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检索条件"机构=Computer Vision and Robotics Laboratory"

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Locally adaptive support-weight approach for visual correspondence search

Locally adaptive support-weight approach for visual correspo...

引用

Conference on computer vision and Pattern Recognition (CVPR)

作者： Kuk-Jin Yoon In-So Kweon Robotics and Computer Vision Laboratory Department of Electrical Engineering and Computer Science KAIST Daejeon South Korea

ISBN: (纸本)0769523722

In this paper, we present a new area-based method for visual correspondence search that focuses on the dissimilarity computation. Local and area-based matching methods generally measure the similarity (or dissimilarity) between the image pixels using local support windows. In this approach, an appropriate support window should be selected adaptively for each pixel to make the measure reliable and certain. Finding the optimal support window with an arbitrary shape and size is, however, very difficult and generally known as an NP-hard problem. For this reason, unlike the existing methods that try to find an optimal support window, we adjusted the support-weight of each pixel in a given support window. The adaptive support-weight of a pixel is computed based on the photometric and geometric relationship with the pixel under consideration. Dissimilarity is then computed using the raw matching costs and support-weights of both support windows, and the correspondence is finally selected by the WTA (winner-takes-all) method. The experimental results for the rectified real images show that the proposed method successfully produces piecewise smooth disparity maps while preserving sharp depth discontinuities accurately.

关键词： Shape Pixel Costs computer vision NP-hard problem Robot vision systems Laboratories computer science Area measurement Photometry

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A dynamic pruning and feature selection strategy for real-time tracking

A dynamic pruning and feature selection strategy for real-ti...

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International Conference on Advanced Information Networking and Applications (AINA)

作者： D.F. Hsu D.M. Lyons Computer Vision & Robotics Laboratory Department of Computer & Information Science Fordham University Bronx NY USA

Automated video tracking is useful in a number of applications such as surveillance, multisensor networks, robotics and virtual reality. In this paper we investigate an approach to tracking based on fusing the output of a collection of video trackers, each attending to a different feature or cue on the target. We show both theoretically and experimentally that the method used to prune the growth of target hypotheses can have a great impact on the trackers performance, and indirectly, change the benefit of using linear score combination as opposed to a non-linear rank combination for fusion. We also show that the rank-score graph defined by Hsu and Taksa can be used to select a subset of features to fuse to reduce classification error.

关键词： Target tracking robotics and automation Fuses computer vision Surveillance Virtual reality Time measurement Robot vision systems Laboratories Information science

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Selecting and Evaluating Combinatorial Fusion Criteria to Improve Multitarget Tracking

Selecting and Evaluating Combinatorial Fusion Criteria to Im...

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International Conference on Information Fusion

作者： D. Frank Hsu Damian M. Lyons Jizhou Ai Computer Vision & Robotics Laboratory Department of Computer & Information Science Fordham University Bronx NY USA

ISBN: (纸本)1424409535

In many useful video tracking situations, targets move through repeated mutual occlusions. As targets undergo occlusions, the feature subsets and combinations of those features that are effective in identifying the target and improving tracking performance may change. We use combinatorial fusion analysis to select and evaluate criteria by which to identify the combination of features that will produce the most accurate tracking. In particular we show that the combination of a pair of features A and B will improve the accuracy only if (a) A and B have relative high performance, and (b) A and B are diverse. We present experimental results from three diverse video sequences to illustrate the performance of the proposed criteria

关键词： Target tracking Target recognition Humans Hidden Markov models computer vision Video sequences Layout Performance analysis Diversity reception Shape

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Combinatorial Fusion Criteria for Real-Time Tracking

Combinatorial Fusion Criteria for Real-Time Tracking

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International Conference on Advanced Information Networking and Applications (AINA)

作者： D.F. Hsu D.M. Lyons J. Ai Computer Vision & Robotics Laboratory Department of Computer & Information Science Fordham University Bronx NY USA

We address the problem of automated video tracking of targets when targets undergo multiple mutual occlusions. Our approach is based on the idea that as targets are occluded, selection of feature subsets and combinations of those features are effective in identifying the target and improving tracking performance. We use combinatorial fusion analysis to develop a metric to select which subset of features will produce the most accurate tracking. In particular we show that the combination of a pair of features A and B will improve the accuracy only if (a) A and B have relative high performance, and (b) A and B are diverse. We present experimental results to illustrate the performance of the proposed metric

关键词： Target tracking Target recognition computer vision robotics and automation Diversity reception Robot vision systems Laboratories Information science Surveillance Robot sensing systems

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Rotational legged locomotion

Rotational legged locomotion

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International Conference on Advanced robotics (ICAR)

作者： D.M. Lyons K. Pamnany Robotics and Computer Vision Laboratory Department of Computer and Information Science Fordham University Bronx NY USA

In this paper we presented a novel, agile robot mechanism, which we call a rotopod, which combines aspects of wheeled and legged locomotion. A general description of how a tripedal rotopod can be made to step, rotating the mechanism about one leg, and moving the center of the mechanism, is presented. The concept of a gait for this mechanism is defined, and is used to show how extremely agile the mechanism can be. Specific resistance is employed as a way to explore the relative efficiency of this mechanism versus a wheel. Finally, we describe our first prototype rotopod and report on experiments conducted to characterize stepping

关键词： Legged locomotion Leg Mobile robots Wheels Vehicles Energy efficiency Prototypes Propulsion Weight control Robot control

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Selection and recognition of landmarks using terrain spatiograms

Selection and recognition of landmarks using terrain spatiog...

