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4th International Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging, CAe 2008

作者： Neumann, L. Hegedus, R. Horvath, G. Garcia, R. Computer Vision and Robotics Group University of Girona Icrea Spain Department of Biological Physics Biooptics Laboratory Eötvös University Budapest Hungary Computer Vision and Robotics Group University of Girona Spain

ISBN: (纸本)9783905674088

We propose the use of imaging polarimetry for general photography, which is a relatively young technique allowing the determination of polarized components of the light coming from extended objects or scenes. In this paper high resolution and accurate methods are introduced to determine the two linearly polarized components (Q;U) of light. The CIE Luv color space is used in this work to visualize the triplet of (I;Q;U) polarization image components. The structure of this color space is also highly appropriate to represent other attributes of linearly polarized light, such as the polarized intensity, degree and the angle of polarization. The accurately measured polarization components can also be efficiently used for image enhancement. In this direction, a new, polarization-based de-reflection method is proposed. This method is an optimal pixel-wise extension of the widely used photographical polarization filtering. Our method is also capable of amplifying the specular effects. Another application is de-hazing, which removes the linearly polarized component of the haze present in natural scenes, and results in a sharp and color-corrected image. Furthermore, the different combinations of visible and infrared polarization channels enable great possibilities in further de-hazing and to create artistic images. © The Eurographics Association 2008.

关键词： Image enhancement

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An adaptive scene description for activity analysis in surveillance video

An adaptive scene description for activity analysis in surve...

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作者： Morris, Brendan Trivedi, Mohan Computer Vision and Robotics Research Laboratory University of California San Diego San Diego CA 92093-0434 United States

ISBN: (纸本)9781424421756

This paper presents an adaptive framework for live video analysis. The activities of surveillance subjects are described using a spatio-temporal vocabulary learned from recurrent motion patterns. The repetitive nature of object trajectories are used to build a topographical map, where nodes are points of interest and the edges correspond to activities, to describe a scene. The graph is learned in an unsupervised manner but is flexible and able to adjust to changes in the environment or other scene variations. © 2008 IEEE.

关键词： Security systems

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Multimodal voxelization and kinematically constrained Gaussian mixture models for full hand pose estimation: An integrated systems approach

Multimodal voxelization and kinematically constrained Gaussi...

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Fourth IEEE International Conference on computer vision Systems, ICVS'06

作者： Cheng, Shinko Y. Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego 9500 Oilman Drive San Diego CA 92037

ISBN: (纸本)0769525067

We propose a new approach to the hand pose estimation problem using only volume information. We describe a thermal and color image-based approach to generate silhouettes of the hand with which voxel images are produced using shape-from-silhouette. We assume a 16-component, 27 degree-of-freedom kinematically constrained Gaussian mixture model, and fit it over the voxel images. We constrain the otherwise freely-arranged components of this model by a system of equations describing joint characteristics parameterized by component centroids and orientations. We use the EM algorithm and steepest descent to estimate the parameters of the model such that they yield the maximum likelihood and kinematically correct pose estimates. We demonstrate the effectiveness of the proposed system on synthesized as well as captured voxel images of the hand, and show that given appropriate initial conditions, the iterative model parameter estimation procedure effectively converges to and tracks the "voxelized" articulated hand. © 2006 IEEE.

关键词： Motion estimation

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Satellite imagery based adaptive background models and shadow suppression

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Signal, Image and Video Processing 2007年第2期1卷 119-132页

作者： Doshi, Anup Trivedi, Mohan Manubhai Computer Vision and Robotics Research Laboratory University of California at San Diego San Diego CA 92093-0434 9500 Gilman Drive United States

Accurate segmentation of foreground objects in video scenes is critical for assuring reliable performance of vision systems for object tracking and situational awareness in outdoor scenes. Most existing techniques for background modeling and shadow suppression require that a number of parameters be "hand-tuned" based on environmental conditions. This paper presents two contributions to overcome such limitations. First, we develop and demonstrate a satellite imagery based approach for selecting appropriate background and shadow models. It is shown that the illumination conditions (i.e. cloud cover) of a scene can be reliably inferred from visible satellite images in the local region of the camera. The second contribution presented in the paper is introduction and evaluation of a Hybrid Cone-Cylinder Codebook (HC3) model which combines an adaptive efficient background model with HSV-color space shadow suppression into a single coherent framework. The structure of the HC3 model allows for seamless fusion of the satellite data. We are thereby able to exploit the fact that, for example, shadows are more pronounced on sunny days than cloudy days, allowing for more sensitive detection. The paper presents a set of experiments using day long sequences of videos from an operational surveillance system testbed. Results of these experimental analyses quantitatively illustrate the benefits of using satellite imagery to inform and adaptively adjust background and shadow modeling. © Springer-Verlag London Limited 2007.

关键词： Cloud radiance Foreground-background segmentation Hybrid Cone-Cylinder Codebook (HC3) Shadow suppression Satellite imagery

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Distributed interactive video arrays for event based analysis of incidents 5

Distributed interactive video arrays for event based analysi...

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5th IEEE International Conference on Intelligent Transportation Systems, ITSC 2002

作者： Trivedi, Mohan M. Prati, Andrea Kogut, Greg Computer Vision and Robotics Research Laboratory University of California at San Diego San DiegoCA92093-0434 United States

ISBN: (纸本)0780373898

The Distributed Interactive Video Array (DIVA) system is developed to provide a large-scale, redundant cluster of video streams to observe a remote scene and to supply automatic focus of-attentian with event-driven serving to capture desired events at appropriate resolutions and perspectives. Installing multiple sensors introduces several new research issues related to the system design, including handoff schemes for passing tracked objects between sensors and clusters, methods for determining the "best view" given the context of the traffic scene, and sensor fusion algorithms to best employ the strengths of a given sensor or sensor modality. This paper describes our research focused on the development of DIVA system for traffic and incident monitoring. The paper describes the overall architecture of the DIVA system. Algorithms (or vehicle and platoon tracking using multiple cameras, and experimental results using novel distributed video networks deployed on the campus and the interstate 1-5. © 2002 IEEE.

