)

Fan Ling

2011-01-01

Full Text Available Abstract In this article, we present a particle filter (PF-based track-before-detect (PF TBD procedure for detection of extended objects whose shape is modeled by an ellipse. By incorporating of an existence variable and the target shape parameters into the state vector, the proposed algorithm performs joint estimation of the target presence/absence, trajectory and shape parameters under unknown nuisance parameters (target power and noise variance. Simulation results show that the proposed algorithm has good detection and tracking capabilities for extended objects.

Adaptive gaze control for object detection

NARCIS (Netherlands)

De Croon, G.C.H.E.; Postma, E.O.; Van den Herik, H.J.

2011-01-01

We propose a novel gaze-control model for detecting objects in images. The model, named act-detect, uses the information from local image samples in order to shift its gaze towards object locations. The model constitutes two main contributions. The first contribution is that the model’s setup makes

Determining root correspondence between previously and newly detected objects

Science.gov (United States)

Paglieroni, David W.; Beer, N Reginald

2014-06-17

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

Combined Electrophysiological and Behavioral Evidence for the Suppression of Salient Distractors.

Science.gov (United States)

Gaspelin, Nicholas; Luck, Steven J

2018-05-15

Researchers have long debated how salient-but-irrelevant features guide visual attention. Pure stimulus-driven theories claim that salient stimuli automatically capture attention irrespective of goals, whereas pure goal-driven theories propose that an individual's attentional control settings determine whether salient stimuli capture attention. However, recent studies have suggested a hybrid model in which salient stimuli attract visual attention but can be actively suppressed by top-down attentional mechanisms. Support for this hybrid model has primarily come from ERP studies demonstrating that salient stimuli, which fail to capture attention, also elicit a distractor positivity (P D ) component, a putative neural index of suppression. Other support comes from a handful of behavioral studies showing that processing at the salient locations is inhibited compared with other locations. The current study was designed to link the behavioral and neural evidence by combining ERP recordings with an experimental paradigm that provides a behavioral measure of suppression. We found that, when a salient distractor item elicited the P D component, processing at the location of this distractor was suppressed below baseline levels. Furthermore, the magnitude of behavioral suppression and the magnitude of the P D component covaried across participants. These findings provide a crucial connection between the behavioral and neural measures of suppression, which opens the door to using the P D component to assess the timing and neural substrates of the behaviorally observed suppression.

Object Detection: Current and Future Directions

Directory of Open Access Journals (Sweden)

Rodrigo eVerschae

2015-11-01

Full Text Available Object detection is a key ability required by most computer and robot vision systems. The latest research on this area has been making great progress in many directions. In the current manuscript we give an overview of past research on object detection, outline the current main research directions, and discuss open problems and possible future directions.

Minimum Delay Moving Object Detection

KAUST Repository

Lao, Dong

2017-01-01

This thesis presents a general framework and method for detection of an object in a video based on apparent motion. The object moves, at some unknown time, differently than the “background” motion, which can be induced from camera motion. The goal

Salient cues improve prospective remembering in Korsakoff's syndrome.

Science.gov (United States)

Altgassen, Mareike; Ariese, Laura; Wester, Arie J; Kessels, Roy P C

2016-06-01

Korsakoff's syndrome is characterized by deficits in episodic memory and executive functions. Both cognitive functions are needed to remember to execute delayed intentions (prospective memory, PM), an ability that is crucial for independent living in everyday life. So far, PM has only been targeted by one study in Korsakoff's syndrome. This study explored the effects of executive control demands on PM to shed further light on a possible interdependence of memory and executive functions in Korsakoff's syndrome, Twenty-five individuals with Korsakoff's syndrome and 23 chronic alcoholics (without amnesia) performed a categorization task into which a PM task was embedded that put either high or low demands on executive control processes (using low vs. high salient cues). Overall, Korsakoff patients had fewer PM hits than alcoholic controls. Across groups, participants had fewer PM hits when cues were low salient as compared to high salient. Korsakoff patients performed better on PM when highly salient cues were presented than cues of low salience, while there were no differential effects for alcoholic controls. While overall Korsakoff patients' showed a global PM deficit, the extent of this deficit was moderated by the executive control demands of the task applied. This indicated further support for an interrelation of executive functions and memory performance in Korsakoff. Positive clinical implications of the work Prospective memory (PM) performance in Korsakoff's syndrome is related to executive control load. Increasing cues' salience improves PM performance in Korsakoff's syndrome. Salient visual aids may be used in everyday life to improve Korsakoff individuals' planning and organization skills. Cautions or limitations of the study Results were obtained in a structured laboratory setting and need to be replicated in a more naturalistic setting to assess their transferability to everyday life. Given the relatively small sample size, individual predictors of PM

A-Track: Detecting Moving Objects in FITS images

Science.gov (United States)

Atay, T.; Kaplan, M.; Kilic, Y.; Karapinar, N.

2017-04-01

A-Track is a fast, open-source, cross-platform pipeline for detecting moving objects (asteroids and comets) in sequential telescope images in FITS format. The moving objects are detected using a modified line detection algorithm.

Current superimposition variable flux reluctance motor with 8 salient poles

Science.gov (United States)

Takahara, Kazuaki; Hirata, Katsuhiro; Niguchi, Noboru; Kohara, Akira

2017-12-01

We propose a current superimposition variable flux reluctance motor for a traction motor of electric vehicles and hybrid electric vehicles, which consists of 10 salient poles in the rotor and 12 slots in the stator. However, iron losses of this motor in high rotation speed ranges is large because the number of salient poles is large. In this paper, we propose a current superimposition variable flux reluctance motor that consists of 8 salient poles and 12 slots. The characteristics of the 10-pole-12-slot and 8-pole-12-slot current superimposition variable flux reluctance motors are compared using finite element analysis under vector control.

Current superimposition variable flux reluctance motor with 8 salient poles

Directory of Open Access Journals (Sweden)

Takahara Kazuaki

2017-12-01

Full Text Available We propose a current superimposition variable flux reluctance motor for a traction motor of electric vehicles and hybrid electric vehicles, which consists of 10 salient poles in the rotor and 12 slots in the stator. However, iron losses of this motor in high rotation speed ranges is large because the number of salient poles is large. In this paper, we propose a current superimposition variable flux reluctance motor that consists of 8 salient poles and 12 slots. The characteristics of the 10-pole-12-slot and 8-pole-12-slot current superimposition variable flux reluctance motors are compared using finite element analysis under vector control.

Distributed dendritic processing facilitates object detection: a computational analysis on the visual system of the fly.

Science.gov (United States)

Hennig, Patrick; Möller, Ralf; Egelhaaf, Martin

2008-08-28

Detecting objects is an important task when moving through a natural environment. Flies, for example, may land on salient objects or may avoid collisions with them. The neuronal ensemble of Figure Detection cells (FD-cells) in the visual system of the fly is likely to be involved in controlling these behaviours, as these cells are more sensitive to objects than to extended background structures. Until now the computations in the presynaptic neuronal network of FD-cells and, in particular, the functional significance of the experimentally established distributed dendritic processing of excitatory and inhibitory inputs is not understood. We use model simulations to analyse the neuronal computations responsible for the preference of FD-cells for small objects. We employed a new modelling approach which allowed us to account for the spatial spread of electrical signals in the dendrites while avoiding detailed compartmental modelling. The models are based on available physiological and anatomical data. Three models were tested each implementing an inhibitory neural circuit, but differing by the spatial arrangement of the inhibitory interaction. Parameter optimisation with an evolutionary algorithm revealed that only distributed dendritic processing satisfies the constraints arising from electrophysiological experiments. In contrast to a direct dendro-dendritic inhibition of the FD-cell (Direct Distributed Inhibition model), an inhibition of its presynaptic retinotopic elements (Indirect Distributed Inhibition model) requires smaller changes in input resistance in the inhibited neurons during visual stimulation. Distributed dendritic inhibition of retinotopic elements as implemented in our Indirect Distributed Inhibition model is the most plausible wiring scheme for the neuronal circuit of FD-cells. This microcircuit is computationally similar to lateral inhibition between the retinotopic elements. Hence, distributed inhibition might be an alternative explanation of

Slow speed object detection for haul trucks

Energy Technology Data Exchange (ETDEWEB)

NONE

2009-09-15

Caterpillar integrates radar technology with its current camera based system. Caterpillar has developed the Integrated Object Detection System, a slow speed object detection system for mining haul trucks. Object detection is a system that aids the truck operator's awareness of their surroundings. The system consists of a color touch screen display along with medium- and short-range radar as well as cameras, harnesses and mounting hardware. It is integrated into the truck's Work Area Vision System (WAVS). After field testing in 2007, system commercialization began in 2008. Prototype systems are in operation in Australia, Utah and Arizona and the Integrated Object Detection System will be available in the fourth quarter of 2009 and on production trucks 785C, 789C, 793D and 797B. The article is adapted from a presentation by Mark Richards of Caterpillar to the Haulage & Loading 2009 conference, May, held in Phoenix, AZ. 1 fig., 5 photos.

In-plane object detection : detection algorithms and visibility problems

NARCIS (Netherlands)

Jovanovic, N.

2011-01-01

A large number of devices today incorporate some form of detection of objects and people in a given environment. Various detection technologies have been developed over the years, as a response to many different demands. The devices such as video surveillance systems, scanners, touch screens and

Real-time object detection, tracking and occlusion reasoning

Science.gov (United States)

Divakaran, Ajay; Yu, Qian; Tamrakar, Amir; Sawhney, Harpreet Singh; Zhu, Jiejie; Javed, Omar; Liu, Jingen; Cheng, Hui; Eledath, Jayakrishnan

2018-02-27

A system for object detection and tracking includes technologies to, among other things, detect and track moving objects, such as pedestrians and/or vehicles, in a real-world environment, handle static and dynamic occlusions, and continue tracking moving objects across the fields of view of multiple different cameras.

REAL-TIME OBJECT DETECTION IN PARALLEL THROUGH ATOMIC TRANSACTIONS

Directory of Open Access Journals (Sweden)

K Sivakumar

2016-11-01

Full Text Available Object detection and tracking is important operation involved in embedded systems like video surveillance, Traffic monitoring, campus security system, machine vision applications and other areas. Detecting and tracking multiple objects in a video or image is challenging problem in machine vision and computer vision based embedded systems. Implementation of such a object detection and tracking systems are done in sequential way of processing and also it was implemented using hardware synthesize tools like verilog HDL with FPGA, achieves considerably lesser performance in speed and it does support lesser atomic transactions. There are many object detection and tracking algorithm were proposed and implemented, among them background subtraction is one of them. This paper proposes a implementation of detecting and tracking multiple objects based on background subtraction algorithm using java and .NET and also discuss about the architecture concept for object detection through atomic transactional, modern hardware synthesizes language called Bluespec.

Detecting potential ship objects from satellite pictures

International Nuclear Information System (INIS)

Luo, B.; Yang, C.C.; Chang, S.K.; Yang, M.C.K.

1984-01-01

Heuristic techniques are presented to detect potential ship objects from satellite pictures. These techniques utilize some noise structures of the pixel gray levels, and certain inherent features of a ship in a satellite picture. The scheme has been implemented and successfully tested on SEASAT satellite pictures. A general approach for database-oriented object detection is also suggested

Object detection from images obtained through underwater turbulence medium

Science.gov (United States)

Furhad, Md. Hasan; Tahtali, Murat; Lambert, Andrew

2017-09-01

Imaging through underwater experiences severe distortions due to random fluctuations of temperature and salinity in water, which produces underwater turbulence through diffraction limited blur. Lights reflecting from objects perturb and attenuate contrast, making the recognition of objects of interest difficult. Thus, the information available for detecting underwater objects of interest becomes a challenging task as they have inherent confusion among the background, foreground and other image properties. In this paper, a saliency-based approach is proposed to detect the objects acquired through an underwater turbulent medium. This approach has drawn attention among a wide range of computer vision applications, such as image retrieval, artificial intelligence, neuro-imaging and object detection. The image is first processed through a deblurring filter. Next, a saliency technique is used on the image for object detection. In this step, a saliency map that highlights the target regions is generated and then a graph-based model is proposed to extract these target regions for object detection.

Analysis of Salient Feature Jitter in the Cochlea for Objective Prediction of Temporally Localized Distortion in Synthesized Speech

Directory of Open Access Journals (Sweden)

Wenliang Lu

2009-01-01

Full Text Available Temporally localized distortions account for the highest variance in subjective evaluation of coded speech signals (Sen (2001 and Hall (2001. The ability to discern and decompose perceptually relevant temporally localized coding noise from other types of distortions is both of theoretical importance as well as a valuable tool for deploying and designing speech synthesis systems. The work described within uses a physiologically motivated cochlear model to provide a tractable analysis of salient feature trajectories as processed by the cochlea. Subsequent statistical analysis shows simple relationships between the jitter of these trajectories and temporal attributes of the Diagnostic Acceptability Measure (DAM.

Research on moving object detection based on frog's eyes

Science.gov (United States)

Fu, Hongwei; Li, Dongguang; Zhang, Xinyuan

2008-12-01

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

Image Processing Methods Usable for Object Detection on the Chessboard

Directory of Open Access Journals (Sweden)

Beran Ladislav

2016-01-01

Full Text Available Image segmentation and object detection is challenging problem in many research. Although many algorithms for image segmentation have been invented, there is no simple algorithm for image segmentation and object detection. Our research is based on combination of several methods for object detection. The first method suitable for image segmentation and object detection is colour detection. This method is very simply, but there is problem with different colours. For this method it is necessary to have precisely determined colour of segmented object before all calculations. In many cases it is necessary to determine this colour manually. Alternative simply method is method based on background removal. This method is based on difference between reference image and detected image. In this paper several methods suitable for object detection are described. Thisresearch is focused on coloured object detection on chessboard. The results from this research with fusion of neural networks for user-computer game checkers will be applied.

Multi-scale salient feature extraction on mesh models

KAUST Repository

Yang, Yongliang; Shen, ChaoHui

2012-01-01

We present a new method of extracting multi-scale salient features on meshes. It is based on robust estimation of curvature on multiple scales. The coincidence between salient feature and the scale of interest can be established straightforwardly, where detailed feature appears on small scale and feature with more global shape information shows up on large scale. We demonstrate this multi-scale description of features accords with human perception and can be further used for several applications as feature classification and viewpoint selection. Experiments exhibit that our method as a multi-scale analysis tool is very helpful for studying 3D shapes. © 2012 Springer-Verlag.

The Role of Inhibition in Avoiding Distraction by Salient Stimuli.

Science.gov (United States)

Gaspelin, Nicholas; Luck, Steven J

2018-01-01

Researchers have long debated whether salient stimuli can involuntarily 'capture' visual attention. We review here evidence for a recently discovered inhibitory mechanism that may help to resolve this debate. This evidence suggests that salient stimuli naturally attempt to capture attention, but capture can be avoided if the salient stimulus is suppressed before it captures attention. Importantly, the suppression process can be more or less effective as a result of changing task demands or lapses in cognitive control. Converging evidence for the existence of this suppression mechanism comes from multiple sources, including psychophysics, eye-tracking, and event-related potentials (ERPs). We conclude that the evidence for suppression is strong, but future research will need to explore the nature and limits of this mechanism. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

DEFF Research Database (Denmark)

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

2017-01-01

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

Salience Is Only Briefly Represented: Evidence from Probe-Detection Performance

Science.gov (United States)

Donk, Mieke; Soesman, Leroy

2010-01-01

Salient objects in the visual field tend to capture attention. The present study aimed to examine the time-course of salience effects using a probe-detection task. Eight experiments investigated how the salience of different orientation singletons affected probe reaction time as a function of stimulus onset asynchrony (SOA) between the…

Selecting salient frames for spatiotemporal video modeling and segmentation.

Science.gov (United States)

Song, Xiaomu; Fan, Guoliang

2007-12-01

We propose a new statistical generative model for spatiotemporal video segmentation. The objective is to partition a video sequence into homogeneous segments that can be used as "building blocks" for semantic video segmentation. The baseline framework is a Gaussian mixture model (GMM)-based video modeling approach that involves a six-dimensional spatiotemporal feature space. Specifically, we introduce the concept of frame saliency to quantify the relevancy of a video frame to the GMM-based spatiotemporal video modeling. This helps us use a small set of salient frames to facilitate the model training by reducing data redundancy and irrelevance. A modified expectation maximization algorithm is developed for simultaneous GMM training and frame saliency estimation, and the frames with the highest saliency values are extracted to refine the GMM estimation for video segmentation. Moreover, it is interesting to find that frame saliency can imply some object behaviors. This makes the proposed method also applicable to other frame-related video analysis tasks, such as key-frame extraction, video skimming, etc. Experiments on real videos demonstrate the effectiveness and efficiency of the proposed method.

Research on Daily Objects Detection Based on Deep Neural Network

Science.gov (United States)

Ding, Sheng; Zhao, Kun

2018-03-01

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

A survey on object detection in optical remote sensing images

Science.gov (United States)

Cheng, Gong; Han, Junwei

2016-07-01

Object detection in optical remote sensing images, being a fundamental but challenging problem in the field of aerial and satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. While enormous methods exist, a deep review of the literature concerning generic object detection is still lacking. This paper aims to provide a review of the recent progress in this field. Different from several previously published surveys that focus on a specific object class such as building and road, we concentrate on more generic object categories including, but are not limited to, road, building, tree, vehicle, ship, airport, urban-area. Covering about 270 publications we survey (1) template matching-based object detection methods, (2) knowledge-based object detection methods, (3) object-based image analysis (OBIA)-based object detection methods, (4) machine learning-based object detection methods, and (5) five publicly available datasets and three standard evaluation metrics. We also discuss the challenges of current studies and propose two promising research directions, namely deep learning-based feature representation and weakly supervised learning-based geospatial object detection. It is our hope that this survey will be beneficial for the researchers to have better understanding of this research field.

Exploring dietitians' salient beliefs about shared decision-making behaviors

Directory of Open Access Journals (Sweden)

Gagnon Marie-Pierre

2011-06-01

Full Text Available Abstract Background Shared decision making (SDM, a process by which health professionals and patients go through the decision-making process together to agree on treatment, is a promising strategy for promoting diet-related decisions that are informed and value based and to which patients adhere well. The objective of the present study was to identify dietitians' salient beliefs regarding their exercise of two behaviors during the clinical encounter, both of which have been deemed essential for SDM to take place: (1 presenting patients with all dietary treatment options for a given health condition and (2 helping patients clarify their values and preferences regarding the options. Methods Twenty-one dietitians were allocated to four focus groups. Facilitators conducted the focus groups using a semistructured interview guide based on the Theory of Planned Behavior. Discussions were audiotaped, transcribed verbatim, coded, and analyzed with NVivo8 (QSR International, Cambridge, MA software. Results Most participants stated that better patient adherence to treatment was an advantage of adopting the two SDM behaviors. Dietitians identified patients, physicians, and the multidisciplinary team as normative referents who would approve or disapprove of their adoption of the SDM behaviors. The most often reported barriers and facilitators for the behaviors concerned patients' characteristics, patients' clinical situation, and time. Conclusions The implementation of SDM in nutrition clinical practice can be guided by addressing dietitians' salient beliefs. Identifying these beliefs also provides the theoretical framework needed for developing a quantitative survey questionnaire to further study the determinants of dietitians' adoption of SDM behaviors.

Moving object detection using background subtraction

CERN Document Server

Shaikh, Soharab Hossain; Chaki, Nabendu

2014-01-01

This Springer Brief presents a comprehensive survey of the existing methodologies of background subtraction methods. It presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes. This well-known methodology has applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition. The authors identify common challenges faced by researchers including gradual or sudden illumination change, dynamic bac

Visual memory for objects following foveal vision loss.

Science.gov (United States)

Geringswald, Franziska; Herbik, Anne; Hofmüller, Wolfram; Hoffmann, Michael B; Pollmann, Stefan

2015-09-01

Allocation of visual attention is crucial for encoding items into visual long-term memory. In free vision, attention is closely linked to the center of gaze, raising the question whether foveal vision loss entails suboptimal deployment of attention and subsequent impairment of object encoding. To investigate this question, we examined visual long-term memory for objects in patients suffering from foveal vision loss due to age-related macular degeneration. We measured patients' change detection sensitivity after a period of free scene exploration monocularly with their worse eye when possible, and under binocular vision, comparing sensitivity and eye movements to matched normal-sighted controls. A highly salient cue was used to capture attention to a nontarget location before a target change occurred in half of the trials, ensuring that change detection relied on memory. Patients' monocular and binocular sensitivity to object change was comparable to controls, even after more than 4 intervening fixations, and not significantly correlated with visual impairment. We conclude that extrafoveal vision suffices for efficient encoding into visual long-term memory. (c) 2015 APA, all rights reserved).

Edge and line detection of complicated and blurred objects

OpenAIRE

Haugsdal, Kari

2010-01-01

This report deals with edge and line detection in pictures with complicated and/or blurred objects. It explores the alternatives available, in edge detection, edge linking and object recognition. Choice of methods are the Canny edge detection and Local edge search processing combined with regional edge search processing in the form of polygon approximation.

Preschoolers Benefit from Visually Salient Speech Cues

Science.gov (United States)

Lalonde, Kaylah; Holt, Rachael Frush

2015-01-01

Purpose: This study explored visual speech influence in preschoolers using 3 developmentally appropriate tasks that vary in perceptual difficulty and task demands. They also examined developmental differences in the ability to use visually salient speech cues and visual phonological knowledge. Method: Twelve adults and 27 typically developing 3-…

Nonnegative Matrix Factorizations Performing Object Detection and Localization

Directory of Open Access Journals (Sweden)

G. Casalino

2012-01-01

Full Text Available We study the problem of detecting and localizing objects in still, gray-scale images making use of the part-based representation provided by nonnegative matrix factorizations. Nonnegative matrix factorization represents an emerging example of subspace methods, which is able to extract interpretable parts from a set of template image objects and then to additively use them for describing individual objects. In this paper, we present a prototype system based on some nonnegative factorization algorithms, which differ in the additional properties added to the nonnegative representation of data, in order to investigate if any additional constraint produces better results in general object detection via nonnegative matrix factorizations.

Methods and Algorithms for Detecting Objects in Video Files

Directory of Open Access Journals (Sweden)

Nguyen The Cuong

2018-01-01

Full Text Available Video files are files that store motion pictures and sounds like in real life. In today's world, the need for automated processing of information in video files is increasing. Automated processing of information has a wide range of application including office/home surveillance cameras, traffic control, sports applications, remote object detection, and others. In particular, detection and tracking of object movement in video file plays an important role. This article describes the methods of detecting objects in video files. Today, this problem in the field of computer vision is being studied worldwide.

When message-frame fits salient cultural-frame, messages feel more persuasive

OpenAIRE

Uskul, Ayse K.; Oyserman, Daphna

2010-01-01

The present study examines the persuasive effects of tailored health messages comparing those tailored to match (versus not match) both chronic cultural frame and momentarily salient cultural frame. Evidence from two studies (Study 1: n = 72 European Americans; Study 2: n = 48 Asian Americans) supports the hypothesis that message persuasiveness increases when chronic cultural frame, health message tailoring and momentarily salient cultural frame all match. The hypothesis was tested using a me...

Line-up member similarity influences the effectiveness of a salient rejection option for eyewitnesses

OpenAIRE

Bruer, Kaila C.; Fitzgerald, Ryan J.; Therrien, Natalie M.; Price, Heather L.

2015-01-01

Visually salient line-up rejection options have not been systematically studied with adult eyewitnesses. We explored the impact of using a non-verbal, salient rejection option on adults' identification accuracy for line-ups containing low- or high-similarity fillers. The non-verbal, salient rejection option had minimal impact on accuracy in low-similarity line-ups, but in high-similarity line-ups its inclusion increased correct rejections for target-absent line-ups as well as incorrect reject...

An Improved Saliency Detection Approach for Flying Apsaras in the Dunhuang Grotto Murals, China

Directory of Open Access Journals (Sweden)

Zhong Chen

2015-01-01

Full Text Available Saliency can be described as the ability of an item to be detected from its background in any particular scene, and it aims to estimate the probable location of the salient objects. Due to the salient map that computed by local contrast features can extract and highlight the edge parts including painting lines of Flying Apsaras, in this paper, we proposed an improved approach based on a frequency-tuned method for visual saliency detection of Flying Apsaras in the Dunhuang Grotto Murals, China. This improved saliency detection approach comprises three important steps: (1 image color and gray channel decomposition; (2 gray feature value computation and color channel convolution; (3 visual saliency definition based on normalization of previous visual saliency and spatial attention function. Unlike existing approaches that rely on many complex image features, this proposed approach only used local contrast and spatial attention information to simulate human’s visual attention stimuli. This improved approach resulted in a much more efficient salient map in the aspect of computing performance. Furthermore, experimental results on the dataset of Flying Apsaras in the Dunhuang Grotto Murals showed that the proposed visual saliency detection approach is very effective when compared with five other state-of-the-art approaches.

AUTOMATIC FAST VIDEO OBJECT DETECTION AND TRACKING ON VIDEO SURVEILLANCE SYSTEM

Directory of Open Access Journals (Sweden)

V. Arunachalam

2012-08-01

Full Text Available This paper describes the advance techniques for object detection and tracking in video. Most visual surveillance systems start with motion detection. Motion detection methods attempt to locate connected regions of pixels that represent the moving objects within the scene; different approaches include frame-to-frame difference, background subtraction and motion analysis. The motion detection can be achieved by Principle Component Analysis (PCA and then separate an objects from background using background subtraction. The detected object can be segmented. Segmentation consists of two schemes: one for spatial segmentation and the other for temporal segmentation. Tracking approach can be done in each frame of detected Object. Pixel label problem can be alleviated by the MAP (Maximum a Posteriori technique.

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection.

Science.gov (United States)

Zhao, Baojun; Zhao, Boya; Tang, Linbo; Han, Yuqi; Wang, Wenzheng

2018-03-04

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).

A Moving Object Detection Algorithm Based on Color Information

International Nuclear Information System (INIS)

Fang, X H; Xiong, W; Hu, B J; Wang, L T

2006-01-01

This paper designed a new algorithm of moving object detection for the aim of quick moving object detection and orientation, which used a pixel and its neighbors as an image vector to represent that pixel and modeled different chrominance component pixel as a mixture of Gaussians, and set up different mixture model of Gauss for different YUV chrominance components. In order to make full use of the spatial information, color segmentation and background model were combined. Simulation results show that the algorithm can detect intact moving objects even when the foreground has low contrast with background

Are All Pixels Equally Important?

CERN Multimedia

CERN. Geneva

2015-01-01

When we look at our environment, we primarily pay attention to visually distinctive objects. We refer to these objects as visually important or salient. For efficient visual processing, the human visual system identifies salients objects and dedicates most of its processing resources to them. An analogous resource allocation can be performed by salient-object detection algorithms, which identify objects of interest in an image. Consequently, thanks to salient-object detection, complex visual computing operations can focus on the important parts of the visual data and can save time and resources. About the speaker Dr. Gokhan Yildirim is a research assistant in the School of Computer and Communication Sciences (IC) at the École Polytechnique Fédérale de Lausanne (EPFL). His research interests include image understanding, multimedia, pattern recognition, machine learning, salient-object detection on images & videos and its applications on image proces...

System and method for smoothing a salient rotor in electrical machines

Science.gov (United States)

Raminosoa, Tsarafidy; Alexander, James Pellegrino; El-Refaie, Ayman Mohamed Fawzi; Torrey, David A.

2016-12-13

An electrical machine exhibiting reduced friction and windage losses is disclosed. The electrical machine includes a stator and a rotor assembly configured to rotate relative to the stator, wherein the rotor assembly comprises a rotor core including a plurality of salient rotor poles that are spaced apart from one another around an inner hub such that an interpolar gap is formed between each adjacent pair of salient rotor poles, with an opening being defined by the rotor core in each interpolar gap. Electrically non-conductive and non-magnetic inserts are positioned in the gaps formed between the salient rotor poles, with each of the inserts including a mating feature formed an axially inner edge thereof that is configured to mate with a respective opening being defined by the rotor core, so as to secure the insert to the rotor core against centrifugal force experienced during rotation of the rotor assembly.

Ecological origins of object salience: reward, uncertainty, aversiveness and novelty

Directory of Open Access Journals (Sweden)

Ali Ghazizadeh

2016-08-01

Full Text Available Among many objects around us, some of them are more salient than others (i.e., attract our attention automatically. Some objects may be inherently salient (e.g., brighter, but others may become salient by virtue of their ecological relevance through experience. However, the importance of ecological experience in guiding attention has not been studied systematically. To address this question, we let subjects (macaque monkeys view a large number of complex objects (>300, each experienced repeatedly (>5 days with rewarding, aversive or no outcome association (mere-perceptual exposure. Test of salience was done on separate days using free viewing with no outcome. We found that gaze was biased among the objects from the outset, affecting saccades to objects or fixations within objects. When the outcome was rewarding, gaze preference was stronger (i.e. positive for objects with larger or equal but uncertain rewards. The effects of aversive outcomes were variable. Gaze preference was positive for some outcome associations (e.g. airpuff, but negative for others (e.g. time-out, possibly due to differences in threat levels. Finally, novel objects attracted gaze, but mere perceptual exposure of objects reduced their salience (learned negative salience. Our results show that, in primates, object salience is strongly influenced by previous ecological experience and is supported by a large memory capacity. Owing to such learned salience, the capacity to rapidly choose important objects can grow during the entire life to promote biological fitness.

Herbig-Haro objects

International Nuclear Information System (INIS)

Schwartz, R.D.

1983-01-01

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

Localization-Aware Active Learning for Object Detection

OpenAIRE

Kao, Chieh-Chi; Lee, Teng-Yok; Sen, Pradeep; Liu, Ming-Yu

2018-01-01

Active learning - a class of algorithms that iteratively searches for the most informative samples to include in a training dataset - has been shown to be effective at annotating data for image classification. However, the use of active learning for object detection is still largely unexplored as determining informativeness of an object-location hypothesis is more difficult. In this paper, we address this issue and present two metrics for measuring the informativeness of an object hypothesis,...

Deep Spatial-Temporal Joint Feature Representation for Video Object Detection

Directory of Open Access Journals (Sweden)

Baojun Zhao

2018-03-01

Full Text Available With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP.

Identification and evaluation of the salient physical activity beliefs of colorectal cancer survivors.

Science.gov (United States)

Speed-Andrews, Amy E; McGowan, Erin L; Rhodes, Ryan E; Blanchard, Chris M; Culos-Reed, S Nicole; Friedenreich, Christine M; Courneya, Kerry S

2014-01-01

Physical activity (PA) has been associated with lower risk of disease recurrence and longer survival in colorectal cancer (CRC) survivors; however, less than one-third of CRC survivors are meeting PA guidelines. Interventions to promote PA in CRC survivors need to understand the most critical beliefs that influence PA behavior. The objective of this study was to examine the strength of associations between the most common PA beliefs of CRC survivors and motivational constructs from the Theory of Planned Behavior (TPB) as well as PA behavior. Colorectal cancer survivors (n = 600) residing in Alberta, Canada, completed self-report questionnaires assessing medical and demographic variables, PA beliefs, constructs from the TPB, and PA behavior. Colorectal cancer survivors identified improved fitness (69.5%), family members (67.3%), and medical/health problems (8.8%) as the most prevalent behavioral, normative, and control beliefs, respectively. All PA beliefs were significantly correlated with all TPB constructs and PA. Physical activity interventions for CRC survivors should target many salient beliefs including behavioral, normative, and control beliefs. Insights into the salient beliefs for PA in CRC survivors can guide nurses in developing successful strategies to promote PA in this population and likely improve quality of life and possibly disease outcomes.

X-ray fluoroscopy spatio-temporal filtering with object detection

International Nuclear Information System (INIS)

Aufrichtig, R.; Wilson, D.L.; University Hospitals of Cleveland, OH

1995-01-01

One potential way to reduce patient and staff x-ray fluoroscopy dose is to reduce the quantum exposure to the detector and compensate the additional noise with digital filtering. A new filtering method, spatio-temporal filtering with object detection, is described that reduces noise while minimizing motion and spatial blur. As compared to some conventional motion-detection filtering schemes, this object-detection method incorporates additional a priori knowledge of image content; i.e. much of the motion occurs in isolated long thin objects (catheters, guide wires, etc.). The authors create object-likelihood images and use these to control spatial and recursive temporal filtering such as to reduce blurring the objects of interest. They use automatically computed receiver operating characteristic (ROC) curves to optimize the object-likelihood enhancement method and determine that oriented matched filter kernels with 4 orientations are appropriate. The matched filter kernels are simple projected cylinders. The authors demonstrate the method on several representative x-ray fluoroscopy sequences to which noise is added to simulate very low dose acquisitions. With processing, they find that noise variance is significantly reduced with slightly less noise reduction near moving objects. They estimate an effective exposure reduction greater than 80%

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

Directory of Open Access Journals (Sweden)

Yiliang Zeng

2015-03-01

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

Object detection via eye tracking and fringe restraint

Science.gov (United States)

Pan, Fei; Zhang, Hanming; Zeng, Ying; Tong, Li; Yan, Bin

2017-07-01

Object detection is a computer vision problem which caught a large amount of attention. But the candidate boundingboxes extracted from only image features may end up with false-detection due to the semantic gap between the top-down and the bottom up information. In this paper, we propose a novel method for generating object bounding-boxes proposals using the combination of eye fixation point, saliency detection and edges. The new method obtains a fixation orientated Gaussian map, optimizes the map through single-layer cellular automata, and derives bounding-boxes from the optimized map on three levels. Then we score the boxes by combining all the information above, and choose the box with the highest score to be the final box. We perform an evaluation of our method by comparing with previous state-ofthe art approaches on the challenging POET datasets, the images of which are chosen from PASCAL VOC 2012. Our method outperforms them on small scale objects while comparable to them in general.

A Space Object Detection Algorithm using Fourier Domain Likelihood Ratio Test

Science.gov (United States)

Becker, D.; Cain, S.

Space object detection is of great importance in the highly dependent yet competitive and congested space domain. Detection algorithms employed play a crucial role in fulfilling the detection component in the situational awareness mission to detect, track, characterize and catalog unknown space objects. Many current space detection algorithms use a matched filter or a spatial correlator to make a detection decision at a single pixel point of a spatial image based on the assumption that the data follows a Gaussian distribution. This paper explores the potential for detection performance advantages when operating in the Fourier domain of long exposure images of small and/or dim space objects from ground based telescopes. A binary hypothesis test is developed based on the joint probability distribution function of the image under the hypothesis that an object is present and under the hypothesis that the image only contains background noise. The detection algorithm tests each pixel point of the Fourier transformed images to make the determination if an object is present based on the criteria threshold found in the likelihood ratio test. Using simulated data, the performance of the Fourier domain detection algorithm is compared to the current algorithm used in space situational awareness applications to evaluate its value.

Unsupervised Object Modeling and Segmentation with Symmetry Detection for Human Activity Recognition

Directory of Open Access Journals (Sweden)

Jui-Yuan Su

2015-04-01

Full Text Available In this paper we present a novel unsupervised approach to detecting and segmenting objects as well as their constituent symmetric parts in an image. Traditional unsupervised image segmentation is limited by two obvious deficiencies: the object detection accuracy degrades with the misaligned boundaries between the segmented regions and the target, and pre-learned models are required to group regions into meaningful objects. To tackle these difficulties, the proposed approach aims at incorporating the pair-wise detection of symmetric patches to achieve the goal of segmenting images into symmetric parts. The skeletons of these symmetric parts then provide estimates of the bounding boxes to locate the target objects. Finally, for each detected object, the graphcut-based segmentation algorithm is applied to find its contour. The proposed approach has significant advantages: no a priori object models are used, and multiple objects are detected. To verify the effectiveness of the approach based on the cues that a face part contains an oval shape and skin colors, human objects are extracted from among the detected objects. The detected human objects and their parts are finally tracked across video frames to capture the object part movements for learning the human activity models from video clips. Experimental results show that the proposed method gives good performance on publicly available datasets.

Objective correlates of pitch salience using pupillometry

DEFF Research Database (Denmark)

Bianchi, Federica; Santurette, Sébastien; Wendt, Dorothea

2014-01-01

the frequency region and F 0 , were considered. Pupil size was measured for each condition, while the subjects’ task was to detect the deviants by pressing a response button. The expected trend was that pupil size would increase with decreasing salience. Results for musically trained listeners showed...... the expected trend, whereby pupil size significantly increased with decreasing salience of the stimuli. Non-musically trained listeners showed, however, a smaller pupil size for the least salient condition as compared to a medium salient condition, probably due to a too demanding task...

The detection of 'virtual' objects using echoes by humans: Spectral cues.

