Only a small part of the large intensities interval found in high dynamic range scenes can be captured with usual image sensors. This is why delivered images may contain under or overexposed pixels. A popular approach to overcome this problem is to take several images using different exposure parameters, and then fuse them into one single image. This exposure fusion is mostly performed as a weighted average between the corresponding pixels. The challenge is to find weights that produce best fused image quality and in a minimum amount of operations to meet real time requirements. In this paper we present a supervised learning method to estimate generalized exposure fusion weights and we demonstrate how they can be used to fuse any exposures very fast. Subjective and objective comparisons with some relevant works are conducted to prove the effectiveness of the proposed method.
{"title":"Generalized Exposure Fusion Weights Estimation","authors":"Mohammed Elamine Moumene, R. Nourine, D. Ziou","doi":"10.1109/CRV.2014.8","DOIUrl":"https://doi.org/10.1109/CRV.2014.8","url":null,"abstract":"Only a small part of the large intensities interval found in high dynamic range scenes can be captured with usual image sensors. This is why delivered images may contain under or overexposed pixels. A popular approach to overcome this problem is to take several images using different exposure parameters, and then fuse them into one single image. This exposure fusion is mostly performed as a weighted average between the corresponding pixels. The challenge is to find weights that produce best fused image quality and in a minimum amount of operations to meet real time requirements. In this paper we present a supervised learning method to estimate generalized exposure fusion weights and we demonstrate how they can be used to fuse any exposures very fast. Subjective and objective comparisons with some relevant works are conducted to prove the effectiveness of the proposed method.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129114664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We address the problem of multimedia event detection from videos captured 'in the wild,' in particular the fusion of cues from multiple aspects of the video's content: detected objects, observed motion, audio signatures, etc. We employ score fusion, also known as late fusion, and propose a method that learns local weightings of the various base classifier scores which respect the performance differences arising from the video quality. Classifiers working with visual texture features, for instance, are given reduced weight when applied to subsets of the video corpus with high compression, and the weights associated with the other classifiers are adjusted to reflect this lack of confidence. We present a method to automatically partition the video corpus into relevant subsets, and to learn local weightings which optimally fuse scores on a particular subset. Improvements in event detection performance are demonstrated on the TRECVid Multimedia Event Detection (MED) MED Test dataset, and comparisons are provided to several other score fusion methods.
{"title":"Metadata-Weighted Score Fusion for Multimedia Event Detection","authors":"Scott McCloskey, Jingchen Liu","doi":"10.1109/CRV.2014.47","DOIUrl":"https://doi.org/10.1109/CRV.2014.47","url":null,"abstract":"We address the problem of multimedia event detection from videos captured 'in the wild,' in particular the fusion of cues from multiple aspects of the video's content: detected objects, observed motion, audio signatures, etc. We employ score fusion, also known as late fusion, and propose a method that learns local weightings of the various base classifier scores which respect the performance differences arising from the video quality. Classifiers working with visual texture features, for instance, are given reduced weight when applied to subsets of the video corpus with high compression, and the weights associated with the other classifiers are adjusted to reflect this lack of confidence. We present a method to automatically partition the video corpus into relevant subsets, and to learn local weightings which optimally fuse scores on a particular subset. Improvements in event detection performance are demonstrated on the TRECVid Multimedia Event Detection (MED) MED Test dataset, and comparisons are provided to several other score fusion methods.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"1_OS 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127817916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. P. Quintero, R. T. Fomena, A. Shademan, Oscar A. Ramirez, Martin Jägersand
We propose and develop an interactive semi-autonomous control of robot arms. Our system controls two interactions: (1) A user can naturally control a robot arm by a direct linkage to the arm motion from the tracked human skeleton. (2) An autonomous image-based visual servoing routine can be triggered for precise positioning. Coarse motions are executed by human teleoperation and fine motions by image-based visual servoing. A successful application of our proposed interaction is presented for a WAM arm equipped with an eye-in-hand camera.
{"title":"Interactive Teleoperation Interface for Semi-autonomous Control of Robot Arms","authors":"C. P. Quintero, R. T. Fomena, A. Shademan, Oscar A. Ramirez, Martin Jägersand","doi":"10.1109/CRV.2014.55","DOIUrl":"https://doi.org/10.1109/CRV.2014.55","url":null,"abstract":"We propose and develop an interactive semi-autonomous control of robot arms. Our system controls two interactions: (1) A user can naturally control a robot arm by a direct linkage to the arm motion from the tracked human skeleton. (2) An autonomous image-based visual servoing routine can be triggered for precise positioning. Coarse motions are executed by human teleoperation and fine motions by image-based visual servoing. A successful application of our proposed interaction is presented for a WAM arm equipped with an eye-in-hand camera.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121994130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a control method of robotic system using electromyography (EMG) signals collected by surface EMG electrodes. The EMG signals are analyzed using a neuromusculoskeletal (NMS) model that represents at the same time the muscle and the skeleton of the body. It has the advantage of adding external forces to the model without changing the initial parameters which is particularly useful for the control of exoskeletons. The algorithm has been validated through experiments consisting of moving only the elbow joint freely or while handling a barbell having various sets of loads. The results of our algorithm are then compared to the motions obtained by a motion capture system during the same session. The comparison points out the efficiency of our algorithm for predicting and estimating the arm motion using only EMG signals.
