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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

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引用

26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Yan Liu Florent Perronnin Xerox Research Centre Europe (XRCE) Textual and Visual Pattern Analysis (TVPA)

We present a novel approach to compute the similarity between two unordered variable-sized vector sets. To solve this problem, several authors have proposed to model each vector set with a Gaussian mixture model (GMM) and to compute a probabilistic measure of similarity between the GMMs. the main contribution of this paper is to model each vector set with a GMM adapted from a common "universal" GMM using the maximum a posteriori (MAP) criterion. the advantages of this approach are twofold. MAP provides a more accurate estimate of the GMM parameters compared to standard maximum likelihood estimation (MLE) in the challenging case where the cardinality of the vector set is small. Moreover, there is a correspondence between the Gaussians of two GMMs adapted from a common distribution and one can take advantage of this fact to compute efficiently the probabilistic similarity. this work is applied to the image categorization problem: images are modeled as bags of low-level features and classification is performed using a kernel classifier based on the proposed similarity measure. Experimental results on the PASCAL VOC 2006 and VOC 2007 databases show the excellent performance of our approach.

关键词： Vocabulary Costs Maximum likelihood estimation Kernel pattern analysis Distributed computing Performance evaluation Histograms Feature extraction Sampling methods

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Learning Bayesian Networks with Qualitative Constraints

Learning Bayesian Networks with Qualitative Constraints

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Yan Tong Qiang Ji GE Global Research Center Niskayuna NY USA Rensselaer Polytechnic Institute Troy NY USA

Graphical models such as Bayesian Networks (BNs) are being increasingly applied to various computer vision problems. One bottleneck in using BN is that learning the BN model parameters often requires a large amount of reliable and representative training data, which proves to be difficult to acquire for many computer vision tasks. On the other hand, there is often available qualitative prior knowledge about the model. Such knowledge comes either from domain experts based on their experience or from various physical or geometric constraints that govern the objects we try to model. Unlike the quantitative prior, the qualitative prior is often ignored due to the difficulty of incorporating them into the model learning process. In this paper, we introduce a closed-form solution to systematically combine the limited training data with some generic qualitative knowledge for BN parameter learning. To validate our method, we compare it with the Maximum Likelihood (ML) estimation method under sparse data and with the Expectation Maximization (EM) algorithm under incomplete data respectively. To further demonstrate its applications for computer vision, we apply it to learn a BN model for facial Action Unit (AU) recognition from real image data. the experimental results show that with simple and generic qualitative constraints and using only a small amount of training data, our method can robustly and accurately estimate the BN model parameters.

关键词： Bayesian methods computer vision Training data Maximum likelihood estimation Graphical models computer network reliability Solid modeling Closed-form solution Application software Gold

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Connected Segmentation Tree - A Joint Representation of Region Layout and Hierarchy

Connected Segmentation Tree - A Joint Representation of Regi...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.7

作者： Narendra Ahuja Sinisa Todorovic Beckman Institute University of Illinois Urbana-Champaign USA

this paper proposes a new object representation, called Connected Segmentation Tree (CST), which captures canonical characteristics of the object in terms of the photometric, geometric, and spatial adjacency and containment properties of its constituent image regions. CST is obtained by augmenting the object's segmentation tree (ST) with inter-region neighbor links, in addition to their recursive embedding structure already present in ST. this makes CST a hierarchy of region adjacency graphs. A region's neighbors are computed using an extension to regions of the Voronoi diagram for point patterns. Unsupervised learning of the CST model of a category is formulated as matching the CST graph representations of unlabeled training images, and fusing their maximally matching subgraphs. A new learning algorithm is proposed that optimizes the model structure by simultaneously searching for both the most salient nodes (regions) and the most salient edges (containment and neighbor relationships of regions) across the image graphs. Matching of the category model to the CST of a new image results in simultaneous detection, segmentation and recognition of all occurrences of the category, and a semantic explanation of these results.

