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检索条件"机构=Computer Vision and Robotics"

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3D Shape Context Based Gesture Analysis Integrated with Tracking using Omni Video Array

3D Shape Context Based Gesture Analysis Integrated with Trac...

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Kohsia S. Huang M.M. Trivedi Computer Vision and Robotics Research (CVRR) Laboratory University of California San Diego USA

In this paper we introduce a multilayer cylindrical histogram based feature space for representing 3D shape context of human body. Dynamics of gestures are analyzed using discrete hidden Markov models (DHMM) with quantized feature vectors. Extensive experimental trials with multiple subjects and a range of gesture classes are presented. Gesture recognition accuracies of over 85% (for nine gestures, and 9 subjects) and over 95% (for seven gestures) support the basic feasibility and robustness of the approach.

关键词： Shape Humans Hidden Markov models Biological system modeling Cameras Robustness Context modeling Information analysis Pattern analysis computer vision

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Vehicle mounted wide FOV stereo for traffic and pedestrian detection

Vehicle mounted wide FOV stereo for traffic and pedestrian d...

引用

IEEE International Conference on Image Processing

作者： T. Gandhi M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego CA USA

This paper describes an approach for detecting objects in front of an automobile using wide field of view stereo with a pair of omni cameras. Several configurations are suggested for effective detection of vehicles and pedestrians. The omni cameras are calibrated using sets of parallel lines on a parking lot. The calibration is used to rectify the omni images. Stereo matching is performed on the rectified images to detect other vehicles and pedestrians. Experimental results show promise of detecting these objects on the road.

关键词： Vehicle detection Cameras Roads Stereo vision Object detection computer vision Calibration Motion analysis Robot vision systems Intelligent vehicles

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computer vision for homeland security: a perspective on its promise and pitfalls

Computer vision for homeland security: a perspective on its ...

引用

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

作者： M.M. Trivedi Computer Vision and Robotics Research (CVRR) Laboratory University of California San Diego USA

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Omnidirectional depth computation from a single image

Omnidirectional depth computation from a single image

引用

2005 IEEE International Conference on robotics and Automation

作者： Orghidan, Radu Mouaddib, El Mustapha Salvi, Joaquim Centre de Robotique Electrotechnique et d'Automatique Université de Picardie Jules Verne Amiens France Computer Vision and Robotics Group Institute of Informatics and Applications University of Girona Girona Catalonia Spain

ISBN: (纸本)078038914X

Omnidirectional cameras offer a much wider field of view than the perspective ones and alleviate the problems due to occlusions. However, both types of cameras suffer from the lack of depth perception. A practical method for obtaining depth in computer vision is to project a known structured light pattern on the scene avoiding the problems and costs involved by stereo vision. This paper is focused on the idea of combining omnidirectional vision and structured light with the aim to provide 3D information about the scene. The resulting sensor is formed by a single catadioptric camera and an omnidirectional light projector. It is also discussed how this sensor can be used in robot navigation applications. © 2005 IEEE.

关键词： Cameras

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A Binary Tree for Probability Learning in Eye Detection

A Binary Tree for Probability Learning in Eye Detection

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： Junwen Wu M.M. Trivedi Computer Vision and Robotics Research Laboratory Department of Electrical and Computer Engineering University of California San Diego CA USA

In this paper we proposed to solve the eye detection and localization problem under a general statistical model based object detection framework. A binary tree representation is used to discover the objects’ underlying statistical structure. Tree structures enable us to describe the object local statistical structure in a coarse-to-fine fashion. Each subtree explains the statistics for certain local substructure. The tree is built in a top-down fashion. Subsets with negligible conditional independency are found by k-means clustering using mutual information. The conditionally independent features are separated into different subtrees, while more dependent features are tended to appear close in the tree. The distribution of the object can be learned accordingly. Gaussian mixture in the independent component analysis (ICA) subspace is used to model the distribution of each high dependent feature subset, where each independent component explains the local substructure. The use of tree structure enables us to learn the distribution recursively by applying Bayesian criterion. Substantial experiments were done to evaluate the performance over the eyes detection accuracy as well as the localization ability. Experimental results show a better detection accuracy than the Viisage system with a reasonable localization ability, which validate the algorithm.

关键词： Binary trees Object detection Bayesian methods computer vision Eyes Tree data structures Probability Statistical distributions Independent component analysis Face detection

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Automatic classification of breast density

Automatic classification of breast density

引用

IEEE International Conference on Image Processing

作者： A. Oliver J. Freixenet R. Zwiggelaar Computer Vision and Robotics Group University of Girona Girona Spain Department of Computer Science University of Wales Aberystwyth UK

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques.

