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A Distributed Approach for Real Time 3D Modeling

A Distributed Approach for Real Time 3D Modeling

引用

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

作者： J.-S. Franco C. Menier E. Boyer B. Raffin GRAVIR-INRIA Rhône-Alpes France ID-INRIA-Rhône-Alpes France

This paper addresses the problem of real time 3D modeling from images with multiple cameras. Environments where multiple cameras and PCs are present are becoming usual, mainly due to new camera technologies and high computing power of modern PCs. However most applications in computer vision are based on a single, or few PCs for computations and do not scale. Our motivation in this paper is therefore to propose a distributed framework which allows to compute precise 3D models in real time with a variable number of cameras, this through an optimal use of the several PCs which are generally present. We focus in this paper on silhouette based modeling approaches and investigate how to efficiently partition the associated tasks over a set of PCs. Our contribution is a distribution scheme that applies to the different types of approaches in this field and allows for real time applications. Such a scheme relies on different accessible levels of parallelization, from individual task partitions to concurrent executions, yielding in turn controls on both latency and frame rate of the modeling system. We report on the application of the presented framework to visual hull modeling applications. In particular, we show that precise surface models can be computed in real time with standard components. Results with synthetic data and preliminary results in real contexts are presented.

关键词： Personal communication networks computer vision Digital cameras Concurrent computing Robot vision systems Application software Layout Computational modeling Power system modeling Real time systems

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Learning a Probabilistic Similarity Function for Segmentation

Learning a Probabilistic Similarity Function for Segmentatio...

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 methods for measuring "similarity" of or "distance" between two image pixels or image patches. These methods generally involve computing some informative features of the image patches and then describing distances between patches based on simple functions of those feature values, e.g., Euclidean distance. Unfortunately, these measures are often frail and difficult to interpret. The goal of this paper is to learn the "similarity" of two patches as an approximation to the likelihood that two patches were drawn from the same surface in the world. This measure is well-defined and allows for a maximization of meaningful values when it is combined with common segmentation algorithms. We introduce a general approximation technique that involves learning a codebook, learning the likelihood function for pairs of codebook entries, and applying the resulting likelihood function in segmentation tasks. These steps can be performed independently, even on different data sets drawn from the same domain. The likelihood can be learned from pre-segmented image set or heuristically approximated from an unsegmented image set. We show examples of probabilistic segmentation of the codebooks themselves based on our similarity measure for multiple types of codebooks including color codebooks and Epitome codebooks. These segmentations illustrate the usefulness of our technique as an image patch similarity measure.

关键词： Image segmentation Pixel Euclidean distance Focusing Clustering algorithms Application software Learning computer science Artificial intelligence Laboratories

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The Dense Estimation of Motion and Appearance in Layers

The Dense Estimation of Motion and Appearance in Layers

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

作者： H. Yalcin M.J. Black R. Fablet Division of Engineering Brown University Providence RI USA Computer Science Brown University Providence RI USA LASAA IFREMER Plouzane France

Segmenting image sequences into meaningful layers is fundamental to many applications such as surveillance, tracking, and video summarization. Background subtraction techniques are popular for their simplicity and, while they provide a dense (pixelwise) estimate of foreground/background, they typically ignore image motion which can provide a rich source of information about scene structure. Conversely, layered motion estimation techniques typically ignore the temporal persistence of image appearance and provide parametric (rather than dense) estimates of optical flow. Recent work adaptively combines motion and appearance estimation in a mixture model framework to achieve robust tracking. Here we extend mixture model approaches to cope with dense motion and appearance estimation. We develop a unified Bayesian framework to simultaneously estimate the appearance of multiple image layers and their corresponding dense flow fields from image sequences. Both the motion and appearance models adapt over time and the probabilistic formulation can be used to provideasegmentation of thescene into foreground/background regions. This extension of mixture models includes prior probability models for the spatial and temporal coherence of motion and appearance. Experimental results show that the simultaneous estimation of appearance models and flow fields in multiple layers improves the estimation of optical flow at motion boundaries.

关键词： Motion estimation Image motion analysis Image sequences Image segmentation Surveillance Subtraction techniques Pixel Information resources Layout Image converters

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Non-rigid motion estimation using the robust tensor method

Non-rigid motion estimation using the robust tensor method

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

作者： K. Palaniappan H.S. Jiang T.I. Baskin Multimedia Communications and Visualization Laboratory Department of Computer Science University of Missouri Columbia Columbia MO USA Biology Department University of Massachusetts Amherst MA USA

This work presents a new technique that couples tensor based image flow with robust statistical estimation models to produce dense accurate velocity fields. The 3D orientation structure tensor of the spatiotemporal varying light intensity field is used to estimate local non-rigid image motions. Previous work has shown that in highly uniform regions that lack salient corner and edge information or with noisy measurements the structure tensor approach can lead to spurious velocity estimates. The proposed algorithm improves estimates of the deformable image flow in these regions by minimizing a robust tensor functional that includes regularization and penalty terms. The robust tensor functional explicitly incorporates a temporal photometric consistency term. The combined robust tensor method is formulated as a global constrained optimization over the 3D spatiotemporal light intensity volume and recovers scientifically accurate velocity fields. An attractive aspect of the robust tensor method is simultaneous segmentation and velocity estimation. Experimental results using image sequences of biological motion captured using a calibrated video microscope reveals new biophysical insights at subcellular micron scale resolutions.