关键词： Image segmentation Tree graphs Photometry Image recognition Sociotechnical systems Encoding Object segmentation Unsupervised learning Image edge detection Solid modeling

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Joint Conditional Random Field of Multiple Views with Online Learning for Image-based Rendering

Joint Conditional Random Field of Multiple Views with Online...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Wenfeng Li Baoxin Li Department of Computer Science and Engineering Arizona State University USA

there are many applications, such as image-based rendering, where multiple views of a scene are considered simultaneously for improved analysis through employing strong correlation among the set of pixels corresponding to the same physical scene point. While being a useful tool for modeling pixel interactions, Markov Random Field (MRF) models encounter challenges in such cases since they assume strong independence of the observed data for tractability, rendering it difficult to take advantage of having multiple correlated views. In this paper we propose joint Conditional Random Field (CRF) for multiple views in the context of virtual view synthesis in image-based rendering. the model is enabled by the adoption of steerable spatial filters for capturing not only the pixel dependence in a single image but also their correlations among multiple views. Furthermore, a novel on-line learning scheme is proposed for the CRF model, which learns the CRF parameters from the same input data for synthesizing virtual views. this effectively makes the model adaptive to the input and thus optimal results can be expected. Experiments are designed to validate the proposed approach and its effectiveness.

关键词： Rendering (computer graphics) Layout Pixel Geometry Markov random fields Cameras Maximum likelihood estimation Dictionaries computer science Application software

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Structure Learning in Random Fields for Heart Motion Abnormality Detection

Structure Learning in Random Fields for Heart Motion Abnorma...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Mark Schmidt Kevin Murphy Glenn Fung Romer Rosales Computer Science Department University of British Columbia Vancouver BC Canada Siemens Medical Solutions IKM CAD and Knowledge Solutions USA Inc. Malvern PA USA

Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are difficult to interpret leading to high intra-observer variability. Previous work indicates that in order to approach this problem, the interactions between the different heart regions and their overall influence on the clinical condition of the heart need to be considered. To do this, we propose a method for jointly learning the structure and parameters of conditional random fields, formulating these tasks as a convex optimization problem. We consider block-L1 regularization for each set of features associated with an edge, and formalize an efficient projection method to find the globally optimal penalized maximum likelihood solution. We perform extensive numerical experiments comparing the presented method with related methods that approach the structure learning problem differently. We verify the robustness of our method on echocardiograms collected in routine clinical practice at one hospital.

关键词： Heart Motion detection Ultrasonic imaging Cardiac disease Image segmentation Biomedical imaging Muscles computer science Coronary arteriosclerosis Medical diagnostic imaging

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Bayesian Tactile Face

Bayesian Tactile Face

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.1

作者： Zheshen Wang Xinyu Xu Baoxin Li Computer Science and Engineering Arizona State University Tempe AZ USA

computer users with visual impairment cannot access the rich graphical contents in print or digital media unless relying on visual-to-tactile conversion, which is done primarily by human specialists. Automated approaches to this conversion are an emerging research field, in which currently only simple graphics such as diagrams are handled. this paper proposes a systematic method for automatically converting a human portrait image into its tactile form. We model the face based on deformable Active Shape Model (ASM)[4], which is enriched by local appearance models in terms of gradient profiles along the shape. the generic face model including the appearance components is learnt from a set of training face images. Given a new portrait image, the prior model is updated through Bayesian inference. To facilitate the incorporation of a pose-dependent appearance model, we propose a statistical sampling scheme for the inference task. Furthermore, to compensate for the simplicity of the face model, edge segments of a given image are used to enrich the basic face model in generating the final tactile printout. Experiments are designed to evaluate the performance of the proposed method.

关键词： Bayesian methods Image converters Image edge detection Humans Active shape model computer graphics Face detection Deformable models Image segmentation Image sampling

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On Errors-In-Variables Regression with Arbitrary Covariance and its Application to Optical Flow Estimation

On Errors-In-Variables Regression with Arbitrary Covariance ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Bjorn Andres Claudia Kondermann Daniel Kondermann Ullrich Kothe Fred A. Hamprecht Christoph S. Garbe Interdisciplinary Center for Scientific Computing University of Heidelberg Germany

Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated errors in variables. Established total least squares methods estimate the most likely corrections A and b to a given data matrix [A, b] perturbed by additive Gaussian noise, such that there exists a solution y with [A + A, b + b]y = 0. In practice, regression imposes a more restrictive constraint namely the existence of a solution x with [A + A]x = [b + b]. In addition, more complicated correlations arise canonically from the use of linear filters. We, therefore, propose a maximum likelihood estimator for regression in the general case of arbitrary positive definite covariance matrices. We show that A, b and x can be found simultaneously by the unconstrained minimization of a multivariate polynomial which can, in principle, be carried out by means of a Grobner basis. Results for plane fitting and optical flow computation indicate the superiority of the proposed method.

