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Hidden Conditional Random Fields for Gesture recognition

Hidden Conditional Random Fields for Gesture Recognition

引用

conference on computer vision and pattern recognition (cvpr)

作者： Sy Bor Wang A. Quattoni L.-P. Morency D. Demirdjian T. Darrell Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA USA

We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models.

关键词： Hidden Markov models pattern recognition Humans computer vision State estimation computer science Artificial intelligence Laboratories Application software Power generation

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Fast recognition using adaptive subdivisions of transformation space

Fast recognition using adaptive subdivisions of transformati...

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conference on computer vision and pattern recognition (cvpr)

作者： T.M. Breuel MIT Artificial Intelligence Laboratory Cambridge MA USA IDIAP Martigny Switzerland

An algorithm, RAST, for solving the bounded error recognition problem efficiently using adaptive subdivisions of transformation space, is presented. The RAST algorithm uses no heuristics and loses no solutions. It is a simple, efficient algorithm combining the ideas of multiresolution matching, Hough transform, search-based recognition, and bounded error recognition. Its performance is better than that of alignment and Hough transform methods, and, as opposed to these methods; RAST finds solutions satisfying simple, well-defined bounded error criteria.< >

关键词： Clustering algorithms Artificial intelligence Laboratories Contracts Polynomials Biological system modeling Heuristic algorithms Object recognition Image recognition Solid modeling

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Combined Depth and Outlier Estimation in Multi-View Stereo

Combined Depth and Outlier Estimation in Multi-View Stereo

引用

conference on computer vision and pattern recognition (cvpr)

作者： C. Strecha R. Fransens L. Van Gool K.U.Leuven ESAT-PSI Leuven Belgium

In this paper, we present a generative model based approach to solve the multi-view stereo problem. The input images are considered to be generated by either one of two processes: (i) an inlier process, which generates the pixels which are visible from the reference camera and which obey the constant brightness assumption, and (ii) an outlier process which generates all other pixels. Depth and visibility are jointly modelled as a hiddenMarkov Random Field, and the spatial correlations of both are explicitly accounted for. Inference is made tractable by an EM-algorithm, which alternates between estimation of visibility and depth, and optimisation of model parameters. We describe and compare two implementations of the E-step of the algorithm, which correspond to the Mean Field and Bethe approximations of the free energy. The approach is validated by experiments on challenging real-world scenes, of which two are contaminated by independently moving objects.

关键词： Layout Hidden Markov models Stereo vision Pixel Brightness Noise level Kernel Cameras Inference algorithms Rendering (computer graphics)

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引用

conference on computer vision and pattern recognition (cvpr)

作者： Tao Xiong Jieping Ye V. Cherkassky Department of ECE University of Minnesota Minneapolis USA Department of CSE Arizona State University Tempe USA

Several kernel algorithms have recently been proposed for nonlinear discriminant analysis. However, these methods mainly address the singularity problem in the high dimensional feature space. Less attention has been focused on the properties of the resulting discriminant vectors and feature vectors in the reduced dimensional space. In this paper, we present a new formulation for kernel discriminant analysis. The proposed formulation includes, as special cases, kernel uncorrelated discriminant analysis (KUDA) and kernel orthogonal discriminant analysis (KODA). The feature vectors of KUDA are uncorrelated, while the discriminant vectors of KODA are orthogonal to each other in the feature space. We present theoretical derivations of proposed KUDA and KODA algorithms. The experimental results show that both KUDA and KODA are very competitive in comparison with other nonlinear discriminant algorithms in terms of classification accuracy.

关键词： Kernel Linear discriminant analysis Scattering Algorithm design and analysis Feature extraction Eigenvalues and eigenfunctions computer vision Supervised learning Support vector machines Matrix decomposition

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Flexible Object Models for Category-Level 3D Object recognition

Flexible Object Models for Category-Level 3D Object Recognit...

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conference on computer vision and pattern recognition (cvpr)

作者： Akash Kushal Cordelia Schmid Jean Ponce Computer Science Department and Beckman Institute University of Illinois Urbana-Champaign USA Lear Team INRIA Montbonnot France

Today's category-level object recognition systems largely focus on fronto-parallel views of objects with characteristic texture patterns. To overcome these limitations, we propose a novel framework for visual object recognition where object classes are represented by assemblies of partial surface models (PSMs) obeying loose local geometric constraints. The PSMs themselves are formed of dense, locally rigid assemblies of image features. Since our model only enforces local geometric consistency, both at the level of model parts and at the level of individual features within the parts, it is robust to viewpoint changes and intra-class variability. The proposed approach has been implemented, and it outperforms the state-of-the-art algorithms for object detection and localization recently compared in [14] on the Pascal 2005 VOC Challenge Cars Test 1 data.