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2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Damian M. Lyons Robotics & Computer Vision Laboratory Computer and Information Science Department Fordham University Bronx NY USA

A team of robots working to explore and map an area may need to share information about landmarks so as to register their local maps and to plan effective exploration strategies. In previous papers we have introduced a combined image and spatial representation for landmarks: terrain spatiograms. We have shown that for manually selected views, terrain spatiograms provide an effective, shared representation for occlusion filtering and for combination of multiple views. In this paper, we present a landmark saliency architecture (LSA) that will automatically select candidate landmarks given some preference settings. Using a dataset of 21 outdoor stereo images generated by LSA, we show that the terrain spatiogram representation reliably recognizes automatically selected landmarks. The terrain spatiogram results are shown to improve on two purely appearance based approaches: template matching and image histogram matching.

关键词： Image color analysis Visualization Robots Histograms Pixel Semantics Humans

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Combinatorial Fusion Criteria for Robot Mapping

Combinatorial Fusion Criteria for Robot Mapping

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International Conference on Advanced Information Networking and Applications (AINA)

作者： Damian M. Lyons D. Frank Hsu Qiang Ma Liang Wang Computer Vision & Robotics Laboratory Department of Computer & Information Science Fordham University Bronx NY USA

We address the problem of sensor fusion for stereo and ultrasound depth measurements for map building for a robot operating in a cluttered environment. In such a situation it's difficult to make useful and realistic assumptions about the sensor or environment statistics. Combinatorial Fusion Analysis is used to develop an approach to fusion with unknown sensor and environment statistics. A metric is proposed that shows when fusion from a set of fusion alternatives will produce a more accurate estimation of depth than either sonar or stereo alone and when not. The metric consists of two criteria: (a) the performance ratio PR(A,B) between sensors A and B, and (b) the diversity d(A,B) between A and B as captured by the rank-score function fA and fB. Experimental results are reported to illustrate that these two CFA criteria are viable predictors to distinguish between positive cases (the combined system performs better than or equal to the individual systems) and negative cases.

关键词： Sensor fusion Robot sensing systems Robot vision systems Statistics computer vision Stereo vision Sonar Mobile robots Ultrasonic imaging Statistical analysis

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A Single 2D Pose with Context is Worth Hundreds for 3D Human Pose Estimation 37

A Single 2D Pose with Context is Worth Hundreds for 3D Human...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Zhao, Qitao Zheng, Ce Liu, Mengyuan Chen, Chen Robotics Institute Carnegie Mellon University United States Center for Research in Computer Vision University of Central Florida United States Key Laboratory of Machine Perception Peking University Shenzhen Graduate School China

ISBN: (纸本)9781713899921

The dominant paradigm in 3D human pose estimation that lifts a 2D pose sequence to 3D heavily relies on long-term temporal clues (i.e., using a daunting number of video frames) for improved accuracy, which incurs performance saturation, intractable computation and the non-causal problem. This can be attributed to their inherent inability to perceive spatial context as plain 2D joint coordinates carry no visual cues. To address this issue, we propose a straightforward yet powerful solution: leveraging the readily available intermediate visual representations produced by off-the-shelf (pre-trained) 2D pose detectors - no finetuning on the 3D task is even needed. The key observation is that, while the pose detector learns to localize 2D joints, such representations (e.g., feature maps) implicitly encode the joint-centric spatial context thanks to the regional operations in backbone networks. We design a simple baseline named Context-Aware PoseFormer to showcase its effectiveness. Without access to any temporal information, the proposed method significantly outperforms its context-agnostic counterpart, PoseFormer [74], and other state-of-the-art methods using up to hundreds of video frames regarding both speed and precision. Project page: ***/ContextAware-PoseFormer. © 2023 Neural information processing systems foundation. All rights reserved.

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Learning trajectory patterns by clustering: Experimental studies and comparative evaluation

Learning trajectory patterns by clustering: Experimental stu...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Brendan Morris Mohan Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego CA USA

Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized for the trajectory learning problem. The clustering performance is measured by evaluating the correct clustering rate on different datasets with varying characteristics.

关键词： Principal component analysis Euclidean distance computer vision Prototypes Hidden Markov models Robot vision systems robotics and automation Laboratories Pattern analysis Layout

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Rank-based multisensory fusion in multitarget video tracking

Rank-based multisensory fusion in multitarget video tracking

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IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： D.M. Lyons D.F. Hsu Robotics and Computer Vision Laboratory Department of Computer and Information Science Fordham University Bronx NY USA

An attractive approach to improve tracking performance for visual surveillance is to use information from multiple visual sensory cues such as position, color, shape, etc. Previous work in fusion for tracking has tended to focus on fusion by numerically combining the scores assigned by each cue. We argue that for video scenes with many targets in a crowded situation, the splitting and merging of regions associated with targets, and the subsequent dramatic changes in cue values and reliabilities, renders this form of fusion less effective. In this paper we present experimental results showing that use of cue rank information in fusion produces a significantly better tracking result in crowded scenes. We also present a formalization of this fusion problem as a step in understanding why this effect occurs and how to build a tracking system that exploits it.

关键词： Target tracking Layout Voting computer vision Shape Merging Bayesian methods Robot sensing systems Robot vision systems Laboratories

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