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A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis

A Lagrangian Particle Dynamics Approach for Crowd Flow Segme...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Saad Ali Mubarak Shah Computer Vision Laboratory University of Central Florida USA

This paper proposes a framework in which Lagrangian particle dynamics is used for the segmentation of high density crowd flows and detection of flow instabilities. For this purpose, a flow field generated by a moving crowd is treated as an aperiodic dynamical system. A grid of particles is overlaid on the flow field, and is advected using a numerical integration scheme. The evolution of particles through the flow is tracked using a flow map, whose spatial gradients are subsequently used to setup a Cauchy Green deformation tensor for quantifying the amount by which the neighboring particles have diverged over the length of the integration. The maximum eigenvalue of the tensor is used to construct a finite time Lyapunov exponent (FTLE) field, which reveals the Lagrangian coherent structures (LCS) present in the underlying flow. The LCS divide flow into regions of qualitatively different dynamics and are used to locate boundaries of the flow segments in a normalized cuts framework. Any change in the number of flow segments over time is regarded as an instability, which is detected by establishing correspondences between flow segments over time. The experiments are conducted on a challenging set of videos taken from Google Video and a National Geographic documentary.

关键词： Lagrangian functions Stability analysis Layout computer vision Bridges Cities and towns Safety Tensile stress Videos Fluid dynamics

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Iterative MAP and ML Estimations for Image Segmentation

Iterative MAP and ML Estimations for Image Segmentation

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Shifeng Chen Liangliang Cao Jianzhuang Liu Xiaoou Tang Department of IE Chinese University of Hong Kong Hong Kong China Department of ECE University of Illinois Urbana-Champaign USA Microsoft Research Asia Beijing China

Image segmentation plays an important role in computer vision and image analysis. In this paper, the segmentation problem is formulated as a labeling problem under a probability maximization framework. To estimate the label configuration, an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori (MAP) estimation and the maximum-likelihood (ML) estimation. The MAP estimation problem is modeled with Markov random fields (MRFs). A graph-cut algorithm is used to find the solution to the MAP-MRF estimation. The ML estimation is achieved by finding the means of region features. Our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance. In addition, under the same framework, it can be extended to another algorithm that extracts objects of a particular class from a group of images. Extensive experiments have shown the effectiveness of our approach.

关键词： Maximum likelihood estimation Image segmentation computer vision Iterative algorithms Clustering algorithms Asia Image color analysis Image texture analysis Labeling Markov random fields

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Multiview normal field integration using level set methods

Multiview normal field integration using level set methods

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Conference on computer vision and pattern recognition (CVPR)

作者： Ju Yong Chang Kyoung Mu Lee Sang Uk Lee School of Electrical Engineering Seoul National University Seoul South Korea

In this paper, we propose a new method to integrate multiview normal fields using level sets. In contrast with conventional normal integration algorithms used in shape from shading and photometric stereo that reconstruct a 2.5D surface using a single-view normal field, our algorithm can combine multiview normal fields simultaneously and recover the full 3D shape of a target object. We formulate this multiview normal integration problem by an energy minimization framework and find an optimal solution in a least square sense using a variational technique. A level set method is applied to solve the resultant geometric PDE that minimizes the proposed error functional. It is shown that the resultant flow is composed of the well known mean curvature and flux maximizing flows. In particular, we apply the proposed algorithm to the problem of 3D shape modelling in a multiview photometric stereo setting. Experimental results for various synthetic data show the validity of our approach.

关键词： Level set Shape Photometry Noise shaping Stereo vision Surface reconstruction computer vision Poisson equations computer errors Least squares methods

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Object Tracking by Asymmetric Kernel Mean Shift with Automatic Scale and Orientation Selection

Object Tracking by Asymmetric Kernel Mean Shift with Automat...