Science.gov (United States)

Rowan, Daniel; Papadopoulos, Timos; Archer, Lauren; Goodhew, Amanda; Cozens, Hayley; Lopez, Ricardo Guzman; Edwards, David; Holmes, Hannah; Allen, Robert

2017-07-01

Some blind people use echoes to detect discrete, silent objects to support their spatial orientation/navigation, independence, safety and wellbeing. The acoustical features that people use for this are not well understood. Listening to changes in spectral shape due to the presence of an object could be important for object detection and avoidance, especially at short range, although it is currently not known whether it is possible with echolocation-related sounds. Bands of noise were convolved with recordings of binaural impulse responses of objects in an anechoic chamber to create 'virtual objects', which were analysed and played to sighted and blind listeners inexperienced in echolocation. The sounds were also manipulated to remove cues unrelated to spectral shape. Most listeners could accurately detect hard flat objects using changes in spectral shape. The useful spectral changes for object detection occurred above approximately 3 kHz, as with object localisation. However, energy in the sounds below 3 kHz was required to exploit changes in spectral shape for object detection, whereas energy below 3 kHz impaired object localisation. Further recordings showed that the spectral changes were diminished by room reverberation. While good high-frequency hearing is generally important for echolocation, the optimal echo-generating stimulus will probably depend on the task. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

Directory of Open Access Journals (Sweden)

Sang-Il Oh

2017-01-01

Full Text Available To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN. The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Software Analysis of Mining Images for Objects Detection

Directory of Open Access Journals (Sweden)

Jan Tomecek

2013-11-01

Full Text Available The contribution is dealing with the development of the new module of robust FOTOMNG system for editing images from a video or miningimage from measurements for subsequent improvement of detection of required objects in the 2D image. The generated module allows create a finalhigh-quality picture by combination of multiple images with the search objects. We can combine input data according to the parameters or basedon reference frames. Correction of detected 2D objects is also part of this module. The solution is implemented intoFOTOMNG system and finishedwork has been tested in appropriate frames, which were validated core functionality and usability. Tests confirmed the function of each part of themodule, its accuracy and implications of integration.

Detecting objects in radiographs for homeland security

Science.gov (United States)

Prasad, Lakshman; Snyder, Hans

2005-05-01

We present a general scheme for segmenting a radiographic image into polygons that correspond to visual features. This decomposition provides a vectorized representation that is a high-level description of the image. The polygons correspond to objects or object parts present in the image. This characterization of radiographs allows the direct application of several shape recognition algorithms to identify objects. In this paper we describe the use of constrained Delaunay triangulations as a uniform foundational tool to achieve multiple visual tasks, namely image segmentation, shape decomposition, and parts-based shape matching. Shape decomposition yields parts that serve as tokens representing local shape characteristics. Parts-based shape matching enables the recognition of objects in the presence of occlusions, which commonly occur in radiographs. The polygonal representation of image features affords the efficient design and application of sophisticated geometric filtering methods to detect large-scale structural properties of objects in images. Finally, the representation of radiographs via polygons results in significant reduction of image file sizes and permits the scalable graphical representation of images, along with annotations of detected objects, in the SVG (scalable vector graphics) format that is proposed by the world wide web consortium (W3C). This is a textual representation that can be compressed and encrypted for efficient and secure transmission of information over wireless channels and on the Internet. In particular, our methods described here provide an algorithmic framework for developing image analysis tools for screening cargo at ports of entry for homeland security.

Feature-fused SSD: fast detection for small objects

Science.gov (United States)

Cao, Guimei; Xie, Xuemei; Yang, Wenzhe; Liao, Quan; Shi, Guangming; Wu, Jinjian

2018-04-01

Small objects detection is a challenging task in computer vision due to its limited resolution and information. In order to solve this problem, the majority of existing methods sacrifice speed for improvement in accuracy. In this paper, we aim to detect small objects at a fast speed, using the best object detector Single Shot Multibox Detector (SSD) with respect to accuracy-vs-speed trade-off as base architecture. We propose a multi-level feature fusion method for introducing contextual information in SSD, in order to improve the accuracy for small objects. In detailed fusion operation, we design two feature fusion modules, concatenation module and element-sum module, different in the way of adding contextual information. Experimental results show that these two fusion modules obtain higher mAP on PASCAL VOC2007 than baseline SSD by 1.6 and 1.7 points respectively, especially with 2-3 points improvement on some small objects categories. The testing speed of them is 43 and 40 FPS respectively, superior to the state of the art Deconvolutional single shot detector (DSSD) by 29.4 and 26.4 FPS.

Magnocellular Bias in Exogenous Attention to Biologically Salient Stimuli as Revealed by Manipulating Their Luminosity and Color.

Science.gov (United States)

Carretié, Luis; Kessel, Dominique; García-Rubio, María J; Giménez-Fernández, Tamara; Hoyos, Sandra; Hernández-Lorca, María

2017-10-01

Exogenous attention is a set of mechanisms that allow us to detect and reorient toward salient events-such as appetitive or aversive-that appear out of the current focus of attention. The nature of these mechanisms, particularly the involvement of the parvocellular and magnocellular visual processing systems, was explored. Thirty-four participants performed a demanding digit categorization task while salient (spiders or S) and neutral (wheels or W) stimuli were presented as distractors under two figure-ground formats: heterochromatic/isoluminant (exclusively processed by the parvocellular system, Par trials) and isochromatic/heteroluminant (preferentially processed by the magnocellular system, Mag trials). This resulted in four conditions: SPar, SMag, WPar, and WMag. Behavioral (RTs and error rates in the task) and electrophysiological (ERPs) indices of exogenous attention were analyzed. Behavior showed greater attentional capture by SMag than by SPar distractors and enhanced modulation of SMag capture as fear of spiders reported by participants increased. ERPs reflected a sequence from magnocellular dominant (P1p, ≃120 msec) to both magnocellular and parvocellular processing (N2p and P2a, ≃200 msec). Importantly, amplitudes in one N2p subcomponent were greater to SMag than to SPar and WMag distractors, indicating greater magnocellular sensitivity to saliency. Taking together, results support a magnocellular bias in exogenous attention toward distractors of any nature during initial processing, a bias that remains in later stages when biologically salient distractors are present.

Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

Science.gov (United States)

Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

2015-07-28

An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

Object detection approach using generative sparse, hierarchical networks with top-down and lateral connections for combining texture/color detection and shape/contour detection

Energy Technology Data Exchange (ETDEWEB)

Paiton, Dylan M.; Kenyon, Garrett T.; Brumby, Steven P.; Schultz, Peter F.; George, John S.

2016-10-25

An approach to detecting objects in an image dataset may combine texture/color detection, shape/contour detection, and/or motion detection using sparse, generative, hierarchical models with lateral and top-down connections. A first independent representation of objects in an image dataset may be produced using a color/texture detection algorithm. A second independent representation of objects in the image dataset may be produced using a shape/contour detection algorithm. A third independent representation of objects in the image dataset may be produced using a motion detection algorithm. The first, second, and third independent representations may then be combined into a single coherent output using a combinatorial algorithm.

Automatic Polyp Detection via A Novel Unified Bottom-up and Top-down Saliency Approach.

Science.gov (United States)

Yuan, Yixuan; Li, Dengwang; Meng, Max Q-H

2017-07-31

In this paper, we propose a novel automatic computer-aided method to detect polyps for colonoscopy videos. To find the perceptually and semantically meaningful salient polyp regions, we first segment images into multilevel superpixels. Each level corresponds to different sizes of superpixels. Rather than adopting hand-designed features to describe these superpixels in images, we employ sparse autoencoder (SAE) to learn discriminative features in an unsupervised way. Then a novel unified bottom-up and top-down saliency method is proposed to detect polyps. In the first stage, we propose a weak bottom-up (WBU) saliency map by fusing the contrast based saliency and object-center based saliency together. The contrast based saliency map highlights image parts that show different appearances compared with surrounding areas while the object-center based saliency map emphasizes the center of the salient object. In the second stage, a strong classifier with Multiple Kernel Boosting (MKB) is learned to calculate the strong top-down (STD) saliency map based on samples directly from the obtained multi-level WBU saliency maps. We finally integrate these two stage saliency maps from all levels together to highlight polyps. Experiment results achieve 0.818 recall for saliency calculation, validating the effectiveness of our method. Extensive experiments on public polyp datasets demonstrate that the proposed saliency algorithm performs favorably against state-of-the-art saliency methods to detect polyps.

The Prototype of Real-time Object Detection System Based on SMS

Directory of Open Access Journals (Sweden)

M. Hana Mirza

2010-08-01

Full Text Available The powerful algorithm to detect object movement in development of room monitoring system is very urgent. The commond algorithm needs complex computation. In this research, the prototype of real-time object detection system using simple algorithm is developed, i.e. using the determination of the max noise/pixel value and the tolerance threshold of image accurately, and then the system automatically send a SMS (short message services to user when the object movement is detected. The developed prototype used a Logitech QuickCam webcam, a Siemens C45 mobile phone and a data cable, and the Borland Delphi 7 with additional components and Serial PortNG Tvideo as system software. The application also includes a database to store the captured images whenever object movement is detected. The test results by varying conditions of light intensities using a 5-watt light bulb, fluorescent lamp 20 and 40 watts indicate that the application is able to automatically detect the presence of moving objects with 100% success rate. The success rate is strongly influenced by the determination of the max noise/pixel value and the tolerance threshold during system configuration. This application is also capable of sending SMS automatically when the system detects a moving object with an average time of 8.35 seconds.

Foreign object detection and removal to improve automated analysis of chest radiographs

International Nuclear Information System (INIS)

Hogeweg, Laurens; Sánchez, Clara I.; Melendez, Jaime; Maduskar, Pragnya; Ginneken, Bram van; Story, Alistair; Hayward, Andrew

2013-01-01

Purpose: Chest radiographs commonly contain projections of foreign objects, such as buttons, brassier clips, jewellery, or pacemakers and wires. The presence of these structures can substantially affect the output of computer analysis of these images. An automated method is presented to detect, segment, and remove foreign objects from chest radiographs.Methods: Detection is performed using supervised pixel classification with a kNN classifier, resulting in a probability estimate per pixel to belong to a projected foreign object. Segmentation is performed by grouping and post-processing pixels with a probability above a certain threshold. Next, the objects are replaced by texture inpainting.Results: The method is evaluated in experiments on 257 chest radiographs. The detection at pixel level is evaluated with receiver operating characteristic analysis on pixels within the unobscured lung fields and an A z value of 0.949 is achieved. Free response operator characteristic analysis is performed at the object level, and 95.6% of objects are detected with on average 0.25 false positive detections per image. To investigate the effect of removing the detected objects through inpainting, a texture analysis system for tuberculosis detection is applied to images with and without pathology and with and without foreign object removal. Unprocessed, the texture analysis abnormality score of normal images with foreign objects is comparable to those with pathology. After removing foreign objects, the texture score of normal images with and without foreign objects is similar, while abnormal images, whether they contain foreign objects or not, achieve on average higher scores.Conclusions: The authors conclude that removal of foreign objects from chest radiographs is feasible and beneficial for automated image analysis

Image objects detection based on boosting neural network

NARCIS (Netherlands)

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

2010-01-01

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

2D-Driven 3D Object Detection in RGB-D Images

KAUST Repository

Lahoud, Jean

2017-12-25

In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D domain, extensive experiments on the well-known SUN RGB-D dataset [29] show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.

Morality salience increases adherence to salient norms and values

NARCIS (Netherlands)

Gailliot, M.T.; Stillman, T.F.; Schmeichel, B.J.; Maner, J.K.; Plant, E.A.

2008-01-01

Four studies indicate that mortality salience increases adherence to social norms and values, but only when cultural norms and values are salient. In Study 1, mortality salience coupled with a reminder about cultural values of egalitarianism reduced prejudice toward Blacks among non-Black

Probabilistic resident space object detection using archival THEMIS fluxgate magnetometer data

Science.gov (United States)

Brew, Julian; Holzinger, Marcus J.

2018-05-01

Recent progress in the detection of small space objects, at geosynchronous altitudes, through ground-based optical and radar measurements is demonstrated as a viable method. However, in general, these methods are limited to detection of objects greater than 10 cm. This paper examines the use of magnetometers to detect plausible flyby encounters with charged space objects using a matched filter signal existence binary hypothesis test approach. Relevant data-set processing and reduction of archival fluxgate magnetometer data from the NASA THEMIS mission is discussed in detail. Using the proposed methodology and a false alarm rate of 10%, 285 plausible detections with probability of detection greater than 80% are claimed and several are reviewed in detail.

Automated gravity gradient tensor inversion for underwater object detection

International Nuclear Information System (INIS)

Wu, Lin; Tian, Jinwen

2010-01-01

Underwater abnormal object detection is a current need for the navigation security of autonomous underwater vehicles (AUVs). In this paper, an automated gravity gradient tensor inversion algorithm is proposed for the purpose of passive underwater object detection. Full-tensor gravity gradient anomalies induced by an object in the partial area can be measured with the technique of gravity gradiometry on an AUV. Then the automated algorithm utilizes the anomalies, using the inverse method to estimate the mass and barycentre location of the arbitrary-shaped object. A few tests on simple synthetic models will be illustrated, in order to evaluate the feasibility and accuracy of the new algorithm. Moreover, the method is applied to a complicated model of an abnormal object with gradiometer and AUV noise, and interference from a neighbouring illusive smaller object. In all cases tested, the estimated mass and barycentre location parameters are found to be in good agreement with the actual values

Younger but not older adults benefit from salient feedback during learning

Directory of Open Access Journals (Sweden)

Michael eHerbert

2011-08-01

Full Text Available Older adults are impaired in reinforcement learning (RL when feedback is partially ambiguous (e.g., Eppinger and Kray, 2011. In this study we examined whether older adults benefit from salient feedback information during learning. We used an electrophysiological approach and investigated 15 younger and 15 older adults with a RL task in which they had to learn stimulus-response associations under two learning conditions. In the positive learning conditions, participants could gain 50 Cents for a correct response but did not gain or lose money (*00 Cent for an incorrect response. In negative learning conditions, they could lose 50 Cents for an incorrect response but did not gain or lose money (*00 Cent for a correct response. As the identical outcome 00 Cent is either better or worse than the alternative outcome depending on the learning condition, this feedback type is ambiguous. To examine the influence of feedback salience we compared this condition with a condition in which positive and negative outcomes were color-coded and thereby clearly separable. The behavioral results indicated that younger adults reached higher accuracy levels under salient feedback conditions. Moreover, the error-related negativity (ERN and the feedback-related negativity (FRN for losses were larger if the good-bad dimension of feedback was salient. Hence, in younger adults salient feedback facilitates the rapid evaluation of outcomes on a good-bad dimension and by this supports learning. In contrast, for older adults we obtained neither behavioral nor electrophysiological effects of feedback salience. The older adults’ performance monitoring system therefore appears less flexible in integrating additional information in this evaluation process.

Wireless Sensor Networks for Heritage Object Deformation Detection and Tracking Algorithm

Directory of Open Access Journals (Sweden)

Zhijun Xie

2014-10-01

Full Text Available Deformation is the direct cause of heritage object collapse. It is significant to monitor and signal the early warnings of the deformation of heritage objects. However, traditional heritage object monitoring methods only roughly monitor a simple-shaped heritage object as a whole, but cannot monitor complicated heritage objects, which may have a large number of surfaces inside and outside. Wireless sensor networks, comprising many small-sized, low-cost, low-power intelligent sensor nodes, are more useful to detect the deformation of every small part of the heritage objects. Wireless sensor networks need an effective mechanism to reduce both the communication costs and energy consumption in order to monitor the heritage objects in real time. In this paper, we provide an effective heritage object deformation detection and tracking method using wireless sensor networks (EffeHDDT. In EffeHDDT, we discover a connected core set of sensor nodes to reduce the communication cost for transmitting and collecting the data of the sensor networks. Particularly, we propose a heritage object boundary detecting and tracking mechanism. Both theoretical analysis and experimental results demonstrate that our EffeHDDT method outperforms the existing methods in terms of network traffic and the precision of the deformation detection.

Detection of a buried object with pulse-compensated wire antennas

NARCIS (Netherlands)

Vossen, S.H.J.A.; Tijhuis, A.G.; Lepelaars, E.S.A.M.; Zwamborn, A.P.M.

2003-01-01

For the detection of a buried object we consider two straight thin-wire antennas above an interface between two homogeneous dielectric half spaces. One antenna is a transmitting wire and the other is a receiving wire. Our aim is to use this simple antenna set up for the detection of buried objects

Human listeners provide insights into echo features used by dolphins (Tursiops truncatus) to discriminate among objects.

Science.gov (United States)

Delong, Caroline M; Au, Whitlow W L; Harley, Heidi E; Roitblat, Herbert L; Pytka, Lisa

2007-08-01

Echolocating bottlenose dolphins (Tursiops truncatus) discriminate between objects on the basis of the echoes reflected by the objects. However, it is not clear which echo features are important for object discrimination. To gain insight into the salient features, the authors had a dolphin perform a match-to-sample task and then presented human listeners with echoes from the same objects used in the dolphin's task. In 2 experiments, human listeners performed as well or better than the dolphin at discriminating objects, and they reported the salient acoustic cues. The error patterns of the humans and the dolphin were compared to determine which acoustic features were likely to have been used by the dolphin. The results indicate that the dolphin did not appear to use overall echo amplitude, but that it attended to the pattern of changes in the echoes across different object orientations. Human listeners can quickly identify salient combinations of echo features that permit object discrimination, which can be used to generate hypotheses that can be tested using dolphins as subjects.

Low-complexity object detection with deep convolutional neural network for embedded systems

Science.gov (United States)

Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

2017-09-01

We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

Moving object detection using dynamic motion modelling from UAV aerial images.

Science.gov (United States)

Saif, A F M Saifuddin; Prabuwono, Anton Satria; Mahayuddin, Zainal Rasyid

2014-01-01

Motion analysis based moving object detection from UAV aerial image is still an unsolved issue due to inconsideration of proper motion estimation. Existing moving object detection approaches from UAV aerial images did not deal with motion based pixel intensity measurement to detect moving object robustly. Besides current research on moving object detection from UAV aerial images mostly depends on either frame difference or segmentation approach separately. There are two main purposes for this research: firstly to develop a new motion model called DMM (dynamic motion model) and secondly to apply the proposed segmentation approach SUED (segmentation using edge based dilation) using frame difference embedded together with DMM model. The proposed DMM model provides effective search windows based on the highest pixel intensity to segment only specific area for moving object rather than searching the whole area of the frame using SUED. At each stage of the proposed scheme, experimental fusion of the DMM and SUED produces extracted moving objects faithfully. Experimental result reveals that the proposed DMM and SUED have successfully demonstrated the validity of the proposed methodology.

Moving object detection in video satellite image based on deep learning

Science.gov (United States)

Zhang, Xueyang; Xiang, Junhua

2017-11-01

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

Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion

Energy Technology Data Exchange (ETDEWEB)

Budavári, Tamás; Szalay, Alexander S. [Department of Physics and Astronomy, The Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 (United States); Loredo, Thomas J. [Cornell Center for Astrophysics and Planetary Science, Cornell University, Ithaca, NY 14853 (United States)

2017-03-20

Astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection and describes an incremental strategy for separating real objects from artifacts in ongoing surveys. The idea is to produce low-threshold single-epoch catalogs and to accumulate information across epochs. This is in contrast to more conventional strategies based on co-added or stacked images. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting that there is no object or one object in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a Gaussian noise setting, comparing results based on single and stacked exposures to results based on a series of single-epoch catalog summaries. Our procedure amounts to generalized cross-matching: it is the product of a factor accounting for the matching of the estimated fluxes of the candidate sources and a factor accounting for the matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalogs can detect sources with similar sensitivity and selectivity compared to stacking. The probabilistic cross-matching framework underlying our approach plays an important role in maintaining detection sensitivity and points toward generalizations that could accommodate variability and complex object structure.

Faint Object Detection in Multi-Epoch Observations via Catalog Data Fusion

International Nuclear Information System (INIS)

Budavári, Tamás; Szalay, Alexander S.; Loredo, Thomas J.

2017-01-01

Astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection and describes an incremental strategy for separating real objects from artifacts in ongoing surveys. The idea is to produce low-threshold single-epoch catalogs and to accumulate information across epochs. This is in contrast to more conventional strategies based on co-added or stacked images. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting that there is no object or one object in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a Gaussian noise setting, comparing results based on single and stacked exposures to results based on a series of single-epoch catalog summaries. Our procedure amounts to generalized cross-matching: it is the product of a factor accounting for the matching of the estimated fluxes of the candidate sources and a factor accounting for the matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalogs can detect sources with similar sensitivity and selectivity compared to stacking. The probabilistic cross-matching framework underlying our approach plays an important role in maintaining detection sensitivity and points toward generalizations that could accommodate variability and complex object structure.

Salient Public Beliefs Underlying Disaster Preparedness Behaviors: A Theory-Based Qualitative Study.

Science.gov (United States)

Najafi, Mehdi; Ardalan, Ali; Akbarisari, Ali; Noorbala, Ahmad Ali; Elmi, Helen

2017-04-01

Introduction Given the increasing importance of disaster preparedness in Tehran, the capital of Iran, interventions encouraging disaster preparedness behavior (DPB) are needed. This study was conducted to show how an elicitation method can be used to identify salient consequences, referents, and circumstances about DPB and provide recommendations for interventions and quantitative research. A theory-based qualitative study using a semi-structured elicitation questionnaire was conducted with 132 heads of households from 22 districts in Tehran, Iran. Following the Theory of Planned Behavior (TPB), six open-ended questions were used to record the opinion of people about DPB: advantages of engaging in DPB; disadvantages of doing so; people who approve; people who disapprove; things that make it easy; and things that make it difficult. Content analysis showed the categories of salient consequences, reference groups, and circumstances. The three most frequently mentioned advantages obtained from inhabitants of Tehran were health outcomes (eg, it helps us to save our lives, it provides basic needs, and it protects us until relief workers arrive); other salient advantages were mentioned (eg, helps family reunification). The main disadvantage was preparedness anxiety. Family members were the most frequently mentioned social referent when people were asked who might approve or disapprove of their DPB. The two main circumstances perceived to obstruct DPB included not having enough knowledge or enough time. The results of this qualitative study suggest that interventions to encourage DPB among Tehran inhabitants should address: perceived consequences of DPB on health and other factors beyond health; barriers of not having enough knowledge and time perceived to hinder DPB; and social approval. More accurate research on salient beliefs with close-ended items developed from these open-ended data and with larger sample sizes of Tehran inhabitants is necessary. Research with other

PROBABILISTIC APPROACH TO OBJECT DETECTION AND RECOGNITION FOR VIDEOSTREAM PROCESSING

Directory of Open Access Journals (Sweden)

Volodymyr Kharchenko

2017-07-01

Full Text Available Purpose: The represented research results are aimed to improve theoretical basics of computer vision and artificial intelligence of dynamical system. Proposed approach of object detection and recognition is based on probabilistic fundamentals to ensure the required level of correct object recognition. Methods: Presented approach is grounded at probabilistic methods, statistical methods of probability density estimation and computer-based simulation at verification stage of development. Results: Proposed approach for object detection and recognition for video stream data processing has shown several advantages in comparison with existing methods due to its simple realization and small time of data processing. Presented results of experimental verification look plausible for object detection and recognition in video stream. Discussion: The approach can be implemented in dynamical system within changeable environment such as remotely piloted aircraft systems and can be a part of artificial intelligence in navigation and control systems.

Estimation of salient regions related to chronic gastritis using gastric X-ray images.

Science.gov (United States)

Togo, Ren; Ishihara, Kenta; Ogawa, Takahiro; Haseyama, Miki

2016-10-01

Since technical knowledge and a high degree of experience are necessary for diagnosis of chronic gastritis, computer-aided diagnosis (CAD) systems that analyze gastric X-ray images are desirable in the field of medicine. Therefore, a new method that estimates salient regions related to chronic gastritis/non-gastritis for supporting diagnosis is presented in this paper. In order to estimate salient regions related to chronic gastritis/non-gastritis, the proposed method monitors the distance between a target image feature and Support Vector Machine (SVM)-based hyperplane for its classification. Furthermore, our method realizes removal of the influence of regions outside the stomach by using positional relationships between the stomach and other organs. Consequently, since the proposed method successfully estimates salient regions of gastric X-ray images for which chronic gastritis and non-gastritis are unknown, visual support for inexperienced clinicians becomes feasible. Copyright © 2016 Elsevier Ltd. All rights reserved.

On the Detectability of Interstellar Objects Like 1I/'Oumuamua

Science.gov (United States)

Ragozzine, Darin

2018-04-01

Almost since Oort's 1950 hypothesis of a tenuously bound cloud of comets, planetary formation theorists have realized that the process of planet formation must have ejected very large numbers of planetesimals into interstellar space. Unforunately, these objects are distributed over galactic volumes, while they are only likely to be detectable if they pass within a few AU of Earth, resulting in an incredibly sparse detectable population. Furthermore, hypotheses for the formation and distribution of these bodies allows for uncertainties of orders of magnitude in the expected detection rate: our analysis suggested LSST would discover 0.01-100 objects during its lifetime (Cook et al. 2016). The discovery of 1I/'Oumuamua by a survey less powerful that LSST indicates either a low probability event and/or that properties of this population are on the more favorable end of the spectrum. We revisit the detailed detection analysis of Cook et al. 2016 in light of the detection of 1I/'Oumuamua. We use these results to better understand 1I/'Oumuamua and to update our assessment of future detections of interstellar objects. We highlight some key questions that can be answered only by additional discoveries.

System and method for automated object detection in an image

Science.gov (United States)

Kenyon, Garrett T.; Brumby, Steven P.; George, John S.; Paiton, Dylan M.; Schultz, Peter F.

2015-10-06

A contour/shape detection model may use relatively simple and efficient kernels to detect target edges in an object within an image or video. A co-occurrence probability may be calculated for two or more edge features in an image or video using an object definition. Edge features may be differentiated between in response to measured contextual support, and prominent edge features may be extracted based on the measured contextual support. The object may then be identified based on the extracted prominent edge features.

Detection and Classification of Buried Metallic Objects UX-1225

Energy Technology Data Exchange (ETDEWEB)

Morrison, Frank; Smith, Torquil; Becker, Alex; Gasperikova, Erika

2005-03-31

In summary the technical objectives of this project were: (1) To develop and demonstrate a methodology for the quantitative evaluation of existing active electromagnetic (AEM) systems and for the design of new systems. (2) To implement a new methodology for optimizing an AEM system for detecting and classifying UXO of a given class in a specified geologic setting and in a given noise environment. (3) To design and build a prototype of an active EM system for detecting and characterizing a metallic object in the ground.

Performance evaluation software moving object detection and tracking in videos

CERN Document Server

Karasulu, Bahadir

2013-01-01

Performance Evaluation Software: Moving Object Detection and Tracking in Videos introduces a software approach for the real-time evaluation and performance comparison of the methods specializing in moving object detection and/or tracking (D&T) in video processing. Digital video content analysis is an important item for multimedia content-based indexing (MCBI), content-based video retrieval (CBVR) and visual surveillance systems. There are some frequently-used generic algorithms for video object D&T in the literature, such as Background Subtraction (BS), Continuously Adaptive Mean-shift (CMS),

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

Directory of Open Access Journals (Sweden)

Yun Ren

2018-01-01

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

Hough transform methods used for object detection

International Nuclear Information System (INIS)

Qussay A Salih; Abdul Rahman Ramli; Md Mahmud Hassan Prakash

2001-01-01

The Hough transform (HT) is a robust parameter estimator of multi-dimensional features in images. The HT is an established technique which evidences a shape by mapping image edge points into a parameter space. The HT is technique which is used to isolate curves of a give shape in an image. The classical HT requires that the curve be specified in some parametric from and, hence is most commonly used in the detection of regular curves. The HT has been generalized so that it is capable of detecting arbitrary curved shapes. The main advantage of this transform technique is that it is very tolerant of gaps in the actual object boundaries the classical HT for the detection of line , we will indicate how it can be applied to the detection of arbitrary shapes. Sometimes the straight line HT is efficient enough to detect features such as artificial curves. The HT is an established technique for extracting geometric shapes based on the duality definition of the points on a curve and their parameters. This technique has been developed for extracting simple geometric shapes such as lines, circles and ellipses as well as arbitrary shapes. The HT provides robustness against discontinuous or missing features, points or edges are mapped into a partitioned parameter of Hough space as individual votes where peaks denote the feature of interest represented in a non-analytically tabular form. The main drawback of the HT technique is the computational requirement which has an exponential growth of memory space and processing time as the number of parameters used to represent a primitive increases. For this reason most of the research on the HT has focused on reducing the computational burden for extracting of arbitrary shapes under more general transformations include a overview of describing the methods for the detection image processing programs are frequently required to detect and particle classification in an industrial setting, a standard algorithms for this detection lines

Covariation of Color and Luminance Facilitate Object Individuation in Infancy

Science.gov (United States)

Woods, Rebecca J.; Wilcox, Teresa

2010-01-01

The ability to individuate objects is one of our most fundamental cognitive capacities. Recent research has revealed that when objects vary in color or luminance alone, infants fail to individuate those objects until 11.5 months. However, color and luminance frequently covary in the natural environment, thus providing a more salient and reliable…

Detection of moving objects from a moving platform in urban scenes

NARCIS (Netherlands)

Haar, F.B. ter; Hollander, R.J.M. den; Dijk, J.

2010-01-01

Moving object detection in urban scenes is important for the guidance of autonomous vehicles, robot navigation, and monitoring. In this paper moving objects are automatically detected using three sequential frames and tracked over a longer period. To this extend we modify the plane+parallax,

Water Detection Based on Object Reflections

Science.gov (United States)

Rankin, Arturo L.; Matthies, Larry H.

2012-01-01

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

Detection of foreign objects using bobbin probe eddy current test

Energy Technology Data Exchange (ETDEWEB)

Jung, Hee Sung; Kweon, Young Ho; Lee, Dong Ha; Shin, Wook Jo; Yim, Chan Ki [ECT Group, Sae-An Engineering Corporation, Seoul (Korea, Republic of)

2016-08-15

Residual foreign objects at the secondary side (top of the tubesheet and tube support plates) of a steam generator are likely to cause a leak by causing wear in the tube. The extent of wear is significantly affected by the material, shape, and size of the foreign object, and the corrosion properties of the tube. The presence of foreign objects at the top of the tubesheet and tube support plates has been identified using remote visual inspection methods such as the foreign object search and retrieval and eddy current test (ECT). The detection of the residual foreign object at the secondary side of a steam generator has limitations that depend on the material properties and the condition of contact with the tube. In this study, which is vertical and horizontal from the upper tubesheet, the corresponding bobbin ECT signals were collected and analyzed to measure its ability to detect foreign objects.

Effect of chewing speed on the detection of a foreign object in food.

Science.gov (United States)

Paphangkorakit, J; Ladsena, V; Rukyuttithamkul, T; Khamtad, T

2016-03-01

Accidentally biting hard on a piece of hard foreign object in food is among the causes of tooth fracturing and could be associated with oral sensibility. This study has investigated the effect of chewing speed on the ability to detect a foreign object in food in human. Fourteen healthy subjects were asked to randomly chew one of 10 cooked rice balls, five of which containing a foreign object made from a tiny uncooked rice grain, until they detected the rice grain. Each subject chewed the test foods both at 50 (slow) and 100 (fast) chews min(-1). The accuracy of detection and the number of chews before detection (CBD) were recorded and compared between the two chewing speeds using paired t-tests. The results showed that almost all subjects detected the foreign object by biting. The accuracy of detection was more than 90% and not significantly different between slow and fast chewing but the mean CBD in slow chewing (11·7 ± 1·3 chews) was significantly different from that in fast chewing (20·7 ± 1·9 chews; P chews before a foreign object in food could be detected and was, presumably, more effective in detecting the object compared to fast chewers. If each chew bears equal probability of teeth encountering the foreign object, slow chewing might also reduce the chance of accidentally biting hard on the foreign object and fracturing the tooth. © 2015 John Wiley & Sons Ltd.

Real-time object detection and semantic segmentation for autonomous driving

Science.gov (United States)

Li, Baojun; Liu, Shun; Xu, Weichao; Qiu, Wei

2018-02-01

In this paper, we proposed a Highly Coupled Network (HCNet) for joint objection detection and semantic segmentation. It follows that our method is faster and performs better than the previous approaches whose decoder networks of different tasks are independent. Besides, we present multi-scale loss architecture to learn better representation for different scale objects, but without extra time in the inference phase. Experiment results show that our method achieves state-of-the-art results on the KITTI datasets. Moreover, it can run at 35 FPS on a GPU and thus is a practical solution to object detection and semantic segmentation for autonomous driving.

Visual SLAM and Moving-object Detection for a Small-size Humanoid Robot

Directory of Open Access Journals (Sweden)

Yin-Tien Wang

2010-09-01

Full Text Available In the paper, a novel moving object detection (MOD algorithm is developed and integrated with robot visual Simultaneous Localization and Mapping (vSLAM. The moving object is assumed to be a rigid body and its coordinate system in space is represented by a position vector and a rotation matrix. The MOD algorithm is composed of detection of image features, initialization of image features, and calculation of object coordinates. Experimentation is implemented on a small-size humanoid robot and the results show that the performance of the proposed algorithm is efficient for robot visual SLAM and moving object detection.

Exploiting Higher Order and Multi-modal Features for 3D Object Detection

DEFF Research Database (Denmark)

Kiforenko, Lilita

that describe object visual appearance such as shape, colour, texture etc. This thesis focuses on robust object detection and pose estimation of rigid objects using 3D information. The thesis main contributions are novel feature descriptors together with object detection and pose estimation algorithms....... The initial work introduces a feature descriptor that uses edge categorisation in combination with a local multi-modal histogram descriptor in order to detect objects with little or no texture or surface variation. The comparison is performed with a state-of-the-art method, which is outperformed...... of the methods work well for one type of objects in a specific scenario, in another scenario or with different objects they might fail, therefore more robust solutions are required. The typical problem solution is the design of robust feature descriptors, where feature descriptors contain information...

Polarized object detection in crabs: a two-channel system.

Science.gov (United States)

Basnak, Melanie Ailín; Pérez-Schuster, Verónica; Hermitte, Gabriela; Berón de Astrada, Martín

2018-05-25

Many animal species take advantage of polarization vision for vital tasks such as orientation, communication and contrast enhancement. Previous studies have suggested that decapod crustaceans use a two-channel polarization system for contrast enhancement. Here, we characterize the polarization contrast sensitivity in a grapsid crab . We estimated the polarization contrast sensitivity of the animals by quantifying both their escape response and changes in heart rate when presented with polarized motion stimuli. The motion stimulus consisted of an expanding disk with an 82 deg polarization difference between the object and the background. More than 90% of animals responded by freezing or trying to avoid the polarized stimulus. In addition, we co-rotated the electric vector (e-vector) orientation of the light from the object and background by increments of 30 deg and found that the animals' escape response varied periodically with a 90 deg period. Maximum escape responses were obtained for object and background e-vectors near the vertical and horizontal orientations. Changes in cardiac response showed parallel results but also a minimum response when e-vectors of object and background were shifted by 45 deg with respect to the maxima. These results are consistent with an orthogonal receptor arrangement for the detection of polarized light, in which two channels are aligned with the vertical and horizontal orientations. It has been hypothesized that animals with object-based polarization vision rely on a two-channel detection system analogous to that of color processing in dichromats. Our results, obtained by systematically varying the e-vectors of object and background, provide strong empirical support for this theoretical model of polarized object detection. © 2018. Published by The Company of Biologists Ltd.

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

Science.gov (United States)

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

2017-05-01

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

Foreign Object Detection by Sub-Terahertz Quasi-Bessel Beam Imaging

Directory of Open Access Journals (Sweden)

Hyang Sook Chun

2012-12-01

Full Text Available Food quality monitoring, particularly foreign object detection, has recently become a critical issue for the food industry. In contrast to X-ray imaging, terahertz imaging can provide a safe and ionizing-radiation-free nondestructive inspection method for foreign object sensing. In this work, a quasi-Bessel beam (QBB known to be nondiffracting was generated by a conical dielectric lens to detect foreign objects in food samples. Using numerical evaluation via the finite-difference time-domain (FDTD method, the beam profiles of a QBB were evaluated and compared with the results obtained via analytical calculation and experimental characterization (knife edge method, point scanning method. The FDTD method enables a more precise estimation of the beam profile. Foreign objects in food samples, namely crickets, were then detected with the QBB, which had a deep focus and a high spatial resolution at 210 GHz. Transmitted images using a Gaussian beam obtained with a conventional lens were compared in the sub-terahertz frequency experimentally with those using a QBB generated using an axicon.

Wind turbine extraction from high spatial resolution remote sensing images based on saliency detection

Science.gov (United States)

Chen, Jingbo; Yue, Anzhi; Wang, Chengyi; Huang, Qingqing; Chen, Jiansheng; Meng, Yu; He, Dongxu

2018-01-01

The wind turbine is a device that converts the wind's kinetic energy into electrical power. Accurate and automatic extraction of wind turbine is instructive for government departments to plan wind power plant projects. A hybrid and practical framework based on saliency detection for wind turbine extraction, using Google Earth image at spatial resolution of 1 m, is proposed. It can be viewed as a two-phase procedure: coarsely detection and fine extraction. In the first stage, we introduced a frequency-tuned saliency detection approach for initially detecting the area of interest of the wind turbines. This method exploited features of color and luminance, was simple to implement, and was computationally efficient. Taking into account the complexity of remote sensing images, in the second stage, we proposed a fast method for fine-tuning results in frequency domain and then extracted wind turbines from these salient objects by removing the irrelevant salient areas according to the special properties of the wind turbines. Experiments demonstrated that our approach consistently obtains higher precision and better recall rates. Our method was also compared with other techniques from the literature and proves that it is more applicable and robust.

T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos

OpenAIRE

Kang, Kai; Li, Hongsheng; Yan, Junjie; Zeng, Xingyu; Yang, Bin; Xiao, Tong; Zhang, Cong; Wang, Zhe; Wang, Ruohui; Wang, Xiaogang; Ouyang, Wanli

2016-01-01

The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as GoogleNet and VGG, novel object detection frameworks such as R-CNN and its successors, Fast R-CNN and Faster R-CNN, play an essential role in improving the state-of-the-art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and context...