关键词： Breast cancer Mammography computer aided diagnosis Image segmentation Lesions computer vision Robot vision systems robotics and automation computer science Data mining

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Performance characterization for Gaussian mixture model based motion detection algorithms

Performance characterization for Gaussian mixture model base...

引用

IEEE International Conference on Image Processing

作者： Junwen Wu M. Trivedi Computer Vision and Robotics Research Laboratory ECE Department University of California San Diego CA USA

Pixelwise Gaussian mixture based background modeling algorithm proposed in S. Stauffer and W.E.L. Grimson (1999 and 2000) has been proved to be robust for many motion detection applications. However, the algorithm is not sensitive to fast motion. One possible solution is to introduce local correlations. Starting from this, this paper proposes to use the Gaussian mixture globally for modeling the distribution of the difference image between the new frame and the estimated background. Experimental evaluation validates the algorithm. Motivated by the demands of selecting the more appropriate algorithm for a specific application, qualitative and quantitative performance comparisons of these two approaches are presented. We proposes three metrics. One is to characterize the pixel level accuracy and the other two are to evaluate the errors in the object level. Pros and cons of both algorithms are summarized.

关键词： Motion detection computer vision Noise robustness Guidelines Statistical distributions Robot sensing systems Robot vision systems Laboratories Application software computer errors

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Dynamic Panoramic Surround Map: Motivation and Omni Video Based Approach

Dynamic Panoramic Surround Map: Motivation and Omni Video Ba...

引用

IEEE computer Society Conference on computer vision and Pattern Recognition Workshops (CVPRW)

作者： T. Gandhi M.M. Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

Awareness of what surrounds a vehicle directly affects the safe driving and maneuvering of an automobile. Surround information or maps can help in ethnographic studies of driver behavior as well as provide a critical input in the development of effective driver assistance system. In this paper, we introduce the concept of Dynamic Panoramic Surround (DPS) map that shows the nearby surroundings of the vehicle, and detects the objects of importance on the road. Omnidirectional cameras which give a panoramic view of the surroundings can be useful for visualizing and analyzing the nearby surroundings of the vehicle. A novel approach for synthesizing the DPS using stereo and motion analysis of video images from a pair of omni-directional cameras on the vehicle is developed. Successful generation of DPS in experimental runs on an instrumented vehicle testbed is demonstrated. These experiments prove the basic feasibility and show promise of omni video based DPS capture algorithm to provide useful semantic descriptors of the state of moving vehicles and obstacles in the vicinity of a vehicle.

关键词： Vehicle dynamics Vehicle detection Cameras Vehicle driving Vehicle safety Automobiles Road vehicles Object detection Visualization Motion analysis

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Looking-in and looking-out of a vehicle: selected investigations in computer vision based enhanced vehicle safety

Looking-in and looking-out of a vehicle: selected investigat...

引用

IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： M.M. Trivedi T. Gandhi J. McCall Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

This paper presents an overview of investigations into the role of computer vision technology in developing safer automobiles. We consider vision systems which cannot only look out of the vehicle to detect and track roads, avoid hitting obstacles or pedestrian, but simultaneously look inside the vehicle to monitor the attentiveness of the driver and even predict her intentions. In this paper, a systems-oriented framework for developing computer vision technology for safer automobiles is presented. We will consider three main components of the system, driver, vehicle, and vehicle surround. We will discuss various issues and ideas for developing models for these main components as well as activities associated with the complex task of safe driving. The paper includes discussion of novel sensory systems and algorithms for capturing not only the dynamic surround information of the vehicle but also the state, intent and activity patterns of drivers.

关键词： Vehicle safety computer vision Vehicle driving Road vehicles Vehicle dynamics Automobiles Cameras Laboratories Machine vision Vehicle detection

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An Object Tracking and Visual Servoing System for the Visually Impaired

An Object Tracking and Visual Servoing System for the Visual...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： D.J. Jacques R. Rodrigo K.A. McIsaac J. Samarabandu Computer Vision and Mobile Robotics Laboratory Faculty of Engineering University of Western Ontario London ONT Canada

In this work, we have taken the first step towards the creation of a computerized seeing-eye guide dog. The system we presented extends the development of assistive technology for the visually impaired into a new area: object tracking and visual servoing. The system uses computer vision to provide a kind of surrogate sight for the human user; sensing information from the environment and communicating it through haptic signalling. Our proof-of concept prototype is a low-cost wearable system which uses a colour camera to analyze a scene and recognize a desired object, then generate tactile cues to the wearer to steer his or her hand towards the object. We have proved the system in trials with random users in an unstructured environment.

关键词： Visual servoing Layout Robot sensing systems computer vision Robot vision systems Humans Mobile robots Wearable computers Intelligent robots Prototypes

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