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Conference on computer vision and pattern recognition (CVPR)

作者： Alper Yilmaz Photogrammetric Computer Vision Laboratory Ohio State Uinversity USA

Tracking objects using the mean shift method is performed by iteratively translating a kernel in the image space such that the past and current object observations are similar. Traditional mean shift method requires a symmetric kernel, such as a circle or an ellipse, and assumes constancy of the object scale and orientation during the course of tracking. In a tracking scenario, it is not uncommon to observe objects with complex shapes whose scale and orientation constantly change due to the camera and object motions. In this paper, we present an object tracking method based on the asymmetric kernel mean shift, in which the scale and orientation of the kernel adaptively change depending on the observations at each iteration. Proposed method extends the traditional mean shift tracking, which is performed in the image coordinates, by including the scale and orientation as additional dimensions and simultaneously estimates all the unknowns in a few number of mean shift iterations. The experimental results show that the proposed method is superior to the traditional mean shift tracking in the following aspects: 1) it provides consistent object tracking throughout the video; 2) it is not effected by the scale and orientation changes of the tracked objects; 3) it is less prone to the background clutter.

关键词： Kernel Bandwidth computer vision Shape Anisotropic magnetoresistance Tracking Robustness Image segmentation Data analysis Laboratories

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Generic Face Alignment using Boosted Appearance Model

Generic Face Alignment using Boosted Appearance Model

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Conference on computer vision and pattern recognition (CVPR)

作者： Xiaoming Liu Visualization and Computer Vision Laboratory General Electric Global Research Niskayuna NY USA

This paper proposes a discriminative framework for efficiently aligning images. Although conventional active appearance models (AAM)-based approaches have achieved some success, they suffer from the generalization problem, i.e., how to align any image with a generic model. We treat the iterative image alignment problem as a process of maximizing the score of a trained two-class classifier that is able to distinguish correct alignment (positive class) from incorrect alignment (negative class). During the modeling stage, given a set of images with ground truth landmarks, we train a conventional point distribution model (PDM) and a boosting-based classifier, which we call boosted appearance model (BAM). When tested on an image with the initial landmark locations, the proposed algorithm iteratively updates the shape parameters of the PDM via the gradient ascent method such that the classification score of the warped image is maximized. The proposed framework is applied to the face alignment problem. Using extensive experimentation, we show that, compared to the AAM-based approach, this framework greatly improves the robustness, accuracy and efficiency of face alignment by a large margin, especially for unseen data.

关键词： Active appearance model Shape Magnesium compounds Face detection computer vision Iterative algorithms Robustness Optimization methods Boosting Visualization

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Learning Color Names from Real-World Images

Learning Color Names from Real-World Images

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Conference on computer vision and pattern recognition (CVPR)

作者： Joost van de Weijer Cordelia Schmid Jakob Verbeek INRIA Montbonnot France

Within a computer vision context color naming is the action of assigning linguistic color labels to image pixels. In general, research on color naming applies the following paradigm: a collection of color chips is labelled with color names within a well-defined experimental setup by multiple test subjects. The collected data set is subsequently used to label RGB values in real-world images with a color name. Apart from the fact that this collection process is time consuming, it is unclear to what extent color naming within a controlled setup is representative for color naming in real-world images. Therefore we propose to learn color names from real-world images. Furthermore, we avoid test subjects by using Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. The color names are learned using a PLSA model adapted to this task. Experimental results show that color names learned from real-world images significantly outperform color names learned from labelled color chips on retrieval and classification.

关键词： Natural languages Testing Color Image retrieval Humans computer vision Labeling Pixel Psychology Reflection

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Feature Mining for Image Classification

Feature Mining for Image Classification

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Conference on computer vision and pattern recognition (CVPR)

作者： Piotr Dollar Zhuowen Tu Hai Tao Serge Belongie Computer Science & Engineering University of California San Diego USA Lab of Neuro Imaging University of California Los Angeles USA Computer Engineering University of California Santa Cruz USA

The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).

