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检索条件"任意字段=2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2013"

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Learning From a Small Number of Training Examples by Exploiting Object Categories

Learning From a Small Number of Training Examples by Exploit...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： K. Levi M. Fink Y. Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel Center for Neural Computation Hebrew University of Jerusalem Jerusalem Israel

In the last few years, object detection techniques have progressed immensely. Impressive detection results have been achieved for many objects such as faces [11, 14, 9] and cars [11]. The robustness of these systems emerges from a training stage utilizing thousands of positive examples. One approach to enable learning from a small set of training examples is to find an efficient set of features that accurately represent the target object. Unfortunately, automatically selecting such a feature set is a difficult task in itself. In this paper we present a novel feature selection method that is based on the notion of object categories. We assume that when learning to recognize a new object (like an apple) we also know a category it belongs to (fruit). We further assume that features that are useful for learning other objects in the same category (e.g. pear or orange) will also be useful for learning the novel object. This leads to a simple criterion for selecting features and building classifiers. We show that our method gives significant improvement in detection performance in challenging domains.

关键词： Object detection Face detection computer science Robustness Humans computer vision computer displays Switches computer Society pattern recognition

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On-line Learning of Motion patterns using an Expert Learning Framework

On-line Learning of Motion Patterns using an Expert Learning...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S. Jhala S.K. Lodha Department of Computer Science University of California Santa Cruz USA

Tracking uncertain mobile objects such as humans and vehicles is an important problem in computer vision, robotics, and geo-spatial visualization. As the name suggests, predictor-corrector tracking is performed in two steps -prediction and correction. Prediction steps have typically utilized a-priori motion model most common of which is a uniform motion model. In this work, we apply an expert learning framework for on-line prediction and learning the motion of an uncertain mobile object. We define a number of probabilistic experts, each of which predicts the future position of the object with some uncertainty and then combine the predictions of all the experts to produce an estimate of the object's location. Individual experts predictions are weighted adaptively depending on their performance. We show that this adaptive combination is powerful when there are changes in the pattern of the object's motion. Results of our algorithm are compared with linear extrapolation and the best off-line expert predictions. We have tested our algorithm with synthetic data using uniform and non-uniform patterns as well as real data acquired using GPS equipment in presence of intermittent and highly erroneous data.

关键词： Predictive models Mobile computing Humans Vehicles computer vision Mobile robots Robot vision systems Visualization Uncertainty Extrapolation

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EyeKeys: A Real-Time vision Interface Based on Gaze Detection from a Low-Grade Video Camera

EyeKeys: A Real-Time Vision Interface Based on Gaze Detectio...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： J.J. Magee M.R. Scott B.N. Waber M. Betke Computer Science Department Boston University Boston MA USA

There are people that are so severely paralyzed that they only have the ability to control the muscles in their eyes. Communication is limited to the interpretation of eye movements. Currently available human-computer interface systems are often intrusive, require special hardware, or use active infrared illumination. We present a system that runs on an average PC with video input from an inexpensive USB camera. The face is tracked using multi-scale template correlation. Symmetry between left and right eyes is exploited to detect if the computer user is looking at the camera, or off to the left or right side. The detected eye direction can then be used to control applications such as spelling programs or games. We developed the game "BlockEscape" to gather quantitative results to evaluate our interface system with test subjects. We also compared our system to a mouse substitution interface.

关键词： Cameras Eyes Communication system control Muscles Hardware Lighting Universal Serial Bus Face detection Application software System testing

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Efficient Appearance-Based Tracking

Efficient Appearance-Based Tracking

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： J.M. Buenaposada E. Munoz L. Baumela Departmento de Informática Estadística y Telemática ESCET University Rey Juan Carlos Madrid Spain Departamento de Inteligencia Artificial Faculty de Informática Universite Politécnica de Madrid Madrid Spain

One of the major challenges that visual tracking algorithms face nowadays is being able to cope with changes in the appearance of the target during tracking. Linear sub-space models have been extensively studied recently and are possibly the most popular way of modeling target appearance. Unfortunately, efficiency is one of the limitations of present linear subspace models, and this is a key feature for a good tracker. In this paper we present an efficient procedure for tracking based on a linear subspace model of target appearance (grey levels). A set of motion templates is built from the subspace base, which is used to efficiently compute target motion and appearance parameters. It differs from previous works in that we impose no restrictions on the subspace used for modeling appearance. In the experiments conducted we have built a modular PCA-based face tracker which shows that video-rate tracking performance can be achieved with a non optimized implementation of our algorithm.

关键词： Target tracking Principal component analysis Face detection computer vision Intelligent robots Lighting Shape Statistics pattern recognition Face recognition

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Human Gait recognition

Human Gait Recognition

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rong Zhang C. Vogler D. Metaxas Department of Computer Science Rutgers University Piscataway NJ USA Gallaudet Research Institute Gallaudet University Washington D.C. DC USA

The reliable extraction of characteristic gait features from image sequences and their recognition are two important issues in gait recognition. In this paper, we propose a novel 2-step, model-based approach to gait recognition by employing a 5-link biped locomotion human model. We first extract the gait features from image sequences using the Metropolis-Hasting method. Hidden Markov Models are then trained based on the frequencies of these feature trajectories, from which recognition is performed. As it is entirely based on human gait, our approach is robust to different type of clothes the subjects wear. The model-based gait feature extraction step is insensitive to noise, cluttered background or even moving background. Furthermore, this approach also minimizes the size of the data required for recognition compared to model-free algorithms. We applied our method to both the USF Gait Challenge data-set and CMU MoBo data-set, and achieved recognition rate of 61% and 96%, respectively. The results suggest that the recognition rate is significantly limited by the distance of the subject to the camera.

