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Stochastic Diffusion for Correspondence Estimation and Objects Segmentation

Stochastic Diffusion for Correspondence Estimation and Objec...

引用

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

作者： Sang Hwa Lee Nam Ik Cho Jong-Il Park School of Electrical Engineering and Computer Science Seoul National University Seoul South Korea Division of Electrical and Computer Engineering Hanyang University Seoul South Korea

In this paper, a generative model combined with stochastic framework is proposed and applied to the simultaneous correspondence estimation and object segmentation. The correspondence and segment fields are explicitly modelled as Markov random fields, and estimated in the maximum a posteriori framework. Some stochastic models are defined as the potential functions to reflect the interaction of the fields. The potential functions of the fields are stochastically diffused with the probability distributions of the neighboring fields, and the probability spaces of the fields are updated from the diffused potential spaces. The stochastic diffusion proposed as an energy minimization process is a kind of generative model which updates and regenerates the probability spaces of the correspondence and segment fields. Some experiments are performed on the simultaneous correspondence estimation and object segmentation. The results show stable and good performances in estimating the correspondence fields and extracting the objects in the scene.

关键词： Stochastic processes Object segmentation Image segmentation Probability distribution Minimization methods Markov random fields Maximum a posteriori estimation Layout Image coding Video surveillance

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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...

引用

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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Simultaneous Pose Estimation and Camera Calibration from Multiple Views

Simultaneous Pose Estimation and Camera Calibration from Mul...

引用

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

作者： T. Izo W.E.L. Grimson Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

We present an algorithm to estimate the body pose of a walking person given synchronized video input from multiple uncalibrated cameras. We construct an appearance model of human walking motion by generating examples from the space of body poses and camera locations, and clustering them using expectation-maximization. Given a segmented input video sequence, we find the closest matching appearance cluster for each silhouette and use the sequence of matched clusters to extrapolate the position of the camera with respect to the person's direction of motion. For each frame, the matching cluster also provides an estimate of the walking phase. We combine these estimates from all views and find the most likely sequence of walking poses using a cyclical, feed-forward hidden Markov model. Our algorithm requires no manual initialization and no prior knowledge about the locations of the cameras.

关键词： Cameras Calibration Legged locomotion Clustering algorithms Biological system modeling Humans Video sequences Phase estimation Feedforward systems Hidden Markov models

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An Approach to Visualize Image Retrieval Results

An Approach to Visualize Image Retrieval Results

引用

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

作者： A. Schneidewind P. Neumann I. Schmitt Faculty of Computer Science Otto-von-Guericke University of Magdeburg Magdeburg Germany

Many current content-based image retrieval systems suffer from poor result presentation. A sophisticated visualization can be used to identify differences between human perception of similarity and system-determined similarity. Analyzing such discrepancies is a prerequisite for a system trimmed towards the user's comprehension of the underlying retrieval process. The aim of the visualization techniques presented in this paper is to provide a tool to analyze a mismatch between the user's perception and the system's calculation of similarity. We combine techniques of visual image retrieval and information visualization to acquire insight into the extracted feature data. In our project we implemented visualization techniques to present feature data on three different levels of abstraction. We discuss our experiences when working with a Data Table, a Parallel Coordinate Plot, and a Color Space Plot.

关键词： Image retrieval Data visualization Content based retrieval Humans Feature extraction Data mining Information retrieval Feedback Switches computer science

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Learning a Factorized Segmental Representation of Far-Field Tracking Data

Learning a Factorized Segmental Representation of Far-Field ...

引用

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

作者： C. Stauffer Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA

There are many useful observable characteristics of the state of a tracked object. These characteristics could include normalized size, normalized speed, normalized direction, object color, position, and object shape among other characteristics. Although these characteristics are by no means completely independent of each other, it is desirable to determine a separate, compact description of each of each of these aspects. Using this compact factored description, different aspects of individual sequences can be estimated and described without overwhelming computational or storage costs. In this work, we describe Factored Latent Analysis (FLA) and its application to deriving factored models for segmenting sequences in each of K separate characteristics. This method exploits temporally local statistics within each of the latent aspects and their inter-dependencies to derive a model that allows segmentation of each of the observed characteristics. This method is data driven and unsupervised. Activity classification results for multiple challenging environments are shown.

关键词： Shape Statistics Humans Legged locomotion Layout Graphical models Learning computer science Artificial intelligence Laboratories

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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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Graph Partitioning Active Contours for Knowledge-Based Geo-Spatial Segmentation

Graph Partitioning Active Contours for Knowledge-Based Geo-S...

引用

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

作者： B. Sumengen S. Bhagavathy B.S. Manjunath Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

Our contribution in this paper is two-fold. First, we extend our previous curve evolution method based on pairwise similarities. This curve evolution equation combines the grouping abilities of active contours and graph partitioning techniques. Connections of our method to spectral graph partitioning are investigated and comparisons are made. Second, in a model-based segmentation scenario, we propose a method to improve segmentation quality by iteratively modifying the model using feedback from segmentation of a labeled training set. Our purpose here is to segment objects in geo-spatial images by integrating domain knowledge with the segmentation method. We achieve our goal by combining a statistical model for the object with a knowledge-guided segmentation method. Experimental results show that this framework is effective for model-based segmentation of complex geo-spatial objects.

关键词： Active contours Image segmentation Statistics Feedback Region 5 Histograms Energy measurement Partial differential equations Monitoring Minimization methods

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Human Gait recognition

Human Gait Recognition

引用

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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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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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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