关键词： Motion estimation Robustness Tensile stress Spatiotemporal phenomena Velocity measurement Photometry Constraint optimization Image segmentation Image sequences Microscopy

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Event Detection by Eigenvector Decomposition Using Object and Frame Features

Event Detection by Eigenvector Decomposition Using Object an...

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

作者： F. Porikli T. Haga Mitsubishi Electric Research Laboratories Inc. USA

We develop an event detection framework that has two significant advantages over past work. First, we introduce an extended set of time-wise and object-wise statistical features including not only the trajectory coordinates but also the histograms and HMM based representations of object's speed, orientation, location, size, and aspect ratio. These features enable detection of events that cannot be detected with the existing trajectory features reported so far. Second, we introduce a spectral clustering algorithm that can automatically estimate the optimal number of clusters. First, we construct feature-wise affinity matrices from the pair-wise similarity scores of objects using the extended set of features. To determine the usual events, we apply eigen-vector decomposition and obtain object clusters. We show that the number of eigenvectors used in the decomposition is proportional to the optimal number of clusters. Unlike the conventional approaches that try to fit predefined models to events, we analyze the conformity of objects using affinity matrices to find the unusual events. We improve the feature selection process by incorporating feature variances. We prove that the clustering stage is not adversely affected by high dimensionality of data space. Our simulations with synthetic and real data reveal that the proposed detection methods accurately detect usual and unusual events.

关键词： Event detection Hidden Markov models computer vision Matrix decomposition Laboratories Histograms Clustering algorithms Discrete event simulation Supervised learning Legged locomotion

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A Probabilistic Approach to Optical Flow based Super-Resolution

A Probabilistic Approach to Optical Flow based Super-Resolut...

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

作者： R. Fransens C. Strecha L. Van Gool ESAT-PSI University of Leuven Belgium Computer Vision Group (BIWI) ETH Zurich Switzerland

This paper deals with the computation of a single super-resolution image from a set of low-resolution images, where the motion fields are not constrained to be parametric. In our approach, the inversion process, in which the super-resolved image is inferred from the input data, is interleaved with the computation of a set of dense optical flow fields. The case of arbitrary motion presents several significant challenges. First of all, the super-resolution setting dictates that the optic flow computations must be very precise. Furthermore, we have to consider the possibility that certain parts of the scene, which are visible in the super-resolved image, are occluded in some of the input images. Such occlusions must be identified and dealt with in the restoration process. We propose a Bayesian approach to tackle these problems. In this framework, the input images are regarded as sub-sampled and noisy versions of the unknown high-quality image. Also, the input data is considered incomplete, in the sense that we do not know which pixels from the evolving super-resolution image are occluded in particular images from the input set. This will be modeled by introducing so-called visibility maps, which are treated as hidden variables. We describe an EM-algorithm, which iterates between estimating values for the hidden quantities, and optimizing the flow-fields and the super-resolution image. The approach is illustrated with a synthetic and a challenging real-world example.

关键词： Image motion analysis Image resolution Nonlinear optics Optical sensors Optical computing Data flow computing Layout Image restoration Bayesian methods Pixel

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Detection and Tracking of Moving Objects from Overlapping EO and IR Sensors

Detection and Tracking of Moving Objects from Overlapping EO...

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

作者： Jinman Kang K. Gajera I. Cohen G. Medioni IRIS Computer Vision Group University of Southern California Los Angeles CA USA

We present an approach for tracking moving objects observed by EO and IR sensors on a moving platform. Our approach detects and tracks the moving objects after accurately recovering the geometric relationship between different sensors. We address the tracking problem by separately modeling the appearance and motion of the moving regions using stochastic models. The appearance of the detected blobs is described by multiple spatial distribution models of blobs' colors and edges from different sensors. This representation is invariant to 2D rigid and scale transformation. It provides a rich description of the object being tracked and produces an accurate blob similarity measure for tracking - especially when one of sensors fails to provide reliable information. The motion model is obtained using a Kalman Filter (KF) process, which predicts the position of the moving objects while taking into account the camera motion. Tracking is performed by the maximization of a joint probability model reflecting appearance and motion. The novelty of our approach consists in defining a Joint Probability Data Association Filter (JPDAF) for integrating multiple cues from multiple sensors, It provides an unified framework for fusing information from different types of sensors. The proposed method tracks multiple moving objects with partial and total occlusions under various illumination conditions. We demonstrate the performance of the system on several real video surveillance sequences.