关键词： Image motion analysis computer errors Optical filters Optical computing Application software Inverse problems computer vision Motion estimation Shape Image reconstruction

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Recovering Shape Characteristics on Near-flat Specular Surfaces

Recovering Shape Characteristics on Near-flat Specular Surfa...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.11

作者： Yuanyuan Ding Jingyi Yu Department of Computer and Information Sciences University of Delaware Newark DE USA

We consider the problem of capturing shape characteristics on specular (refractive and reflective) surfaces that are nearly flat. these surfaces are difficult to model using traditional methods based on reconstructing the surface positions and normals. these lower-order shape attributes provide little information to identify important surface characteristics related to distortions. In this paper, we present a framework for recovering the higher-order geometry attributes of specular surfaces. Our method models local reflections and refractions in terms of a special class of multiperspective cameras called the general linear cameras (GLCs). We then develop a new theory that correlates the higher-order differential geometry attributes with the local GLCs. Specifically, we show that Gaussian and Mean Curvature can be directly derived from the camera intrinsics of the local GLCs. We validate this theory on both synthetic and real-world specular surfaces. Our method places a known pattern in front of a reflective surface or beneath a refractive surface and captures a distorted image on the surface. We then compute the optimal GLC using a sparse set of correspondences and recover the curvatures from the GLC. Experiments demonstrate that our methods are robust and highly accurate.

关键词： Shape Surface reconstruction Cameras Image reconstruction Geometry Reflection Robustness Mirrors Optical refraction Solid modeling

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Recent Trends in Applied Artificial Intelligence 1

引用

丛书名： Lecture Notes in computer Science

1000年

作者： Moonis Ali Tibor Bosse Jan Treur Koen V. Hindriks Catholijn M. Jonker Mark Hoogendoorn

ISBN: (数字)9783642385773

ISBN: (纸本)9783642385766

this volume constitutes the thoroughly refereed conference proceedings of the 26th International conference on Industrial Engineering and Other Applications of Applied Intelligence Systems, IEA/AIE 2013, held in Amsterdam, the Netherlands, in June 2013. the total of 71 papers selected for the proceedings were carefully reviewed and selected from 185 submissions. the papers focus on the following topics: auctions and negotiation, cognitive modeling, crowd behavior modeling, distributed systems and networks, evolutionary algorithms, knowledge representation and reasoning, pattern recognition, planning, problem solving, robotics, text mining, advances in recommender systems, business process intelligence, decision support for safety-related systems, innovations in intelligent computation and applications, intelligent image and signal processing, and machine learning methods applied to manufacturing processes and production systems.

关键词： Artificial Intelligence Information Systems Applications (incl. Internet) Algorithm Analysis and Problem Complexity Data Mining and Knowledge Discovery computer Imaging, vision, pattern recognition and Graphics computer Science, general

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Boosting Adaptive Linear Weak Classifiers for Online learning and Tracking

Boosting Adaptive Linear Weak Classifiers for Online learnin...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.6

作者： Toufiq Parag Fatih Porikli Ahmed Elgammal Department of Computer Science Rutgers University Piscataway NJ USA Mitsubishi Electric Research Laboratories Inc. Cambridge MA USA

Online boosting methods have recently been used successfully for tracking, background subtraction etc. Conventional online boosting algorithms emphasize on interchanging new weak classifiers/features to adapt with the change over time. We are proposing a new online boosting algorithm where the form of the weak classifiers themselves are modified to cope with scene changes. Instead of replacement, the parameters of the weak classifiers are altered in accordance with the new data subset presented to the online boosting process at each time step. thus we may avoid altogether the issue of how many weak classifiers to be replaced to capture the change in the data or which efficient search algorithm to use for a fast retrieval of weak classifiers. A computationally efficient method has been used in this paper for the adaptation of linear weak classifiers. the proposed algorithm has been implemented to be used both as an online learning and a tracking method. We show quantitative and qualitative results on both UCI datasets and several video sequences to demonstrate improved performance of our algorithm.

关键词： Boosting Target tracking Machine learning algorithms Layout computer science Approximation algorithms Filtering Least squares methods Information retrieval Video sequences

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