Active Exploration for Robust Object Detection

OpenAIRE

Velez, Javier J.; Hemann, Garrett A.; Huang, Albert S.; Posner, Ingmar; Roy, Nicholas

2011-01-01

Today, mobile robots are increasingly expected to operate in ever more complex and dynamic environments. In order to carry out many of the higher-level tasks envisioned a semantic understanding of a workspace is pivotal. Here our field has benefited significantly from successes in machine learning and vision: applications in robotics of off-the-shelf object detectors are plentiful. This paper outlines an online, any-time planning framework enabling the active exploration of such detections. O...

Salient regions detection using convolutional neural networks and color volume

Science.gov (United States)

Liu, Guang-Hai; Hou, Yingkun

2018-03-01

Convolutional neural network is an important technique in machine learning, pattern recognition and image processing. In order to reduce the computational burden and extend the classical LeNet-5 model to the field of saliency detection, we propose a simple and novel computing model based on LeNet-5 network. In the proposed model, hue, saturation and intensity are utilized to extract depth cues, and then we integrate depth cues and color volume to saliency detection following the basic structure of the feature integration theory. Experimental results show that the proposed computing model outperforms some existing state-of-the-art methods on MSRA1000 and ECSSD datasets.

A man-made object detection for underwater TV

Science.gov (United States)

Cheng, Binbin; Wang, Wenwu; Chen, Yao

2018-03-01

It is a great challenging task to complete an automatic search of objects underwater. Usually the forward looking sonar is used to find the target, and then the initial identification of the target is completed by the side-scan sonar, and finally the confirmation of the target is accomplished by underwater TV. This paper presents an efficient method for automatic extraction of man-made sensitive targets in underwater TV. Firstly, the image of underwater TV is simplified with taking full advantage of the prior knowledge of the target and the background; then template matching technology is used for target detection; finally the target is confirmed by extracting parallel lines on the target contour. The algorithm is formulated for real-time execution on limited-memory commercial-of-the-shelf platforms and is capable of detection objects in underwater TV.

Object detection by correlation coefficients using azimuthally averaged reference projections.

Science.gov (United States)

Nicholson, William V

2004-11-01

A method of computing correlation coefficients for object detection that takes advantage of using azimuthally averaged reference projections is described and compared with two alternative methods-computing a cross-correlation function or a local correlation coefficient versus the azimuthally averaged reference projections. Two examples of an application from structural biology involving the detection of projection views of biological macromolecules in electron micrographs are discussed. It is found that a novel approach to computing a local correlation coefficient versus azimuthally averaged reference projections, using a rotational correlation coefficient, outperforms using a cross-correlation function and a local correlation coefficient in object detection from simulated images with a range of levels of simulated additive noise. The three approaches perform similarly in detecting macromolecular views in electron microscope images of a globular macrolecular complex (the ribosome). The rotational correlation coefficient outperforms the other methods in detection of keyhole limpet hemocyanin macromolecular views in electron micrographs.

The fate of object memory traces under change detection and change blindness.

Science.gov (United States)

Busch, Niko A

2013-07-03

Observers often fail to detect substantial changes in a visual scene. This so-called change blindness is often taken as evidence that visual representations are sparse and volatile. This notion rests on the assumption that the failure to detect a change implies that representations of the changing objects are lost all together. However, recent evidence suggests that under change blindness, object memory representations may be formed and stored, but not retrieved. This study investigated the fate of object memory representations when changes go unnoticed. Participants were presented with scenes consisting of real world objects, one of which changed on each trial, while recording event-related potentials (ERPs). Participants were first asked to localize where the change had occurred. In an additional recognition task, participants then discriminated old objects, either from the pre-change or the post-change scene, from entirely new objects. Neural traces of object memories were studied by comparing ERPs for old and novel objects. Participants performed poorly in the detection task and often failed to recognize objects from the scene, especially pre-change objects. However, a robust old/novel effect was observed in the ERP, even when participants were change blind and did not recognize the old object. This implicit memory trace was found both for pre-change and post-change objects. These findings suggest that object memories are stored even under change blindness. Thus, visual representations may not be as sparse and volatile as previously thought. Rather, change blindness may point to a failure to retrieve and use these representations for change detection. Copyright © 2013 Elsevier B.V. All rights reserved.

Boundary Region Detection for Continuous Objects in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yaqiang Zhang

2018-01-01

Full Text Available Industrial Internet of Things has been widely used to facilitate disaster monitoring applications, such as liquid leakage and toxic gas detection. Since disasters are usually harmful to the environment, detecting accurate boundary regions for continuous objects in an energy-efficient and timely fashion is a long-standing research challenge. This article proposes a novel mechanism for continuous object boundary region detection in a fog computing environment, where sensing holes may exist in the deployed network region. Leveraging sensory data that have been gathered, interpolation algorithms have been applied to estimate sensory data at certain geographical locations, in order to estimate a more accurate boundary line. To examine whether estimated sensory data reflect that fact, mobile sensors are adopted to traverse these locations for gathering their sensory data, and the boundary region is calibrated accordingly. Experimental evaluation shows that this technique can generate a precise object boundary region with certain time constraints, and the network lifetime can be prolonged significantly.

Unsupervised sub-categorization for object detection: fInding cars from a driving vehicle

NARCIS (Netherlands)

Wijnhoven, R.G.J.; With, de P.H.N.

2011-01-01

We present a novel algorithm for unsupervised subcategorization of an object class, in the context of object detection. Dividing the detection problem into smaller subproblems simplifies the object vs. background classification. The algorithm uses an iterative split-and-merge procedure and uses both

Evaluating Color Descriptors for Object and Scene Recognition

NARCIS (Netherlands)

van de Sande, K.E.A.; Gevers, T.; Snoek, C.G.M.

2010-01-01

Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been

Dominant object detection for autonomous vision-based surveillance

NARCIS (Netherlands)

Celik, H.

2010-01-01

The deployment of visual surveillance and monitoring systems has reached massive proportions. Consequently, a need to automate the processes involved in retrieving useful information from surveillance videos, such as detecting and counting objects, and interpreting their individual and joint

Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms

Science.gov (United States)

Particke, F.; Kolbenschlag, R.; Hiller, M.; Patiño-Studencki, L.; Thielecke, J.

2017-10-01

Industry 4.0 is one of the most formative terms in current times. Subject of research are particularly smart and autonomous mobile platforms, which enormously lighten the workload and optimize production processes. In order to interact with humans, the platforms need an in-depth knowledge of the environment. Hence, it is required to detect a variety of static and non-static objects. Goal of this paper is to propose an accurate and real-time capable object detection and localization approach for the use on mobile platforms. A method is introduced to use the powerful detection capabilities of a neural network for the localization of objects. Therefore, detection information of a neural network is combined with depth information from a RGB-D camera, which is mounted on a mobile platform. As detection network, YOLO Version 2 (YOLOv2) is used on a mobile robot. In order to find the detected object in the depth image, the bounding boxes, predicted by YOLOv2, are mapped to the corresponding regions in the depth image. This provides a powerful and extremely fast approach for establishing a real-time-capable Object Locator. In the evaluation part, the localization approach turns out to be very accurate. Nevertheless, it is dependent on the detected object itself and some additional parameters, which are analysed in this paper.

Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

Directory of Open Access Journals (Sweden)

Jaehoon Jung

2016-06-01

Full Text Available This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i automatic camera calibration using both moving objects and a background structure; (ii object depth estimation; and (iii detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems.

A system and a method for detecting the position of an object

International Nuclear Information System (INIS)

Brown, M.H.; Harrison, J.G.

1982-01-01

The position of an object e.g. a manipulator, in an enclosure is detected by two video cameras from which signals representative of images in the cameras are supplied to a mini-computer. The mini-computer scans the signals to detect the position of the object in the signals, and relates this position to the spatial coordinates of the object in the enclosure. Means are provided for controlling the movement of the object within the enclosure, which may be a hostile environment e.g. radio-active. (author)

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

Directory of Open Access Journals (Sweden)

K. Khattab

2009-01-01

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

Dual-Layer Density Estimation for Multiple Object Instance Detection

Directory of Open Access Journals (Sweden)

Qiang Zhang

2016-01-01

Full Text Available This paper introduces a dual-layer density estimation-based architecture for multiple object instance detection in robot inventory management applications. The approach consists of raw scale-invariant feature transform (SIFT feature matching and key point projection. The dominant scale ratio and a reference clustering threshold are estimated using the first layer of the density estimation. A cascade of filters is applied after feature template reconstruction and refined feature matching to eliminate false matches. Before the second layer of density estimation, the adaptive threshold is finalized by multiplying an empirical coefficient for the reference value. The coefficient is identified experimentally. Adaptive threshold-based grid voting is applied to find all candidate object instances. Error detection is eliminated using final geometric verification in accordance with Random Sample Consensus (RANSAC. The detection results of the proposed approach are evaluated on a self-built dataset collected in a supermarket. The results demonstrate that the approach provides high robustness and low latency for inventory management application.

Object-based landslide detection in different geographic regions

Science.gov (United States)

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

2015-04-01

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

Object classification and detection with context kernel descriptors

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2014-01-01

Context information is important in object representation. By embedding context cue of image attributes into kernel descriptors, we propose a set of novel kernel descriptors called Context Kernel Descriptors (CKD) for object classification and detection. The motivation of CKD is to use spatial...... consistency of image attributes or features defined within a neighboring region to improve the robustness of descriptor matching in kernel space. For feature selection, Kernel Entropy Component Analysis (KECA) is exploited to learn a subset of discriminative CKD. Different from Kernel Principal Component...

Saliency predicts change detection in pictures of natural scenes.

Science.gov (United States)

Wright, Michael J

2005-01-01

It has been proposed that the visual system encodes the salience of objects in the visual field in an explicit two-dimensional map that guides visual selective attention. Experiments were conducted to determine whether salience measurements applied to regions of pictures of outdoor scenes could predict the detection of changes in those regions. To obtain a quantitative measure of change detection, observers located changes in pairs of colour pictures presented across an interstimulus interval (ISI). Salience measurements were then obtained from different observers for image change regions using three independent methods, and all were positively correlated with change detection. Factor analysis extracted a single saliency factor that accounted for 62% of the variance contained in the four measures. Finally, estimates of the magnitude of the image change in each picture pair were obtained, using nine separate visual filters representing low-level vision features (luminance, colour, spatial frequency, orientation, edge density). None of the feature outputs was significantly associated with change detection or saliency. On the other hand it was shown that high-level (structural) properties of the changed region were related to saliency and to change detection: objects were more salient than shadows and more detectable when changed.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

OpenAIRE

Ren, Shaoqing; He, Kaiming; Girshick, Ross; Sun, Jian

2015-01-01

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultan...

Understanding of Object Detection Based on CNN Family and YOLO

Science.gov (United States)

Du, Juan

2018-04-01

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

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

Directory of Open Access Journals (Sweden)

Gil-beom Lee

2017-03-01

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

System and method for detecting a faulty object in a system

Science.gov (United States)

Gunnels, John A.; Gustavson, Fred Gehrung; Engle, Robert Daniel

2010-12-14

A method (and system) for detecting at least one faulty object in a system including a plurality of objects in communication with each other in an n-dimensional architecture, includes probing a first plane of objects in the n-dimensional architecture and probing at least one other plane of objects in the n-dimensional architecture which would result in identifying a faulty object in the system.

The effects of changes in object location on object identity detection: A simultaneous EEG-fMRI study.

Science.gov (United States)

Yang, Ping; Fan, Chenggui; Wang, Min; Fogelson, Noa; Li, Ling

2017-08-15

Object identity and location are bound together to form a unique integration that is maintained and processed in visual working memory (VWM). Changes in task-irrelevant object location have been shown to impair the retrieval of memorial representations and the detection of object identity changes. However, the neural correlates of this cognitive process remain largely unknown. In the present study, we aim to investigate the underlying brain activation during object color change detection and the modulatory effects of changes in object location and VWM load. To this end we used simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) recordings, which can reveal the neural activity with both high temporal and high spatial resolution. Subjects responded faster and with greater accuracy in the repeated compared to the changed object location condition, when a higher VWM load was utilized. These results support the spatial congruency advantage theory and suggest that it is more pronounced with higher VWM load. Furthermore, the spatial congruency effect was associated with larger posterior N1 activity, greater activation of the right inferior frontal gyrus (IFG) and less suppression of the right supramarginal gyrus (SMG), when object location was repeated compared to when it was changed. The ERP-fMRI integrative analysis demonstrated that the object location discrimination-related N1 component is generated in the right SMG. Copyright © 2017 Elsevier Inc. All rights reserved.

Aerial surveillance based on hierarchical object classification for ground target detection

Science.gov (United States)

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

2015-03-01

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

OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

Directory of Open Access Journals (Sweden)

Y. Jia

2016-06-01

Full Text Available The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

RASW : a Run-time Adaptive Sliding Window to Improve Viola-Jones object detection.

NARCIS (Netherlands)

Comaschi, F.; Stuijk, S.; Basten, T.; Corporaal, H.

2013-01-01

Abstract—In recent years accurate algorithms for detecting objects in images have been developed. Among these algorithms, the object detection scheme proposed by Viola and Jones gained great popularity, especially after the release of high-quality face classifiers by the OpenCV group. However, as

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

Directory of Open Access Journals (Sweden)

Jaehoon Jung

2016-01-01

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

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

Science.gov (United States)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Salient Predictors of School Dropout among Secondary Students with Learning Disabilities

Science.gov (United States)

Doren, Bonnie; Murray, Christopher; Gau, Jeff M.

2014-01-01

The purpose of this study was to identify the unique contributions of a comprehensive set of predictors and the most salient predictors of school dropout among a nationally representative sample of students with learning disabilities (LD). A comprehensive set of theoretically and empirically relevant factors was selected for examination. Analyses…

DETECTION AND CLASSIFICATION OF POLE-LIKE OBJECTS FROM MOBILE MAPPING DATA

Directory of Open Access Journals (Sweden)

K. Fukano

2015-08-01

Full Text Available Laser scanners on a vehicle-based mobile mapping system can capture 3D point-clouds of roads and roadside objects. Since roadside objects have to be maintained periodically, their 3D models are useful for planning maintenance tasks. In our previous work, we proposed a method for detecting cylindrical poles and planar plates in a point-cloud. However, it is often required to further classify pole-like objects into utility poles, streetlights, traffic signals and signs, which are managed by different organizations. In addition, our previous method may fail to extract low pole-like objects, which are often observed in urban residential areas. In this paper, we propose new methods for extracting and classifying pole-like objects. In our method, we robustly extract a wide variety of poles by converting point-clouds into wireframe models and calculating cross-sections between wireframe models and horizontal cutting planes. For classifying pole-like objects, we subdivide a pole-like object into five subsets by extracting poles and planes, and calculate feature values of each subset. Then we apply a supervised machine learning method using feature variables of subsets. In our experiments, our method could achieve excellent results for detection and classification of pole-like objects.

Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection.

Science.gov (United States)

Wei, Pan; Ball, John E; Anderson, Derek T

2018-03-17

A significant challenge in object detection is accurate identification of an object's position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy. Experiments comparing the results both with and without fusion are presented. We demonstrate that the augmented and fused combination results are the best, with respect to higher accuracy rates and reduction of outlier influences. The approach is demonstrated in the context of cone, pedestrian and box detection for Advanced Driver Assistance Systems (ADAS) applications.

Investigating the feasibility of using partial least squares as a method of extracting salient information for the evaluation of digital breast tomosynthesis

Science.gov (United States)

Zhang, George Z.; Myers, Kyle J.; Park, Subok

2013-03-01

Digital breast tomosynthesis (DBT) has shown promise for improving the detection of breast cancer, but it has not yet been fully optimized due to a large space of system parameters to explore. A task-based statistical approach1 is a rigorous method for evaluating and optimizing this promising imaging technique with the use of optimal observers such as the Hotelling observer (HO). However, the high data dimensionality found in DBT has been the bottleneck for the use of a task-based approach in DBT evaluation. To reduce data dimensionality while extracting salient information for performing a given task, efficient channels have to be used for the HO. In the past few years, 2D Laguerre-Gauss (LG) channels, which are a complete basis for stationary backgrounds and rotationally symmetric signals, have been utilized for DBT evaluation2, 3 . But since background and signal statistics from DBT data are neither stationary nor rotationally symmetric, LG channels may not be efficient in providing reliable performance trends as a function of system parameters. Recently, partial least squares (PLS) has been shown to generate efficient channels for the Hotelling observer in detection tasks involving random backgrounds and signals.4 In this study, we investigate the use of PLS as a method for extracting salient information from DBT in order to better evaluate such systems.

The detection of temporally defined objects does not require focused attention.

NARCIS (Netherlands)

Pinto, Y.; Olivers, C.N.L.; Theeuwes, J.

2008-01-01

Perceptual grouping is crucial to distinguish objects from their background. Recent studies have shown that observers can detect an object that does not have any unique qualities other than unique temporal properties. A crucial question is whether focused attention is needed for this type of

Incrementally Detecting Change Types of Spatial Area Object: A Hierarchical Matching Method Considering Change Process

Directory of Open Access Journals (Sweden)

Yanhui Wang

2018-01-01

Full Text Available Detecting and extracting the change types of spatial area objects can track area objects’ spatiotemporal change pattern and provide the change backtracking mechanism for incrementally updating spatial datasets. To respond to the problems of high complexity of detection methods, high redundancy rate of detection factors, and the low automation degree during incrementally update process, we take into account the change process of area objects in an integrated way and propose a hierarchical matching method to detect the nine types of changes of area objects, while minimizing the complexity of the algorithm and the redundancy rate of detection factors. We illustrate in details the identification, extraction, and database entry of change types, and how we achieve a close connection and organic coupling of incremental information extraction and object type-of-change detection so as to characterize the whole change process. The experimental results show that this method can successfully detect incremental information about area objects in practical applications, with the overall accuracy reaching above 90%, which is much higher than the existing weighted matching method, making it quite feasible and applicable. It helps establish the corresponding relation between new-version and old-version objects, and facilitate the linked update processing and quality control of spatial data.

Salient Beliefs of Secondary School Mathematics Teachers Using Dynamic Geometry Software

Science.gov (United States)

Chan, Kan Kan

2015-01-01

Even though dynamic geometry software (DGS) is becoming an emergent instructional tool for mathematics teachers, many teachers are still in the process of consideration about whether to use it. In order to encourage teachers to use DGS, this study seeks to discover mathematics teachers' salient beliefs about the use of DGS in mathematics class.…

Salient Key Features of Actual English Instructional Practices in Saudi Arabia

Science.gov (United States)

Al-Seghayer, Khalid

2015-01-01

This is a comprehensive review of the salient key features of the actual English instructional practices in Saudi Arabia. The goal of this work is to gain insights into the practices and pedagogic approaches to English as a foreign language (EFL) teaching currently employed in this country. In particular, we identify the following central features…

2D-Driven 3D Object Detection in RGB-D Images

KAUST Repository

Lahoud, Jean; Ghanem, Bernard

2017-01-01

In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection

Colour Terms Affect Detection of Colour and Colour-Associated Objects Suppressed from Visual Awareness.

Science.gov (United States)

Forder, Lewis; Taylor, Olivia; Mankin, Helen; Scott, Ryan B; Franklin, Anna

2016-01-01

The idea that language can affect how we see the world continues to create controversy. A potentially important study in this field has shown that when an object is suppressed from visual awareness using continuous flash suppression (a form of binocular rivalry), detection of the object is differently affected by a preceding word prime depending on whether the prime matches or does not match the object. This may suggest that language can affect early stages of vision. We replicated this paradigm and further investigated whether colour terms likewise influence the detection of colours or colour-associated object images suppressed from visual awareness by continuous flash suppression. This method presents rapidly changing visual noise to one eye while the target stimulus is presented to the other. It has been shown to delay conscious perception of a target for up to several minutes. In Experiment 1 we presented greyscale photos of objects. They were either preceded by a congruent object label, an incongruent label, or white noise. Detection sensitivity (d') and hit rates were significantly poorer for suppressed objects preceded by an incongruent label compared to a congruent label or noise. In Experiment 2, targets were coloured discs preceded by a colour term. Detection sensitivity was significantly worse for suppressed colour patches preceded by an incongruent colour term as compared to a congruent term or white noise. In Experiment 3 targets were suppressed greyscale object images preceded by an auditory presentation of a colour term. On congruent trials the colour term matched the object's stereotypical colour and on incongruent trials the colour term mismatched. Detection sensitivity was significantly poorer on incongruent trials than congruent trials. Overall, these findings suggest that colour terms affect awareness of coloured stimuli and colour- associated objects, and provide new evidence for language-perception interaction in the brain.

Colour Terms Affect Detection of Colour and Colour-Associated Objects Suppressed from Visual Awareness.

Directory of Open Access Journals (Sweden)

Lewis Forder

Full Text Available The idea that language can affect how we see the world continues to create controversy. A potentially important study in this field has shown that when an object is suppressed from visual awareness using continuous flash suppression (a form of binocular rivalry, detection of the object is differently affected by a preceding word prime depending on whether the prime matches or does not match the object. This may suggest that language can affect early stages of vision. We replicated this paradigm and further investigated whether colour terms likewise influence the detection of colours or colour-associated object images suppressed from visual awareness by continuous flash suppression. This method presents rapidly changing visual noise to one eye while the target stimulus is presented to the other. It has been shown to delay conscious perception of a target for up to several minutes. In Experiment 1 we presented greyscale photos of objects. They were either preceded by a congruent object label, an incongruent label, or white noise. Detection sensitivity (d' and hit rates were significantly poorer for suppressed objects preceded by an incongruent label compared to a congruent label or noise. In Experiment 2, targets were coloured discs preceded by a colour term. Detection sensitivity was significantly worse for suppressed colour patches preceded by an incongruent colour term as compared to a congruent term or white noise. In Experiment 3 targets were suppressed greyscale object images preceded by an auditory presentation of a colour term. On congruent trials the colour term matched the object's stereotypical colour and on incongruent trials the colour term mismatched. Detection sensitivity was significantly poorer on incongruent trials than congruent trials. Overall, these findings suggest that colour terms affect awareness of coloured stimuli and colour- associated objects, and provide new evidence for language-perception interaction in the brain.

Comparison of Flux Regulation Ability of the Hybrid Excitation Doubly Salient Machines

DEFF Research Database (Denmark)

Chen, ZhiHui; Wang, Bo; Chen, Zhe

2014-01-01

A hybrid excitation doubly salient machine (DSM) (HEDSM) can adjust the air gap flux with the limited field exciting ampere-turns. There are a few studied structures with different air gap flux regulation abilities. In this paper, several HEDSMs with different structures are analyzed by using an ...

Learning Rich Features from RGB-D Images for Object Detection and Segmentation

OpenAIRE

Gupta, Saurabh; Girshick, Ross; Arbeláez, Pablo; Malik, Jitendra

2014-01-01

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height above ground and angle with gravity for each pixel in addition to the horizontal disparity. We demonstrate that this geocentric embedding works better than using raw depth images for learning feature representations with convolutional neural networks. Our final object detection system achieves an av...

Object tracking by occlusion detection via structured sparse learning

KAUST Repository

Zhang, Tianzhu

2013-06-01

Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

DOUBLE COMPACT OBJECTS. III. GRAVITATIONAL-WAVE DETECTION RATES

Energy Technology Data Exchange (ETDEWEB)

Dominik, Michal; Belczynski, Krzysztof; Bulik, Tomasz [Astronomical Observatory, University of Warsaw, Al. Ujazdowskie 4, 00-478 Warsaw (Poland); Berti, Emanuele [Department of Physics and Astronomy, The University of Mississippi, University, MS 38677 (United States); O’Shaughnessy, Richard [Center for Gravitation, Cosmology, and Astrophysics, University of Wisconsin-Milwaukee, Milwaukee, WI (United States); Mandel, Ilya [School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham B15 2TT (United Kingdom); Fryer, Christopher [CCS-2, MSD409, Los Alamos National Laboratory, Los Alamos, NM 87545 (United States); Holz, Daniel E. [Enrico Fermi Institute, Department of Physics, and Kavli Institute for Cosmological Physics University of Chicago, Chicago, IL 60637 (United States); Pannarale, Francesco [School of Physics and Astronomy, Cardiff University, The Parade, Cardiff CF24 3AA (United Kingdom)

2015-06-20

The unprecedented range of second-generation gravitational-wave (GW) observatories calls for refining the predictions of potential sources and detection rates. The coalescence of double compact objects (DCOs)—i.e., neutron star–neutron star (NS–NS), black hole–neutron star (BH–NS), and black hole–black hole (BH–BH) binary systems—is the most promising source of GWs for these detectors. We compute detection rates of coalescing DCOs in second-generation GW detectors using the latest models for their cosmological evolution, and implementing inspiral-merger-ringdown gravitational waveform models in our signal-to-noise ratio calculations. We find that (1) the inclusion of the merger/ringdown portion of the signal does not significantly affect rates for NS–NS and BH–NS systems, but it boosts rates by a factor of ∼1.5 for BH–BH systems; (2) in almost all of our models BH–BH systems yield by far the largest rates, followed by NS–NS and BH–NS systems, respectively; and (3) a majority of the detectable BH–BH systems were formed in the early universe in low-metallicity environments. We make predictions for the distributions of detected binaries and discuss what the first GW detections will teach us about the astrophysics underlying binary formation and evolution.

Fast and objective detection and analysis of structures in downhole images

Science.gov (United States)

Wedge, Daniel; Holden, Eun-Jung; Dentith, Mike; Spadaccini, Nick

2017-09-01

Downhole acoustic and optical televiewer images, and formation microimager (FMI) logs are important datasets for structural and geotechnical analyses for the mineral and petroleum industries. Within these data, dipping planar structures appear as sinusoids, often in incomplete form and in abundance. Their detection is a labour intensive and hence expensive task and as such is a significant bottleneck in data processing as companies may have hundreds of kilometres of logs to process each year. We present an image analysis system that harnesses the power of automated image analysis and provides an interactive user interface to support the analysis of televiewer images by users with different objectives. Our algorithm rapidly produces repeatable, objective results. We have embedded it in an interactive workflow to complement geologists' intuition and experience in interpreting data to improve efficiency and assist, rather than replace the geologist. The main contributions include a new image quality assessment technique for highlighting image areas most suited to automated structure detection and for detecting boundaries of geological zones, and a novel sinusoid detection algorithm for detecting and selecting sinusoids with given confidence levels. Further tools are provided to perform rapid analysis of and further detection of structures e.g. as limited to specific orientations.

Object Detection and Tracking using Modified Diamond Search Block Matching Motion Estimation Algorithm

Directory of Open Access Journals (Sweden)

Apurva Samdurkar

2018-06-01

Full Text Available Object tracking is one of the main fields within computer vision. Amongst various methods/ approaches for object detection and tracking, the background subtraction approach makes the detection of object easier. To the detected object, apply the proposed block matching algorithm for generating the motion vectors. The existing diamond search (DS and cross diamond search algorithms (CDS are studied and experiments are carried out on various standard video data sets and user defined data sets. Based on the study and analysis of these two existing algorithms a modified diamond search pattern (MDS algorithm is proposed using small diamond shape search pattern in initial step and large diamond shape (LDS in further steps for motion estimation. The initial search pattern consists of five points in small diamond shape pattern and gradually grows into a large diamond shape pattern, based on the point with minimum cost function. The algorithm ends with the small shape pattern at last. The proposed MDS algorithm finds the smaller motion vectors and fewer searching points than the existing DS and CDS algorithms. Further, object detection is carried out by using background subtraction approach and finally, MDS motion estimation algorithm is used for tracking the object in color video sequences. The experiments are carried out by using different video data sets containing a single object. The results are evaluated and compared by using the evaluation parameters like average searching points per frame and average computational time per frame. The experimental results show that the MDS performs better than DS and CDS on average search point and average computation time.

Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching

DEFF Research Database (Denmark)

Dimitriou, Michalis; Kounalakis, Tsampikos; Vidakis, Nikolaos

2013-01-01

, connected components detection and filtering approaches, in order to design a complete image processing algorithm for efficient object detection of multiple individual objects in a single scene, even in complex scenes with many objects. Besides, we apply the Linear Spatial Pyramid Matching (LSPM) [1] method......This paper presents a complete system for multiple object detection and classification in a 3D scene using an RGB-D sensor such as the Microsoft Kinect sensor. Successful multiple object detection and classification are crucial features in many 3D computer vision applications. The main goal...... is making machines see and understand objects like humans do. To this goal, the new RGB-D sensors can be utilized since they provide real-time depth map which can be used along with the RGB images for our tasks. In our system we employ effective depth map processing techniques, along with edge detection...

Sensorless Speed Control including zero speed of Non Salient PM Synchronous Drives

DEFF Research Database (Denmark)

Rasmussen, Henrik

This paper presents a position sensorless drive of non salient pole PM synchronous motors for all speeds including zero speed. Using adaptive Lyapunov design a new approach for the design of an observer is developed. The resulting scheme leads to a nonlinear full order observer for the motor states...

Sensorless Speed Control including zero speed of Non Salient PM Synchronous Drives

DEFF Research Database (Denmark)

Rasmussen, Henrik

2005-01-01

This paper presents a position sensorless drive of non salient pole PM synchronous motors for all speeds including zero speed. Using adaptive Lyapunov design a new approach for the design of an observer is developed. The resulting scheme leads to a nonlinear full order observer for the motor states...

Sensorless speed Control including Zero Speed on Non Salient PM Synchronous Drives

DEFF Research Database (Denmark)

Rasmussen, Henrik

2006-01-01

This paper presents a position sensorless drive of non salient pole PM synchronous motors for all speeds including zero speed. Using adaptive Lyapunov design a new approach for the design of an observer is developed. The resulting scheme leads to a nonlinear full order observer for the motor states...

Colour Terms Affect Detection of Colour and Colour-Associated Objects Suppressed from Visual Awareness

Science.gov (United States)

Forder, Lewis; Taylor, Olivia; Mankin, Helen; Scott, Ryan B.; Franklin, Anna

2016-01-01

The idea that language can affect how we see the world continues to create controversy. A potentially important study in this field has shown that when an object is suppressed from visual awareness using continuous flash suppression (a form of binocular rivalry), detection of the object is differently affected by a preceding word prime depending on whether the prime matches or does not match the object. This may suggest that language can affect early stages of vision. We replicated this paradigm and further investigated whether colour terms likewise influence the detection of colours or colour-associated object images suppressed from visual awareness by continuous flash suppression. This method presents rapidly changing visual noise to one eye while the target stimulus is presented to the other. It has been shown to delay conscious perception of a target for up to several minutes. In Experiment 1 we presented greyscale photos of objects. They were either preceded by a congruent object label, an incongruent label, or white noise. Detection sensitivity (d’) and hit rates were significantly poorer for suppressed objects preceded by an incongruent label compared to a congruent label or noise. In Experiment 2, targets were coloured discs preceded by a colour term. Detection sensitivity was significantly worse for suppressed colour patches preceded by an incongruent colour term as compared to a congruent term or white noise. In Experiment 3 targets were suppressed greyscale object images preceded by an auditory presentation of a colour term. On congruent trials the colour term matched the object’s stereotypical colour and on incongruent trials the colour term mismatched. Detection sensitivity was significantly poorer on incongruent trials than congruent trials. Overall, these findings suggest that colour terms affect awareness of coloured stimuli and colour- associated objects, and provide new evidence for language-perception interaction in the brain. PMID:27023274

Salient stimuli in advertising: the effect of contrast interval length and type on recall.

Science.gov (United States)

Olsen, G Douglas

2002-09-01

Salient auditory stimuli (e.g., music or sound effects) are commonly used in advertising to elicit attention. However, issues related to the effectiveness of such stimuli are not well understood. This research examines the ability of a salient auditory stimulus, in the form of a contrast interval (CI), to enhance recall of message-related information. Researchers have argued that the effectiveness of the CI is a function of the temporal duration between the onset and offset of the change in the background stimulus and the nature of this stimulus. Three experiments investigate these propositions and indicate that recall is enhanced, providing the CI is 3 s or less. Information highlighted with silence is recalled better than information highlighted with music.

A novel no-reference objective stereoscopic video quality assessment method based on visual saliency analysis

Science.gov (United States)

Yang, Xinyan; Zhao, Wei; Ye, Long; Zhang, Qin

2017-07-01

This paper proposes a no-reference objective stereoscopic video quality assessment method with the motivation that making the effect of objective experiments close to that of subjective way. We believe that the image regions with different visual salient degree should not have the same weights when designing an assessment metric. Therefore, we firstly use GBVS algorithm to each frame pairs and separate both the left and right viewing images into the regions with strong, general and week saliency. Besides, local feature information like blockiness, zero-crossing and depth are extracted and combined with a mathematical model to calculate a quality assessment score. Regions with different salient degree are assigned with different weights in the mathematical model. Experiment results demonstrate the superiority of our method compared with the existed state-of-the-art no-reference objective Stereoscopic video quality assessment methods.

Earliest Memories and Recent Memories of Highly Salient Events--Are They Similar?

Science.gov (United States)

Peterson, Carole; Fowler, Tania; Brandeau, Katherine M.

2015-01-01

Four- to 11-year-old children were interviewed about 2 different sorts of memories in the same home visit: recent memories of highly salient and stressful events--namely, injuries serious enough to require hospital emergency room treatment--and their earliest memories. Injury memories were scored for amount of unique information, completeness…

The Impact of Salient Advertisements on Reading and Attention on Web Pages

Science.gov (United States)

Simola, Jaana; Kuisma, Jarmo; Oorni, Anssi; Uusitalo, Liisa; Hyona, Jukka

2011-01-01

Human vision is sensitive to salient features such as motion. Therefore, animation and onset of advertisements on Websites may attract visual attention and disrupt reading. We conducted three eye tracking experiments with authentic Web pages to assess whether (a) ads are efficiently ignored, (b) ads attract overt visual attention and disrupt…

Ground Penetrating Radar (GPR) for Detection of Underground Objects

International Nuclear Information System (INIS)

Amry Amin Abas; Mohd Kamal Shah Shamsuddin; Wan Zainal Abidin; Awang Sarfarudin Awang Putra

2011-01-01

Ground Penetrating Radar (GPR) utilizes an electromagnetic microwave that is transmitted into the matter under investigation. Any objects with different dielectric properties from the medium of the matter under investigation will reflect the waves and will be picked up by the receivers embedded in the antenna. We have applied GPR in various application such as concrete inspection, underground utility detection, grave detection, archaeology, oil contamination of soil, soil layer thickness measurement and etc. This paper will give general findings of the application of GPR to provide solutions to the industry and public. The results of the GPR surveys will be discussed. (author)

When message-frame fits salient cultural-frame, messages feel more persuasive.

Science.gov (United States)

Uskul, Ayse K; Oyserman, Daphna

2010-03-01

The present study examines the persuasive effects of tailored health messages comparing those tailored to match (versus not match) both chronic cultural frame and momentarily salient cultural frame. Evidence from two studies (Study 1: n = 72 European Americans; Study 2: n = 48 Asian Americans) supports the hypothesis that message persuasiveness increases when chronic cultural frame, health message tailoring and momentarily salient cultural frame all match. The hypothesis was tested using a message about health risks of caffeine consumption among individuals prescreened to be regular caffeine consumers. After being primed for individualism, European Americans who read a health message that focused on the personal self were more likely to accept the message-they found it more persuasive, believed they were more at risk and engaged in more message-congruent behaviour. These effects were also found among Asian Americans who were primed for collectivism and who read a health message that focused on relational obligations. The findings point to the importance of investigating the role of situational cues in persuasive effects of health messages and suggest that matching content to primed frame consistent with the chronic frame may be a way to know what to match messages to.

Small Vocabulary with Saliency Matching for Video Copy Detection

DEFF Research Database (Denmark)

Ren, Huamin; Moeslund, Thomas B.; Tang, Sheng

2013-01-01

The importance of copy detection has led to a substantial amount of research in recent years, among which Bag of visual Words (BoW) plays an important role due to its ability to effectively handling occlusion and some minor transformations. One crucial issue in BoW approaches is the size of vocab......The importance of copy detection has led to a substantial amount of research in recent years, among which Bag of visual Words (BoW) plays an important role due to its ability to effectively handling occlusion and some minor transformations. One crucial issue in BoW approaches is the size...... matching algorithm based on salient visual words selection. More specifically, the variation of visual words across a given video are represented as trajectories and those containing locally asymptotically stable points are selected as salient visual words. Then we attempt to measure the similarity of two...... videos through saliency matching merely based on the selected salient visual words to remove false positives. Our experiments show that a small codebook with saliency matching is quite competitive in video copy detection. With the incorporation of the proposed saliency matching, the precision can...

Positron emission mammography (PEM): Effect of activity concentration, object size, and object contrast on phantom lesion detection

International Nuclear Information System (INIS)

MacDonald, Lawrence R.; Wang, Carolyn L.; Eissa, Marna; Haseley, David; Kelly, Mary M.; Liu, Franklin; Parikh, Jay R.; Beatty, J. David; Rogers, James V.