关键词： Object detection Infrared detectors Infrared sensors Tracking Sensor phenomena and characterization Stochastic processes Predictive models Cameras Information filtering Information filters

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Visual Hand Tracking Using Nonparametric Belief Propagation

Visual Hand Tracking Using Nonparametric Belief Propagation

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

作者： E.B. Sudderth M.I. Mandel W.T. Freeman A.S. Willsky Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology UK

This paper develops probabilistic methods for visual tracking of a three-dimensional geometric hand model from monocular image sequences. We consider a redundant representation in which each model component is described by its position and orientation in the world coordinate frame. A prior model is then defined which enforces the kinematic constraints implied by the model's joints. We show that this prior has a local structure, and is in fact a pairwise Markov random field. Furthermore, our redundant representation allows color and edge-based likelihood measures, such as the Chamfer distance, to be similarly decomposed in cases where there is no self-occlusion. Given this graphical model of hand kinematics, we may track the hand's motion using the recently proposed nonparametric belief propagation (NBP) algorithm. Like particle filters, NBP approximates the posterior distribution over hand configurations as a collection of samples. However, NBP uses the graphical structure to greatly reduce the dimensionality of these distributions, providing improved robustness. Several methods are used to improve NBP's computational efficiency, including a novel KD-tree based method for fast Chamfer distance evaluation. We provide simulations showing that NBP may be used to refine inaccurate model initializations, as well as track hand motion through extended image sequences.

关键词： Belief propagation Image sequences Kinematics Tracking Solid modeling Markov random fields Graphical models Particle filters Robustness Computational efficiency

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Spline-based Motion Recovery for 3D Surfaces Using Nonrigid Shape Properties

Spline-based Motion Recovery for 3D Surfaces Using Nonrigid ...

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

作者： Min Li C. Kambhamettu M. Stone Department of Computer & Information Sciences University of Delaware Newark DE USA Vocal Tract Visualization Laboratory University of Maryland Dental School Baltimore MD USA

We present a spline-based nonrigid motion and point correspondence recovery method for 3D surfaces. This method is based on differential geometry. Shape information is used to recover the point correspondences. In contrast to the majority of shape-based methods which assume that shape (unit normal, curvature) changes are minimumafter motion, our method focuses on the nonrigid relationship between before-motion and after-motion shapes. This nonrigid shape relationship is described by modeling the underlying non-rigid motion; we model it as a spline transformation which has global control over the entire motion field along with the local deformation integrated within. This provides our method certain advantages over some pure differential geometric methods which also utilize the nonrigid shape relationship but only work on local areas without a global control. For example, motion regularity is hard to implement in these pure differential geometric methods but is not a problem when the motion field is controlled by a spline transformation. Furthermore, the small deformation constraint introduced by the previous works is relaxed in our method. Experiments on both synthetic and real motions have been conducted. The quantitive and qualitative evaluations of our method are presented in this paper.

关键词： Spline Shape control Motion estimation Deformable models Motion control Visualization Dentistry Geometry Biomedical imaging Image registration

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A Particle Filter without Dynamics for Robust 3D Face Tracking

A Particle Filter without Dynamics for Robust 3D Face Tracki...

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

作者： Le Lu Xiang-Tian Dai G. Hager Computational Interaction and Robotics Lab Computer Science Department Johns Hopkins University Baltimore MD USA

Particle filtering is a very popular technique for sequential state estimation problem. However its convergence greatly depends on the balance between the number of particles/hypotheses and the fitness of the dynamic model. In particular, in cases where the dynamics are complex or poorly modeled, thousands of particles are usually required for real applications. This paper presents a hybrid sampling solution that combines the sampling in the image feature space and in the state space via RANSAC and particle filtering, respectively. We show that the number of particles can be reduced to dozens for a full 3D tracking problem which contains considerable noise of different types. For unexpected motions, a specific set of dynamics may not exist, but it is avoided in our algorithm. The theoretical convergence proof [1, 3] for particle filtering when integrating RANSAC is difficult, but we address this problem by analyzing the likelihood distribution of particles from a real tracking example. The sampling efficiency (on the more likely areas) is much higher by the use of RANSAC. We also discuss the tracking quality measurement in the sense of entropy or statistical testing. The algorithm has been applied to the problem of 3D face pose tracking with changing moderate or intense expressions. We demonstrate the validity of our approach with several video sequences acquired in an unstructured environment.

关键词： Particle filters Robustness Particle tracking Filtering Image sampling State estimation State-space methods Noise reduction Convergence Sampling methods

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