{"title":"Toward a Unified Framework for EMG Signals Processing and Controlling an Exoskeleton","authors":"G. Durandau, W. Suleiman","doi":"10.1109/CRV.2014.46","DOIUrl":"https://doi.org/10.1109/CRV.2014.46","url":null,"abstract":"In this paper, we present a control method of robotic system using electromyography (EMG) signals collected by surface EMG electrodes. The EMG signals are analyzed using a neuromusculoskeletal (NMS) model that represents at the same time the muscle and the skeleton of the body. It has the advantage of adding external forces to the model without changing the initial parameters which is particularly useful for the control of exoskeletons. The algorithm has been validated through experiments consisting of moving only the elbow joint freely or while handling a barbell having various sets of loads. The results of our algorithm are then compared to the motions obtained by a motion capture system during the same session. The comparison points out the efficiency of our algorithm for predicting and estimating the arm motion using only EMG signals.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"418 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116402753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
River Allen, Neil MacMillan, D. Marinakis, R. Nishat, Rayhan Rahman, S. Whitesides
Instrumentation of an environment with sensors can provide an effective and scalable localization solution for robots. Where GPS is not available, beacons that provide position estimates to a robot must be placed effectively in order to maximize a robots navigation accuracy and robustness. Sonar range-based beacons are reasonable candidates for low cost position estimate sensors. In this paper we explore heuristics derived from computational geometry to estimate the effectiveness of sonar beacon deployments given a predefined mobile robot path. Results from numerical simulations and experimentation demonstrate the effectiveness and scalability of our approach.
{"title":"The Range Beacon Placement Problem for Robot Navigation","authors":"River Allen, Neil MacMillan, D. Marinakis, R. Nishat, Rayhan Rahman, S. Whitesides","doi":"10.1109/CRV.2014.28","DOIUrl":"https://doi.org/10.1109/CRV.2014.28","url":null,"abstract":"Instrumentation of an environment with sensors can provide an effective and scalable localization solution for robots. Where GPS is not available, beacons that provide position estimates to a robot must be placed effectively in order to maximize a robots navigation accuracy and robustness. Sonar range-based beacons are reasonable candidates for low cost position estimate sensors. In this paper we explore heuristics derived from computational geometry to estimate the effectiveness of sonar beacon deployments given a predefined mobile robot path. Results from numerical simulations and experimentation demonstrate the effectiveness and scalability of our approach.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127726987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Among many illumination robust approaches, scale-space decomposition based methods play an important role to reduce the lighting effects in face images. However, most of the existing scale-space decomposition methods perform recognition, based on the illumination-invariant small-scale features only. We propose a scale-space decomposition based face recognition approach that extracts the features of different scales through the TV+L1 model and wavelet transform. The approach represents a subject's face image via a subspace spanned by linear combination of the features of different scales. To decide the proper identity of the probe, the nearest neighbor (NN) approach is used to measure the similarities between a probe face image and subspace representations of gallery face images. Experiments on various benchmarks have demonstrated that the system outperforms many recognition methods in the same category.
{"title":"Scale-Space Decomposition and Nearest Linear Combination Based Approach for Face Recognition","authors":"F. A. Hoque, Liang Chen","doi":"10.1109/CRV.2014.37","DOIUrl":"https://doi.org/10.1109/CRV.2014.37","url":null,"abstract":"Among many illumination robust approaches, scale-space decomposition based methods play an important role to reduce the lighting effects in face images. However, most of the existing scale-space decomposition methods perform recognition, based on the illumination-invariant small-scale features only. We propose a scale-space decomposition based face recognition approach that extracts the features of different scales through the TV+L1 model and wavelet transform. The approach represents a subject's face image via a subspace spanned by linear combination of the features of different scales. To decide the proper identity of the probe, the nearest neighbor (NN) approach is used to measure the similarities between a probe face image and subspace representations of gallery face images. Experiments on various benchmarks have demonstrated that the system outperforms many recognition methods in the same category.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127913776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Straight line fitting is an important problem in computer and robot vision. We propose a novel method for least squares line fitting that uses both the point coordinates and the local gradient orientation to fit an optimal line by minimizing the proposed algebraic distance. The proposed inclusion of gradient orientation offers several advantages: (a) one data point is sufficient for the line fit, (b) for the same number of points the fit is more precise due to inclusion of gradient orientation, and (c) outliers can be rejected based on the gradient orientation or the distance to line.