2012-01-01

Purpose: To characterize the relationship between lesion detection sensitivity and injected activity as a function of lesion size and contrast on the PEM (positron emission mammography) Flex Solo II scanner using phantom experiments. Methods: Phantom lesions (spheres 4, 8, 12, 16, and 20 mm diameter) were randomly located in uniform background. Sphere activity concentrations were 3 to 21 times the background activity concentration (BGc). BGc was a surrogate for injected activity; BGc ranged from 0.44–4.1 kBq/mL, corresponding to 46–400 MBq injections. Seven radiologists read 108 images containing zero, one, or two spheres. Readers used a 5-point confidence scale to score the presence of spheres. Results: Sensitivity was 100% for lesions ≥12 mm under all conditions except for one 12 mm sphere with the lowest contrast and lowest BGc (60% sensitivity). Sensitivity was 100% for 8 mm spheres when either contrast or BGc was high, and 100% for 4 mm spheres only when both contrast and BGc were highest. Sphere contrast recovery coefficients (CRC) were 49%, 34%, 26%, 14%, and 2.8% for the largest to smallest spheres. Cumulative specificity was 98%. Conclusions: Phantom lesion detection sensitivity depends more on sphere size and contrast than on BGc. Detection sensitivity remained ≥90% for injected activities as low as 100 MBq, for lesions ≥8 mm. Low CRC in 4 mm objects results in moderate detection sensitivity even for 400 MBq injected activity, making it impractical to optimize injected activity for such lesions. Low CRC indicates that when lesions <8 mm are observed on PEM images they are highly tracer avid with greater potential of clinical significance. High specificity (98%) suggests that image statistical noise does not lead to false positive findings. These results apply to the 85 mm thick object used to obtain them; lesion detectability should be better (worse) for thinner (thicker) objects based on the reduced (increased) influence of photon attenuation.

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

Directory of Open Access Journals (Sweden)

Anan Banharnsakun

2014-01-01

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

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

Science.gov (United States)

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

2018-04-01

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

Salient value similarity, social trust, and attitudes toward wildland fire management strategies

Science.gov (United States)

Jerry J. Vaske; James D. Absher; Alan D. Bright

2007-01-01

Using the salient value similarity (SVS) model, we predicted that social trust mediated the relationship between SVS and attitudes toward prescribed burns and mechanical thinning. Data were obtained from a mail survey (n = 532) of Colorado residents living in the wildland-urban interface. Results indicated that respondents shared the same values as U...

Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

Science.gov (United States)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

Dissociable Fronto-Operculum-Insula Control Signals for Anticipation and Detection of Inhibitory Sensory Cue.

Science.gov (United States)

Cai, Weidong; Chen, Tianwen; Ide, Jaime S; Li, Chiang-Shan R; Menon, Vinod

2017-08-01

The ability to anticipate and detect behaviorally salient stimuli is important for virtually all adaptive behaviors, including inhibitory control that requires the withholding of prepotent responses when instructed by external cues. Although right fronto-operculum-insula (FOI), encompassing the anterior insular cortex (rAI) and inferior frontal cortex (rIFC), involvement in inhibitory control is well established, little is known about signaling mechanisms underlying their differential roles in detection and anticipation of salient inhibitory cues. Here we use 2 independent functional magnetic resonance imaging data sets to investigate dynamic causal interactions of the rAI and rIFC, with sensory cortex during detection and anticipation of inhibitory cues. Across 2 different experiments involving auditory and visual inhibitory cues, we demonstrate that primary sensory cortex has a stronger causal influence on rAI than on rIFC, suggesting a greater role for the rAI in detection of salient inhibitory cues. Crucially, a Bayesian prediction model of subjective trial-by-trial changes in inhibitory cue anticipation revealed that the strength of causal influences from rIFC to rAI increased significantly on trials in which participants had higher anticipation of inhibitory cues. Together, these results demonstrate the dissociable bottom-up and top-down roles of distinct FOI regions in detection and anticipation of behaviorally salient cues across multiple sensory modalities. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Ten Salient Practices of Undergraduate Research Mentors: A Review of the Literature

Science.gov (United States)

Shanahan, Jenny Olin; Ackley-Holbrook, Elizabeth; Hall, Eric; Stewart, Kearsley; Walkington, Helen

2015-01-01

This paper identifies salient practices of faculty mentors of undergraduate research (UR) as indicated in the extensive literature of the past two decades on UR. The well-established benefits for students involved in UR are dependent, first and foremost, on high-quality mentoring. Mentorship is a defining feature of UR. As more and different types…

Multi-Model Estimation Based Moving Object Detection for Aerial Video

Directory of Open Access Journals (Sweden)

Yanning Zhang

2015-04-01

Full Text Available With the wide development of UAV (Unmanned Aerial Vehicle technology, moving target detection for aerial video has become a popular research topic in the computer field. Most of the existing methods are under the registration-detection framework and can only deal with simple background scenes. They tend to go wrong in the complex multi background scenarios, such as viaducts, buildings and trees. In this paper, we break through the single background constraint and perceive the complex scene accurately by automatic estimation of multiple background models. First, we segment the scene into several color blocks and estimate the dense optical flow. Then, we calculate an affine transformation model for each block with large area and merge the consistent models. Finally, we calculate subordinate degree to multi-background models pixel to pixel for all small area blocks. Moving objects are segmented by means of energy optimization method solved via Graph Cuts. The extensive experimental results on public aerial videos show that, due to multi background models estimation, analyzing each pixel’s subordinate relationship to multi models by energy minimization, our method can effectively remove buildings, trees and other false alarms and detect moving objects correctly.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

Science.gov (United States)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

Barack Obama Blindness (BOB): Absence of Visual Awareness to a Single Object.

Science.gov (United States)

Persuh, Marjan; Melara, Robert D

2016-01-01

In two experiments, we evaluated whether a perceiver's prior expectations could alone obliterate his or her awareness of a salient visual stimulus. To establish expectancy, observers first made a demanding visual discrimination on each of three baseline trials. Then, on a fourth, critical trial, a single, salient and highly visible object appeared in full view at the center of the visual field and in the absence of any competing visual input. Surprisingly, fully half of the participants were unaware of the solitary object in front of their eyes. Dramatically, observers were blind even when the only stimulus on display was the face of U.S. President Barack Obama. We term this novel, counterintuitive phenomenon, Barack Obama Blindness (BOB). Employing a method that rules out putative memory effects by probing awareness immediately after presentation of the critical stimulus, we demonstrate that the BOB effect is a true failure of conscious vision.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

Science.gov (United States)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

Detection and classification of pole-like road objects from mobile LiDAR data in motorway environment

Science.gov (United States)

Yan, Li; Li, Zan; Liu, Hua; Tan, Junxiang; Zhao, Sainan; Chen, Changjun

2017-12-01

Mobile LiDAR Scanning (MLS) can collect 3-dimensional (3D) road and road-related geospatial information accurately and efficiently. Pole-like objects located in road environment are important street furniture and they are necessary information in road inventory and road mapping. The automatic detection and classification of pole-like road objects from mobile LiDAR data can greatly reduce the cost and improve the efficiency. This paper provides a complete workflow for the detection and classification of pole-like road objects from mobile LiDAR data in motorway environment. The major workflow includes three steps: data preprocessing, pole-like road objects detection and pole-like road objects classification. In data preprocessing step, ground points are removed by an automatic ground filtering algorithm, and then off-ground points are clustered into segments and the overlapped segments containing pole-like road objects are further separated through an iterative min-cut based segmentation approach. In detection step, filters utilizing both prior and shape information are used to detect the target objects. In classification step, features of objects are calculated and classified using Random Forest classifier. Our method was tested on two datasets scanned in motorway environment, and the results showed that the Matthews correlation coefficient of the two datasets in detection step was 93.7% and 95.9% respectively and the overall accuracy of the two datasets in classification step was 96.5% and 97.9% respectively.

Automatic cumulative sums contour detection of FBP-reconstructed multi-object nuclear medicine images.

Science.gov (United States)

Protonotarios, Nicholas E; Spyrou, George M; Kastis, George A

2017-06-01

The problem of determining the contours of objects in nuclear medicine images has been studied extensively in the past, however most of the analysis has focused on a single object as opposed to multiple objects. The aim of this work is to develop an automated method for determining the contour of multiple objects in positron emission tomography (PET) and single photon emission computed tomography (SPECT) filtered backprojection (FBP) reconstructed images. These contours can be used for computing body edges for attenuation correction in PET and SPECT, as well as for eliminating streak artifacts outside the objects, which could be useful in compressive sensing reconstruction. Contour detection has been accomplished by applying a modified cumulative sums (CUSUM) scheme in the sinogram. Our approach automatically detects all objects in the image, without requiring a priori knowledge of the number of distinct objects in the reconstructed image. This method has been tested in simulated phantoms, such as an image-quality (IQ) phantom and two digital multi-object phantoms, as well as a real NEMA phantom and a clinical thoracic study. For this purpose, a GE Discovery PET scanner was employed. The detected contours achieved root mean square accuracy of 1.14 pixels, 1.69 pixels and 3.28 pixels and a Hausdorff distance of 3.13, 3.12 and 4.50 pixels, for the simulated image-quality phantom PET study, the real NEMA phantom and the clinical thoracic study, respectively. These results correspond to a significant improvement over recent results obtained in similar studies. Furthermore, we obtained an optimal sub-pattern assignment (OSPA) localization error of 0.94 and 1.48, for the two-objects and three-objects simulated phantoms, respectively. Our method performs efficiently for sets of convex objects and hence it provides a robust tool for automatic contour determination with precise results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode

Directory of Open Access Journals (Sweden)

Tao Ye

2018-06-01

Full Text Available Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net. It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

When Politics Matters: The Impact of Politicians' and Bureaucrats' Policy Preferences on Salient and Nonsalient Policy Areas

DEFF Research Database (Denmark)

Bækgaard, Martin; Blom-Hansen, Jens; Serritzlew, Søren

2015-01-01

whether politics still matters when bureaucratic preferences are taken into account. We do this in a simultaneous test of political and bureaucratic influences on public budgets, a policy measure often studied in the ‘politics matters’ literature. We find that political preferences trump bureaucratic ones...... in policy areas salient to the public, but not in less salient areas. This might be comforting news from a democratic perspective. However, since public budgets represent an easy case for political influence, it is food for thought that political preferences do not always prevail....

The kinematic evolution of the Serra Central Salient, Eastern Brazil: A Neoproterozoic progressive arc in northern Espinhaço fold-thrust belt

Science.gov (United States)

Bersan, Samuel Moreira; Danderfer, André; Lagoeiro, Leonardo; Costa, Alice Fernanda de Oliveira

2017-12-01

Convex-to-the-foreland map-view curves are common features in fold-thrust belts around cratonic areas. These features are easily identifiable in belts composed of supracrustal rocks but have been rarely described in rocks from relatively deeper crustal levels where plastic deformation mechanisms stand out. Several local salients have been described in Neoproterozoic marginal fold-thrust belts around the São Francisco craton. In the northern part of the Espinhaço fold-thrust belt, which borders the eastern portion of the São Francisco craton, both Archean-Paleoproterozoic basement rocks and Proterozoic cover rocks are involved in the so-called Serra Central salient. A combination of conventional structural analysis and microstructural and paleostress studies were conducted to characterize the kinematic and the overall architecture and processes involved in the generation of this salient. The results allowed us to determine that the deformation along the Serra Central salient occur under low-grade metamorphic conditions and was related to a gently oblique convergence with westward mass transport that developed in a confined flow, controlled by two transverse bounding shear zones. We propose that the Serra Central salient nucleates as a basin-controlled primary arc that evolves to a progressive arc with secondary vertical axis rotation. This secondary rotation, well-illustrated by the presence of two almost orthogonal families of folds, was dominantly controlled by buttress effect exert by a basement high located in the foreland of the Serra Central salient.

AUTONOMOUS DETECTION AND TRACKING OF AN OBJECT AUTONOMOUSLY USING AR.DRONE QUADCOPTER

Directory of Open Access Journals (Sweden)

Futuhal Arifin

2014-08-01

Full Text Available Abstract Nowadays, there are many robotic applications being developed to do tasks autonomously without any interactions or commands from human. Therefore, developing a system which enables a robot to do surveillance such as detection and tracking of a moving object will lead us to more advanced tasks carried out by robots in the future. AR.Drone is a flying robot platform that is able to take role as UAV (Unmanned Aerial Vehicle. Usage of computer vision algorithm such as Hough Transform makes it possible for such system to be implemented on AR.Drone. In this research, the developed algorithm is able to detect and track an object with certain shape and color. Then the algorithm is successfully implemented on AR.Drone quadcopter for detection and tracking.

Fusion of an Ensemble of Augmented Image Detectors for Robust Object Detection

Directory of Open Access Journals (Sweden)

Pan Wei

2018-03-01

Full Text Available A significant challenge in object detection is accurate identification of an object’s position in image space, whereas one algorithm with one set of parameters is usually not enough, and the fusion of multiple algorithms and/or parameters can lead to more robust results. Herein, a new computational intelligence fusion approach based on the dynamic analysis of agreement among object detection outputs is proposed. Furthermore, we propose an online versus just in training image augmentation strategy. Experiments comparing the results both with and without fusion are presented. We demonstrate that the augmented and fused combination results are the best, with respect to higher accuracy rates and reduction of outlier influences. The approach is demonstrated in the context of cone, pedestrian and box detection for Advanced Driver Assistance Systems (ADAS applications.

Invariant Hough Random Ferns for Object Detection and Tracking

Directory of Open Access Journals (Sweden)

Yimin Lin

2014-01-01

Full Text Available This paper introduces an invariant Hough random ferns (IHRF incorporating rotation and scale invariance into the local feature description, random ferns classifier training, and Hough voting stages. It is especially suited for object detection under changes in object appearance and scale, partial occlusions, and pose variations. The efficacy of this approach is validated through experiments on a large set of challenging benchmark datasets, and the results demonstrate that the proposed method outperforms state-of-the-art conventional methods such as bounding-box-based and part-based methods. Additionally, we also propose an efficient clustering scheme based on the local patches’ appearance and their geometric relations that can provide pixel-accurate, top-down segmentations from IHRF back-projections. This refined segmentation can be used to improve the quality of online object tracking because it avoids the drifting problem. Thus, an online tracking framework based on IHRF, which is trained and updated in each frame to distinguish and segment the object from the background, is established. Finally, the experimental results on both object segmentation and long-term object tracking show that this method yields accurate and robust tracking performance in a variety of complex scenarios, especially in cases of severe occlusions and nonrigid deformations.

Spatial discretization methods for air gap permeance calculations in double salient traction motors

NARCIS (Netherlands)

Ilhan, E.; Kremers, M.F.J.; Motoasca, T.E.; Paulides, J.J.H.; Lomonova, E.

2012-01-01

Weight limitations in electric/hybrid cars demand the highest possible power-to-weight ratio from the traction motor, as in double salient permanent magnet (PM) machines. Their high flux densities in the air gap result in nonlinear analytical models, which need to be time optimized. The incorporated

An object-based visual attention model for robotic applications.

Science.gov (United States)

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

2010-10-01

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

Technological and economical analysis of salient pole and permanent magnet synchronous machines designed for wind turbines

Energy Technology Data Exchange (ETDEWEB)

Guendogdu, Tayfun, E-mail: tgundogdu@itu.edu.tr [Istanbul Technical University, Department of Electrical Engineering, Ayazaga Campus, 34469 Maslak/Istanbul (Turkey); Koemuergoez, Gueven, E-mail: komurgoz@itu.edu.tr [Istanbul Technical University, Department of Electrical Engineering, Ayazaga Campus, 34469 Maslak/Istanbul (Turkey)

2012-08-15

Chinese export restrictions already reduced the planning reliability for investments in permanent magnet wind turbines. Today the production of permanent magnets consumes the largest proportion of rare earth elements, with 40% of the rare earth-based magnets used for generators and other electrical machines. The cost and availability of NdFeB magnets will likely determine the production rate of permanent magnet generators. The high volatility of rare earth metals makes it very difficult to quote a price. Prices may also vary from supplier to supplier to an extent of up to 50% for the same size, shape and quantity with a minor difference in quality. The paper presents the analysis and the comparison of salient pole with field winding and of peripheral winding synchronous electrical machines, presenting important advantages. A neodymium alloy magnet rotor structure has been considered and compared to the salient rotor case. The Salient Pole Synchronous Machine and the Permanent Magnet Synchronous Machine were designed so that the plate values remain constant. The Eddy current effect on the windings is taken into account during the design, and the efficiency, output power and the air-gap flux density obtained after the simulation were compared. The analysis results clearly indicate that Salient Pole Synchronous Machine designs would be attractive to wind power companies. Furthermore, the importance of the design of electrical machines and the determination of criteria are emphasized. This paper will be a helpful resource in terms of examination and comparison of the basic structure and magnetic features of the Salient Pole Synchronous Machine and Permanent Magnet Synchronous Machine. Furthermore, an economic analysis of the designed machines was conducted. - Highlights: Black-Right-Pointing-Pointer Importance of the design of electrical machines and the determination of criteria are emphasized. Black-Right-Pointing-Pointer Machines were investigated in terms of

Technological and economical analysis of salient pole and permanent magnet synchronous machines designed for wind turbines

International Nuclear Information System (INIS)

Gündoğdu, Tayfun; Kömürgöz, Güven

2012-01-01

Chinese export restrictions already reduced the planning reliability for investments in permanent magnet wind turbines. Today the production of permanent magnets consumes the largest proportion of rare earth elements, with 40% of the rare earth-based magnets used for generators and other electrical machines. The cost and availability of NdFeB magnets will likely determine the production rate of permanent magnet generators. The high volatility of rare earth metals makes it very difficult to quote a price. Prices may also vary from supplier to supplier to an extent of up to 50% for the same size, shape and quantity with a minor difference in quality. The paper presents the analysis and the comparison of salient pole with field winding and of peripheral winding synchronous electrical machines, presenting important advantages. A neodymium alloy magnet rotor structure has been considered and compared to the salient rotor case. The Salient Pole Synchronous Machine and the Permanent Magnet Synchronous Machine were designed so that the plate values remain constant. The Eddy current effect on the windings is taken into account during the design, and the efficiency, output power and the air-gap flux density obtained after the simulation were compared. The analysis results clearly indicate that Salient Pole Synchronous Machine designs would be attractive to wind power companies. Furthermore, the importance of the design of electrical machines and the determination of criteria are emphasized. This paper will be a helpful resource in terms of examination and comparison of the basic structure and magnetic features of the Salient Pole Synchronous Machine and Permanent Magnet Synchronous Machine. Furthermore, an economic analysis of the designed machines was conducted. - Highlights: ► Importance of the design of electrical machines and the determination of criteria are emphasized. ► Machines were investigated in terms of efficiency, weight and maintenance requirements. ► An

Detection and Localization of Subsurface Two-Dimensional Metallic Objects

Science.gov (United States)

Meschino, S.; Pajewski, L.; Schettini, G.

2009-04-01

"Roma Tre" University, Applied Electronics Dept.v. Vasca Navale 84, 00146 Rome, Italy Non-invasive identification of buried objects in the near-field of a receiver array is a subject of great interest, due to its application to the remote sensing of the earth's subsurface, to the detection of landmines, pipes, conduits, to the archaeological site characterization, and more. In this work, we present a Sub-Array Processing (SAP) approach for the detection and localization of subsurface perfectly-conducting circular cylinders. We consider a plane wave illuminating the region of interest, which is assumed to be a homogeneous, unlossy medium of unknown permittivity containing one or more targets. In a first step, we partition the receiver array so that the field scattered from the targets result to be locally plane at each sub-array. Then, we apply a Direction of Arrival (DOA) technique to obtain a set of angles for each locally plane wave, and triangulate these directions obtaining a collection of crossing crowding in the expected object locations [1]. We compare several DOA algorithms such as the traditional Bartlett and Capon Beamforming, the Pisarenko Harmonic Decomposition (PHD), the Minimum-Norm method, the Multiple Signal Classification (MUSIC) and the Estimation of Signal Parameters via Rotational Techinque (ESPRIT) [2]. In a second stage, we develop a statistical Poisson based model to manage the crossing pattern in order to extract the probable target's centre position. In particular, if the crossings are Poisson distributed, it is possible to feature two different distribution parameters [3]. These two parameters perform two density rate for the crossings, so that we can previously divide the crossing pattern in a certain number of equal-size windows and we can collect the windows of the crossing pattern with low rate parameters (that probably are background windows) and remove them. In this way we can consider only the high rate parameter windows (that most

Optimizing detection of road furniture (pole-like object in Mobile Laser Scanner data

Directory of Open Access Journals (Sweden)

D. Li

2013-10-01

Full Text Available Due to the road safety problem is becoming more and more serious recent years, existing road safety assessment by using automatic method is necessary. Meanwhile, since the pole-like objects have large effect on road safety and are in high demand as facilities to be managed, the automatic pole-like objects extraction is becoming a hot issue. As a result, a robust, quick and automatic pole-like object detection algorithm in MLS data is proposed in this paper. Two datasets are tested to show performance of the proposed algorithm, it demonstrates that it is feasible to detect tree with an overall accuracy of over 92% and other pole-like object of 72% in dataset A and 82% of tree points and 75% of other pole points in dataset B.

Salient beliefs about eating and buying dark green vegetables as told by Mid-western African–American women☆

Science.gov (United States)

Sheats, Jylana L.; Middlestadt, Susan E.

2013-01-01

Vegetables in the dark green group are the most nutritious, yet intake is low. Studies suggest that an increase in fruit and vegetables may improve diet-related health outcomes of African Americans. The aim of this exploratory study was to use the Reasoned Action Approach (RAA) to qualitatively assess salient, top-of-the-mind, beliefs (consequences, circumstances and referents) about eating and buying more dark green leafy vegetables each week over the next 3 months. Adult (n = 30), Midwestern African–American women, who buy and prepare food for their household participated in a face-to-face salient belief elicitation. A content analysis of verbatim text and a descriptive analysis were conducted. Findings suggest that the RAA can be used to identify salient consequences, circumstances and referents about eating and buying more dark green leafy vegetables. The use of the RAA allowed for the extraction of specific beliefs that may aid in the development of nutrition education programs that consider the varying priorities, motivators and barriers that subgroups within the population have in regard to buying and consuming dark green leafy vegetables. PMID:23415980

Biases in the OSSOS Detection of Large Semimajor Axis Trans-Neptunian Objects

Science.gov (United States)

Gladman, Brett; Shankman, Cory; OSSOS Collaboration

2017-10-01

The accumulating but small set of large semimajor axis trans-Neptunian objects (TNOs) shows an apparent clustering in the orientations of their orbits. This clustering must either be representative of the intrinsic distribution of these TNOs, or else have arisen as a result of observation biases and/or statistically expected variations for such a small set of detected objects. The clustered TNOs were detected across different and independent surveys, which has led to claims that the detections are therefore free of observational bias. This apparent clustering has led to the so-called “Planet 9” hypothesis that a super-Earth currently resides in the distant solar system and causes this clustering. The Outer Solar System Origins Survey (OSSOS) is a large program that ran on the Canada-France-Hawaii Telescope from 2013 to 2017, discovering more than 800 new TNOs. One of the primary design goals of OSSOS was the careful determination of observational biases that would manifest within the detected sample. We demonstrate the striking and non-intuitive biases that exist for the detection of TNOs with large semimajor axes. The eight large semimajor axis OSSOS detections are an independent data set, of comparable size to the conglomerate samples used in previous studies. We conclude that the orbital distribution of the OSSOS sample is consistent with being detected from a uniform underlying angular distribution.

Distribution majorization of corner points by reinforcement learning for moving object detection

Science.gov (United States)

Wu, Hao; Yu, Hao; Zhou, Dongxiang; Cheng, Yongqiang

2018-04-01

Corner points play an important role in moving object detection, especially in the case of free-moving camera. Corner points provide more accurate information than other pixels and reduce the computation which is unnecessary. Previous works only use intensity information to locate the corner points, however, the information that former and the last frames provided also can be used. We utilize the information to focus on more valuable area and ignore the invaluable area. The proposed algorithm is based on reinforcement learning, which regards the detection of corner points as a Markov process. In the Markov model, the video to be detected is regarded as environment, the selections of blocks for one corner point are regarded as actions and the performance of detection is regarded as state. Corner points are assigned to be the blocks which are seperated from original whole image. Experimentally, we select a conventional method which uses marching and Random Sample Consensus algorithm to obtain objects as the main framework and utilize our algorithm to improve the result. The comparison between the conventional method and the same one with our algorithm show that our algorithm reduce 70% of the false detection.

Multiple Moving Object Detection for Fast Video Content Description in Compressed Domain

Directory of Open Access Journals (Sweden)

Boris Mansencal

2007-11-01

Full Text Available Indexing deals with the automatic extraction of information with the objective of automatically describing and organizing the content. Thinking of a video stream, different types of information can be considered semantically important. Since we can assume that the most relevant one is linked to the presence of moving foreground objects, their number, their shape, and their appearance can constitute a good mean for content description. For this reason, we propose to combine both motion information and region-based color segmentation to extract moving objects from an MPEG2 compressed video stream starting only considering low-resolution data. This approach, which we refer to as “rough indexing,” consists in processing P-frame motion information first, and then in performing I-frame color segmentation. Next, since many details can be lost due to the low-resolution data, to improve the object detection results, a novel spatiotemporal filtering has been developed which is constituted by a quadric surface modeling the object trace along time. This method enables to effectively correct possible former detection errors without heavily increasing the computational effort.

Static Object Detection Based on a Dual Background Model and a Finite-State Machine

Directory of Open Access Journals (Sweden)

Heras Evangelio Rubén

2011-01-01

Full Text Available Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets.

Barack Obama Blindness (BOB: Absence of visual awareness to a single object

Directory of Open Access Journals (Sweden)

Marjan ePersuh

2016-03-01

Full Text Available In two experiments we evaluated whether a perceiver’s prior expectations could alone obliterate his or her awareness of a salient visual stimulus. To establish expectancy, observers first made a demanding visual discrimination on each of three baseline trials. Then, on a fourth, critical trial, a single, salient and highly visible object appeared in full view at the center of the visual field and in the absence of any competing visual input. Surprisingly, fully half of the participants were unaware of the solitary object in front of their eyes. Dramatically, observers were blind even when the only stimulus on display was the face of U.S. President Barack Obama. We term this novel, counterintuitive phenomenon, Barack Obama Blindness (BOB. Employing a method that rules out putative memory effects by probing awareness immediately after presentation of the critical stimulus, we demonstrate that the BOB effect is a true failure of conscious vision.

Discriminative kernel feature extraction and learning for object recognition and detection

DEFF Research Database (Denmark)

Pan, Hong; Olsen, Søren Ingvor; Zhu, Yaping

2015-01-01

Feature extraction and learning is critical for object recognition and detection. By embedding context cue of image attributes into the kernel descriptors, we propose a set of novel kernel descriptors called context kernel descriptors (CKD). The motivation of CKD is to use the spatial consistency...... even in high-dimensional space. In addition, the latent connection between Rényi quadratic entropy and the mapping data in kernel feature space further facilitates us to capture the geometric structure as well as the information about the underlying labels of the CKD using CSQMI. Thus the resulting...... codebook and reduced CKD are discriminative. We report superior performance of our algorithm for object recognition on benchmark datasets like Caltech-101 and CIFAR-10, as well as for detection on a challenging chicken feet dataset....

Sound effects: Multimodal input helps infants find displaced objects.

Science.gov (United States)

Shinskey, Jeanne L

2017-09-01

Before 9 months, infants use sound to retrieve a stationary object hidden by darkness but not one hidden by occlusion, suggesting auditory input is more salient in the absence of visual input. This article addresses how audiovisual input affects 10-month-olds' search for displaced objects. In AB tasks, infants who previously retrieved an object at A subsequently fail to find it after it is displaced to B, especially following a delay between hiding and retrieval. Experiment 1 manipulated auditory input by keeping the hidden object audible versus silent, and visual input by presenting the delay in the light versus dark. Infants succeeded more at B with audible than silent objects and, unexpectedly, more after delays in the light than dark. Experiment 2 presented both the delay and search phases in darkness. The unexpected light-dark difference disappeared. Across experiments, the presence of auditory input helped infants find displaced objects, whereas the absence of visual input did not. Sound might help by strengthening object representation, reducing memory load, or focusing attention. This work provides new evidence on when bimodal input aids object processing, corroborates claims that audiovisual processing improves over the first year of life, and contributes to multisensory approaches to studying cognition. Statement of contribution What is already known on this subject Before 9 months, infants use sound to retrieve a stationary object hidden by darkness but not one hidden by occlusion. This suggests they find auditory input more salient in the absence of visual input in simple search tasks. After 9 months, infants' object processing appears more sensitive to multimodal (e.g., audiovisual) input. What does this study add? This study tested how audiovisual input affects 10-month-olds' search for an object displaced in an AB task. Sound helped infants find displaced objects in both the presence and absence of visual input. Object processing becomes more

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

Science.gov (United States)

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

2017-10-13

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

Forced to remember: when memory is biased by salient information.

Science.gov (United States)

Santangelo, Valerio

2015-04-15

The last decades have seen a rapid growing in the attempt to understand the key factors involved in the internal memory representation of the external world. Visual salience have been found to provide a major contribution in predicting the probability for an item/object embedded in a complex setting (i.e., a natural scene) to be encoded and then remembered later on. Here I review the existing literature highlighting the impact of perceptual- (based on low-level sensory features) and semantics-related salience (based on high-level knowledge) on short-term memory representation, along with the neural mechanisms underpinning the interplay between these factors. The available evidence reveal that both perceptual- and semantics-related factors affect attention selection mechanisms during the encoding of natural scenes. Biasing internal memory representation, both perceptual and semantics factors increase the probability to remember high- to the detriment of low-saliency items. The available evidence also highlight an interplay between these factors, with a reduced impact of perceptual-related salience in biasing memory representation as a function of the increasing availability of semantics-related salient information. The neural mechanisms underpinning this interplay involve the activation of different portions of the frontoparietal attention control network. Ventral regions support the assignment of selection/encoding priorities based on high-level semantics, while the involvement of dorsal regions reflects priorities assignment based on low-level sensory features. Copyright © 2015 Elsevier B.V. All rights reserved.

What are the underlying units of perceived animacy? Chasing detection is intrinsically object-based.

Science.gov (United States)

van Buren, Benjamin; Gao, Tao; Scholl, Brian J

2017-10-01

One of the most foundational questions that can be asked about any visual process is the nature of the underlying 'units' over which it operates (e.g., features, objects, or spatial regions). Here we address this question-for the first time, to our knowledge-in the context of the perception of animacy. Even simple geometric shapes appear animate when they move in certain ways. Do such percepts arise whenever any visual feature moves appropriately, or do they require that the relevant features first be individuated as discrete objects? Observers viewed displays in which one disc (the "wolf") chased another (the "sheep") among several moving distractor discs. Critically, two pairs of discs were also connected by visible lines. In the Unconnected condition, both lines connected pairs of distractors; but in the Connected condition, one connected the wolf to a distractor, and the other connected the sheep to a different distractor. Observers in the Connected condition were much less likely to describe such displays using mental state terms. Furthermore, signal detection analyses were used to explore the objective ability to discriminate chasing displays from inanimate control displays in which the wolf moved toward the sheep's mirror-image. Chasing detection was severely impaired on Connected trials: observers could readily detect an object chasing another object, but not a line-end chasing another line-end, a line-end chasing an object, or an object chasing a line-end. We conclude that the underlying units of perceived animacy are discrete visual objects.

Novelty detection of foreign objects in food using multi-modal X-ray imaging

DEFF Research Database (Denmark)

Einarsdottir, Hildur; Emerson, Monica Jane; Clemmensen, Line Katrine Harder

2016-01-01

In this paper we demonstrate a method for novelty detection of foreign objects in food products using grating-based multimodal X-ray imaging. With this imaging technique three modalities are available with pixel correspondence, enhancing organic materials such as wood chips, insects and soft...... plastics not detectable by conventional X-ray absorption radiography. We conduct experiments, where several food products are imaged with common foreign objects typically found in the food processing industry. To evaluate the benefit from using this multi-contrast X-ray technique over conventional X......-ray absorption imaging, a novelty detection scheme based on well known image- and statistical analysis techniques is proposed. The results show that the presented method gives superior recognition results and highlights the advantage of grating-based imaging....

Expanded opportunities of THz passive camera for the detection of concealed objects

Science.gov (United States)

Trofimov, Vyacheslav A.; Trofimov, Vladislav V.; Kuchik, Igor E.

2013-10-01

Among the security problems, the detection of object implanted into either the human body or animal body is the urgent problem. At the present time the main tool for the detection of such object is X-raying only. However, X-ray is the ionized radiation and therefore can not be used often. Other way for the problem solving is passive THz imaging using. In our opinion, using of the passive THz camera may help to detect the object implanted into the human body under certain conditions. The physical reason of such possibility arises from temperature trace on the human skin as a result of the difference in temperature between object and parts of human body. Modern passive THz cameras have not enough resolution in temperature to see this difference. That is why, we use computer processing to enhance the passive THz camera resolution for this application. After computer processing of images captured by passive THz camera TS4, developed by ThruVision Systems Ltd., we may see the pronounced temperature trace on the human body skin from the water, which is drunk by person, or other food eaten by person. Nevertheless, there are many difficulties on the way of full soution of this problem. We illustrate also an improvement of quality of the image captured by comercially available passive THz cameras using computer processing. In some cases, one can fully supress a noise on the image without loss of its quality. Using computer processing of the THz image of objects concealed on the human body, one may improve it many times. Consequently, the instrumental resolution of such device may be increased without any additional engineering efforts.

Object detection based on improved color and scale invariant features

Science.gov (United States)

Chen, Mengyang; Men, Aidong; Fan, Peng; Yang, Bo

2009-10-01

A novel object detection method which combines color and scale invariant features is presented in this paper. The detection system mainly adopts the widely used framework of SIFT (Scale Invariant Feature Transform), which consists of both a keypoint detector and descriptor. Although SIFT has some impressive advantages, it is not only computationally expensive, but also vulnerable to color images. To overcome these drawbacks, we employ the local color kernel histograms and Haar Wavelet Responses to enhance the descriptor's distinctiveness and computational efficiency. Extensive experimental evaluations show that the method has better robustness and lower computation costs.

Optical system for object detection and delineation in space

Science.gov (United States)

Handelman, Amir; Shwartz, Shoam; Donitza, Liad; Chaplanov, Loran

2018-01-01

Object recognition and delineation is an important task in many environments, such as in crime scenes and operating rooms. Marking evidence or surgical tools and attracting the attention of the surrounding staff to the marked objects can affect people's lives. We present an optical system comprising a camera, computer, and small laser projector that can detect and delineate objects in the environment. To prove the optical system's concept, we show that it can operate in a hypothetical crime scene in which a pistol is present and automatically recognize and segment it by various computer-vision algorithms. Based on such segmentation, the laser projector illuminates the actual boundaries of the pistol and thus allows the persons in the scene to comfortably locate and measure the pistol without holding any intermediator device, such as an augmented reality handheld device, glasses, or screens. Using additional optical devices, such as diffraction grating and a cylinder lens, the pistol size can be estimated. The exact location of the pistol in space remains static, even after its removal. Our optical system can be fixed or dynamically moved, making it suitable for various applications that require marking of objects in space.

GPR Detection of Buried Symmetrically Shaped Mine-like Objects using Selective Independent Component Analysis

DEFF Research Database (Denmark)

Karlsen, Brian; Sørensen, Helge Bjarup Dissing; Larsen, Jan

2003-01-01

from small-scale anti-personal (AP) mines to large-scale anti-tank (AT) mines were designed. Large-scale SF-GPR measurements on this series of mine-like objects buried in soil were performed. The SF-GPR data was acquired using a wideband monostatic bow-tie antenna operating in the frequency range 750......This paper addresses the detection of mine-like objects in stepped-frequency ground penetrating radar (SF-GPR) data as a function of object size, object content, and burial depth. The detection approach is based on a Selective Independent Component Analysis (SICA). SICA provides an automatic...... ranking of components, which enables the suppression of clutter, hence extraction of components carrying mine information. The goal of the investigation is to evaluate various time and frequency domain ICA approaches based on SICA. Performance comparison is based on a series of mine-like objects ranging...

Torque Analysis With Saturation Effects for Non-Salient Single-Phase Permanent-Magnet Machines

DEFF Research Database (Denmark)

Lu, Kaiyuan; Ritchie, Ewen

2011-01-01

The effects of saturation on torque production for non-salient, single-phase, permanent-magnet machines are studied in this paper. An analytical torque equation is proposed to predict the instantaneous torque with saturation effects. Compared to the existing methods, it is computationally faster......-element results, and experimental results obtained on a prototype single-phase permanent-magnet machine....

CoMIC: Good features for detection and matching at object boundaries

OpenAIRE

Ravindran, Swarna Kamlam; Mittal, Anurag

2014-01-01

Feature or interest points typically use information aggregation in 2D patches which does not remain stable at object boundaries when there is object motion against a significantly varying background. Level or iso-intensity curves are much more stable under such conditions, especially the longer ones. In this paper, we identify stable portions on long iso-curves and detect corners on them. Further, the iso-curve associated with a corner is used to discard portions from the background and impr...

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

Science.gov (United States)

Friedl, B.; Hölbling, D.; Füreder, P.

2012-04-01

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

Rendering visual events as sounds: Spatial attention capture by auditory augmented reality.

Science.gov (United States)

Stone, Scott A; Tata, Matthew S

2017-01-01

Many salient visual events tend to coincide with auditory events, such as seeing and hearing a car pass by. Information from the visual and auditory senses can be used to create a stable percept of the stimulus. Having access to related coincident visual and auditory information can help for spatial tasks such as localization. However not all visual information has analogous auditory percepts, such as viewing a computer monitor. Here, we describe a system capable of detecting and augmenting visual salient events into localizable auditory events. The system uses a neuromorphic camera (DAVIS 240B) to detect logarithmic changes of brightness intensity in the scene, which can be interpreted as salient visual events. Participants were blindfolded and asked to use the device to detect new objects in the scene, as well as determine direction of motion for a moving visual object. Results suggest the system is robust enough to allow for the simple detection of new salient stimuli, as well accurately encoding direction of visual motion. Future successes are probable as neuromorphic devices are likely to become faster and smaller in the future, making this system much more feasible.