关键词： Image classification computer vision Feature extraction Data mining Machine learning Face detection Machine vision Space exploration Filters Detectors

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Sensor Fusion and Environmental Modelling for Multimodal Sentient Computing

Sensor Fusion and Environmental Modelling for Multimodal Sen...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Christopher Town Zhigang Zhu Computer Laboratory University of Cambridge Cambridge UK Computer Science Department CUNY Queensborogh Community College New York NY USA

Sentient computing uses networks of sensors to capture and maintain an internal representation ("world model") of an indoor environment, thereby allowing applications to have greater awareness of users and their requirements. This chapter shows how computer vision information obtained by several cameras can be used to enhance the capabilities of a sentient computing system which previously relied on ultrasound to track people and devices. Integration is achieved at the system level through the metaphor of shared perceptions in the sense that the different modalities are guided by and provide updates for a shared internal model. This world model incorporates aspects of both the static (e.g. positions of office walls and doors) and dynamic (e.g. location and appearance of devices and people) environments. It serves both as an ontology of prior information and as a source of context which is shared between applications. Fusion and inference are performed by Bayesian networks which model the probabilistic dependencies and reliabilities of different sources of information over time. It is shown that the fusion process significantly enhances the capabilities and robustness of the system, thus enabling it to maintain a richer and more accurate world model.

关键词： Sensor fusion computer vision computer networks Indoor environments Application software Cameras Ultrasonic imaging Ontologies Bayesian methods Information resources

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A Multi-Resolution Dynamic Model for Face Aging Simulation

A Multi-Resolution Dynamic Model for Face Aging Simulation

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Conference on computer vision and pattern recognition (CVPR)

作者： Jinli Suo Feng Min Songchun Zhu Shiguang Shan Xilin Chen Chinese Academy of Sciences China IPRAI Huazhong University of Science and Technology China Lotus Hill Institute of Computer Vision and Information Science China

In this paper we present a dynamic model for simulating face aging process. We adopt a high resolution grammatical face model [1] and augment it with age and hair features. This model represents all face images by a multi-layer and-or graph and integrates three most prominent aspects related to aging changes: global appearance changes in hair style and shape, deformations and aging effects of facial components, and wrinkles appearance at various facial zones. Then face aging is modeled as a dynamic Markov process on this graph representation which is learned from a large dataset. Given an input image, we firstly compute the graph representation, and then sample the graph structures over various age groups according to the learned dynamic model. Finally we generate new face images with the sampled graphs. Our approach has three novel aspects: (1) the aging model is learned from a dataset of 50,000 adult faces at different ages; (2) we explicitly model the uncertainty in face aging andean sample multiple plausible aged faces for an input image; and (3) we conduct a simple human experiment to validate the simulated aging process.

关键词： Aging Face detection Computational modeling Shape computer vision Hair Face recognition computer simulation Humans computer graphics

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Dimensionality Reduction and Clustering on Statistical Manifolds

Dimensionality Reduction and Clustering on Statistical Manif...

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Conference on computer vision and pattern recognition (CVPR)

作者： Sang-Mook Lee A. Lynn Abbott Philip A. Araman Virginia Polytechnic Institute and State University Blacksburg VA USA Southern Research Station U.S. Forest Service Blacksburg VA USA

Dimensionality reduction and clustering on statistical manifolds is presented. Statistical manifold [16] is a 2D Riemannian manifold which is statistically defined by maps that transform a parameter domain onto a set of probability density functions. Principal component analysis (PCA) based dimensionality reduction is performed on the manifold, and therefore, estimation of a mean and a variance of the set of probability distributions are needed. First, the probability distributions are transformed by an isometric transform that maps the distributions onto a surface of hyper-sphere. The sphere constructs a Riemannian manifold with a simple geodesic distance measure. Then, a Frechet mean is estimated on the Riemannian manifold to perform the PCA on a tangent plane to the mean. Experimental results show that clustering on the Riemannian space produce more accurate and stable classification than the one on Euclidean space.

关键词： Principal component analysis Probability density function Image segmentation Probability distribution Level measurement Image texture analysis pattern recognition Stochastic processes Parametric statistics Tensile stress

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