关键词： Object recognition Solid modeling Layout Assembly Robustness Object detection Testing computer vision pattern matching Shape

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Towards object-based heuristics

Towards object-based heuristics

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conference on computer vision and pattern recognition (cvpr)

作者： A.D. Gross Center for Research in Intelligent Systems Columbia University USA Department of Computer Science Queens College City University of New York USA

A survey and critique of previous work is given, and two object-based heuristics are developed. The structured nature of objects is the motivation for the nonaccidental alignment criterion; parallel lines within the object's bounding contour are related to the object-centered coordinate system. The regularity and symmetry inherent in many man-made objects is the motivation for the orthogonal basis constraint, an oblique set of coordinate axes in the image is presumed to be the projection of an orthogonal set of 3D coordinate axes in the scene. These heuristics are demonstrated on real and synthetic image contours.< >

关键词： Shape Layout Parametric statistics computer science Intelligent systems computer vision Geometry Labeling

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Interactive Feature Tracking using K-D Trees and Dynamic Programming

Interactive Feature Tracking using K-D Trees and Dynamic Pro...

引用

conference on computer vision and pattern recognition (cvpr)

作者： A. Buchanan A. Fitzgibbon Department Engineering Science University of Oxford UK Microsoft Research Limited Cambridge UK

A new approach to template tracking is presented, incorporating three distinct contributions. Firstly, an explicit definition for a feature track is given. Secondly, the advantages of an image preprocessing stage are demonstrated and, in particular, the effectiveness of highly compressed image patch data stored in k-d trees for fast and discriminatory image patch searches. Thirdly, the k-d trees are used to generate multiple track hypotheses which are efficiently merged to give the optimal solution using dynamic programming. The explicit separation of feature detection and trajectory determination creates the basis for the novel use of k-d trees and dynamic programming. Multiple appearances and occlusion handling are seamlessly integrated into this framework. Appearance variation through the sequence is robustly handled in an iterative process. The work presented is a significant foundation for a powerful off-line feature tracking system, particularly in the context of interactive applications.

关键词： Dynamic programming computer vision Robustness Image coding Mice Optical films Image motion analysis Feathers Tracking Radio access networks

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Model learning and recognition of nonrigid objects

Model learning and recognition of nonrigid objects

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conference on computer vision and pattern recognition (cvpr)

作者： J. Segen AT and T Bell Laboratories Holmdel NJ USA

A method of learning structural models of 2D shape from real data is described and demonstrated. These models can be used to classify nonrigid shapes, even if they are partially occluded, and to label their parts. The representation of a single shape is a layered graph whose vertices correspond to n-ary relations. A class of shapes is represented as a probability model whose outcome is a graph. The method is based on two types of learning: unsupervised learning used to discover relations, and supervised learning used to build class models. The class models are constructed incrementally, by matching and merging graphs representing shape instances. This process uses a fast graph-matching heuristic which seeks a simplest representation of a graph. An important feature is the self-generation of symbolic primitives by an unsupervised learning process. This feature makes it possible to apply the system to any set of shape data without adjustments, while other methods might require the user to provide a different set of primitives for each case.< >

关键词： Shape Biological system modeling Noise shaping Unsupervised learning Supervised learning Merging Humans Layout Character recognition

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Object recognition using invariant profiles

Object recognition using invariant profiles

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conference on computer vision and pattern recognition (cvpr)

作者： D. Slater G. Healey Computer Vision Laboratory Electrical and Computer Engineering University of California Irvine CA USA

We derive a sensitivity analysis for moment invariants of multidimensional distributions. These invariants have many uses in computational systems and have recently been used for illumination-invariant recognition in color images. In this context, the sensitivity analysis predicts the response of moment invariants to partial occlusion. Using the results of the sensitivity analysis, we develop a novel surface representation called the invariant profile which captures color distribution and spatial information while remaining invariant to the spectral content of the scene illumination. Unlike previous representations, the recognition of invariant profiles does not require illumination correction. We demonstrate the sensitivity analysis and the use of invariant profiles for recognition with a set of experiments on color images.

关键词： Object recognition Lighting Indexing Image databases Image recognition Image color analysis Sensitivity analysis Layout Spatial databases computer vision

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Bayesian video matting using learnt image priors

Bayesian video matting using learnt image priors

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conference on computer vision and pattern recognition (cvpr)

作者： N. Apostoloff A. Fitzgibbon Robotics Research Group University of Oxford Oxford UK

Video matting, or layer extraction, is a classic inverse problem in computer vision that involves the extraction of foreground objects, and the alpha mattes that describe their opacity, from a set of images. Modem approaches that work with natural backgrounds often require user-labelled "trimaps" that segment each image into foreground, background and unknown regions. For long sequences, the production of accurate trimaps can be time consuming. In contrast, another class of approach depends on automatic background extraction to automate the process, but existing techniques do not make use of spatiotemporal consistency, and cannot take account of operator hints such as trimaps. This paper presents a method inspired by natural image statistics that cleanly unifies these approaches. A prior is learnt that models the relationship between the spatiotemporal gradients in the image sequence and those in the alpha mattes. This is used in combination with a learnt foreground colour model and a prior on the alpha distribution to help regularize the solution and greatly improve the automatic performance of such systems. The system is applied to several real image sequences that demonstrate the advantage that the unified approach can afford.

关键词： Bayesian methods computer vision Equations Pixel Inverse problems Spatiotemporal phenomena Image sequences Robot vision systems Image segmentation Production

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