Rendering visual events as sounds: Spatial attention capture by auditory augmented reality.

Directory of Open Access Journals (Sweden)

Scott A Stone

Full Text Available Many salient visual events tend to coincide with auditory events, such as seeing and hearing a car pass by. Information from the visual and auditory senses can be used to create a stable percept of the stimulus. Having access to related coincident visual and auditory information can help for spatial tasks such as localization. However not all visual information has analogous auditory percepts, such as viewing a computer monitor. Here, we describe a system capable of detecting and augmenting visual salient events into localizable auditory events. The system uses a neuromorphic camera (DAVIS 240B to detect logarithmic changes of brightness intensity in the scene, which can be interpreted as salient visual events. Participants were blindfolded and asked to use the device to detect new objects in the scene, as well as determine direction of motion for a moving visual object. Results suggest the system is robust enough to allow for the simple detection of new salient stimuli, as well accurately encoding direction of visual motion. Future successes are probable as neuromorphic devices are likely to become faster and smaller in the future, making this system much more feasible.

Prediction of chaos in non-salient permanent-magnet synchronous machines

Energy Technology Data Exchange (ETDEWEB)

Rasoolzadeh, Arsalan [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of); Tavazoei, Mohammad Saleh, E-mail: tavazoei@sharif.edu [Department of Electrical Engineering, Sharif University of Technology, Tehran (Iran, Islamic Republic of)

2012-12-03

This Letter tries to find the area in parameter space of a non-salient Permanent-Magnet Synchronous Machine (PMSM) in which chaos can occur. This area is briefly named as chaotic area. The predicted chaotic area is obtained by checking some conditions which are necessary for existence of chaos in a dynamical system. In this Letter, it is assumed that this machine is in the generator mode, and its model is based on direct and quadrature axis of stator voltages and currents. The information of the predicted area is used in non-chaotic maximum power control of torque in the machine.

Bi-variate statistical attribute filtering : A tool for robust detection of faint objects

NARCIS (Netherlands)

Teeninga, Paul; Moschini, Ugo; Trager, Scott C.; Wilkinson, M.H.F.

2013-01-01

We present a new method for morphological connected attribute filtering for object detection in astronomical images. In this approach, a threshold is set on one attribute (power), based on its distribution due to noise, as a function of object area. The results show an order of magnitude higher

Real-time underwater object detection based on an electrically scanned high-resolution sonar

DEFF Research Database (Denmark)

Henriksen, Lars

1994-01-01

The paper describes an approach to real time detection and tracking of underwater objects, using image sequences from an electrically scanned high-resolution sonar. The use of a high resolution sonar provides a good estimate of the location of the objects, but strains the computers on board, beca...

Simile: the most salient stylistic feature in Kelile and Demne

Directory of Open Access Journals (Sweden)

Maryam Mahmoodi

2014-11-01

Full Text Available Abstract Kelile and Demne is one of the most salient samples of Persian technical prose rhetorical and semantic figures and figures of speech namely simile, metaphor, metonymy and irony are among the stylistic features of this book. Among these, simile, as the most influential imagination tool, play a dominant role in the illustrations of the book. In this article, simile has been analyzed and investigated in all its variations in Kelile and Demne. In this book, simile appears from its most laconic form (eloquent simile to its most extensive form. But the major feature of theirs is their outspokenness, explicitness and sometimes their novelty. Among the likening components, the range of image vocabulary is one of the likening features in this book. Also the point of similarity has been usually abstracted from man's states, shape, place, space, volume and generally affairs concerning visual and tactile senses. So, its perception is not too much difficult. The variety and extension of likening vehicles in this work are worth of contemplating. In analysis of simile on the credit of both parties, we can conclude that ratio-emotional similes are of the most frequent kinds of simile. And Nasrollah Monshi has extended the field of emotional similes and has manipulated the relations between objects in a novel way. Allegoric simile has been used abundantly in Kelile and Demne.  It justifies the didactic function of this text. Allegory approaches its main role in this book. i.e. arguing and convincing. The contents of allegories in this book are moral and political and in terms of from, they are anecdotes of animals and human beings. The types of similes on the credit of form - namely equalization similes implied comparative similes and subtrahend similes - have been also used. Among the salient features of this book, several images together or in interference with each other have been used in one word or sentence. Sometimes similes accompany other

Simile: the most salient stylistic feature in Kelile and Demne

Directory of Open Access Journals (Sweden)

Maryam Mahmoodi

2014-12-01

Full Text Available Abstract Kelile and Demne is one of the most salient samples of Persian technical prose rhetorical and semantic figures and figures of speech namely simile, metaphor, metonymy and irony are among the stylistic features of this book. Among these, simile, as the most influential imagination tool, play a dominant role in the illustrations of the book. In this article, simile has been analyzed and investigated in all its variations in Kelile and Demne. In this book, simile appears from its most laconic form (eloquent simile to its most extensive form. But the major feature of theirs is their outspokenness, explicitness and sometimes their novelty. Among the likening components, the range of image vocabulary is one of the likening features in this book. Also the point of similarity has been usually abstracted from man's states, shape, place, space, volume and generally affairs concerning visual and tactile senses. So, its perception is not too much difficult. The variety and extension of likening vehicles in this work are worth of contemplating. In analysis of simile on the credit of both parties, we can conclude that ratio-emotional similes are of the most frequent kinds of simile. And Nasrollah Monshi has extended the field of emotional similes and has manipulated the relations between objects in a novel way. Allegoric simile has been used abundantly in Kelile and Demne.  It justifies the didactic function of this text. Allegory approaches its main role in this book. i.e. arguing and convincing. The contents of allegories in this book are moral and political and in terms of from, they are anecdotes of animals and human beings. The types of similes on the credit of form - namely equalization similes implied comparative similes and subtrahend similes - have been also used. Among the salient features of this book, several images together or in interference with each other have been used in one word or sentence. Sometimes similes accompany other

A salient region detection model combining background distribution measure for indoor robots.

Science.gov (United States)

Li, Na; Xu, Hui; Wang, Zhenhua; Sun, Lining; Chen, Guodong

2017-01-01

Vision system plays an important role in the field of indoor robot. Saliency detection methods, capturing regions that are perceived as important, are used to improve the performance of visual perception system. Most of state-of-the-art methods for saliency detection, performing outstandingly in natural images, cannot work in complicated indoor environment. Therefore, we propose a new method comprised of graph-based RGB-D segmentation, primary saliency measure, background distribution measure, and combination. Besides, region roundness is proposed to describe the compactness of a region to measure background distribution more robustly. To validate the proposed approach, eleven influential methods are compared on the DSD and ECSSD dataset. Moreover, we build a mobile robot platform for application in an actual environment, and design three different kinds of experimental constructions that are different viewpoints, illumination variations and partial occlusions. Experimental results demonstrate that our model outperforms existing methods and is useful for indoor mobile robots.

Nonlinear Speed Control of Permanent Magnet Synchronous Motor with Salient Poles

Directory of Open Access Journals (Sweden)

K. Kyslan

2015-12-01

Full Text Available This paper presents the speed control of permanent magnet synchronous motor with salient poles based on two-step linearization method. In the first step, the direct compensation of the nonlinearities in the equations of current is used. In the second step, the input-output linearization in the state space is used for the decoupling of flux and torque axis. Simulated results are compared to the field oriented vector control structure with PI controllers in order to show differences in the performance of both approaches.

Accelerating object detection via a visual-feature-directed search cascade: algorithm and field programmable gate array implementation

Science.gov (United States)

Kyrkou, Christos; Theocharides, Theocharis

2016-07-01

Object detection is a major step in several computer vision applications and a requirement for most smart camera systems. Recent advances in hardware acceleration for real-time object detection feature extensive use of reconfigurable hardware [field programmable gate arrays (FPGAs)], and relevant research has produced quite fascinating results, in both the accuracy of the detection algorithms as well as the performance in terms of frames per second (fps) for use in embedded smart camera systems. Detecting objects in images, however, is a daunting task and often involves hardware-inefficient steps, both in terms of the datapath design and in terms of input/output and memory access patterns. We present how a visual-feature-directed search cascade composed of motion detection, depth computation, and edge detection, can have a significant impact in reducing the data that needs to be examined by the classification engine for the presence of an object of interest. Experimental results on a Spartan 6 FPGA platform for face detection indicate data search reduction of up to 95%, which results in the system being able to process up to 50 1024×768 pixels images per second with a significantly reduced number of false positives.

Computer aided detection of surgical retained foreign object for prevention

International Nuclear Information System (INIS)

Hadjiiski, Lubomir; Marentis, Theodore C.; Rondon, Lucas; Chan, Heang-Ping; Chaudhury, Amrita R.; Chronis, Nikolaos

2015-01-01

Purpose: Surgical retained foreign objects (RFOs) have significant morbidity and mortality. They are associated with approximately $1.5 × 10 9 annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. The authors address the RFO problem with two complementary technologies: a three-dimensional (3D) gossypiboma micro tag, the μTag that improves the visibility of RFOs on radiographs, and a computer aided detection (CAD) system that detects the μTag. It is desirable for the CAD system to operate in a high specificity mode in the operating room (OR) and function as a first reader for the surgeon. This allows for fast point of care results and seamless workflow integration. The CAD system can also operate in a high sensitivity mode as a second reader for the radiologist to ensure the highest possible detection accuracy. Methods: The 3D geometry of the μTag produces a similar two dimensional (2D) depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. The authors created a data set of 1800 cadaver images with the 3D μTag and other common man-made surgical objects positioned randomly. A total of 1061 cadaver images contained a single μTag and the remaining 739 were without μTag. A radiologist marked the location of the μTag using an in-house developed graphical user interface. The data set was partitioned into three independent subsets: a training set, a validation set, and a test set, consisting of 540, 560, and 700 images, respectively. A CAD system with modules that included preprocessing μTag enhancement, labeling, segmentation, feature analysis, classification, and detection was developed. The CAD system was developed using the training and the validation sets. Results: On the training set, the CAD achieved 81.5% sensitivity with 0.014 false positives (FPs) per image in a high specificity mode for the surgeons in the OR and 96

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

Directory of Open Access Journals (Sweden)

ZHANG Zhiqiang

2018-01-01

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

Demand artifact: objectively detecting biased participants in advertising research.

Science.gov (United States)

Miller, Felicia; Schertzer, Susan

2014-12-01

Detecting and reducing the effect of biased participants continues to be an important task for researchers. However, the lack of objective measures to assess demand artifact has made it difficult to effectively address this issue. This paper reports two experiments that apply a theory-based post-experimental inquiry that can systematically identify biased participants in consumer research. The results demonstrate how easily and effectively researchers can incorporate this tool into experimental studies of all types and reduce the likelihood of systematic error.

Early Detection of Brain Pathology Suggestive of Early AD Using Objective Evaluation of FDG-PET Scans

Directory of Open Access Journals (Sweden)

James C. Patterson

2011-01-01

Full Text Available The need for early detection of AD becomes critical as disease-modifying agents near the marketplace. Here, we present results from a study focused on improvement in detection of metabolic deficits related to neurodegenerative changes consistent with possible early AD with statistical evaluation of FDG-PET brain images. We followed 31 subjects at high risk or diagnosed with MCI/AD for 3 years. 15 met criteria for diagnosis of MCI, and five met criteria for AD. FDG-PET scans were completed at initiation and termination of the study. PET scans were read clinically and also evaluated objectively using Statistical Parametric Mapping (SPM. Using standard clinical evaluation of the FDG-PET scans, 11 subjects were detected, while 18 were detected using SPM evaluation. These preliminary results indicate that objective analyses may improve detection; however, early detection in at-risk normal subjects remains tentative. Several FDA-approved software packages are available that use objective analyses, thus the capacity exists for wider use of this method for MCI/AD.

Real-time power angle determination of salient-pole synchronous machine based on air gap measurements

Energy Technology Data Exchange (ETDEWEB)

Despalatovic, Marin; Jadric, Martin; Terzic, Bozo [FESB University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, R. Boskovica bb, 21000 Split (Croatia)

2008-11-15

This paper presents a new method for the real-time power angle determination of the salient-pole synchronous machines. This method is based on the terminal voltage and air gap measurements, which are the common features of the hydroturbine generator monitoring system. The raw signal of the air gap sensor is used to detect the rotor displacement with reference to the fundamental component of the terminal voltage. First, the algorithm developed for the real-time power angle determination is tested using the synthetic data obtained by the standard machine model simulation. Thereafter, the experimental investigation is carried out on the 26 MVA utility generator. The validity of the method is verified by comparing with another method, which is based on a tooth gear mounted on the rotor shaft. The proposed real-time algorithm has an adequate accuracy and needs a very short processing time. For applications that do not require real-time processing, such as the estimation of the synchronous machine parameters, the accuracy is additionally increased by applying an off-line data-processing algorithm. (author)

Characterization of the seismically imaged Tuscarora fold system and implications for layer parallel shortening in the Pennsylvania salient

Science.gov (United States)

Mount, Van S.; Wilkins, Scott; Comiskey, Cody S.

2017-12-01

The Tuscarora fold system (TFS) is located in the Pennsylvania salient in the foreland of the Valley and Ridge province. The TFS is imaged in high quality 3D seismic data and comprises a system of small-scale folds within relatively flat-lying Lower Silurian Tuscarora Formation strata. We characterize the TFS structures and infer layer parallel shortening (LPS) directions and magnitudes associated with deformation during the Alleghany Orogeny. Previously reported LPS data in our study area are from shallow Devonian and Carboniferous strata (based on outcrop and core analyses) above the shallowest of three major detachments recognized in the region. Seismic data allows us to characterize LPS at depth in strata beneath the shallow detachment. Our LPS data (orientations and inferred magnitudes) are consistent with the shallow data leading us to surmise that LPS during Alleghanian deformation fanned around the salient and was distributed throughout the stratigraphic section - and not isolated to strata above the shallow detachment. We propose that a NW-SE oriented Alleghanian maximum principal stress was perturbed by deep structure associated with the non-linear margin of Laurentia resulting in fanning of shortening directions within the salient.

Orexin receptor antagonist-induced sleep does not impair the ability to wake in response to emotionally salient acoustic stimuli in dogs

Directory of Open Access Journals (Sweden)

Pamela L. Tannenbaum

2014-05-01

Full Text Available The ability to awaken from sleep in response to important stimuli is a critical feature of normal sleep, as is maintaining sleep continuity in the presence of irrelevant background noise. Dual orexin receptor antagonists (DORAs effectively promote sleep across species by targeting the evolutionarily conserved wake-promoting orexin signaling pathway. This study in dogs investigated whether DORA-induced sleep preserved the ability to awaken appropriately to salient acoustic stimuli but remain asleep when exposed to irrelevant stimuli. Sleep and wake in response to DORAs, vehicle, GABA-A receptor modulators (diazepam, eszopiclone and zolpidem and antihistamine (diphenhydramine administration were evaluated in telemetry-implanted adult dogs with continuous electrocorticogram, electromyogram, electrooculogram, and activity recordings. DORAs induced sleep, but GABA-A modulators and antihistamine induced paradoxical hyperarousal. Thus, salience gating studies were conducted during DORA-22 (0.3, 1, and 5 mg/kg; day and night and vehicle nighttime sleep. The acoustic stimuli were either classically conditioned using food reward and positive attention (salient stimulus or presented randomly (neutral stimulus. Once conditioned, the tones were presented at sleep times corresponding to maximal DORA-22 exposure. In response to the salient stimuli, dogs woke completely from vehicle and orexin-antagonized sleep across all sleep stages but rarely awoke to neutral stimuli. Notably, acute pharmacological antagonism of orexin receptors paired with emotionally salient anticipation produced wake, not cataplexy, in a species where genetic (chronic loss of orexin receptor signaling leads to narcolepsy/cataplexy. DORA-induced sleep in this species thereby retains the desired capacity to awaken to emotionally salient acoustic stimuli while preserving uninterrupted sleep in response to irrelevant stimuli.

Adaptive Colour Feature Identification in Image for Object Tracking

Directory of Open Access Journals (Sweden)

Feng Su

2012-01-01

Full Text Available Identification and tracking of a moving object using computer vision techniques is important in robotic surveillance. In this paper, an adaptive colour filtering method is introduced for identifying and tracking a moving object appearing in image sequences. This filter is capable of automatically identifying the most salient colour feature of the moving object in the image and using this for a robot to track the object. The method enables the selected colour feature to adapt to surrounding condition when it is changed. A method of determining the region of interest of the moving target is also developed for the adaptive colour filter to extract colour information. Experimental results show that by using a camera mounted on a robot, the proposed methods can perform robustly in tracking a randomly moving object using adaptively selected colour features in a crowded environment.

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

DEFF Research Database (Denmark)

Kraft, Dirk; Pugeault, Nicolas; Baseski, Emre

2008-01-01

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

Long Baseline Stereovision for Automatic Detection and Ranging of Moving Objects in the Night Sky

Directory of Open Access Journals (Sweden)

Vlad Turcu

2012-09-01

Full Text Available As the number of objects in Earth’s atmosphere and in low Earth orbit is continuously increasing; accurate surveillance of these objects has become important. This paper presents a generic, low cost sky surveillance system based on stereovision. Two cameras are placed 37 km apart and synchronized by a GPS-controlled external signal. The intrinsic camera parameters are calibrated before setup in the observation position, the translation vectors are determined from the GPS coordinates and the rotation matrices are continuously estimated using an original automatic calibration methodology based on following known stars. The moving objects in the sky are recognized as line segments in the long exposure images, using an automatic detection and classification algorithm based on image processing. The stereo correspondence is based on the epipolar geometry and is performed automatically using the image detection results. The resulting experimental system is able to automatically detect moving objects such as planes, meteors and Low Earth Orbit satellites, and measure their 3D position in an Earth-bound coordinate system.

77 FR 66651 - Salient Advisors, L.P. and MarketShares ETF Trust; Notice of Application

Science.gov (United States)

2012-11-06

... Advisors, L.P. and MarketShares ETF Trust; Notice of Application October 31, 2012 AGENCY: Securities and... ETF Trust (the ``Trust''). Summary of Application: Applicants request an order that permits: (a... statutory trust. The Trust will initially offer one series, the Salient MLP and Energy Infrastructure ETF...

Two-stage sparse coding of region covariance via Log-Euclidean kernels to detect saliency.

Science.gov (United States)

Zhang, Ying-Ying; Yang, Cai; Zhang, Ping

2017-05-01

In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel. In the second stage, an improvement of the initial result is achieved by calculating reconstruction errors of the superpixels on foreground dictionary, which is extracted from the first stage saliency map. The sparse coding in the second stage is similar to the first stage, but is able to effectively highlight the salient objects uniformly from the background. Finally, three post-processing methods-highlight-inhibition function, context-based saliency weighting, and the graph cut-are adopted to further refine the saliency map. Experiments on four public benchmark datasets show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall and mean absolute error, and demonstrate the robustness and efficiency of the proposed method. Copyright © 2017 Elsevier Ltd. All rights reserved.

Observance and development of salient quality overprint for tablecloths embroidery with use of RFID technology

Directory of Open Access Journals (Sweden)

Jana Strauszová

2011-04-01

Full Text Available Mission of this paper is to enable those interested in observance and development quality embroidery typical of the region use with RFID technologies for designing, implementing and providing overprint for tablecloths embroidery. The starting point is present situation and method of observance and development of overprint embroidery. The solution is in scanning of patterns and their saving into database of industrial patterns with implemented RFID tag. This will allow indentifying, evaluating and using overprint for tablecloths embroidery. RFID technology can be applied for observance and development of salient quality any products of individual, organizations and their protected pattern and support creative and innovative acting of individuals and organizations in region. The paper is intended especially for specialists, who are interested in issue observance salient quality in sense of cultural heritage of regions. The paper has been compiled in connection with resolving project KEGA 3/6411/08 Transformation of the already existing study programme Management of production quality to a university-wide bilingual study programme.

OBSERVANCE AND DEVELOPMENT OF SALIENT QUALITY OVERPRINT FOR TABLECLOTHS EMBROIDERY WITH USE OF RFID TECHNOLOGY

Directory of Open Access Journals (Sweden)

KRISTÍNA ZGODAVOVÁ

2010-12-01

Full Text Available Mission of this paper is to enable those interested in observance and development quality embroidery typical of the region use with RFID technologies for designing, implementing and providing overprint for tablecloths embroidery. The starting point is present situation and method of observance and development of overprint embroidery. The solution is in scanning of patterns and their saving into database of industrial patterns with implemented RFID tag. This will allow indentifying, evaluating and using overprint for tablecloths embroidery. RFID technology can be applied for observance and development of salient quality any products of individual, organizations and their protected pattern and support creative and innovative acting of individuals and organizations in region. The paper is intended especially for specialists, who are interested in issue observance salient quality in sense of cultural heritage of regions. The paper has been compiled in connection with resolving project KEGA 3/6411/08 Transformation of the already existing study programme Management of production quality to a university-wide bilingual study programme.

Paleozoic age of high-pressure metamorphic rocks of the Dakh salient, North-Western Caucasus: results of U-Pb-geochronological study

International Nuclear Information System (INIS)

Somin, M.L.; Levchenkov, O.A.; Kotov, A.B.; Makeev, A.F.; Komarov, A.N.; Ro, N.I.; Lavrishchev, V.A.; Lebedev, V.A.

2007-01-01

U-Pb geochronological studies of an ancient component of the Dakh salient, i.e. metaaplites, which are vein fine-grained rocks made up by albite, microcline, quartz and potash mica, were made. Besides, K-Ar dating of granodiorites breaking through metamorphic rocks was conducted. U-Pb dating of accessory zircons (353 mln. years) defines the lower age boundary of the Dakh salient rock metamorphism. Its upper boundary was identified by K-Ar dating (301 ± 10 mln. years) of hornfels blende of nonmetamophized granodiorites [ru

Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

Science.gov (United States)

Chen, Shuoyang; Xu, Tingfa; Li, Daqun; Zhang, Jizhou; Jiang, Shenwang

2016-01-01

During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern)” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor) and FPGA (Field Programmable Gate Array) platforms and the high-precision intelligent holder. PMID:27775671

Moving Object Detection Using Scanning Camera on a High-Precision Intelligent Holder

Directory of Open Access Journals (Sweden)

Shuoyang Chen

2016-10-01

Full Text Available During the process of moving object detection in an intelligent visual surveillance system, a scenario with complex background is sure to appear. The traditional methods, such as “frame difference” and “optical flow”, may not able to deal with the problem very well. In such scenarios, we use a modified algorithm to do the background modeling work. In this paper, we use edge detection to get an edge difference image just to enhance the ability of resistance illumination variation. Then we use a “multi-block temporal-analyzing LBP (Local Binary Pattern” algorithm to do the segmentation. In the end, a connected component is used to locate the object. We also produce a hardware platform, the core of which consists of the DSP (Digital Signal Processor and FPGA (Field Programmable Gate Array platforms and the high-precision intelligent holder.

A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

Science.gov (United States)

Barat, Christian; Phlypo, Ronald

2010-12-01

We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

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

Science.gov (United States)

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

2018-03-01

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

Detection and Classification of Multiple Objects using an RGB-D Sensor and Linear Spatial Pyramid Matching

OpenAIRE

Dimitriou, Michalis; Kounalakis, Tsampikos; Vidakis, Nikolaos; Triantafyllidis, Georgios

2013-01-01

This paper presents a complete system for multiple object detection and classification in a 3D scene using an RGB-D sensor such as the Microsoft Kinect sensor. Successful multiple object detection and classification are crucial features in many 3D computer vision applications. The main goal is making machines see and understand objects like humans do. To this goal, the new RGB-D sensors can be utilized since they provide real-time depth map which can be used along with the RGB images for our ...

The detection of objects in a turbid underwater medium using orbital angular momentum (OAM)

Science.gov (United States)

Cochenour, Brandon; Rodgers, Lila; Laux, Alan; Mullen, Linda; Morgan, Kaitlyn; Miller, Jerome K.; Johnson, Eric G.

2017-05-01

We present an investigation of the optical property of orbital angular momentum (OAM) for use in the detection of objects obscured by a turbid underwater channel. In our experiment, a target is illuminated by a Gaussian beam. An optical vortex is formed by passing the object-reflected and backscattered light through a diffractive spiral phase plate at the receiver, which allows for the spatial separation of coherent and non-coherent light. This provides a method for discriminating target from environment. Initial laboratory results show that the ballistic target return can be detected 2-3 orders of magnitude below the backscatter clutter level. Furthermore, the detection of this coherent component is accomplished with the use of a complicated optical heterodyning scheme. The results suggest new optical sensing techniques for underwater imaging or LIDAR.

Moving object detection in top-view aerial videos improved by image stacking

Science.gov (United States)

Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

2017-08-01

Image stacking is a well-known method that is used to improve the quality of images in video data. A set of consecutive images is aligned by applying image registration and warping. In the resulting image stack, each pixel has redundant information about its intensity value. This redundant information can be used to suppress image noise, resharpen blurry images, or even enhance the spatial image resolution as done in super-resolution. Small moving objects in the videos usually get blurred or distorted by image stacking and thus need to be handled explicitly. We use image stacking in an innovative way: image registration is applied to small moving objects only, and image warping blurs the stationary background that surrounds the moving objects. Our video data are coming from a small fixed-wing unmanned aerial vehicle (UAV) that acquires top-view gray-value images of urban scenes. Moving objects are mainly cars but also other vehicles such as motorcycles. The resulting images, after applying our proposed image stacking approach, are used to improve baseline algorithms for vehicle detection and segmentation. We improve precision and recall by up to 0.011, which corresponds to a reduction of the number of false positive and false negative detections by more than 3 per second. Furthermore, we show how our proposed image stacking approach can be implemented efficiently.

Acoustic signature recognition technique for Human-Object Interactions (HOI) in persistent surveillance systems

Science.gov (United States)

Alkilani, Amjad; Shirkhodaie, Amir

2013-05-01

Handling, manipulation, and placement of objects, hereon called Human-Object Interaction (HOI), in the environment generate sounds. Such sounds are readily identifiable by the human hearing. However, in the presence of background environment noises, recognition of minute HOI sounds is challenging, though vital for improvement of multi-modality sensor data fusion in Persistent Surveillance Systems (PSS). Identification of HOI sound signatures can be used as precursors to detection of pertinent threats that otherwise other sensor modalities may miss to detect. In this paper, we present a robust method for detection and classification of HOI events via clustering of extracted features from training of HOI acoustic sound waves. In this approach, salient sound events are preliminary identified and segmented from background via a sound energy tracking method. Upon this segmentation, frequency spectral pattern of each sound event is modeled and its features are extracted to form a feature vector for training. To reduce dimensionality of training feature space, a Principal Component Analysis (PCA) technique is employed to expedite fast classification of test feature vectors, a kd-tree and Random Forest classifiers are trained for rapid classification of training sound waves. Each classifiers employs different similarity distance matching technique for classification. Performance evaluations of classifiers are compared for classification of a batch of training HOI acoustic signatures. Furthermore, to facilitate semantic annotation of acoustic sound events, a scheme based on Transducer Mockup Language (TML) is proposed. The results demonstrate the proposed approach is both reliable and effective, and can be extended to future PSS applications.

Robust video object cosegmentation.

Science.gov (United States)

Wang, Wenguan; Shen, Jianbing; Li, Xuelong; Porikli, Fatih

2015-10-01

With ever-increasing volumes of video data, automatic extraction of salient object regions became even more significant for visual analytic solutions. This surge has also opened up opportunities for taking advantage of collective cues encapsulated in multiple videos in a cooperative manner. However, it also brings up major challenges, such as handling of drastic appearance, motion pattern, and pose variations, of foreground objects as well as indiscriminate backgrounds. Here, we present a cosegmentation framework to discover and segment out common object regions across multiple frames and multiple videos in a joint fashion. We incorporate three types of cues, i.e., intraframe saliency, interframe consistency, and across-video similarity into an energy optimization framework that does not make restrictive assumptions on foreground appearance and motion model, and does not require objects to be visible in all frames. We also introduce a spatio-temporal scale-invariant feature transform (SIFT) flow descriptor to integrate across-video correspondence from the conventional SIFT-flow into interframe motion flow from optical flow. This novel spatio-temporal SIFT flow generates reliable estimations of common foregrounds over the entire video data set. Experimental results show that our method outperforms the state-of-the-art on a new extensive data set (ViCoSeg).

Moving Object Detection in Heterogeneous Conditions in Embedded Systems.

Science.gov (United States)

Garbo, Alessandro; Quer, Stefano

2017-07-01

This paper presents a system for moving object exposure, focusing on pedestrian detection, in external, unfriendly, and heterogeneous environments. The system manipulates and accurately merges information coming from subsequent video frames, making small computational efforts in each single frame. Its main characterizing feature is to combine several well-known movement detection and tracking techniques, and to orchestrate them in a smart way to obtain good results in diversified scenarios. It uses dynamically adjusted thresholds to characterize different regions of interest, and it also adopts techniques to efficiently track movements, and detect and correct false positives. Accuracy and reliability mainly depend on the overall receipt, i.e., on how the software system is designed and implemented, on how the different algorithmic phases communicate information and collaborate with each other, and on how concurrency is organized. The application is specifically designed to work with inexpensive hardware devices, such as off-the-shelf video cameras and small embedded computational units, eventually forming an intelligent urban grid. As a matter of fact, the major contribution of the paper is the presentation of a tool for real-time applications in embedded devices with finite computational (time and memory) resources. We run experimental results on several video sequences (both home-made and publicly available), showing the robustness and accuracy of the overall detection strategy. Comparisons with state-of-the-art strategies show that our application has similar tracking accuracy but much higher frame-per-second rates.

OSSOS. VI. Striking Biases in the Detection of Large Semimajor Axis Trans-Neptunian Objects

Science.gov (United States)

Shankman, Cory; Kavelaars, J. J.; Bannister, Michele T.; Gladman, Brett J.; Lawler, Samantha M.; Chen, Ying-Tung; Jakubik, Marian; Kaib, Nathan; Alexandersen, Mike; Gwyn, Stephen D. J.; Petit, Jean-Marc; Volk, Kathryn

2017-08-01

The accumulating but small set of large semimajor axis trans-Neptunian objects (TNOs) shows an apparent clustering in the orientations of their orbits. This clustering must either be representative of the intrinsic distribution of these TNOs, or else have arisen as a result of observation biases and/or statistically expected variations for such a small set of detected objects. The clustered TNOs were detected across different and independent surveys, which has led to claims that the detections are therefore free of observational bias. This apparent clustering has led to the so-called “Planet 9” hypothesis that a super-Earth currently resides in the distant solar system and causes this clustering. The Outer Solar System Origins Survey (OSSOS) is a large program that ran on the Canada–France–Hawaii Telescope from 2013 to 2017, discovering more than 800 new TNOs. One of the primary design goals of OSSOS was the careful determination of observational biases that would manifest within the detected sample. We demonstrate the striking and non-intuitive biases that exist for the detection of TNOs with large semimajor axes. The eight large semimajor axis OSSOS detections are an independent data set, of comparable size to the conglomerate samples used in previous studies. We conclude that the orbital distribution of the OSSOS sample is consistent with being detected from a uniform underlying angular distribution.

Evaluation of a miniature microscope objective designed for fluorescence array microscopy detection of Mycobacterium tuberculosis.

Science.gov (United States)

McCall, Brian; Olsen, Randall J; Nelles, Nicole J; Williams, Dawn L; Jackson, Kevin; Richards-Kortum, Rebecca; Graviss, Edward A; Tkaczyk, Tomasz S

2014-03-01

A prototype miniature objective that was designed for a point-of-care diagnostic array microscope for detection of Mycobacterium tuberculosis and previously fabricated and presented in a proof of concept is evaluated for its effectiveness in detecting acid-fast bacteria. To evaluate the ability of the microscope to resolve submicron features and details in the image of acid-fast microorganisms stained with a fluorescent dye, and to evaluate the accuracy of clinical diagnoses made with digital images acquired with the objective. The lens prescription data for the microscope design are presented. A test platform is built by combining parts of a standard microscope, a prototype objective, and a digital single-lens reflex camera. Counts of acid-fast bacteria made with the prototype objective are compared to counts obtained with a standard microscope over matched fields of view. Two sets of 20 smears, positive and negative, are diagnosed by 2 pathologists as sputum smear positive or sputum smear negative, using both a standard clinical microscope and the prototype objective under evaluation. The results are compared to a reference diagnosis of the same sample. More bacteria are counted in matched fields of view in digital images taken with the prototype objective than with the standard clinical microscope. All diagnostic results are found to be highly concordant. An array microscope built with this miniature lens design will be able to detect M tuberculosis with high sensitivity and specificity.

A New Method Based on Two-Stage Detection Mechanism for Detecting Ships in High-Resolution SAR Images

Directory of Open Access Journals (Sweden)

Xu Yongli

2017-01-01

Full Text Available Ship detection in synthetic aperture radar (SAR remote sensing images, being a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Aiming at the requirements of ship detection in high-resolution SAR images, the accuracy, the intelligent level, a better real-time operation and processing efficiency, The characteristics of ocean background and ship target in high-resolution SAR images were analyzed, we put forward a ship detection algorithm in high-resolution SAR images. The algorithm consists of two detection stages: The first step designs a pre-training classifier based on improved spectral residual visual model to obtain the visual salient regions containing ship targets quickly, then achieve the purpose of probably detection of ships. In the second stage, considering the Bayesian theory of binary hypothesis detection, a local maximum posterior probability (MAP classifier is designed for the classification of pixels. After the parameter estimation and judgment criterion, the classification of pixels are carried out in the target areas to achieve the classification of two types of pixels in the salient regions. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X, Radarsat-2, are used to evaluate the performance of detection methods. Comparing with classical CFAR detection algorithms, experimental results show that the algorithm can achieve a better effect of suppressing false alarms, which caused by the speckle noise and ocean clutter background inhomogeneity. At the same time, the detection speed is increased by 25% to 45%.

The Rio Pardo salient, northern Araçuaí orogen: an example of a complex basin-controlled fold-thrust belt curve

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Eliza Peixoto

Full Text Available ABSTRACT: The Rio Pardo salient, the large antitaxial curve described by the Araçuaí fold-and-thrust belt along the southeastern edge of the São Francisco craton, is one of the most prominent and one of the least studied features of the Brasiliano Araçuaí-West Congo orogenic system (AWCO. In addition to the Archean/Paleoproterozoic basement, the salient is comprised of metasedimentary rocks mainly from the Neoproterozoic Macaúbas Group and the Salinas Formation. Its western limb occupies a portion of the Espinhaço ridge, where the NS-trending structures of the Araçuaí belt progressively curve NE and E, thereby defining the hinge zone along the Serra Geral on the Minas-Bahia boundary. The eastern limb is NW-trending and marked by a major shear zone. In models postulated to generate the AWCO through the closure of the Neoproterozoic Macaúbas basin, this large curve plays a critical kinematic role. Yet, in spite of this, its development is still not fully understood. How did this curve originate? Which factors controlled its generation? Our field study performed in the northern Araçuaí orogen characterized the kinematic picture of the salient, and led to a model that addresses these questions. The results we obtained indicate that the Rio Pardo salient developed in response to four deformation phases. The contractional D1 and D2 phases are coaxial and responsible for a craton-directed tectonic transport along the salient’s outer arc, which is coupled with an overall southward motion of the inner arc, thereby giving rise to a rather complex kinematic picture. Furthermore, structures of the D1/D2 phases define a zigzag pattern with alternating NE- and NW-trending segments along the salient’s leading edge. Along the NE-trending segments, the metasedimentary rocks are thrust northwestwards on top of the craton basement, while along the NW-trending segments, the supracrustal rocks are displaced along dextral to reverse

Detection of High-Z Objects using Multiple Scattering of Cosmic Ray Muons

International Nuclear Information System (INIS)

Hogan, Gary E.; Borozdin, Konstantin N.; Gomez, John; Morris, Christopher; Priedhorsky, William C.; Saunders, Alexander; Schultz, Larry J.; Teasdale, Margaret E.

2004-01-01

Detection of high-Z material hidden inside a large volume of ordinary cargo is an important and timely task given the danger associated with illegal transport of uranium and heavier elements. Existing radiography techniques are inefficient for shielded material, often expensive and involve radiation hazards, real and perceived. We recently demonstrated that radiographs can be formed using cosmic-ray muons. Here, we show that compact, high-Z objects can be detected and located in 3 dimensions with muon radiography. The natural flux of cosmic-ray muons, approximately 10,000 m-2min-1, can generate a reliable detection signal in a fraction of a minute, using large-area muon detectors as used in particle and nuclear physics

A novel visual saliency detection method for infrared video sequences

Science.gov (United States)

Wang, Xin; Zhang, Yuzhen; Ning, Chen

2017-12-01

Infrared video applications such as target detection and recognition, moving target tracking, and so forth can benefit a lot from visual saliency detection, which is essentially a method to automatically localize the ;important; content in videos. In this paper, a novel visual saliency detection method for infrared video sequences is proposed. Specifically, for infrared video saliency detection, both the spatial saliency and temporal saliency are considered. For spatial saliency, we adopt a mutual consistency-guided spatial cues combination-based method to capture the regions with obvious luminance contrast and contour features. For temporal saliency, a multi-frame symmetric difference approach is proposed to discriminate salient moving regions of interest from background motions. Then, the spatial saliency and temporal saliency are combined to compute the spatiotemporal saliency using an adaptive fusion strategy. Besides, to highlight the spatiotemporal salient regions uniformly, a multi-scale fusion approach is embedded into the spatiotemporal saliency model. Finally, a Gestalt theory-inspired optimization algorithm is designed to further improve the reliability of the final saliency map. Experimental results demonstrate that our method outperforms many state-of-the-art saliency detection approaches for infrared videos under various backgrounds.