{"title":"Using Gradient Orientation to Improve Least Squares Line Fitting","authors":"T. Petković, S. Lončarić","doi":"10.1109/CRV.2014.38","DOIUrl":"https://doi.org/10.1109/CRV.2014.38","url":null,"abstract":"Straight line fitting is an important problem in computer and robot vision. We propose a novel method for least squares line fitting that uses both the point coordinates and the local gradient orientation to fit an optimal line by minimizing the proposed algebraic distance. The proposed inclusion of gradient orientation offers several advantages: (a) one data point is sufficient for the line fit, (b) for the same number of points the fit is more precise due to inclusion of gradient orientation, and (c) outliers can be rejected based on the gradient orientation or the distance to line.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Collision avoidance for small unmanned aerial vehicles operating in a variety of environments is limited by the types of available depth sensors. Currently, there are no sensors that are lightweight, function outdoors in sunlight, and cover enough of a field of view to be useful in complex environments, although many sensors excel in one or two of these areas. We present a new depth estimation method, based on concepts from multi-view stereo and structured light methods, that uses only lightweight miniature cameras and a small laser dot matrix projector to produce measurements in the range of 1-12 meters. The field of view of the system is limited only by the number and type of cameras/projectors used, and can be fully Omni directional if desired. The sensitivity of the system to design and calibration parameters is tested in simulation, and results from a functional prototype are presented.
{"title":"Towards Full Omnidirectional Depth Sensing Using Active Vision for Small Unmanned Aerial Vehicles","authors":"A. Harmat, I. Sharf","doi":"10.1109/CRV.2014.12","DOIUrl":"https://doi.org/10.1109/CRV.2014.12","url":null,"abstract":"Collision avoidance for small unmanned aerial vehicles operating in a variety of environments is limited by the types of available depth sensors. Currently, there are no sensors that are lightweight, function outdoors in sunlight, and cover enough of a field of view to be useful in complex environments, although many sensors excel in one or two of these areas. We present a new depth estimation method, based on concepts from multi-view stereo and structured light methods, that uses only lightweight miniature cameras and a small laser dot matrix projector to produce measurements in the range of 1-12 meters. The field of view of the system is limited only by the number and type of cameras/projectors used, and can be fully Omni directional if desired. The sensitivity of the system to design and calibration parameters is tested in simulation, and results from a functional prototype are presented.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114381817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visual Odometry (VO) is an integral part of many navigation techniques in mobile robotics. In this work, we investigate how the orientation of the camera affects the overall position estimates recovered from stereo VO. Through simulations and experimental work, we demonstrate that this error can be significantly reduced by changing the perspective of the stereo camera in relation to the moving platform. Specifically, we show that orienting the camera at an oblique angle to the direction of travel can reduce VO error by up to 82% in simulations and up to 59% in experimental data. A variety of parameters are investigated for their effects on this trend including frequency of captured images and camera resolution.
{"title":"Optimizing Camera Perspective for Stereo Visual Odometry","authors":"Valentin Peretroukhin, Jonathan Kelly, T. Barfoot","doi":"10.1109/CRV.2014.9","DOIUrl":"https://doi.org/10.1109/CRV.2014.9","url":null,"abstract":"Visual Odometry (VO) is an integral part of many navigation techniques in mobile robotics. In this work, we investigate how the orientation of the camera affects the overall position estimates recovered from stereo VO. Through simulations and experimental work, we demonstrate that this error can be significantly reduced by changing the perspective of the stereo camera in relation to the moving platform. Specifically, we show that orienting the camera at an oblique angle to the direction of travel can reduce VO error by up to 82% in simulations and up to 59% in experimental data. A variety of parameters are investigated for their effects on this trend including frequency of captured images and camera resolution.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125505800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main idea of this paper is to use distances between camera positions to recover the trajectory of a mobile robot. We consider a mobile platform equipped with a single fixed camera using images of the floor and their associated inter-image homographies to find these distances. We show that under the assumptions that the camera is rigidly mounted with a constant tilt and travelling at a constant height above the floor, the distance between two camera positions may be expressed in terms of the condition number of the inter-image homography. Experiments are conducted on synthetic data to verify that the derived distance formula gives distances close to the true ones and is not too sensitive to noise. We also describe how the robot trajectory may be represented as a graph with edge lengths determined by the distances computed using the formula above, and present one possible method to construct this graph given some of these distances. The experiments show promising results.
{"title":"Trajectory Estimation Using Relative Distances Extracted from Inter-image Homographies","authors":"Mårten Wadenbäck, A. Heyden","doi":"10.1109/CRV.2014.39","DOIUrl":"https://doi.org/10.1109/CRV.2014.39","url":null,"abstract":"The main idea of this paper is to use distances between camera positions to recover the trajectory of a mobile robot. We consider a mobile platform equipped with a single fixed camera using images of the floor and their associated inter-image homographies to find these distances. We show that under the assumptions that the camera is rigidly mounted with a constant tilt and travelling at a constant height above the floor, the distance between two camera positions may be expressed in terms of the condition number of the inter-image homography. Experiments are conducted on synthetic data to verify that the derived distance formula gives distances close to the true ones and is not too sensitive to noise. We also describe how the robot trajectory may be represented as a graph with edge lengths determined by the distances computed using the formula above, and present one possible method to construct this graph given some of these distances. The experiments show promising results.","PeriodicalId":385422,"journal":{"name":"2014 Canadian Conference on Computer and Robot Vision","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128296578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：