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Robust face recognition using key-point descriptors 10

Robust face recognition using key-point descriptors

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10th International Conference on computer vision Theory and Applications, VISAPP 2015

作者： Klemm, Soeren Andreu, Yasmina Henriquez, Pedro Matuszewski, Bogdan J. Robotics and Computer Vision Research Laboratory School of Computing Engineering and Physical Sciences University of Central Lancashire Preston United Kingdom

ISBN: (纸本)9789897580901

Key-point based techniques have demonstrated a good performance for recognition of various objects in numerous computer vision applications. This paper investigates the use of some of the most popular key-point descriptors for face recognition. The emphasis is put on the experimental performance evaluation of the key-point based face recognition methods against some of the most popular and best performing techniques, utilising both global (Eigenfaces) and local (LBP, Gabor filters) information extracted from the whole face image. Most of the results reported in literature so far, on the use of the key-points descriptors for the face recognition, concluded that the methods based on processing of the full face image have somewhat better performances than methods using exclusively key-points. The results reported in this paper suggest that the performance of the key-point based methods could be at least comparable to the leading "whole face" methods and are often better suited to handle face recognition in practical applications, as they do not require face image co-registration, and perform well even with significantly occluded faces.

关键词： Face recognition

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Learning and classification of trajectories in dynamic scenes: A general framework for live video analysis

Learning and classification of trajectories in dynamic scene...

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IEEE 5th International Conference on Advanced Video and Signal Based Surveillance, AVSS 2008

作者： Morris, Brendan T. Trivedi, Mohan M. Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA 92093-0434 United States

ISBN: (纸本)9780769533414

This paper presents a general framework for live video analysis. The activities of surveillance subjects are described using a spatio-temporal vocabulary learned from recurrent motion patterns. The repetitive nature of object trajectories is used to build a topographical scene description where nodes are points of interest (POI) and the edges correspond to activity paths (AP). The POI are learned through as a mixture of Gaussians and AP by clustering trajectories. The paths are probabilistically represented by hidden Markov models and adapt to temporal variations using online maximum likelihood regression (MLLR) and through a periodic batch update. Using the scene graph, new trajectories can be analyzed in online fashion to categorize past and present activity, predict future behavior, and detect abnormalities. ©2008 IEEE.

关键词： Trajectories

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Unsupervised learning of motion patterns of rear surrounding vehicles

Unsupervised learning of motion patterns of rear surrounding...

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2009 IEEE International Conference on Vehicular Electronics and Safety, ICVES 2009

作者： Morris, Brendan Trivedi, Mohan Computer Vision and Robotics Research Laboratory University of California San Diego San Diego CA 92093-0434 United States

ISBN: (纸本)9781424454419

Awareness to a vehicle's surrounding is necessary for safe driving. Current surround technologies focus on the detection of obstacles in hard-to-view places but may neglect temporal information. This paper seeks the causes of dangerous situations by examining surround behavior. A general hierarchical learning framework is introduced to automatically learn surround behaviors. By observing motion trajectories during natural driving, models of rear vehicle behaviors are obtained in an unsupervised fashion. The extracted behaviors are shown to correspond to typical driving scenarios, vehicle overtake and surround overtake, demonstrating the effectiveness of the learning framework. ©2009 IEEE.

关键词： Vehicles

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Constraint-free topological mapping and path planning by maxima detection of the kernel spatial clearance density

Constraint-free topological mapping and path planning by max...

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1st International Conference on Pattern Recognition Applications and Methods, ICPRAM 2012

作者： Papadakis, Panagiotis Gianni, Mario Pizzoli, Matia Pirri, Fiora Department of Computer and System Sciences ALCOR Vision Perception and Cognitive Robotics Laboratory University of Rome La Sapienza Rome Italy

ISBN: (纸本)9789898425980

Asserting the inherent topology of the environment perceived by a robot is a key prerequisite of high-level decision making. This is achieved through the construction of a concise representation of the environment that endows a robot with the ability to operate in a coarse-to-fine strategy. In this paper, we propose a novel topological segmentation method of generic metric maps operating concurrently as a path-planning algorithm. First, we apply a Gaussian Distance Transform on the map that weighs points belonging to free space according to the proximity of the surrounding free area in a noise resilient mode. We define a region as the set of all the points that locally converge to a common point of maximum space clearance and employ a weighed meanshift gradient ascent onto the kernel space clearance density in order to detect the maxima that characterize the regions. The spatial intra-connectivity of each cluster is ensured by allowing only for linearly unobstructed mean-shifts which in parallel serves as a path-planning algorithm by concatenating the consecutive mean-shift vectors of the convergence paths. Experiments on structured and unstructured environments demonstrate the effectiveness and potential of the proposed approach.

关键词： Motion planning

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Hidden Markov models for silhouette classification

Hidden Markov models for silhouette classification

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作者： Abd-Almageed, Wael Smith, Christopher Robotics Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico United States

ISBN: (纸本)1889335185

In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov Models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach and show its invariance under severe rotation conditions. Also, a comparison with other techniques that use Hidden Markov Models for object classification from silhouettes is presented.

关键词： Hidden Markov models

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