Algorithms for detection of objects in image sequences captured from an airborne imaging system

Science.gov (United States)

Kasturi, Rangachar; Camps, Octavia; Tang, Yuan-Liang; Devadiga, Sadashiva; Gandhi, Tarak

1995-01-01

This research was initiated as a part of the effort at the NASA Ames Research Center to design a computer vision based system that can enhance the safety of navigation by aiding the pilots in detecting various obstacles on the runway during critical section of the flight such as a landing maneuver. The primary goal is the development of algorithms for detection of moving objects from a sequence of images obtained from an on-board video camera. Image regions corresponding to the independently moving objects are segmented from the background by applying constraint filtering on the optical flow computed from the initial few frames of the sequence. These detected regions are tracked over subsequent frames using a model based tracking algorithm. Position and velocity of the moving objects in the world coordinate is estimated using an extended Kalman filter. The algorithms are tested using the NASA line image sequence with six static trucks and a simulated moving truck and experimental results are described. Various limitations of the currently implemented version of the above algorithm are identified and possible solutions to build a practical working system are investigated.

A Comparative Study of Multiple Object Detection Using Haar-Like Feature Selection and Local Binary Patterns in Several Platforms

Directory of Open Access Journals (Sweden)

Souhail Guennouni

2015-01-01

Full Text Available Object detection has been attracting much interest due to the wide spectrum of applications that use it. It has been driven by an increasing processing power available in software and hardware platforms. In this work we present a developed application for multiple objects detection based on OpenCV libraries. The complexity-related aspects that were considered in the object detection using cascade classifier are described. Furthermore, we discuss the profiling and porting of the application into an embedded platform and compare the results with those obtained on traditional platforms. The proposed application deals with real-time systems implementation and the results give a metric able to select where the cases of object detection applications may be more complex and where it may be simpler.

The objects of visuospatial short-term memory: Perceptual organization and change detection.

Science.gov (United States)

Nikolova, Atanaska; Macken, Bill

2016-01-01

We used a colour change-detection paradigm where participants were required to remember colours of six equally spaced circles. Items were superimposed on a background so as to perceptually group them within (a) an intact ring-shaped object, (b) a physically segmented but perceptually completed ring-shaped object, or (c) a corresponding background segmented into three arc-shaped objects. A nonpredictive cue at the location of one of the circles was followed by the memory items, which in turn were followed by a test display containing a probe indicating the circle to be judged same/different. Reaction times for correct responses revealed a same-object advantage; correct responses were faster to probes on the same object as the cue than to equidistant probes on a segmented object. This same-object advantage was identical for physically and perceptually completed objects, but was only evident in reaction times, and not in accuracy measures. Not only, therefore, is it important to consider object-level perceptual organization of stimulus elements when assessing the influence of a range of factors (e.g., number and complexity of elements) in visuospatial short-term memory, but a more detailed picture of the structure of information in memory may be revealed by measuring speed as well as accuracy.

Leukocyte Image Segmentation Using Novel Saliency Detection Based on Positive Feedback of Visual Perception

Directory of Open Access Journals (Sweden)

Chen Pan

2018-01-01

Full Text Available This paper presents a novel method for salient object detection in nature image by simulating microsaccades in fixational eye movements. Due to a nucleated cell usually stained that is salient obviously, the proposed method is suitable to segment nucleated cell. Firstly, the existing fixation prediction method is utilized to produce an initial fixation area. Followed EPELM (ensemble of polyharmonic extreme learning machine is trained on-line by the pixels sampling from the fixation and nonfixation area. Then the model of EPELM could be used to classify image pixels to form new binary fixation area. Depending upon the updated fixation area, the procedure of “pixel sampling-learning-classification” could be performed iteratively. If the previous binary fixation area and the latter one were similar enough in iteration, it indicates that the perception is saturated and the loop should be terminated. The binary output in iteration could be regarded as a kind of visual stimulation. So the multiple outputs of visual stimuli can be accumulated to form a new saliency map. Experiments on three image databases show the validity of our method. It can segment nucleated cells successfully in different imaging conditions.

Reprint of "Two-stage sparse coding of region covariance via Log-Euclidean kernels to detect saliency".

Science.gov (United States)

Zhang, Ying-Ying; Yang, Cai; Zhang, Ping

2017-08-01

In this paper, we present a novel bottom-up saliency detection algorithm from the perspective of covariance matrices on a Riemannian manifold. Each superpixel is described by a region covariance matrix on Riemannian Manifolds. We carry out a two-stage sparse coding scheme via Log-Euclidean kernels to extract salient objects efficiently. In the first stage, given background dictionary on image borders, sparse coding of each region covariance via Log-Euclidean kernels is performed. The reconstruction error on the background dictionary is regarded as the initial saliency of each superpixel. In the second stage, an improvement of the initial result is achieved by calculating reconstruction errors of the superpixels on foreground dictionary, which is extracted from the first stage saliency map. The sparse coding in the second stage is similar to the first stage, but is able to effectively highlight the salient objects uniformly from the background. Finally, three post-processing methods-highlight-inhibition function, context-based saliency weighting, and the graph cut-are adopted to further refine the saliency map. Experiments on four public benchmark datasets show that the proposed algorithm outperforms the state-of-the-art methods in terms of precision, recall and mean absolute error, and demonstrate the robustness and efficiency of the proposed method. Copyright © 2017 Elsevier Ltd. All rights reserved.

Affectively salient meaning in random noise: a task sensitive to psychosis liability.

Science.gov (United States)

Galdos, Mariana; Simons, Claudia; Fernandez-Rivas, Aranzazu; Wichers, Marieke; Peralta, Concepción; Lataster, Tineke; Amer, Guillermo; Myin-Germeys, Inez; Allardyce, Judith; Gonzalez-Torres, Miguel Angel; van Os, Jim

2011-11-01

Stable differences in the tendency to attribute meaning and emotional value to experience may represent an indicator of liability to psychosis. A brief task was developed assessing variation in detecting affectively meaningful speech (speech illusion) in neutral random signals (white noise) and the degree to which this was associated with psychometric and familial vulnerability for psychosis. Thirty patients, 28 of their siblings, and 307 controls participated. The rate of speech illusion was compared between cases and controls. In controls, the association between speech illusion and interview-based positive schizotypy was assessed. The hypothesis of a dose-response increase in rate of speech illusion across increasing levels of familial vulnerability for psychosis (controls, siblings of patients, and patients) was examined. Patients were more likely to display speech illusions than controls (odds ratio [OR] = 4.0, 95% confidence interval [CI] = 1.4-11.7), also after controlling for neurocognitive variables (OR = 3.8, 95% CI = 1.04-14.1). The case-control difference was more accentuated for speech illusion perceived as affectively salient (positively or negatively appraised) than for neutrally appraised speech illusions. Speech illusion in the controls was strongly associated with positive schizotypy but not with negative schizotypy. In addition, the rate of speech illusion increased with increasing level of familial risk for psychotic disorder. The data suggest that the white noise task may be sensitive to psychometric and familial vulnerability for psychosis associated with alterations in top-down processing and/or salience attribution.

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

Directory of Open Access Journals (Sweden)

Ye Yao

2017-12-01

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

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

Science.gov (United States)

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

2017-09-01

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

Robust vehicle detection in aerial images based on salient region selection and superpixel classification

Science.gov (United States)

Sahli, Samir; Duval, Pierre-Luc; Sheng, Yunlong; Lavigne, Daniel A.

2011-05-01

For detecting vehicles in large scale aerial images we first used a non-parametric method proposed recently by Rosin to define the regions of interest, where the vehicles appear with dense edges. The saliency map is a sum of distance transforms (DT) of a set of edges maps, which are obtained by a threshold decomposition of the gradient image with a set of thresholds. A binary mask for highlighting the regions of interest is then obtained by a moment-preserving thresholding of the normalized saliency map. Secondly, the regions of interest were over-segmented by the SLIC superpixels proposed recently by Achanta et al. to cluster pixels into the color constancy sub-regions. In the aerial images of 11.2 cm/pixel resolution, the vehicles in general do not exceed 20 x 40 pixels. We introduced a size constraint to guarantee no superpixels exceed the size of a vehicle. The superpixels were then classified to vehicle or non-vehicle by the Support Vector Machine (SVM), in which the Scale Invariant Feature Transform (SIFT) features and the Linear Binary Pattern (LBP) texture features were used. Both features were extracted at two scales with two size patches. The small patches capture local structures and the larger patches include the neighborhood information. Preliminary results show a significant gain in the detection. The vehicles were detected with a dense concentration of the vehicle-class superpixels. Even dark color cars were successfully detected. A validation process will follow to reduce the presence of isolated false alarms in the background.

Noise reduction in muon tomography for detecting high density objects

International Nuclear Information System (INIS)

Benettoni, M; Checchia, P; Cossutta, L; Furlan, M; Gonella, F; Pegoraro, M; Garola, A Rigoni; Ronchese, P; Vanini, S; Viesti, G; Bettella, G; Bonomi, G; Donzella, A; Subieta, M; Zenoni, A; Calvagno, G; Cortelazzo, G; Zanuttigh, P; Calvini, P; Squarcia, S

2013-01-01

The muon tomography technique, based on multiple Coulomb scattering of cosmic ray muons, has been proposed as a tool to detect the presence of high density objects inside closed volumes. In this paper a new and innovative method is presented to handle the density fluctuations (noise) of reconstructed images, a well known problem of this technique. The effectiveness of our method is evaluated using experimental data obtained with a muon tomography prototype located at the Legnaro National Laboratories (LNL) of the Istituto Nazionale di Fisica Nucleare (INFN). The results reported in this paper, obtained with real cosmic ray data, show that with appropriate image filtering and muon momentum classification, the muon tomography technique can detect high density materials, such as lead, albeit surrounded by light or medium density material, in short times. A comparison with algorithms published in literature is also presented

An elicitation study of critical care nurses' salient hand hygiene beliefs.

Science.gov (United States)

Piras, Susan E; Lauderdale, Jana; Minnick, Ann

2017-10-01

To describe critical care nurses' hand hygiene attitudinal, normative referent, and control beliefs. Hand hygiene is the primary strategy to prevent healthcare-associated infections. Social influence is an underdeveloped hand hygiene strategy. This qualitative descriptive study was conducted with 25 ICU nurses in the southeastern United States. Data were collected using the Nurses' Salient Belief Instrument. Thematic analysis generated four themes: Hand Hygiene is Protective; Nurses look to Nurses; Time-related Concerns; and Convenience is Essential. Nurses look to nurses as hand hygiene referents and believe hand hygiene is a protective behaviour that requires time and functional equipment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Object detection and tracking system

Energy Technology Data Exchange (ETDEWEB)

Ma, Tian J.

2017-05-30

Methods and apparatuses for analyzing a sequence of images for an object are disclosed herein. In a general embodiment, the method identifies a region of interest in the sequence of images. The object is likely to move within the region of interest. The method divides the region of interest in the sequence of images into sections and calculates signal-to-noise ratios for a section in the sections. A signal-to-noise ratio for the section is calculated using the section in the image, a prior section in a prior image to the image, and a subsequent section in a subsequent image to the image. The signal-to-noise ratios are for potential velocities of the object in the section. The method also selects a velocity from the potential velocities for the object in the section using a potential velocity in the potential velocities having a highest signal-to-noise ratio in the signal-to-noise ratios.

Torque ripple minimization in a doubly salient permanent magnet motors by skewing the rotor teeth

International Nuclear Information System (INIS)

Sheth, N.K.; Sekharbabu, A.R.C.; Rajagopal, K.R.

2006-01-01

This paper presents the effects of skewing the rotor teeth on the performance of an 8/6 doubly salient permanent magnet motor using a simple method, which utilizes the results obtained from the 2-D FE analysis. The optimum skewing angle is obtained as 12-15 o for the least ripple torque without much reduction in the back-emf

Detecting multiple moving objects in crowded environments with coherent motion regions

Science.gov (United States)

Cheriyadat, Anil M.; Radke, Richard J.

2013-06-11

Coherent motion regions extend in time as well as space, enforcing consistency in detected objects over long time periods and making the algorithm robust to noisy or short point tracks. As a result of enforcing the constraint that selected coherent motion regions contain disjoint sets of tracks defined in a three-dimensional space including a time dimension. An algorithm operates directly on raw, unconditioned low-level feature point tracks, and minimizes a global measure of the coherent motion regions. At least one discrete moving object is identified in a time series of video images based on the trajectory similarity factors, which is a measure of a maximum distance between a pair of feature point tracks.

Refining Visually Detected Object poses

DEFF Research Database (Denmark)

Holm, Preben; Petersen, Henrik Gordon

2010-01-01

to the particular object and in order to handle the demand for flexibility, there is an increasing demand for avoiding such dedicated mechanical alignment systems. Rather, it would be desirable to automatically locate and grasp randomly placed objects from tables, conveyor belts or even bins with a high accuracy...

Colour Terms Affect Detection of Colour and Colour-Associated Objects Suppressed from Visual Awareness

OpenAIRE

Forder, Lewis; Taylor, Olivia; Mankin, Helen; Scott, Ryan B.; Franklin, Anna

2016-01-01

The idea that language can affect how we see the world continues to create controversy. A potentially important study in this field has shown that when an object is suppressed from visual awareness using continuous flash suppression (a form of binocular rivalry), detection of the object is differently affected by a preceding word prime depending on whether the prime matches or does not match the object. This may suggest that language can affect early stages of vision. We replicated this parad...

Statistical Hypothesis Testing using CNN Features for Synthesis of Adversarial Counterexamples to Human and Object Detection Vision Systems

Energy Technology Data Exchange (ETDEWEB)

Raj, Sunny [Univ. of Central Florida, Orlando, FL (United States); Jha, Sumit Kumar [Univ. of Central Florida, Orlando, FL (United States); Pullum, Laura L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ramanathan, Arvind [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-05-01

Validating the correctness of human detection vision systems is crucial for safety applications such as pedestrian collision avoidance in autonomous vehicles. The enormous space of possible inputs to such an intelligent system makes it difficult to design test cases for such systems. In this report, we present our tool MAYA that uses an error model derived from a convolutional neural network (CNN) to explore the space of images similar to a given input image, and then tests the correctness of a given human or object detection system on such perturbed images. We demonstrate the capability of our tool on the pre-trained Histogram-of-Oriented-Gradients (HOG) human detection algorithm implemented in the popular OpenCV toolset and the Caffe object detection system pre-trained on the ImageNet benchmark. Our tool may serve as a testing resource for the designers of intelligent human and object detection systems.

Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task

International Nuclear Information System (INIS)

Goldstein, R.Z.; Woicik, P.A.; Maloney, T.; Tomasi, D.; Alia-Klein, N.; Shan, J.; Honorario, J.; Samaras, D.; Wang, R.; Telang, F.; Wang, G.-J.; Volkow, N.D.

2010-01-01

Anterior cingulate cortex (ACC) hypoactivations during cognitive demand are a hallmark deficit in drug addiction. Methylphenidate (MPH) normalizes cortical function, enhancing task salience and improving associated cognitive abilities, in other frontal lobe pathologies; however, in clinical trials, MPH did not improve treatment outcome in cocaine addiction. We hypothesized that oral MPH will attenuate ACC hypoactivations and improve associated performance during a salient cognitive task in individuals with cocaine-use disorders (CUD). In the current functional MRI study, we used a rewarded drug cue-reactivity task previously shown to be associated with hypoactivations in both major ACC subdivisions (implicated in default brain function) in CUD compared with healthy controls. The task was performed by 13 CUD and 14 matched healthy controls on 2 d: after ingesting a single dose of oral MPH (20 mg) or placebo (lactose) in a counterbalanced fashion. Results show that oral MPH increased responses to this salient cognitive task in both major ACC subdivisions (including the caudal-dorsal ACC and rostroventromedial ACC extending to the medial orbitofrontal cortex) in the CUD. These functional MRI results were associated with reduced errors of commission (a common impulsivity measure) and improved task accuracy, especially during the drug (vs. neutral) cue-reactivity condition in all subjects. The clinical application of such MPH-induced brain-behavior enhancements remains to be tested.

Oral methylphenidate normalizes cingulate activity in cocaine addiction during a salient cognitive task

Energy Technology Data Exchange (ETDEWEB)

Goldstein, R.Z.; Goldstein, R.Z.; Woicik, P.A.; Maloney, T.; Tomasi, D.; Alia-Klein, N.; Shan, J.; Honorario, J.; Samaras, d.; Wang, R.; Telang, F.; Wang, G.-J.; Volkow, N.D.

2010-09-21

Anterior cingulate cortex (ACC) hypoactivations during cognitive demand are a hallmark deficit in drug addiction. Methylphenidate (MPH) normalizes cortical function, enhancing task salience and improving associated cognitive abilities, in other frontal lobe pathologies; however, in clinical trials, MPH did not improve treatment outcome in cocaine addiction. We hypothesized that oral MPH will attenuate ACC hypoactivations and improve associated performance during a salient cognitive task in individuals with cocaine-use disorders (CUD). In the current functional MRI study, we used a rewarded drug cue-reactivity task previously shown to be associated with hypoactivations in both major ACC subdivisions (implicated in default brain function) in CUD compared with healthy controls. The task was performed by 13 CUD and 14 matched healthy controls on 2 d: after ingesting a single dose of oral MPH (20 mg) or placebo (lactose) in a counterbalanced fashion. Results show that oral MPH increased responses to this salient cognitive task in both major ACC subdivisions (including the caudal-dorsal ACC and rostroventromedial ACC extending to the medial orbitofrontal cortex) in the CUD. These functional MRI results were associated with reduced errors of commission (a common impulsivity measure) and improved task accuracy, especially during the drug (vs. neutral) cue-reactivity condition in all subjects. The clinical application of such MPH-induced brain-behavior enhancements remains to be tested.

An object cue is more effective than a word in ERP-based detection of deception.

Science.gov (United States)

Cutmore, Tim R H; Djakovic, Tatjana; Kebbell, Mark R; Shum, David H K

2009-03-01

Recent studies of deception have used a form of the guilty knowledge test along with the oddball P300 event-related potential (ERP) to uncover hidden memories. These studies typically have used words as the cuing stimuli. In the present study, a mock crime was enacted by participants to prime their episodic memory and different memory cue types (Words, Pictures of Objects and Faces) were created to investigate their relative efficacy in identifying guilt. A peak-to peak (p-p) P300 response was computed for rare known non-guilty item (target), rare guilty knowledge item (probe) and frequently presented unknown items (irrelevant). Difference in this P300 measure between the probe and irrelevant was the key dependent variable. Object cues were found to be the most effective, particularly at the parietal site. A bootstrap procedure commonly used to detect deception in individual participants by comparing their probe and irrelevant P300 p-p showed the object cues to provide the best discrimination. Furthermore, using all three of the cue types together provided high detection accuracy (94%). These results confirm prior findings on the utility of ERPs for detecting deception. More importantly, they provide support for the hypothesis that direct cueing with a picture of the crime object may be more effective than using a word (consistent with the picture superiority effect reported in the literature). Finally, a face cue (e.g., crime victim) may also provide a useful probe for detection of guilty knowledge but this stimulus form needs to be chosen with due caution.

A New 3D Object Pose Detection Method Using LIDAR Shape Set.

Science.gov (United States)

Kim, Jung-Un; Kang, Hang-Bong

2018-03-16

In object detection systems for autonomous driving, LIDAR sensors provide very useful information. However, problems occur because the object representation is greatly distorted by changes in distance. To solve this problem, we propose a LIDAR shape set that reconstructs the shape surrounding the object more clearly by using the LIDAR point information projected on the object. The LIDAR shape set restores object shape edges from a bird's eye view by filtering LIDAR points projected on a 2D pixel-based front view. In this study, we use this shape set for two purposes. The first is to supplement the shape set with a LIDAR Feature map, and the second is to divide the entire shape set according to the gradient of the depth and density to create a 2D and 3D bounding box proposal for each object. We present a multimodal fusion framework that classifies objects and restores the 3D pose of each object using enhanced feature maps and shape-based proposals. The network structure consists of a VGG -based object classifier that receives multiple inputs and a LIDAR-based Region Proposal Networks (RPN) that identifies object poses. It works in a very intuitive and efficient manner and can be extended to other classes other than vehicles. Our research has outperformed object classification accuracy (Average Precision, AP) and 3D pose restoration accuracy (3D bounding box recall rate) based on the latest studies conducted with KITTI data sets.

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

Science.gov (United States)

Sofina, N.; Ehlers, M.

2012-08-01

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

Detecting Target Objects by Natural Language Instructions Using an RGB-D Camera

Directory of Open Access Journals (Sweden)

Jiatong Bao

2016-12-01

Full Text Available Controlling robots by natural language (NL is increasingly attracting attention for its versatility, convenience and no need of extensive training for users. Grounding is a crucial challenge of this problem to enable robots to understand NL instructions from humans. This paper mainly explores the object grounding problem and concretely studies how to detect target objects by the NL instructions using an RGB-D camera in robotic manipulation applications. In particular, a simple yet robust vision algorithm is applied to segment objects of interest. With the metric information of all segmented objects, the object attributes and relations between objects are further extracted. The NL instructions that incorporate multiple cues for object specifications are parsed into domain-specific annotations. The annotations from NL and extracted information from the RGB-D camera are matched in a computational state estimation framework to search all possible object grounding states. The final grounding is accomplished by selecting the states which have the maximum probabilities. An RGB-D scene dataset associated with different groups of NL instructions based on different cognition levels of the robot are collected. Quantitative evaluations on the dataset illustrate the advantages of the proposed method. The experiments of NL controlled object manipulation and NL-based task programming using a mobile manipulator show its effectiveness and practicability in robotic applications.

Object detection and recognition in digital images theory and practice

CERN Document Server

Cyganek, Boguslaw

2013-01-01

Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields. Key features: Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.

An investigation of visual selection priority of objects with texture and crossed and uncrossed disparities

Science.gov (United States)

Khaustova, Dar'ya; Fournier, Jérôme; Wyckens, Emmanuel; Le Meur, Olivier

2014-02-01

The aim of this research is to understand the difference in visual attention to 2D and 3D content depending on texture and amount of depth. Two experiments were conducted using an eye-tracker and a 3DTV display. Collected fixation data were used to build saliency maps and to analyze the differences between 2D and 3D conditions. In the first experiment 51 observers participated in the test. Using scenes that contained objects with crossed disparity, it was discovered that such objects are the most salient, even if observers experience discomfort due to the high level of disparity. The goal of the second experiment is to decide whether depth is a determinative factor for visual attention. During the experiment, 28 observers watched the scenes that contained objects with crossed and uncrossed disparities. We evaluated features influencing the saliency of the objects in stereoscopic conditions by using contents with low-level visual features. With univariate tests of significance (MANOVA), it was detected that texture is more important than depth for selection of objects. Objects with crossed disparity are significantly more important for selection processes when compared to 2D. However, objects with uncrossed disparity have the same influence on visual attention as 2D objects. Analysis of eyemovements indicated that there is no difference in saccade length. Fixation durations were significantly higher in stereoscopic conditions for low-level stimuli than in 2D. We believe that these experiments can help to refine existing models of visual attention for 3D content.

Real-time detection of natural objects using AM-coded spectral matching imager

Science.gov (United States)

Kimachi, Akira

2005-01-01

This paper describes application of the amplitude-modulation (AM)-coded spectral matching imager (SMI) to real-time detection of natural objects such as human beings, animals, vegetables, or geological objects or phenomena, which are much more liable to change with time than artificial products while often exhibiting characteristic spectral functions associated with some specific activity states. The AM-SMI produces correlation between spectral functions of the object and a reference at each pixel of the correlation image sensor (CIS) in every frame, based on orthogonal amplitude modulation (AM) of each spectral channel and simultaneous demodulation of all channels on the CIS. This principle makes the SMI suitable to monitoring dynamic behavior of natural objects in real-time by looking at a particular spectral reflectance or transmittance function. A twelve-channel multispectral light source was developed with improved spatial uniformity of spectral irradiance compared to a previous one. Experimental results of spectral matching imaging of human skin and vegetable leaves are demonstrated, as well as a preliminary feasibility test of imaging a reflective object using a test color chart.

Impulse radar imaging system for concealed object detection

Science.gov (United States)

Podd, F. J. W.; David, M.; Iqbal, G.; Hussain, F.; Morris, D.; Osakue, E.; Yeow, Y.; Zahir, S.; Armitage, D. W.; Peyton, A. J.

2013-10-01

-to-noise parameter to determine how the frequencies contained in the echo dataset are normalised. The chosen image reconstruction algorithm is based on the back-projection method. The algorithm was implemented in MATLAB and uses a pre-calculated sensitivity matrix to increase the computation speed. The results include both 2D and 3D image datasets. The 3D datasets were obtained by scanning the dual sixteen element linear antenna array over the test object. The system has been tested on both humans and mannequin test objects. The front surface of an object placed on the human/mannequin torso is clearly visible, but its presence is also seen from a tell-tale imaging characteristic. This characteristic is caused by a reduction in the wave velocity as the electromagnetic radiation passes through the object, and manifests as an indentation in the reconstructed image that is readily identifiable. The prototype system has been shown to easily detect a 12 mm x 30 mm x70 mm plastic object concealed under clothing.

Rapid Object Detection Systems, Utilising Deep Learning and Unmanned Aerial Systems (uas) for Civil Engineering Applications

Science.gov (United States)

Griffiths, D.; Boehm, J.

2018-05-01

With deep learning approaches now out-performing traditional image processing techniques for image understanding, this paper accesses the potential of rapid generation of Convolutional Neural Networks (CNNs) for applied engineering purposes. Three CNNs are trained on 275 UAS-derived and freely available online images for object detection of 3m2 segments of railway track. These includes two models based on the Faster RCNN object detection algorithm (Resnet and Incpetion-Resnet) as well as the novel onestage Focal Loss network architecture (Retinanet). Model performance was assessed with respect to three accuracy metrics. The first two consisted of Intersection over Union (IoU) with thresholds 0.5 and 0.1. The last assesses accuracy based on the proportion of track covered by object detection proposals against total track length. In under six hours of training (and two hours of manual labelling) the models detected 91.3 %, 83.1 % and 75.6 % of track in the 500 test images acquired from the UAS survey Retinanet, Resnet and Inception-Resnet respectively. We then discuss the potential for such applications of such systems within the engineering field for a range of scenarios.

Design and analysis of a flux intensifying permanent magnet embedded salient pole wind generator

Science.gov (United States)

Guo, Yujing; Jin, Ping; Lin, Heyun; Yang, Hui; Lyu, Shukang

2018-05-01

This paper presents an improved flux intensifying permanent magnet embedded salient pole wind generator (FI-PMESPWG) with mirror symmetrical magnetizing directions permanent magnet (PM) for improving generator's performances. The air-gap flux densities, the output voltage, the cogging torque and the d- and q-axis inductances of FI-PMESPWG are all calculated and analyzed by using the finite element method (FEM). To highlight the advantages of the proposed FI-PMESPWG, an original permanent magnet embedded salient pole wind generator (PMESPWG) model is adopted for comparison under the same operating conditions. The calculating results show that the air-gap flux densities of FI-PMESPWG are intensified with the same magnet amounts because the PMs are set in a form of V shape in each pole. The difference between d-axis inductance and q-axis inductance of the proposed FI-PMESPWG is reduced. Thus, the output power of the proposed FI-PMESPWG reaches a higher value than that of the original PMESPWG at the same current phase angle. The cogging torque is diminished because the flux path is changed. All the analysis results indicate that the electromagnetic characteristics of the proposed FI-PMESPWG are significantly better than that of the original PMESPWG.

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

Directory of Open Access Journals (Sweden)

Bin Guo

2008-01-01

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

Red to Green or Fast to Slow? Infants' Visual Working Memory for "Just Salient Differences"

Science.gov (United States)

Kaldy, Zsuzsa; Blaser, Erik

2013-01-01

In this study, 6-month-old infants' visual working memory for a static feature (color) and a dynamic feature (rotational motion) was compared. Comparing infants' use of different features can only be done properly if experimental manipulations to those features are equally salient (Kaldy & Blaser, 2009; Kaldy, Blaser, & Leslie,…

RT-based memory detection : Item saliency effects in the single-probe and the multiple-probe protocol

NARCIS (Netherlands)

Verschuere, B.; Kleinberg, B.; Theocharidou, K.

RT-based memory detection may provide an efficient means to assess recognition of concealed information. There is, however, considerable heterogeneity in detection rates, and we explored two potential moderators: item saliency and test protocol. Participants tried to conceal low salient (e.g.,

Dual view x-ray inspection system for foreign objects detection in canned food

Science.gov (United States)

Lu, Zhiwen; Peng, Ningsong

2013-04-01

X-ray inspection technique for foreign objects in food products can determine and mark the presence of contaminants within the product by using image processing and pattern recognition technique on the X-ray transmission images. This paper presents the dual view X-ray inspection technique for foreign objects in food jar via analyzing the weak points of the traditional single view X-ray inspection technique. In addition, a prototype with the new technique is developed in accordance with glass splinters detection within the food jar (glass jar especially) which is a typical tickler. Some algorithms such as: adaptive image segmentation based on contour tracking, nonlinear arctan function transform and etc., are applied to improve image quality and achieve effective inspection results. The false recognition rate is effectively reduced and the detection sensitivity is highly enhanced. Finally the actual test results of this prototype are given.

Support vector machine as a binary classifier for automated object detection in remotely sensed data

International Nuclear Information System (INIS)

Wardaya, P D

2014-01-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result

Support vector machine as a binary classifier for automated object detection in remotely sensed data

Science.gov (United States)

Wardaya, P. D.

2014-02-01

In the present paper, author proposes the application of Support Vector Machine (SVM) for the analysis of satellite imagery. One of the advantages of SVM is that, with limited training data, it may generate comparable or even better results than the other methods. The SVM algorithm is used for automated object detection and characterization. Specifically, the SVM is applied in its basic nature as a binary classifier where it classifies two classes namely, object and background. The algorithm aims at effectively detecting an object from its background with the minimum training data. The synthetic image containing noises is used for algorithm testing. Furthermore, it is implemented to perform remote sensing image analysis such as identification of Island vegetation, water body, and oil spill from the satellite imagery. It is indicated that SVM provides the fast and accurate analysis with the acceptable result.

Development of a smartphone application for the objective detection of attentional deficits in delirium

OpenAIRE

Tieges, Zoe; Stiobhairt, Antaine; Scott, Katie; Suchorab, Klaudia; Weir, Alexander; Parks, Stuart; Shenkin, Susan; Maclullich, Alasdair

2015-01-01

BackgroundDelirium is an acute, severe deterioration in mental functioning. Inattention is the core feature, yet there are few objective methods for assessing attentional deficits in delirium. We previously developed a novel, graded test for objectively detecting inattention in delirium, implemented on a computerised device (Edinburgh Delirium Test Box (EDTB)). Although the EDTB is effective, tests on universally available devices have potential for greater impact. Here we assessed feasibilit...

Threat Object Detection using Covariance Matrix Modeling in X-ray Images

International Nuclear Information System (INIS)

Jeon, Byoun Gil; Kim, Jong Yul; Moon, Myung Kook

2016-01-01

The X-ray imaging system for the aviation security is one of the applications. In airports, all passengers and properties should be inspected and accepted by security machines before boarding on aircrafts to avoid all treat factors. That treat factors might be directly connected on terrorist threats awfully hazardous to not only passengers but also people in highly populated area such as major cities or buildings. Because the performance of the system is increasing along with the growth of IT technology, information that has various type and good quality can be provided for security check. However, human factors are mainly affected on the inspections. It means that human inspectors should be proficient corresponding to the growth of technology for efficient and effective inspection but there is clear limit of proficiency. Human being is not a computer. Because of the limitation, the aviation security techniques have the tendencies to provide not only numerous and nice information but also effective assistance for security inspectors. Many image processing applications already have been developed to provide efficient assistance for the security systems. Naturally, the security check procedure should not be altered by automatic software because it's not guaranteed that the automatic system will never make any mistake. This paper addressed an application of threat object detection using the covariance matrix modeling. The algorithm is implemented in MATLAB environment and evaluated the performance by comparing with other detection algorithms. Considering the shape of an object on an image is changed by the attitude of that to the imaging machine, the implemented detector has the robustness for rotation and scale of an object

Threat Object Detection using Covariance Matrix Modeling in X-ray Images

Energy Technology Data Exchange (ETDEWEB)

Jeon, Byoun Gil; Kim, Jong Yul; Moon, Myung Kook [KAERI, Daejeon (Korea, Republic of)

2016-05-15

The X-ray imaging system for the aviation security is one of the applications. In airports, all passengers and properties should be inspected and accepted by security machines before boarding on aircrafts to avoid all treat factors. That treat factors might be directly connected on terrorist threats awfully hazardous to not only passengers but also people in highly populated area such as major cities or buildings. Because the performance of the system is increasing along with the growth of IT technology, information that has various type and good quality can be provided for security check. However, human factors are mainly affected on the inspections. It means that human inspectors should be proficient corresponding to the growth of technology for efficient and effective inspection but there is clear limit of proficiency. Human being is not a computer. Because of the limitation, the aviation security techniques have the tendencies to provide not only numerous and nice information but also effective assistance for security inspectors. Many image processing applications already have been developed to provide efficient assistance for the security systems. Naturally, the security check procedure should not be altered by automatic software because it's not guaranteed that the automatic system will never make any mistake. This paper addressed an application of threat object detection using the covariance matrix modeling. The algorithm is implemented in MATLAB environment and evaluated the performance by comparing with other detection algorithms. Considering the shape of an object on an image is changed by the attitude of that to the imaging machine, the implemented detector has the robustness for rotation and scale of an object.

Somatosensory BOLD fMRI reveals close link between salient blood pressure changes and the murine neuromatrix.

Science.gov (United States)

Reimann, Henning Matthias; Todiras, Mihail; Hodge, Russ; Huelnhagen, Till; Millward, Jason Michael; Turner, Robert; Seeliger, Erdmann; Bader, Michael; Pohlmann, Andreas; Niendorf, Thoralf

2018-05-15

The neuromatrix, or "pain matrix", is a network of cortical brain areas which is activated by noxious as well as salient somatosensory stimulation. This has been studied in mice and humans using blood oxygenation level-dependent (BOLD) fMRI. Here we demonstrate that BOLD effects observed in the murine neuromatrix in response to salient somatosensory stimuli are prone to reflect mean arterial blood pressure (MABP) changes, rather than neural activity. We show that a standard electrostimulus typically used in murine somatosensory fMRI can induce substantial elevations in MABP. Equivalent drug-induced MABP changes - without somatosensory stimulation - evoked BOLD patterns in the neuromatrix strikingly similar to those evoked by electrostimulation. This constitutes a serious caveat for murine fMRI. The regional specificity of these BOLD patterns can be attributed to the co-localization of the neuromatrix with large draining veins. Based on these findings we propose a cardiovascular support mechanism whereby abrupt elevations in MABP provide additional energy supply to the neuromatrix and other essential brain areas in fight-or-flight situations. Copyright © 2018 Elsevier Inc. All rights reserved.

Binary Large Object-Based Approach for QR Code Detection in Uncontrolled Environments

Directory of Open Access Journals (Sweden)

Omar Lopez-Rincon

2017-01-01

Full Text Available Quick Response QR barcode detection in nonarbitrary environment is still a challenging task despite many existing applications for finding 2D symbols. The main disadvantage of recent applications for QR code detection is a low performance for rotated and distorted single or multiple symbols in images with variable illumination and presence of noise. In this paper, a particular solution for QR code detection in uncontrolled environments is presented. The proposal consists in recognizing geometrical features of QR code using a binary large object- (BLOB- based algorithm with subsequent iterative filtering QR symbol position detection patterns that do not require complex processing and training of classifiers frequently used for these purposes. The high precision and speed are achieved by adaptive threshold binarization of integral images. In contrast to well-known scanners, which fail to detect QR code with medium to strong blurring, significant nonuniform illumination, considerable symbol deformations, and noising, the proposed technique provides high recognition rate of 80%–100% with a speed compatible to real-time applications. In particular, speed varies from 200 ms to 800 ms per single or multiple QR code detected simultaneously in images with resolution from 640 × 480 to 4080 × 2720, respectively.

Change detection algorithms for surveillance in visual iot: a comparative study

International Nuclear Information System (INIS)

Akram, B.A.; Zafar, A.; Akbar, A.H.; Chaudhry, A.

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule’s Coefficient) and JC (Jaccard’s Coefficient), execution time and memory consumption. Experimental results showed that Kapur’s algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes. (author)

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

NARCIS (Netherlands)

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

2014-01-01

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

Foundations of computer vision computational geometry, visual image structures and object shape detection

CERN Document Server

Peters, James F

2017-01-01

This book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classification of image regions. Algorithms provide a practical, step-by-step means of viewing image structures. The implementations of CV methods in Matlab and Mathematica, classification of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, examples and connections with the fabric of C...