关键词： Humans Hidden Markov models Image sequences Character recognition Image recognition Feature extraction Legged locomotion Frequency Noise robustness Background noise

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An Audio-Based Sports Video Segmentation and Event Detection Algorithm

An Audio-Based Sports Video Segmentation and Event Detection...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M. Baillie J.M. Jose Department of Computing Science University of Glasgow Glasgow UK

In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection. This decision process eliminated the need for defining a heuristic set of rules for segmentation. Each audio segment is then labelled using a series of Hidden Markov model (HMM) classifiers, each a representation of one of 6 predefined semantic content classes found in Soccer video. Exciting events are identified as those segments belonging to a crowd cheering class. Experimentation indicated that the algorithm was more effective for classifying crowd response when compared to traditional model-based segmentation and classification techniques.

关键词： Event detection Hidden Markov models pattern recognition Auditory displays Indexing Speech coding Cepstral analysis Bayesian methods Digital video broadcasting TV broadcasting

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Junction Inference and Classification for Figure Completion using Tensor Voting

Junction Inference and Classification for Figure Completion ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： P. Mordohai G. Medioni Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

We address the issues associated with figure completion, a perceptual grouping task. Endpoints and junctions play a critical role in contour completion by the human visual system and should be an integral part of a computational process that attempts to emulate human perception. A significant body of evidence in the psychology literature points to two types of completion, modal (or orthogonal) and amodal (or parallel). We provide a computational framework which implements both types of completion and integrates a fully automatic decision making mechanism for selecting between them. It proceeds directly from tokens or binary image input, infers descriptions in terms of overlapping layers and labels junctions as T, L and endpoints. It is based on first and second order tensor voting, which facilitate the propagation of local support among tokens. The addition of first order information to the original framework is crucial, since it makes the inference of endpoints and the labeling of junctions possible. We illustrate the approach on several classical inputs, producing interpretations consistent with those of the human visual system.

关键词： Tensile stress Voting Humans Visual system computer vision Intelligent robots Intelligent systems Psychology Decision making Labeling

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Head and Facial Animation Tracking using Appearance-Adaptive Models and Particle Filters

Head and Facial Animation Tracking using Appearance-Adaptive...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： F. Dornaika F. Davoine CNRS HEUDIASYC Compiègne University of Technology Compiegne France

This paper introduces two frameworks for head and facial animation tracking. The first framework introduces a particle-filter tracker capable of tracking the 3D head pose using a statistical facial texture model. The second framework introduces an appearance-adaptive tracker capable of tracking the 3D head pose and the facial animations in real-time. This framework has the merits of both deterministic and stochastic approaches. It consists of an online adaptive observation model of the face texture together with an adaptive transition motion model. The latter is based on a registration technique between the appearance model and the incoming observation. The second framework extends the concept of Online Appearance Models to the case of tracking 3D non-rigid face motion (3D head pose and facial animations). Tracking long video sequences demonstrated the effectiveness of the developed methods. Accurate tracking was obtained even in the presence of perturbing factors such as illumination changes, significant head pose and facial expression variations as well as occlusions.

关键词： Facial animation Particle tracking Particle filters Face detection Head Stochastic processes Facial features computer vision Video sequences Application software

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Learning to Detect Scene Text Using a Higher-Order MRF with Belief Propagation

Learning to Detect Scene Text Using a Higher-Order MRF with ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dong-Qing Zhang Shih-Fu Chang Department of Electrical Engineering Columbia University New York NY USA

Detecting text in natural 3D scenes is a challenging problem due to background clutter and photometric/gemetric variations of scene text. Most prior systems adopt approaches based on deterministic rules, lacking a systematic and scalable framework. In this paper, we present a parts-based approach for 3D scene text detection using a higher-order MRF model. The higher-order structure is used to capture the spatial-feature relations among multiple parts in scene text. The use of higher-order structure and the feature-dependent potential function represents significant departure from the conventional pairwise MRF, which has been successfully applied in several low-level applications. We further develop a variational approximation method, in the form of belief propagation, for inference in the higher-order model. Our experiments using the ICDAR'03 benchmark showed promising results in detecting scene text with significant geometric variations, background clutter on planar surfaces or non-planar surfaces with limited angles.

关键词： Layout Belief propagation Object detection Biological system modeling Application software Tree graphs Humans computer vision Photometry Approximation methods

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A Mathematical Comparison of Point Detectors

A Mathematical Comparison of Point Detectors

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M. Zuliani C. Kenney B.S. Manjunath Vision Research Lab Department of Electrical and Computer Engineering University of California슠Santa Barbara Santa Barbara CA USA

Selecting salient points from two or more images for computing correspondences is a fundamental problem in image analysis. Three methods originally proposed by Harris et al. in [A combined corner and edge detector], by Noble et al. in [Descriptions of image surfaces] and by Shi et al. in [Good features to track] proved to be quite effective and robust and have been widely used by the computer vision community. The goal of this paper is to analyze these point detectors starting from the algebraic and numerical properties of the image auto-correlation matrix. To accomplish this task we will first introduce a "natural" constraint that needs to be satisfied by any point detector based on the auto-correlation matrix. Then, by casting the point detection problem in a mathematical framework based on condition theory [A condition number for point matching with application to registration and post-registration error estimation], we will show that under certain hypothesis the point detectors are equivalent modulo the choice of a specific matrix norm. The results presented in this paper will provide a novel unifying description for the most commonly used point detection algorithms.

关键词： Detectors Transmission line matrix methods computer vision Autocorrelation Image analysis Layout Robustness Casting Detection algorithms Image reconstruction

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