Laser methods for detecting explosive residues on surfaces of distant objects

Energy Technology Data Exchange (ETDEWEB)

Skvortsov, L A [Institute of Cryptography, Communications and Informatics, Moscow (Russian Federation)

2012-01-31

The basic methods of laser spectroscopy that are used for standoff detection and identification of explosive traces in the form of particles on the surfaces of objects tested under real or close-toreal conditions are briefly considered. The advantages and drawbacks of all methods are discussed and their characteristics are compared. Particular attention has been given to the prospects of development and practical implementation of the technologies discussed and justification of their most preferred applications. (review)

Multi-Frame Convolutional Neural Networks for Object Detection in Temporal Data

Science.gov (United States)

2017-03-01

of low-cost autonomous drones. The on-station time will no longer be dictated by human factors, but instead by the platforms’ capabilities. A...Imagine the task of detecting only moving cars but ignoring stationary cars . An object detector could probably do very well by looking for clues in a...single frame of video: cars in parking spots are usually stationary, moving cars may have a motion blur, and if it had an infrared sensor it could even

Salient measures of inhibition and switching are associated with frontal lobe gray matter volume in healthy middle-aged and older adults.

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Adólfsdóttir, Steinunn; Haász, Judit; Wehling, Eike; Ystad, Martin; Lundervold, Arvid; Lundervold, Astri J

2014-11-01

To investigate brain-behavior relationships between morphometric brain measures and salient executive function (EF) measures of inhibition and switching. One hundred participants (49-80 years) performed the Color Word Interference Test from the Delis-Kaplan Executive Function System (D-KEFS). Salient measures of EF components of inhibition and switching, of which the effect of more fundamental skills were regressed out, were analyzed using linear models and a conditional inference trees analysis taking intercorrelations between predictor variables (brain volumes, age, gender, and education) into account. The conditional inference trees analysis demonstrated a primary role of the middle frontal gyrus (MFG) in explaining variations in the salient EF measure of switching and combined inhibition/switching. Age predicted measures of inhibition. The study highlights the importance of considering fundamental cognitive skills and the use of a statistical method taking possible complex relationships between predictor variables into account when interpreting standard EF test results. Further studies should include MRI measures representing neural networks that may relate to CWIT performance, and longitudinal studies are required to investigate any causal relationships. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Quantization and training of object detection networks with low-precision weights and activations

Science.gov (United States)

Yang, Bo; Liu, Jian; Zhou, Li; Wang, Yun; Chen, Jie

2018-01-01

As convolutional neural networks have demonstrated state-of-the-art performance in object recognition and detection, there is a growing need for deploying these systems on resource-constrained mobile platforms. However, the computational burden and energy consumption of inference for these networks are significantly higher than what most low-power devices can afford. To address these limitations, this paper proposes a method to train object detection networks with low-precision weights and activations. The probability density functions of weights and activations of each layer are first directly estimated using piecewise Gaussian models. Then, the optimal quantization intervals and step sizes for each convolution layer are adaptively determined according to the distribution of weights and activations. As the most computationally expensive convolutions can be replaced by effective fixed point operations, the proposed method can drastically reduce computation complexity and memory footprint. Performing on the tiny you only look once (YOLO) and YOLO architectures, the proposed method achieves comparable accuracy to their 32-bit counterparts. As an illustration, the proposed 4-bit and 8-bit quantized versions of the YOLO model achieve a mean average precision of 62.6% and 63.9%, respectively, on the Pascal visual object classes 2012 test dataset. The mAP of the 32-bit full-precision baseline model is 64.0%.

A System based on Adaptive Background Subtraction Approach for Moving Object Detection and Tracking in Videos

Directory of Open Access Journals (Sweden)

Bahadır KARASULU

2013-04-01

Full Text Available Video surveillance systems are based on video and image processing research areas in the scope of computer science. Video processing covers various methods which are used to browse the changes in existing scene for specific video. Nowadays, video processing is one of the important areas of computer science. Two-dimensional videos are used to apply various segmentation and object detection and tracking processes which exists in multimedia content-based indexing, information retrieval, visual and distributed cross-camera surveillance systems, people tracking, traffic tracking and similar applications. Background subtraction (BS approach is a frequently used method for moving object detection and tracking. In the literature, there exist similar methods for this issue. In this research study, it is proposed to provide a more efficient method which is an addition to existing methods. According to model which is produced by using adaptive background subtraction (ABS, an object detection and tracking system’s software is implemented in computer environment. The performance of developed system is tested via experimental works with related video datasets. The experimental results and discussion are given in the study

Ships as salient objects in synthetic aperture radar imaginary

CSIR Research Space (South Africa)

Schwegmann, Colin P

2016-07-01

Full Text Available The widespread access to Synthetic Aperture Radar data has created a need for more precise ship extraction, specifically in low-to-medium resolution imagery. While Synthetic Aperture Radar pixel resolution is improving for a large swaths...

DEEP-SEE: Joint Object Detection, Tracking and Recognition with Application to Visually Impaired Navigational Assistance

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Ruxandra Tapu

2017-10-01

Full Text Available In this paper, we introduce the so-called DEEP-SEE framework that jointly exploits computer vision algorithms and deep convolutional neural networks (CNNs to detect, track and recognize in real time objects encountered during navigation in the outdoor environment. A first feature concerns an object detection technique designed to localize both static and dynamic objects without any a priori knowledge about their position, type or shape. The methodological core of the proposed approach relies on a novel object tracking method based on two convolutional neural networks trained offline. The key principle consists of alternating between tracking using motion information and predicting the object location in time based on visual similarity. The validation of the tracking technique is performed on standard benchmark VOT datasets, and shows that the proposed approach returns state-of-the-art results while minimizing the computational complexity. Then, the DEEP-SEE framework is integrated into a novel assistive device, designed to improve cognition of VI people and to increase their safety when navigating in crowded urban scenes. The validation of our assistive device is performed on a video dataset with 30 elements acquired with the help of VI users. The proposed system shows high accuracy (>90% and robustness (>90% scores regardless on the scene dynamics.

Demadroid: Object Reference Graph-Based Malware Detection in Android

Directory of Open Access Journals (Sweden)

Huanran Wang

2018-01-01

Full Text Available Smartphone usage has been continuously increasing in recent years. In addition, Android devices are widely used in our daily life, becoming the most attractive target for hackers. Therefore, malware analysis of Android platform is in urgent demand. Static analysis and dynamic analysis methods are two classical approaches. However, they also have some drawbacks. Motivated by this, we present Demadroid, a framework to implement the detection of Android malware. We obtain the dynamic information to build Object Reference Graph and propose λ-VF2 algorithm for graph matching. Extensive experiments show that Demadroid can efficiently identify the malicious features of malware. Furthermore, the system can effectively resist obfuscated attacks and the variants of known malware to meet the demand for actual use.

Change Detection Algorithms for Surveillance in Visual IoT: A Comparative Study

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Akram, Beenish Ayesha; Zafar, Amna; Akbar, Ali Hammad; Wajid, Bilal; Chaudhry, Shafique Ahmad

2018-01-01

The VIoT (Visual Internet of Things) connects virtual information world with real world objects using sensors and pervasive computing. For video surveillance in VIoT, ChD (Change Detection) is a critical component. ChD algorithms identify regions of change in multiple images of the same scene recorded at different time intervals for video surveillance. This paper presents performance comparison of histogram thresholding and classification ChD algorithms using quantitative measures for video surveillance in VIoT based on salient features of datasets. The thresholding algorithms Otsu, Kapur, Rosin and classification methods k-means, EM (Expectation Maximization) were simulated in MATLAB using diverse datasets. For performance evaluation, the quantitative measures used include OSR (Overall Success Rate), YC (Yule's Coefficient) and JC (Jaccard's Coefficient), execution time and memory consumption. Experimental results showed that Kapur's algorithm performed better for both indoor and outdoor environments with illumination changes, shadowing and medium to fast moving objects. However, it reflected degraded performance for small object size with minor changes. Otsu algorithm showed better results for indoor environments with slow to medium changes and nomadic object mobility. k-means showed good results in indoor environment with small object size producing slow change, no shadowing and scarce illumination changes.

Design of salient pole PM synchronous machines for a vehicle traction application. Analysis and implementation

Energy Technology Data Exchange (ETDEWEB)

Rilla, M.

2012-07-01

This doctoral thesis presents a study on the development of a liquid-cooled frame salient pole permanent-magnet-exited traction machine for a four-wheel-driven electric car. The emphasis of the thesis is put on a radial flux machine design in order to achieve a light-weight machine structure for traction applications. The design features combine electromagnetic and thermal design methods, because traction machine operation does not have a strict operating point. Arbitrary load cycles and the flexible supply require special attention in the design process. It is shown that accurate modelling of the machine magnetic state is essential for high-performance operation. The saturation effect related to the cross-saturation has to be taken carefully into account in order to achieve the desired operation. Two prototype machines have been designed and built for testing: one totally enclosed machine with a special magnet module pole arrangement and another through-ventilated machine with a more traditional embedded magnet structure. Both structures are built with magnetically salient structures in order to increase the torque production capability with the reluctance torque component. Both machine structures show potential for traction usage. However, the traditional embedded magnet design turns out to be mechanically the more secure one of these two machine options. (orig.)

Amplitude modulation of sexy phrases is salient for song attractiveness in female canaries (Serinus canaria).

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Pasteau, Magali; Ung, Davy; Kreutzer, Michel; Aubin, Thierry

2012-07-01

Song discrimination and recognition in songbird species have usually been studied by measuring responses to song playbacks. In female canaries, Serinus canaria, copulation solicitation displays (CSDs) are used as an index of female preferences, which are related to song recognition. Despite the fact that many studies underline the role of song syntax in this species, we observed that short segments of songs (a few seconds long) are enough for females to discriminate between conspecific and heterospecific songs, whereas such a short duration is not sufficient to identify the syntax rules. This suggests that other cues are salient for song recognition. In this experiment, we investigated the influence of amplitude modulation (AM) on the responses (CSDs) of female canaries to song playbacks. We used two groups of females: (1) raised in acoustic isolation and (2) raised in normal conditions. When adult, we tested their preferences for sexy phrases with different AMs. We broadcast three types of stimuli: (1) songs with natural canary AM, (2) songs with AM removed, or (3) song with wren Troglodytes troglodytes AM. Results indicate that female canaries prefer and have predispositions for a song type with the natural canary AM. Thus, this acoustic parameter is a salient cue for song attractiveness.

The role of objects and effects in action imitation: Comparing the imitation of object-related actions vs. gestures in 18-month-old infants.

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Kim, Ziyon; Óturai, Gabriella; Király, Ildikó; Knopf, Monika

2015-11-01

This study aimed to systematically investigate 18-month-old infants' imitation of object-related actions compared to motorically similar gestures. An additional goal of the study was to examine the role of action effects on infants' imitation of target actions. One group of infants (n=17) observed object-related actions and gestures leading to salient effects (sounds or visual resp. social effects), and the other group (n=16) watched the same actions without effects. Furthermore, this study examined whether infants show a consistent imitation ability for object-related actions and gestures. First, the present study showed that 18-month-old infants imitated object-related actions more frequently than gestures. Second, the presence of an effect significantly increased the imitation rate of object-related actions; however, this difference was not found for gestures. Third, indications for a general imitation ability were found as results on an individual level showed that object-related action imitation significantly correlated with gesture imitation. Implications of the results for theory and future studies are discussed with a focus on the role of objects and effects in 18-month-old infants' action imitation. Copyright © 2015 Elsevier Inc. All rights reserved.

Spatio-Temporal Saliency Perception via Hypercomplex Frequency Spectral Contrast

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Zhiqiang Tian

2013-03-01

Full Text Available Salient object perception is the process of sensing the salient information from the spatio-temporal visual scenes, which is a rapid pre-attention mechanism for the target location in a visual smart sensor. In recent decades, many successful models of visual saliency perception have been proposed to simulate the pre-attention behavior. Since most of the methods usually need some ad hoc parameters or high-cost preprocessing, they are difficult to rapidly detect salient object or be implemented by computing parallelism in a smart sensor. In this paper, we propose a novel spatio-temporal saliency perception method based on spatio-temporal hypercomplex spectral contrast (HSC. Firstly, the proposed HSC algorithm represent the features in the HSV (hue, saturation and value color space and features of motion by a hypercomplex number. Secondly, the spatio-temporal salient objects are efficiently detected by hypercomplex Fourier spectral contrast in parallel. Finally, our saliency perception model also incorporates with the non-uniform sampling, which is a common phenomenon of human vision that directs visual attention to the logarithmic center of the image/video in natural scenes. The experimental results on the public saliency perception datasets demonstrate the effectiveness of the proposed approach compared to eleven state-of-the-art approaches. In addition, we extend the proposed model to moving object extraction in dynamic scenes, and the proposed algorithm is superior to the traditional algorithms.

Low cost, robust and real time system for detecting and tracking moving objects to automate cargo handling in port terminals

NARCIS (Netherlands)

Vaquero, V.; Repiso, E.; Sanfeliu, A.; Vissers, J.; Kwakkernaat, M.

2016-01-01

The presented paper addresses the problem of detecting and tracking moving objects for autonomous cargo handling in port terminals using a perception system which input data is a single layer laser scanner. A computationally low cost and robust Detection and Tracking Moving Objects (DATMO) algorithm

A Fault-Tolerant Parallel Structure of Single-Phase Full-Bridge Rectifiers for a Wound-Field Doubly Salient Generator

DEFF Research Database (Denmark)

Chen, Zhihui; Chen, Ran; Chen, Zhe

2013-01-01

The fault-tolerance design is widely adopted for high-reliability applications. In this paper, a parallel structure of single-phase full-bridge rectifiers (FBRs) (PS-SPFBR) is proposed for a wound-field doubly salient generator. The analysis shows the potential fault-tolerance capability of the PS...

The flexible engagement of monitoring processes in non-focal and focal prospective memory tasks with salient cues.

Science.gov (United States)

Hefer, Carmen; Cohen, Anna-Lisa; Jaudas, Alexander; Dreisbach, Gesine

2017-09-01

Prospective memory (PM) refers to the ability to remember to perform a delayed intention. Here, we aimed to investigate the ability to suspend such an intention and thus to confirm previous findings (Cohen, Gordon, Jaudas, Hefer, & Dreisbach, 2016) demonstrating the ability to flexibly engage in monitoring processes. In the current study, we presented a perceptually salient PM cue (bold and red) to rule out that previous findings were limited to non-salient and, thus, easy to ignore PM cues. Moreover, we used both a non-focal (Experiment 1) and a focal PM (Experiment 2) cue. In both Experiments, three groups of participants performed an Eriksen flanker task as an ongoing task with an embedded PM task (they had to remember to press the F1 key if a pre-specified cue appeared). Participants were assigned to either a control condition (performed solely the flanker task), a standard PM condition (performed the flanker task along with the PM task), or a PM delayed condition (performed the flanker task but were instructed to postpone their PM task intention). The results of Experiment 1 with the non-focal PM cue closely replicated those of Cohen et al. (2016) and confirmed that participants were able to successfully postpone the PM cue intention without additional costs even when the PM cue was a perceptually salient one. However, when the PM cue was focal (Experiment 2), it was much more difficult for participants to ignore it as evidenced by commission errors and slower latencies on PM cue trials. In sum, results showed that the focality of the PM cue plays a more crucial role in the flexibility of the monitoring process whereas the saliency of the PM cue does not. Copyright © 2017 Elsevier B.V. All rights reserved.

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

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Xin Wang

2018-02-01

Full Text Available To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

Object-centered representations support flexible exogenous visual attention across translation and reflection.

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Lin, Zhicheng

2013-11-01

Visual attention can be deployed to stimuli based on our willful, top-down goal (endogenous attention) or on their intrinsic saliency against the background (exogenous attention). Flexibility is thought to be a hallmark of endogenous attention, whereas decades of research show that exogenous attention is attracted to the retinotopic locations of the salient stimuli. However, to the extent that salient stimuli in the natural environment usually form specific spatial relations with the surrounding context and are dynamic, exogenous attention, to be adaptive, should embrace these structural regularities. Here we test a non-retinotopic, object-centered mechanism in exogenous attention, in which exogenous attention is dynamically attracted to a relative, object-centered location. Using a moving frame configuration, we presented two frames in succession, forming either apparent translational motion or in mirror reflection, with a completely uninformative, transient cue presented at one of the item locations in the first frame. Despite that the cue is presented in a spatially separate frame, in both translation and mirror reflection, behavioralperformance in visual search is enhanced when the target in the second frame appears at the same relative location as the cue location than at other locations. These results provide unambiguous evidence for non-retinotopic exogenous attention and further reveal an object-centered mechanism supporting flexible exogenous attention. Moreover, attentional generalization across mirror reflection may constitute an attentional correlate of perceptual generalization across lateral mirror images, supporting an adaptive, functional account of mirror images confusion. Copyright © 2013 Elsevier B.V. All rights reserved.

Buried Object Detection Method Using Optimum Frequency Range in Extremely Shallow Underground

Science.gov (United States)

Sugimoto, Tsuneyoshi; Abe, Touma

2011-07-01

We propose a new detection method for buried objects using the optimum frequency response range of the corresponding vibration velocity. Flat speakers and a scanning laser Doppler vibrometer (SLDV) are used for noncontact acoustic imaging in the extremely shallow underground. The exploration depth depends on the sound pressure, but it is usually less than 10 cm. Styrofoam, wood (silver fir), and acrylic boards of the same size, different size styrofoam boards, a hollow toy duck, a hollow plastic container, a plastic container filled with sand, a hollow steel can and an unglazed pot are used as buried objects which are buried in sand to about 2 cm depth. The imaging procedure of buried objects using the optimum frequency range is given below. First, the standardized difference from the average vibration velocity is calculated for all scan points. Next, using this result, underground images are made using a constant frequency width to search for the frequency response range of the buried object. After choosing an approximate frequency response range, the difference between the average vibration velocity for all points and that for several points that showed a clear response is calculated for the final confirmation of the optimum frequency range. Using this optimum frequency range, we can obtain the clearest image of the buried object. From the experimental results, we confirmed the effectiveness of our proposed method. In particular, a clear image of the buried object was obtained when the SLDV image was unclear.

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

Directory of Open Access Journals (Sweden)

Fonseca Carlos M

2010-10-01

detection of moderately irregularly shaped clusters. The multi-objective cohesion scan is most effective for the detection of highly irregularly shaped clusters.

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization.

Science.gov (United States)

Rao, Jinmeng; Qiao, Yanjun; Ren, Fu; Wang, Junxing; Du, Qingyun

2017-08-24

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device's built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

Directory of Open Access Journals (Sweden)

Jinmeng Rao

2017-08-01

Full Text Available The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction.

A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

Science.gov (United States)

Rao, Jinmeng; Qiao, Yanjun; Ren, Fu; Wang, Junxing; Du, Qingyun

2017-01-01

The purpose of this study was to develop a robust, fast and markerless mobile augmented reality method for registration, geovisualization and interaction in uncontrolled outdoor environments. We propose a lightweight deep-learning-based object detection approach for mobile or embedded devices; the vision-based detection results of this approach are combined with spatial relationships by means of the host device’s built-in Global Positioning System receiver, Inertial Measurement Unit and magnetometer. Virtual objects generated based on geospatial information are precisely registered in the real world, and an interaction method based on touch gestures is implemented. The entire method is independent of the network to ensure robustness to poor signal conditions. A prototype system was developed and tested on the Wuhan University campus to evaluate the method and validate its results. The findings demonstrate that our method achieves a high detection accuracy, stable geovisualization results and interaction. PMID:28837096

The role of play objects and object play in human cognitive evolution and innovation.

Science.gov (United States)

Riede, Felix; Johannsen, Niels N; Högberg, Anders; Nowell, April; Lombard, Marlize

2018-01-01

In this contribution, we address a major puzzle in the evolution of human material culture: If maturing individuals just learn their parental generation's material culture, then what is the origin of key innovations as documented in the archeological record? We approach this question by coupling a life-history model of the costs and benefits of experimentation with a niche-construction perspective. Niche-construction theory suggests that the behavior of organisms and their modification of the world around them have important evolutionary ramifications by altering developmental settings and selection pressures. Part of Homo sapiens' niche is the active provisioning of children with play objects - sometimes functional miniatures of adult tools - and the encouragement of object play, such as playful knapping with stones. Our model suggests that salient material culture innovation may occur or be primed in a late childhood or adolescence sweet spot when cognitive and physical abilities are sufficiently mature but before the full onset of the concerns and costs associated with reproduction. We evaluate the model against a series of archeological cases and make suggestions for future research. © 2018 The Authors Evolutionary Anthropology Published by Wiley Periodicals, Inc.

The role of play objects and object play in human cognitive evolution and innovation

Science.gov (United States)

Johannsen, Niels N.; Högberg, Anders; Nowell, April; Lombard, Marlize

2018-01-01

Abstract In this contribution, we address a major puzzle in the evolution of human material culture: If maturing individuals just learn their parental generation's material culture, then what is the origin of key innovations as documented in the archeological record? We approach this question by coupling a life‐history model of the costs and benefits of experimentation with a niche‐construction perspective. Niche‐construction theory suggests that the behavior of organisms and their modification of the world around them have important evolutionary ramifications by altering developmental settings and selection pressures. Part of Homo sapiens' niche is the active provisioning of children with play objects — sometimes functional miniatures of adult tools — and the encouragement of object play, such as playful knapping with stones. Our model suggests that salient material culture innovation may occur or be primed in a late childhood or adolescence sweet spot when cognitive and physical abilities are sufficiently mature but before the full onset of the concerns and costs associated with reproduction. We evaluate the model against a series of archeological cases and make suggestions for future research. PMID:29446561

The Novel Artificial Intelligence Based Sub-Surface Inclusion Detection Device and Algorithm

Directory of Open Access Journals (Sweden)

Jong-Ha LEE

2017-05-01

Full Text Available We design, implement, and test a novel tactile elasticity imaging sensor to detect the elastic modulus of a contacted object. Emulating a human finger, a multi-layer polydimethylsiloxane waveguide has been fabricated as the sensing probe. The light is illuminated under the critical angle to totally reflect within the flexible and transparent waveguide. When a waveguide is compressed by an object, the contact area of the waveguide deforms and causes the light to scatter. The scattered light is captured by a high resolution camera. Multiple images are taken from slightly different loading values. The distributed forces have been estimated using the integrated pixel values of diffused lights. The displacements of the contacted object deformation have been estimated by matching the series of tactile images. For this purpose, a novel pattern matching algorithm is developed. The salient feature of this sensor is that it is capable of measuring the absolute elastic modulus value of soft materials without additional measurement units. The measurements were validated by comparing the measured elasticity of the commercial rubber samples with the known elasticity. The evaluation results showed that this type of sensor can measure elasticity within ±5.38 %.

Object-based detection of vehicles using combined optical and elevation data

Science.gov (United States)

Schilling, Hendrik; Bulatov, Dimitri; Middelmann, Wolfgang

2018-02-01

The detection of vehicles is an important and challenging topic that is relevant for many applications. In this work, we present a workflow that utilizes optical and elevation data to detect vehicles in remotely sensed urban data. This workflow consists of three consecutive stages: candidate identification, classification, and single vehicle extraction. Unlike in most previous approaches, fusion of both data sources is strongly pursued at all stages. While the first stage utilizes the fact that most man-made objects are rectangular in shape, the second and third stages employ machine learning techniques combined with specific features. The stages are designed to handle multiple sensor input, which results in a significant improvement. A detailed evaluation shows the benefits of our workflow, which includes hand-tailored features; even in comparison with classification approaches based on Convolutional Neural Networks, which are state of the art in computer vision, we could obtain a comparable or superior performance (F1 score of 0.96-0.94).

Beyond Screening for Risk Factors Objective Detection of Strabismus and Amblyopia

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Jost, Reed M.; Yanni, Susan E.; Beauchamp, Cynthia L.; Stager, David R.; Stager, David; Dao, Lori; Birch, Eileen E.

2015-01-01

IMPORTANCE Commercially available automated vision screening devices assess refractive risk factors, not amblyopia or strabismus, underreferring affected children and overreferring healthy children. Nearly half of affected children are not identified until after age 5 years, when treatment is less effective. OBJECTIVES To determine the diagnostic accuracy of the Pediatric Vision Scanner (PVS), a binocular retinal birefringence scanner, to objectively identify strabismus and amblyopia, and to compare retinal birefringence screening with a widely used automated pediatric screening device. DESIGN, SETTING, AND PARTICIPANTS Three hundred consecutive preschool children (aged 2-6 years) were screened using the PVS and the SureSight Autorefractor at 2 pediatric ophthalmology private practices. A masked comprehensive pediatric ophthalmic examination provided the gold standard for determining sensitivity and specificity for each screening device. MAIN OUTCOMES AND MEASURES The primary outcome was sensitivity and specificity of the PVS for detecting the targeted conditions, strabismus and amblyopia, in children aged 2 to 6 years. Secondary outcomes included the positive and negative likelihood ratios of the PVS for identifying the targeted conditions. In addition, sensitivity, specificity, and positive and negative likelihood ratios of the SureSight Autorefractor for the targeted conditions were assessed in the same cohort of children. RESULTS Of the 300 patients, 188 had strabismus only, amblyopia only, or both, and 112 had no strabismus or amblyopia. The sensitivity of the PVS to detect strabismus and amblyopia (0.97; 95% CI, 0.94-1.00) was significantly higher than that of the SureSight Autorefractor (0.74; 95% CI, 0.66-0.83). Specificity of the PVS for strabismus and amblyopia (0.87; 95% CI, 0.80-0.95) was significantly higher than that of the SureSight Autorefractor (0.62; 95% CI, 0.50-0.73). CONCLUSIONS AND RELEVANCE The PVS identified children with strabismus and

Generation and Detection of Alignments in Gabor Patterns

Directory of Open Access Journals (Sweden)

Samy Blusseau

2016-11-01

Full Text Available This paper presents a method to be used in psychophysical experiments to compare directly visual perception to an a contrario algorithm, on a straight patterns detection task. The method is composed of two parts. The first part consists in building a stimulus, namely an array of oriented elements, in which an alignment is present with variable salience. The second part focuses on a detection algorithm, based on the a contrario theory, which is designed to predict which alignment will be considered as the most salient in a given stimulus.

Detecting Chloride Contamination of Objects and Buildings – Evaluating a New Testing Process

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Lynda Skipper

2018-02-01

Full Text Available Soluble salts play a key factor in damage to a variety of materials, including stone, ceramics and metals. Particularly, salt contamination can lead to weakening of porous materials through salt crystallisation events, and increases the rate of metal corrosion. Over time, this results in physical damage to affected objects and buildings. It is therefore important to be able to monitor the salt content of materials, in order to understand levels of salt contamination and the potential for damage to occur. This research discusses the further development of the testing method for surface chlorides originally proposed by Piechota and Drake Piechota (2016 in their article “A simple survey kit for chloride detection on cuneiform tablets and other collections”. It introduces new and revised steps into the original protocol in order to make the achieved results semi-quantifiable, as well as identifying the limits of detection of the test kit. A comparison to alternative testing methods showed that comparable results were achievable using this methodology. The revised methodology was tested for efficacy on a range of salt contaminated objects, as well as on samples from buildings.

Do already grasped objects activate motor affordances?

Science.gov (United States)

Iani, Cristina; Ferraro, Luca; Maiorana, Natale Vincenzo; Gallese, Vittorio; Rubichi, Sandro

2018-04-07

This study investigated whether in a stimulus-response compatibility (SRC) task affordance effects in response to picture of graspable objects emerge when these objects appear as already grasped. It also assessed whether the observed effects could be explained as due to spatial compatibility between the most salient part in the object/display and the hand of response rather than to action potentiation. To this aim, we conducted three behavioural experiments in which participants were required to discriminate the vertical orientation (upright vs. inverted) of an object presented in the centre of the screen, while ignoring the right-left orientation of its handle. The object could be presented alone, as already grasped, as partially masked (Experiment 1) or with a human hand close to its graspable side (Experiment 2). In addition, to assess the role of perceptual salience, the object could be presented with a human hand or a non-biological (a geometrical shape) distractor located opposite to the object's graspable side. Results showed faster responses when the object's handle was located on the same side of the responding hand with a larger effect when upright objects were shown as already grasped (Experiment 1) or when a hand was displayed close to its handle (Experiment 2), and a smaller reversed effect when the hand or the geometrical shape was located opposite to the handled side (Experiment 3). We interpreted these findings as indicating that handle orientation effects emerging in SRC tasks may result from the interplay between motor affordance and spatial compatibility mechanisms.

A robust object-based shadow detection method for cloud-free high resolution satellite images over urban areas and water bodies

Science.gov (United States)

Tatar, Nurollah; Saadatseresht, Mohammad; Arefi, Hossein; Hadavand, Ahmad

2018-06-01

Unwanted contrast in high resolution satellite images such as shadow areas directly affects the result of further processing in urban remote sensing images. Detecting and finding the precise position of shadows is critical in different remote sensing processing chains such as change detection, image classification and digital elevation model generation from stereo images. The spectral similarity between shadow areas, water bodies, and some dark asphalt roads makes the development of robust shadow detection algorithms challenging. In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels. In this paper, a new object-based shadow detection framework is introduced. In the proposed method a pixel-level shadow mask is built by extending established thresholding methods with a new C4 index which enables to solve the ambiguity of shadow and water bodies. Then the pixel-based results are further processed in an object-based majority analysis to detect the final shadow objects. Four different high resolution satellite images are used to validate this new approach. The result shows the superiority of the proposed method over some state-of-the-art shadow detection method with an average of 96% in F-measure.

Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

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Mohendra Roy

2016-05-01

Full Text Available Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al., we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings.

Object detection using categorised 3D edges

DEFF Research Database (Denmark)

Kiforenko, Lilita; Buch, Anders Glent; Bodenhagen, Leon

2015-01-01

is made possible by the explicit use of edge categories in the feature descriptor. We quantitatively compare our approach with the state-of-the-art template based Linemod method, which also provides an effective way of dealing with texture-less objects, tests were performed on our own object dataset. Our...... categorisation algorithm for describing objects in terms of its different edge types. Relying on edge information allow our system to deal with objects with little or no texture or surface variation. We show that edge categorisation improves matching performance due to the higher level of discrimination, which...

Salient features, response and operation of Lead-Free Gulmarg Neutron Monitor

International Nuclear Information System (INIS)

Mufti, S.; Chatterjee, S.; Ishtiaq, P.M.; Darzi, M.A.; Mir, T.A.; Shah, G.N.

2016-01-01

Lead-Free Gulmarg Neutron Monitor (LFGNM) provides continuous ground level intensity measurements of atmospheric secondary neutrons produced in interactions of primary cosmic rays with the Earth's constituent atmosphere. We report the LFGNM detector salient features and simulation of its energy response for 10"−"1"1 MeV to 10"4 MeV energy incident neutrons using the FLUKA Monte Carlo package. An empirical calibration of the LFGNM detector carried out with a Pu–Be neutron source for maximising its few MeV neutron counting sensitivity is also presented. As an illustration of its functionality a single representative transient solar modulation event recorded by LFGNM depicting Forbush decrease in integrated neutron data for which the geospace consequences are well known is also presented. Performance of LFGNM under actual observation conditions for effectively responding to transient solar modulation is seen to compare well with other world-wide conventional neutron monitors.

Higher-Order Statistics for the Detection of Small Objects in a Noisy Background Application on Sonar Imaging

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M. Amate

2007-01-01

Full Text Available An original algorithm for the detection of small objects in a noisy background is proposed. Its application to underwater objects detection by sonar imaging is addressed. This new method is based on the use of higher-order statistics (HOS that are locally estimated on the images. The proposed algorithm is divided into two steps. In a first step, HOS (skewness and kurtosis are estimated locally using a square sliding computation window. Small deterministic objects have different statistical properties from the background they are thus highlighted. The influence of the signal-to-noise ratio (SNR on the results is studied in the case of Gaussian noise. Mathematical expressions of the estimators and of the expected performances are derived and are experimentally confirmed. In a second step, the results are focused by a matched filter using a theoretical model. This enables the precise localization of the regions of interest. The proposed method generalizes to other statistical distributions and we derive the theoretical expressions of the HOS estimators in the case of a Weibull distribution (both when only noise is present or when a small deterministic object is present within the filtering window. This enables the application of the proposed technique to the processing of synthetic aperture sonar data containing underwater mines whose echoes have to be detected and located. Results on real data sets are presented and quantitatively evaluated using receiver operating characteristic (ROC curves.

Objective evaluation of Tl-201 image efficacy for detection of myocardial infarction

International Nuclear Information System (INIS)

Nagai, Teruo; Murata, Kazuhiko; Torizuka, Kanji

1982-01-01

As the 3rd report of the objective analysis of radioactive 201 Tl scintigraphy of myocardial infarction, detection of infarction and extent of the lesion was discussed. In 114 cases with relatively definite findings, their images were rereaded and evaluated by 2 physicians. Segmental analysis in each direction was employed for localization of perfusion defects. Comparison between the sites of myocardial infarction on ECG and that of perfusion defects by segmental analysis revealed that, in infarction of the anterior wall, many segments showed defects, and that the lesions of the posterior and lateral wall had a few segments showing defects. This standard of correlation was applied to other cases of myocardial infarction, and the result suggested possible improvement in detection of infarction. As regards the extent of the lesion, no significant correlation between number of segments with defect and ECG and/or the serum enzyme levels were seen. (Ueda, J.)

A comparison of signal detection theory to the objective threshold/strategic model of unconscious perception.

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Haase, Steven J; Fisk, Gary D

2011-08-01

A key problem in unconscious perception research is ruling out the possibility that weak conscious awareness of stimuli might explain the results. In the present study, signal detection theory was compared with the objective threshold/strategic model as explanations of results for detection and identification sensitivity in a commonly used unconscious perception task. In the task, 64 undergraduate participants detected and identified one of four briefly displayed, visually masked letters. Identification was significantly above baseline (i.e., proportion correct > .25) at the highest detection confidence rating. This result is most consistent with signal detection theory's continuum of sensory states and serves as a possible index of conscious perception. However, there was limited support for the other model in the form of a predicted "looker's inhibition" effect, which produced identification performance that was significantly below baseline. One additional result, an interaction between the target stimulus and type of mask, raised concerns for the generality of unconscious perception effects.

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

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Stéphanie Jehan-Besson

2002-06-01

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

Attentional capture by irrelevant transients leads to perceptual errors in a competitive change detection task

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Daniel eSchneider

2012-05-01

Full Text Available Theories on visual change detection imply that attention is a necessary but not sufficient prerequisite for aware perception. Misguidance of attention due to salient irrelevant distractors can therefore lead to severe deficits in change detection. The present study investigates the mechanisms behind such perceptual errors and their relation to error processing on higher cognitive levels. Participants had to detect a luminance change that occasionally occurred simultaneously with an irrelevant orientation change in the opposite hemi-field (conflict condition. By analyzing event-related potentials in the EEG separately in those error prone conflict trials for correct and erroneous change detection, we demonstrate that only correct change detection was associated with the allocation of attention to the relevant luminance change. Erroneous change detection was associated with an initial capture of attention towards the irrelevant orientation change in the N1 time window and a lack of subsequent target selection processes (N2pc. Errors were additionally accompanied by an increase of the fronto-central N2 and a kind of error negativity (Ne or ERN, which, however, peaked prior to the response. These results suggest that a strong perceptual conflict by salient distractors can disrupt the further processing of relevant information and thus affect its aware perception. Yet, it does not impair higher cognitive processes for conflict and error detection, indicating that these processes are independent from awareness.

A Low-Power Wireless Image Sensor Node with Noise-Robust Moving Object Detection and a Region-of-Interest Based Rate Controller

Science.gov (United States)

2017-03-01

from both environment and hardware further reduces the transmission energy with negligible computation and memory overhead. The rate controller...detection, Region-of-interest, Rate control Introduction In wireless image sensor nodes for moving object surveillance, energy efficiency can be...noise, reliable moving object detection is required to avoid unnecessary transmission of background scenes [1]. Transmission energy can be further

Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

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Min-Yin Liu

2017-05-01

Full Text Available Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz measured by electroencephalography (EEG mostly during non-rapid eye movement (NREM stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1 the lack of common benchmark databases, and (2 the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA, the Strength Pareto Evolutionary Algorithm (SPEA2, to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT, and two Hilbert-Huang transform (HHT based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.

Human listening studies reveal insights into object features extracted by echolocating dolphins

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Delong, Caroline M.; Au, Whitlow W. L.; Roitblat, Herbert L.

2004-05-01

Echolocating dolphins extract object feature information from the acoustic parameters of object echoes. However, little is known about which object features are salient to dolphins or how they extract those features. To gain insight into how dolphins might be extracting feature information, human listeners were presented with echoes from objects used in a dolphin echoic-visual cross-modal matching task. Human participants performed a task similar to the one the dolphin had performed; however, echoic samples consisting of 23-echo trains were presented via headphones. The participants listened to the echoic sample and then visually selected the correct object from among three alternatives. The participants performed as well as or better than the dolphin (M=88.0% correct), and reported using a combination of acoustic cues to extract object features (e.g., loudness, pitch, timbre). Participants frequently reported using the pattern of aural changes in the echoes across the echo train to identify the shape and structure of the objects (e.g., peaks in loudness or pitch). It is likely that dolphins also attend to the pattern of changes across echoes as objects are echolocated from different angles.

Appraisals of Salient Visual Elements in Web Page Design

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Johanna M. Silvennoinen

2016-01-01

Full Text Available Visual elements in user interfaces elicit emotions in users and are, therefore, essential to users interacting with different software. Although there is research on the relationship between emotional experience and visual user interface design, the focus has been on the overall visual impression and not on visual elements. Additionally, often in a software development process, programming and general usability guidelines are considered as the most important parts of the process. Therefore, knowledge of programmers’ appraisals of visual elements can be utilized to understand the web page designs we interact with. In this study, appraisal theory of emotion is utilized to elaborate the relationship of emotional experience and visual elements from programmers’ perspective. Participants (N=50 used 3E-templates to express their visual and emotional experiences of web page designs. Content analysis of textual data illustrates how emotional experiences are elicited by salient visual elements. Eight hierarchical visual element categories were found and connected to various emotions, such as frustration, boredom, and calmness, via relational emotion themes. The emotional emphasis was on centered, symmetrical, and balanced composition, which was experienced as pleasant and calming. The results benefit user-centered visual interface design and researchers of visual aesthetics in human-computer interaction.

A strategy to objectively evaluate the necessity of correcting detected target deviations in image guided radiotherapy

International Nuclear Information System (INIS)

Yue, Ning J.; Kim, Sung; Jabbour, Salma; Narra, Venkat; Haffty, Bruce G.

2007-01-01

Image guided radiotherapy technologies are being increasingly utilized in the treatment of various cancers. These technologies have enhanced the ability to detect temporal and spatial deviations of the target volume relative to planned radiation beams. Correcting these detected deviations may, in principle, improve the accuracy of dose delivery to the target. However, in many situations, a clinical decision has to be made as to whether it is necessary to correct some of the deviations since the relevant dosimetric impact may or may not be significant, and the corresponding corrective action may be either impractical or time consuming. Ideally this decision should be based on objective and reproducible criteria rather than subjective judgment. In this study, a strategy is proposed for the objective evaluation of the necessity of deviation correction during the treatment verification process. At the treatment stage, without any alteration from the planned beams, the treatment beams should provide the desired dose coverage to the geometric volume identical to the planning target volume (PTV). Given this fact, the planned dose distribution and PTV geometry were used to compute the dose coverage and PTV enclosure of the clinical target volume (CTV) that was detected from imaging during the treatment setup verification. The spatial differences between the detected CTV and the planning CTV are essentially the target deviations. The extent of the PTV enclosure of the detected CTV as well as its dose coverage were used as criteria to evaluate the necessity of correcting any of the target deviations. This strategy, in principle, should be applicable to any type of target deviations, including both target deformable and positional changes and should be independent of how the deviations are detected. The proposed strategy was used on two clinical prostate cancer cases. In both cases, gold markers were implanted inside the prostate for the purpose of treatment setup

Detection of nuclei in 4D Nomarski DIC microscope images of early Caenorhabditis elegans embryos using local image entropy and object tracking

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Hamahashi Shugo

2005-05-01

Full Text Available Abstract Background The ability to detect nuclei in embryos is essential for studying the development of multicellular organisms. A system of automated nuclear detection has already been tested on a set of four-dimensional (4D Nomarski differential interference contrast (DIC microscope images of Caenorhabditis elegans embryos. However, the system needed laborious hand-tuning of its parameters every time a new image set was used. It could not detect nuclei in the process of cell division, and could detect nuclei only from the two- to eight-cell stages. Results We developed a system that automates the detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. Local image entropy is used to produce regions of the images that have the image texture of the nucleus. From these regions, those that actually detect nuclei are manually selected at the first and last time points of the image set, and an object-tracking algorithm then selects regions that detect nuclei in between the first and last time points. The use of local image entropy makes the system applicable to multiple image sets without the need to change its parameter values. The use of an object-tracking algorithm enables the system to detect nuclei in the process of cell division. The system detected nuclei with high sensitivity and specificity from the one- to 24-cell stages. Conclusion A combination of local image entropy and an object-tracking algorithm enabled highly objective and productive detection of nuclei in a set of 4D DIC microscope images of C. elegans embryos. The system will facilitate genomic and computational analyses of C. elegans embryos.

Automated detection of qualitative spatio-temporal features in electrocardiac activation maps.

Science.gov (United States)

Ironi, Liliana; Tentoni, Stefania

2007-02-01

This paper describes a piece of work aiming at the realization of a tool for the automated interpretation of electrocardiac maps. Such maps can capture a number of electrical conduction pathologies, such as arrhytmia, that can be missed by the analysis of traditional electrocardiograms. But, their introduction into the clinical practice is still far away as their interpretation requires skills that belongs to very few experts. Then, an automated interpretation tool would bridge the gap between the established research outcome and clinical practice with a consequent great impact on health care. Qualitative spatial reasoning can play a crucial role in the identification of spatio-temporal patterns and salient features that characterize the heart electrical activity. We adopted the spatial aggregation (SA) conceptual framework and an interplay of numerical and qualitative information to extract features from epicardial maps, and to make them available for reasoning tasks. Our focus is on epicardial activation isochrone maps as they are a synthetic representation of spatio-temporal aspects of the propagation of the electrical excitation. We provide a computational SA-based methodology to extract, from 3D epicardial data gathered over time, (1) the excitation wavefront structure, and (2) the salient features that characterize wavefront propagation and visually correspond to specific geometric objects. The proposed methodology provides a robust and efficient way to identify salient pieces of information in activation time maps. The hierarchical structure of the abstracted geometric objects, crucial in capturing the prominent information, facilitates the definition of general rules necessary to infer the correlation between pathophysiological patterns and wavefront structure and propagation.

The artificial object detection and current velocity measurement using SAR ocean surface images

Science.gov (United States)

Alpatov, Boris; Strotov, Valery; Ershov, Maksim; Muraviev, Vadim; Feldman, Alexander; Smirnov, Sergey

2017-10-01

Due to the fact that water surface covers wide areas, remote sensing is the most appropriate way of getting information about ocean environment for vessel tracking, security purposes, ecological studies and others. Processing of synthetic aperture radar (SAR) images is extensively used for control and monitoring of the ocean surface. Image data can be acquired from Earth observation satellites, such as TerraSAR-X, ERS, and COSMO-SkyMed. Thus, SAR image processing can be used to solve many problems arising in this field of research. This paper discusses some of them including ship detection, oil pollution control and ocean currents mapping. Due to complexity of the problem several specialized algorithm are necessary to develop. The oil spill detection algorithm consists of the following main steps: image preprocessing, detection of dark areas, parameter extraction and classification. The ship detection algorithm consists of the following main steps: prescreening, land masking, image segmentation combined with parameter measurement, ship orientation estimation and object discrimination. The proposed approach to ocean currents mapping is based on Doppler's law. The results of computer modeling on real SAR images are presented. Based on these results it is concluded that the proposed approaches can be used in maritime applications.

Most people do not ignore salient invalid cues in memory-based decisions.

Science.gov (United States)

Platzer, Christine; Bröder, Arndt

2012-08-01

Former experimental studies have shown that decisions from memory tend to rely only on a few cues, following simple noncompensatory heuristics like "take the best." However, it has also repeatedly been demonstrated that a pictorial, as opposed to a verbal, representation of cue information fosters the inclusion of more cues in compensatory strategies, suggesting a facilitated retrieval of cue patterns. These studies did not properly control for visual salience of cues, however. In the experiment reported here, the cue salience hierarchy established in a pilot study was either congruent or incongruent with the validity order of the cues. Only the latter condition increased compensatory decision making, suggesting that the apparent representational format effect is, rather, a salience effect: Participants automatically retrieve and incorporate salient cues irrespective of their validity. Results are discussed with respect to reaction time data.

Detection of Buried Objects : The MUD Project

NARCIS (Netherlands)

Quesson, B.A.J.; Vossen, R. van; Zampolli, M.; Beckers, A.L.D.

2011-01-01

The aim of the Mine Underwater Detection (MUD) project at TNO is to experimentally investigate the acoustic and magnetic detection of explosives underwater, buried in a soft sediment layer. This problem is relevant for the protection of harbors and littoral assets against terrorist attacks and for

Inverse modelling and pulsating torque minimization of salient pole non-sinusoidal synchronous machines

Energy Technology Data Exchange (ETDEWEB)

Ait-gougam, Y.; Ibtiouen, R.; Touhami, O. [Laboratoire de Recherche en Electrotechnique, Ecole Nationale Polytechnique, BP 182, El-Harrach 16200 (Algeria); Louis, J.-P.; Gabsi, M. [Systemes et Applications des Technologies de l' Information et de l' Energie (SATIE), CNRS UMR 8029, Ecole Normale Superieure de Cachan, 61 Avenue du President Wilson, 94235 Cachan Cedex (France)

2008-01-15

Sinusoidal motor's mathematical models are usually obtained using classical d-q transformation in the case of salient pole synchronous motors having sinusoidal field distribution. In this paper, a new inverse modelling for synchronous motors is presented. This modelling is derived from the properties of constant torque curves in the Concordia's reference frame. It takes into account the non-sinusoidal field distribution; EMF, self and mutual inductances having non-sinusoidal variations with respect to the angular rotor position. Both copper losses and torque ripples are minimized by adapted currents waveforms calculated from this model. Experimental evaluation was carried out on a DSP-controlled PMSM drive platform. Test results obtained demonstrate the effectiveness of the proposed method in reducing torque ripple. (author)

Deep Salient Feature Based Anti-Noise Transfer Network for Scene Classification of Remote Sensing Imagery

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Xi Gong

2018-03-01

Full Text Available Remote sensing (RS scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises. This paper proposes a deep salient feature based anti-noise transfer network (DSFATN method that effectively enhances and explores the high-level features for RS scene classification in different scales and noise conditions. In DSFATN, a novel discriminative deep salient feature (DSF is introduced by saliency-guided DSF extraction, which conducts a patch-based visual saliency (PBVS algorithm using “visual attention” mechanisms to guide pre-trained CNNs for producing the discriminative high-level features. Then, an anti-noise network is proposed to learn and enhance the robust and anti-noise structure information of RS scene by directly propagating the label information to fully-connected layers. A joint loss is used to minimize the anti-noise network by integrating anti-noise constraint and a softmax classification loss. The proposed network architecture can be easily trained with a limited amount of training data. The experiments conducted on three different scale RS scene datasets show that the DSFATN method has achieved excellent performance and great robustness in different scales and noise conditions. It obtains classification accuracy of 98.25%, 98.46%, and 98.80%, respectively, on the UC Merced Land Use Dataset (UCM, the Google image dataset of SIRI-WHU, and the SAT-6 dataset, advancing the state-of-the-art substantially.

The use of the Emotional-Object Recognition as an assay to assess learning and memory associated to an aversive stimulus in rodents.

Science.gov (United States)

Brancato, Anna; Lavanco, Gianluca; Cavallaro, Angela; Plescia, Fulvio; Cannizzaro, Carla

2016-12-01

Emotionally salient experiences induce the formation of explicit memory traces, besides eliciting automatic or implicit emotional memory in rodents. This study aims at investigating the implementation of a novel task for studying the formation of limbic memory engrams as a result of the acquisition- and retrieval- of fear-conditioning - biased declarative memory traces, measured by animal discrimination of an "emotional-object". Moreover, by using this new method we investigated the potential interactions between stimulation of cannabinoid transmission and integration of emotional information and cognitive functioning. The Emotional-Object Recognition task is composed of 3 following sessions: habituation; cued fear-conditioned learning; emotional recognition. Rats are exposed to Context "B chamber" for habituation and cued fear-conditioning, and tested in Context "A chamber" for emotional-object recognition. Cued fear-conditioning induces a reduction in emotional-object exploration time during the Emotional-Object Recognition task in controls. The activation of cannabinoid signalling impairs limbic memory formation, with respect to vehicle. The Emotional-Object Recognition test overcomes several limitations of commonly employed methods that explore declarative-, spatial memory and fear-conditioning in a non-integrated manner. It allows the assessment of unbiased cognitive indicators of emotional learning and memory. The Emotional-Object Recognition task is a valuable tool for investigating whether, and at what extent, specific drugs or pathological conditions that interfere with the individual affective/emotional homeostasis, can modulate the formation of emotionally salient explicit memory traces, thus jeopardizing control and regulation of animal behavioural strategy. Copyright © 2016 Elsevier B.V. All rights reserved.

Detection of shallow buried objects using an autoregressive model on the ground penetrating radar signal

Science.gov (United States)

Nabelek, Daniel P.; Ho, K. C.

2013-06-01

The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.

Detection, Location and Grasping Objects Using a Stereo Sensor on UAV in Outdoor Environments

Directory of Open Access Journals (Sweden)

Pablo Ramon Soria

2017-01-01

Full Text Available The article presents a vision system for the autonomous grasping of objects with Unmanned Aerial Vehicles (UAVs in real time. Giving UAVs the capability to manipulate objects vastly extends their applications, as they are capable of accessing places that are difficult to reach or even unreachable for human beings. This work is focused on the grasping of known objects based on feature models. The system runs in an on-board computer on a UAV equipped with a stereo camera and a robotic arm. The algorithm learns a feature-based model in an offline stage, then it is used online for detection of the targeted object and estimation of its position. This feature-based model was proved to be robust to both occlusions and the presence of outliers. The use of stereo cameras improves the learning stage, providing 3D information and helping to filter features in the online stage. An experimental system was derived using a rotary-wing UAV and a small manipulator for final proof of concept. The robotic arm is designed with three degrees of freedom and is lightweight due to payload limitations of the UAV. The system has been validated with different objects, both indoors and outdoors.

Change Analysis and Decision Tree Based Detection Model for Residential Objects across Multiple Scales

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CHEN Liyan

2018-03-01

Full Text Available Change analysis and detection plays important role in the updating of multi-scale databases.When overlap an updated larger-scale dataset and a to-be-updated smaller-scale dataset,people usually focus on temporal changes caused by the evolution of spatial entities.Little attention is paid to the representation changes influenced by map generalization.Using polygonal building data as an example,this study examines the changes from different perspectives,such as the reasons for their occurrence,their performance format.Based on this knowledge,we employ decision tree in field of machine learning to establish a change detection model.The aim of the proposed model is to distinguish temporal changes that need to be applied as updates to the smaller-scale dataset from representation changes.The proposed method is validated through tests using real-world building data from Guangzhou city.The experimental results show the overall precision of change detection is more than 90%,which indicates our method is effective to identify changed objects.

A study of earthquake-induced building detection by object oriented classification approach

Science.gov (United States)

Sabuncu, Asli; Damla Uca Avci, Zehra; Sunar, Filiz

2017-04-01

Among the natural hazards, earthquakes are the most destructive disasters and cause huge loss of lives, heavily infrastructure damages and great financial losses every year all around the world. According to the statistics about the earthquakes, more than a million earthquakes occur which is equal to two earthquakes per minute in the world. Natural disasters have brought more than 780.000 deaths approximately % 60 of all mortality is due to the earthquakes after 2001. A great earthquake took place at 38.75 N 43.36 E in the eastern part of Turkey in Van Province on On October 23th, 2011. 604 people died and about 4000 buildings seriously damaged and collapsed after this earthquake. In recent years, the use of object oriented classification approach based on different object features, such as spectral, textural, shape and spatial information, has gained importance and became widespread for the classification of high-resolution satellite images and orthophotos. The motivation of this study is to detect the collapsed buildings and debris areas after the earthquake by using very high-resolution satellite images and orthophotos with the object oriented classification and also see how well remote sensing technology was carried out in determining the collapsed buildings. In this study, two different land surfaces were selected as homogenous and heterogeneous case study areas. In the first step of application, multi-resolution segmentation was applied and optimum parameters were selected to obtain the objects in each area after testing different color/shape and compactness/smoothness values. In the next step, two different classification approaches, namely "supervised" and "unsupervised" approaches were applied and their classification performances were compared. Object-based Image Analysis (OBIA) was performed using e-Cognition software.

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

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-01-01

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

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

Science.gov (United States)

Chen, Qiang; Chen, Yunhao; Jiang, Weiguo

2016-07-30

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

Children's use of comparison and function in novel object categorization.

Science.gov (United States)

Kimura, Katherine; Hunley, Samuel B; Namy, Laura L

2018-06-01

Although young children often rely on salient perceptual cues, such as shape, when categorizing novel objects, children eventually shift towards deeper relational reasoning about category membership. This study investigates what information young children use to classify novel instances of familiar categories. Specifically, we investigated two sources of information that have the potential to facilitate the classification of novel exemplars: (1) comparison of familiar category instances, and (2) attention to function information that might direct children's attention to functionally relevant perceptual features. Across two experiments, we found that comparing two perceptually similar category members-particularly when function information was also highlighted-led children to discover non-obvious relational features that supported their categorization of novel category instances. Together, these findings demonstrate that comparison may aid in novel object categorization by heightening the salience of less obvious, yet functionally relevant, relational structures that support conceptual reasoning. Copyright © 2018. Published by Elsevier Inc.

Underwater Cylindrical Object Detection Using the Spectral Features of Active Sonar Signals with Logistic Regression Models

Directory of Open Access Journals (Sweden)

Yoojeong Seo

2018-01-01

Full Text Available The issue of detecting objects bottoming on the sea floor is significant in various fields including civilian and military areas. The objective of this study is to investigate the logistic regression model to discriminate the target from the clutter and to verify the possibility of applying the model trained by the simulated data generated by the mathematical model to the real experimental data because it is not easy to obtain sufficient data in the underwater field. In the first stage of this study, when the clutter signal energy is so strong that the detection of a target is difficult, the logistic regression model is employed to distinguish the strong clutter signal and the target signal. Previous studies have found that if the clutter energy is larger, false detection occurs even for the various existing detection schemes. For this reason, the discrete Fourier transform (DFT magnitude spectrum of acoustic signals received by active sonar is applied to train the model to distinguish whether the received signal contains a target signal or not. The goodness of fit of the model is verified in terms of receiver operation characteristic (ROC, area under ROC curve (AUC, and classification table. The detection performance of the proposed model is evaluated in terms of detection rate according to target to clutter ratio (TCR. Furthermore, the real experimental data are employed to test the proposed approach. When using the experimental data to test the model, the logistic regression model is trained by the simulated data that are generated based on the mathematical model for the backscattering of the cylindrical object. The mathematical model is developed according to the size of the cylinder used in the experiment. Since the information on the experimental environment including the sound speed, the sediment type and such is not available, once simulated data are generated under various conditions, valid simulated data are selected using 70% of the

An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces.

Science.gov (United States)

Descombes, Xavier; Kruggel, Frithjof; Wollny, Gert; Gertz, Hermann Josef

2004-02-01

This paper is concerned with the detection of multiple small brain lesions from magnetic resonance imaging (MRI) data. A model based on the marked point process framework is designed to detect Virchow-Robin spaces (VRSs). These tubular shaped spaces are due to retraction of the brain parenchyma from its supplying arteries. VRS are described by simple geometrical objects that are introduced as small tubular structures. Their radiometric properties are embedded in a data term. A prior model includes interactions describing the clustering property of VRS. A Reversible Jump Markov Chain Monte Carlo algorithm (RJMCMC) optimizes the proposed model, obtained by multiplying the prior and the data model. Example results are shown on T1-weighted MRI datasets of elderly subjects.

Multispectral image analysis for object recognition and classification

Science.gov (United States)

Viau, C. R.; Payeur, P.; Cretu, A.-M.

2016-05-01

Computer and machine vision applications are used in numerous fields to analyze static and dynamic imagery in order to assist or automate decision-making processes. Advancements in sensor technologies now make it possible to capture and visualize imagery at various wavelengths (or bands) of the electromagnetic spectrum. Multispectral imaging has countless applications in various fields including (but not limited to) security, defense, space, medical, manufacturing and archeology. The development of advanced algorithms to process and extract salient information from the imagery is a critical component of the overall system performance. The fundamental objective of this research project was to investigate the benefits of combining imagery from the visual and thermal bands of the electromagnetic spectrum to improve the recognition rates and accuracy of commonly found objects in an office setting. A multispectral dataset (visual and thermal) was captured and features from the visual and thermal images were extracted and used to train support vector machine (SVM) classifiers. The SVM's class prediction ability was evaluated separately on the visual, thermal and multispectral testing datasets.

WE-G-204-05: Relative Object Detectability Evaluation of a New High Resolution A-Se Direct Detection System Compared to Indirect Micro-Angiographic Fluoroscopic (MAF) Detectors

International Nuclear Information System (INIS)

Russ, M; Nagesh, S Setlur; Ionita, C; Bednarek, D; Rudin, S; Scott, C; Karim, K

2015-01-01

Purpose: To evaluate the task specific imaging performance of a new 25µm pixel pitch, 1000µm thick amorphous selenium direct detection system with CMOS readout for typical angiographic exposure parameters using the relative object detectability (ROD) metric. Methods: The ROD metric uses a simulated object function weighted at each spatial frequency by the detectors’ detective quantum efficiency (DQE), which is an intrinsic performance metric. For this study, the simulated objects were aluminum spheres of varying diameter (0.05–0.6mm). The weighted object function is then integrated over the full range of detectable frequencies inherent to each detector, and a ratio is taken of the resulting value for two detectors. The DQE for the 25µm detector was obtained from a simulation of a proposed a-Se detector using an exposure of 200µR for a 50keV x-ray beam. This a-Se detector was compared to two microangiographic fluoroscope (MAF) detectors [the MAF-CCD with pixel size of 35µm and Nyquist frequency of 14.2 cycles/mm and the MAF-CMOS with pixel size of 75µm and Nyquist frequency of 6.6 cycles/mm] and a standard flat-panel detector (FPD with pixel size of 194µm and Nyquist frequency of 2.5cycles/mm). Results: ROD calculations indicated vastly superior performance by the a-Se detector in imaging small aluminum spheres. For the 50µm diameter sphere, the ROD values for the a-Se detector compared to the MAF-CCD, the MAF-CMOS, and the FPD were 7.3, 9.3 and 58, respectively. Detector performance in the low frequency regime was dictated by each detector’s DQE(0) value. Conclusion: The a-Se with CMOS readout is unique and appears to have distinctive advantages of incomparable high resolution, low noise, no readout lag, and expandable design. The a-Se direct detection system will be a powerful imaging tool in angiography, with potential break-through applications in diagnosis and treatment of neuro-vascular disease. Supported by NIH Grant: 2R01EB002873 and an

WE-G-204-05: Relative Object Detectability Evaluation of a New High Resolution A-Se Direct Detection System Compared to Indirect Micro-Angiographic Fluoroscopic (MAF) Detectors

Energy Technology Data Exchange (ETDEWEB)

Russ, M; Nagesh, S Setlur; Ionita, C; Bednarek, D; Rudin, S [Toshiba Stroke and Vascular Research Center, University at Buffalo (SUNY), Buffalo, NY (United States); Scott, C; Karim, K [University of Waterloo, Waterloo, ON (Canada)

2015-06-15

Purpose: To evaluate the task specific imaging performance of a new 25µm pixel pitch, 1000µm thick amorphous selenium direct detection system with CMOS readout for typical angiographic exposure parameters using the relative object detectability (ROD) metric. Methods: The ROD metric uses a simulated object function weighted at each spatial frequency by the detectors’ detective quantum efficiency (DQE), which is an intrinsic performance metric. For this study, the simulated objects were aluminum spheres of varying diameter (0.05–0.6mm). The weighted object function is then integrated over the full range of detectable frequencies inherent to each detector, and a ratio is taken of the resulting value for two detectors. The DQE for the 25µm detector was obtained from a simulation of a proposed a-Se detector using an exposure of 200µR for a 50keV x-ray beam. This a-Se detector was compared to two microangiographic fluoroscope (MAF) detectors [the MAF-CCD with pixel size of 35µm and Nyquist frequency of 14.2 cycles/mm and the MAF-CMOS with pixel size of 75µm and Nyquist frequency of 6.6 cycles/mm] and a standard flat-panel detector (FPD with pixel size of 194µm and Nyquist frequency of 2.5cycles/mm). Results: ROD calculations indicated vastly superior performance by the a-Se detector in imaging small aluminum spheres. For the 50µm diameter sphere, the ROD values for the a-Se detector compared to the MAF-CCD, the MAF-CMOS, and the FPD were 7.3, 9.3 and 58, respectively. Detector performance in the low frequency regime was dictated by each detector’s DQE(0) value. Conclusion: The a-Se with CMOS readout is unique and appears to have distinctive advantages of incomparable high resolution, low noise, no readout lag, and expandable design. The a-Se direct detection system will be a powerful imaging tool in angiography, with potential break-through applications in diagnosis and treatment of neuro-vascular disease. Supported by NIH Grant: 2R01EB002873 and an

Ground penetrating radar system and method for detecting an object on or below a ground surface

NARCIS (Netherlands)

De Jongth, R.; Yarovoy, A.; Schukin, A.

2001-01-01

Ground penetrating radar system for detecting objects (17) on or below a ground surface (18), comprising at least one transmit antenna (13) having a first foot print (14) at the ground surface, at least one receive antenna (15) having a second foot print (16) at the ground surface, and processing

Does long-term object priming depend on the explicit detection of object identity at encoding?

Directory of Open Access Journals (Sweden)

Carlos Alexandre Gomes

2015-03-01

Full Text Available It is currently unclear whether objects have to be explicitly identified at encoding for reliable behavioural long-term object priming to occur. We conducted two experiments that investigated long-term object and non-object priming using a selective-attention encoding manipulation that reduces explicit object identification. In Experiment 1, participants either counted dots flashed within an object picture (shallow encoding or engaged in an animacy task (deep encoding at study, whereas, at test, they performed an object-decision task. Priming, as measured by reaction times, was observed for both types of encoding, and was of equivalent magnitude. In Experiment 2, non-object priming (faster reaction times for studied relative to unstudied non-objects was also obtained under the same selective-attention encoding manipulation as in Experiment 1, and the magnitude of the priming effect was equivalent between experiments. In contrast, we observed a linear decrement in recognition memory accuracy across conditions (deep encoding of Experiment 1 > shallow encoding Experiment 1 > shallow encoding of Experiment 2, suggesting that priming was not contaminated by explicit memory strategies. We argue that our results are more consistent with the identification/production framework than the perceptual/conceptual distinction, and we conclude that priming of pictures largely ignored at encoding can be subserved by the automatic retrieval of two types of instances: one at the motor-level and another at an object-decision level.

Field of attention for instantaneous object recognition.

Directory of Open Access Journals (Sweden)

Jian-Gao Yao

Full Text Available BACKGROUND: Instantaneous object discrimination and categorization are fundamental cognitive capacities performed with the guidance of visual attention. Visual attention enables selection of a salient object within a limited area of the visual field; we referred to as "field of attention" (FA. Though there is some evidence concerning the spatial extent of object recognition, the following questions still remain unknown: (a how large is the FA for rapid object categorization, (b how accuracy of attention is distributed over the FA, and (c how fast complex objects can be categorized when presented against backgrounds formed by natural scenes. METHODOLOGY/PRINCIPAL FINDINGS: To answer these questions, we used a visual perceptual task in which subjects were asked to focus their attention on a point while being required to categorize briefly flashed (20 ms photographs of natural scenes by indicating whether or not these contained an animal. By measuring the accuracy of categorization at different eccentricities from the fixation point, we were able to determine the spatial extent and the distribution of accuracy over the FA, as well as the speed of categorizing objects using stimulus onset asynchrony (SOA. Our results revealed that subjects are able to rapidly categorize complex natural images within about 0.1 s without eye movement, and showed that the FA for instantaneous image categorization covers a visual field extending 20° × 24°, and accuracy was highest (>90% at the center of FA and declined with increasing eccentricity. CONCLUSIONS/SIGNIFICANCE: In conclusion, human beings are able to categorize complex natural images at a glance over a large extent of the visual field without eye movement.

PastVision+: Thermovisual Inference of Recent Medicine Intake by Detecting Heated Objects and Cooled Lips

Directory of Open Access Journals (Sweden)

Martin Cooney

2017-11-01

Full Text Available This article addresses the problem of how a robot can infer what a person has done recently, with a focus on checking oral medicine intake in dementia patients. We present PastVision+, an approach showing how thermovisual cues in objects and humans can be leveraged to infer recent unobserved human–object interactions. Our expectation is that this approach can provide enhanced speed and robustness compared to existing methods, because our approach can draw inferences from single images without needing to wait to observe ongoing actions and can deal with short-lasting occlusions; when combined, we expect a potential improvement in accuracy due to the extra information from knowing what a person has recently done. To evaluate our approach, we obtained some data in which an experimenter touched medicine packages and a glass of water to simulate intake of oral medicine, for a challenging scenario in which some touches were conducted in front of a warm background. Results were promising, with a detection accuracy of touched objects of 50% at the 15 s mark and 0% at the 60 s mark, and a detection accuracy of cooled lips of about 100 and 60% at the 15 s mark for cold and tepid water, respectively. Furthermore, we conducted a follow-up check for another challenging scenario in which some participants pretended to take medicine or otherwise touched a medicine package: accuracies of inferring object touches, mouth touches, and actions were 72.2, 80.3, and 58.3% initially, and 50.0, 81.7, and 50.0% at the 15 s mark, with a rate of 89.0% for person identification. The results suggested some areas in which further improvements would be possible, toward facilitating robot inference of human actions, in the context of medicine intake monitoring.

Detection of water masers toward young stellar objects in the Large Magellanic Cloud

International Nuclear Information System (INIS)

Johanson, A. K.; Migenes, V.; Breen, S. L.

2014-01-01

We present results from a search for water maser emission toward N4A, N190, and N206, three regions of massive star formation in the Large Magellanic Cloud (LMC). Four water masers were detected; two toward N4A, and two toward N190. In the latter region, no previously known maser emission has been reported. Future studies of maser proper motion to determine the galactic dynamics of the LMC will benefit from the independent data points the new masers in N190 provide. Two of these masers are associated with previously identified massive young stellar objects (YSOs), which strongly supports the authenticity of the classification. We argue that the other two masers identify previously unknown YSOs. No masers were detected toward N206, but it does host a newly discovered 22 GHz continuum source, also associated with a massive YSO. We suggest that future surveys for water maser emission in the LMC be targeted toward the more luminous, massive YSOs.

Binary compact object inspiral: Detection expectations

Indian Academy of Sciences (India)

... events/yr. These predictions, for the first time, bring the expectations for DNS detections by initial LIGO .... our galactic event rate out to the local group. ..... VK thanks her collaborators in the work reviewed here: C Kim, P Grandclément,. C Ihm ...

Object-Based Image Analysis for Detection of Japanese Knotweed s.l. taxa (Polygonaceae in Wales (UK

Directory of Open Access Journals (Sweden)

Daniel Jones

2011-02-01

Full Text Available Japanese Knotweed s.l. taxa are amongst the most aggressive vascular plant Invasive Alien Species (IAS in the world. These taxa form dense, suppressive monocultures and are persistent, pervasive invaders throughout the more economically developed countries (MEDCs of the world. The current paper utilises the Object-Based Image Analysis (OBIA approach of Definiens Imaging Developer software, in combination with very high spatial resolution (VHSR colour infra-red (CIR and visible‑band (RGB aerial photography in order to detect Japanese Knotweed s.l. taxa in Wales (UK. An algorithm was created using Definiens in order to detect these taxa, using variables found to effectively distinguish them from landscape and vegetation features. The results of the detection algorithm were accurate, as confirmed by field validation and desk‑based studies. Further, these results may be incorporated into Geographical Information Systems (GIS research as they are readily transferable as vector polygons (shapefiles. The successful detection results developed within the Definiens software should enable greater management and control efficacy. Further to this, the basic principles of the detection process could enable detection of these taxa worldwide, given the (relatively limited technical requirements necessary to conduct further analyses.

Salient aspects of PBP2A-inhibition; A QSAR Study.

Science.gov (United States)

Ogunleye, Adewale J; Eniafe, Gabriel O; Inyang, Olumide K; Adewumi, Benjamin; Omotuyi, Olaposi I

2018-05-15

Backgound: Inhibition of penicillin binding protein 2A (PBP2A) represents a sound drug design strategy in combatting Methicillin resistant Staphylococcus aureus (MRSA). Considering the urgent need for effective antimicrobials in combatting MRSA infections, we have developed a statistically robust ensemble of molecular descriptors (1, 2, & 3-D) from compounds targeting PBP2A in vivo. 37 (training set: 26, test set: 11) PBP2A-inhibitors were submitted for descriptor generation after which an unsupervised, non-exhaustive genetic algorithm (GA) was deployed for fishing out the best descriptor subset. Assignment of descriptors to a regression model was accomplished with the Partial Least Square (PLS) algorithm. At the end, an ensemble of 30 descriptors accurately predicted the ligand bioactivity, IC50 (R = 0.9996, R2 = 0.9992, R2a = 0.9949, SEE =, 0.2297 Q2LOO = 0.9741). Inferentially, we noticed that the overall efficacy of this model greatly depends on atomic polarizability and negative charge (electron) density. Besides the formula derived, the high dimensional model also offers critical insights into salient cheminformatics parameter to note during hit-to-lead PBP2A-antagonist optimization. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Automatic landslide detection from LiDAR DTM derivatives by geographic-object-based image analysis based on open-source software

Science.gov (United States)

Knevels, Raphael; Leopold, Philip; Petschko, Helene

2017-04-01

With high-resolution airborne Light Detection and Ranging (LiDAR) data more commonly available, many studies have been performed to facilitate the detailed information on the earth surface and to analyse its limitation. Specifically in the field of natural hazards, digital terrain models (DTM) have been used to map hazardous processes such as landslides mainly by visual interpretation of LiDAR DTM derivatives. However, new approaches are striving towards automatic detection of landslides to speed up the process of generating landslide inventories. These studies usually use a combination of optical imagery and terrain data, and are designed in commercial software packages such as ESRI ArcGIS, Definiens eCognition, or MathWorks MATLAB. The objective of this study was to investigate the potential of open-source software for automatic landslide detection based only on high-resolution LiDAR DTM derivatives in a study area within the federal state of Burgenland, Austria. The study area is very prone to landslides which have been mapped with different methodologies in recent years. The free development environment R was used to integrate open-source geographic information system (GIS) software, such as SAGA (System for Automated Geoscientific Analyses), GRASS (Geographic Resources Analysis Support System), or TauDEM (Terrain Analysis Using Digital Elevation Models). The implemented geographic-object-based image analysis (GEOBIA) consisted of (1) derivation of land surface parameters, such as slope, surface roughness, curvature, or flow direction, (2) finding optimal scale parameter by the use of an objective function, (3) multi-scale segmentation, (4) classification of landslide parts (main scarp, body, flanks) by k-mean thresholding, (5) assessment of the classification performance using a pre-existing landslide inventory, and (6) post-processing analysis for the further use in landslide inventories. The results of the developed open-source approach demonstrated good

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

Science.gov (United States)

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

2018-02-01

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

Learning Photometric Invariance for Object Detection

NARCIS (Netherlands)

Álvarez, J.M.; Gevers, T.; López, A.M.

2010-01-01

Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian

Biosensor technology for the detection of illegal drugs I: objectives, preparatory work, and drug enrichment

Science.gov (United States)

Hilpert, Reinhold; Binder, Florian; Grol, Michael; Hallermayer, Klaus; Josel, Hans-Peter; Klein, Christian; Maier, Josef; Oberpriller, Helmut; Ritter, Josef; Scheller, Frieder W.

1994-10-01

In a joint project of Deutsche Aerospace, Boehringer Mannheim and the University of Potsdam portable devices for the detection of illegal drugs, based on biosensor technology, are being developed. The concept enrichment of the drug from the gas phase and detection by immunological means. This publication covers the description of our objectives, preparatory work and results concerning enrichment of drugs from the gas phase. Vapor pressures of cocaine and cannabinoids have been determined. A test gas generator has been constructed which allows for reproducible preparation of cocaine concentrations between 2 ng/l and 2 pg/l. Coupling of a thermodesorption unit with GC/MS has been established for reference analysis. As another analytical tool, an ELISA with a lower detection limit of about 0,5 pg cocaine/assay has been developed. Applying fleece-type adsorbers, enrichment factors for cocaine in the range of 105 have been realized. No significant interference was found with potentially disturbing substances.

Crowded Field Photometry and Moving Object Detection with Optimal Image Subtraction

Science.gov (United States)

Lee, Austin A. T.; Scheulen, F.; Sauro, C. M.; McMahon, C. T.; Berry, S. J.; Robinson, C. H.; Buie, M. W.; Little, P.

2010-05-01

High precision photometry and moving object detection are essential in the study of Pluto and the Kuiper Belt. In particular, the New Horizons mission would benefit from an accurate and fast method of performing image subtraction to locate faint Kuiper Belt Objects (KBO) among large data sets. The optimal image subtraction (OIS) algorithm was optimized for IDL to decrease execution time by a factor of about 140 from a previous implementation (Miller 2008, PASP, 120, 449). In addition, a powerful image transformation and interpolation routine was written to provide OIS with well-aligned input images using astrometric fit data. The first half of this project is complete including the code optimization and the alignment routine. The second half of the project is focused on using these tools to search a 5 x 10 degree search area to find KBOs for possible targets for the New Horizons mission. We will present examples of how these tools work and along with resulting Pluto photometry and KBO target lists. The optimized OIS and transformation routines are available in Marc Buie's IDL library at http://www.boulder.swri.edu/ buie/idl/ as ois.pro and dewarp.pro. This project was conducted for Harvey Mudd College's Clinic Program with financial support from the NASA Planetary Astronomy Program grant number NNX09AB43G.

Visual long-term memory and change blindness: Different effects of pre- and post-change information on one-shot change detection using meaningless geometric objects.