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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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Fusion of Time-of-Flight Depth and Stereo for High Accuracy Depth Maps

Fusion of Time-of-Flight Depth and Stereo for High Accuracy ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Jiejie Zhu Liang Wang Ruigang Yang James Davis Center for Visualization and Virtual Environments University of Kentucky USA Computer Science Department University of California Santa Cruz USA

Time-of-flight range sensors have error characteristics which are complementary to passive stereo. they provide real time depth estimates in conditions where passive stereo does not work well, such as on white walls. In contrast, these sensors are noisy and often perform poorly on the textured scenes for which stereo excels. We introduce a method for combining the results from both methods that performs better than either alone. A depth probability distribution function from each method is calculated and then merged. In addition, stereo methods have long used global methods such as belief propagation and graph cuts to improve results, and we apply these methods to this sensor. Since time-of-flight devices have primarily been used as individual sensors, they are typically poorly calibrated. We introduce a method that substantially improves upon the manufacturer's calibration. We show that these techniques lead to improved accuracy and robustness.

关键词： Stereo vision Sensor phenomena and characterization Calibration Layout Probability distribution Belief propagation Manufacturing Robustness Application software Table lookup

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Locally Adaptive Learning for Translation-Variant MRF Image Priors

Locally Adaptive Learning for Translation-Variant MRF Image ...

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

作者： Masayuki Tanaka Masatoshi Okutomi Tokyo Institute of Technology Tokyo Japan

Markov random field (MRF) models are a powerful tool in machine vision applications. However, learning the model parameters is still a challenging problem and a burdensome task. the main contribution of this paper is to propose a locally adaptive learning framework. the proposed learning framework is simple and effective learning framework for translation-variant MRF models. the key idea is to use neighboring patches as a locally adaptive training set. We use multivariate Gaussian MRF models for local image prior models. Although the Gaussian MRF models are too simple for whole natural image priors, the locally adaptive framework enables to express the prior distributions of the every observed image. these locally adaptive learning framework and the multivariate Gaussian translation-variant MRF models simplify the learning procedures. this paper also includes other two contributions;a novel iteration framework by updating the prior information, and a simple and intuitive derivation of the well-known bilateral filter. Experimental results of denoising applications demonstrate that the denoising based on the proposed locally adaptive learning framework outperforms existing high-performance denoising algorithms.

关键词： Noise reduction Markov random fields Machine vision Information filtering Information filters Adaptive filters Machine learning Image denoising Image resolution Optical filters

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Sparsity, Redundancy and Optimal Image Support towards Knowledge-based Segmentation

Sparsity, Redundancy and Optimal Image Support towards Knowl...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.4

作者： Salma Essafi Georg Langs Nikos Paragios Equipe GALEN INRIA Saclay-Ile-de-France Orsay France Laboratoire MAS Ecole Centrale Paris Malabry France

In this paper, we propose a novel approach to model shape variations. It encodes sparsity, exploits geometric redundancy, and accounts for the different degrees of local variation and image support. In this context we consider a control-point based shape representation. their sparse distribution is derived based on a shape model metric learned from the training data, and the ambiguity of local appearance with regard to segmentation changes. the resulting sparse model of the object improves reconstruction and search behavior, in particular for data that exhibit a heterogeneous distribution of image information and shape complexity. Furthermore, it goes beyond conventional image-based segmentation approaches since it is able to identify reliable image structures which are then encoded within the model and used to determine the optimal segmentation map. We report promising experimental results comparing our approach with standard models on MRI data of calf muscles - an application where traditional image-based methods fail - and CT data of the left heart ventricle.

关键词： Image segmentation Image reconstruction Biomedical imaging Redundancy Shape control computer vision Interpolation Training data Magnetic resonance imaging Muscles

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Regularizing 3D Medial Axis Using Medial Scaffold Transforms

Regularizing 3D Medial Axis Using Medial Scaffold Transforms

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

作者： Ming-Ching Chang Benjamin B. Kimia LEMS Engineering Brown University USA

this paper addresses a key bottleneck in the use of the 3D medial axis (MA) representation, namely, how the complex MA structure can be regularized so that similar, within-category 3D shapes yield similar 3D MA that are distinct from the non-category shapes. We rely on previous work which (i) constructs a hierarchical MA hypergraph, the medial scaffold (MS), and (ii) the theoretical classification of the instabilities of this structure, or transitions (sudden topological changes due to a small perturbation). the shapes at transition point are degenerate. Our approach is to recognize the transitions which are close-by to a given shape and transform the shape to this transition point, and repeat until no close-by transitions exists. this move towards degeneracy is the basis of simplification of shape. We derive 11 transforms from 7 transitions and follow a greedy scheme in applying the transform. the results show that the simplified MA preserves with-in-category similarity, thus indicating its potential use in various applications including shape analysis, manipulation, and matching.

关键词： Shape Surface morphology computer vision Topology Noise shaping Fingers Application software Animation Neck Power generation

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Motion Segmentation via Robust Subspace Separation in the Presence of Outlying, Incomplete, or Corrupted Trajectories

Motion Segmentation via Robust Subspace Separation in the Pr...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Shankar R. Rao Roberto Tron Rene Vidal Yi Ma Coordinated Science Laboratory University of Illinois Urbana-Champaign USA Center for Imaging Science Johns Hopkins University USA

We examine the problem of segmenting tracked feature point trajectories of multiple moving objects in an image sequence. Using the affine camera model, this motion segmentation problem can be cast as the problem of segmenting samples drawn from a union of linear subspaces. Due to limitations of the tracker, occlusions and the presence of nonrigid objects in the scene, the obtained motion trajectories may contain grossly mistracked features, missing entries, or not correspond to any valid motion model. In this paper, we develop a robust subspace separation scheme that can deal with all of these practical issues in a unified framework. Our methods draw strong connections between lossy compression, rank minimization, and sparse representation. We test our methods extensively and compare their performance to several extant methods with experiments on the Hopkins 155 database. Our results are on par with state-of-the-art results, and in many cases exceed them. All MAT-LAB code and segmentation results are publicly available for peer evaluation at .

关键词： Motion segmentation computer vision Robustness Image segmentation Trajectory Mathematical model Image sequences Cameras Tracking Layout

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Learning Class-Specific Affinities for Image Labelling

Learning Class-Specific Affinities for Image Labelling

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Dhruv Batra Rahul Sukthankar Tsuhan Chen Carnegie Mellon University USA Intel Research Pittsburgh USA

Spectral clustering and eigenvector-based methods have become increasingly popular in segmentation and recognition. Although the choice of the pairwise similarity metric (or affinities) greatly influences the quality of the results, this choice is typically specified outside the learning framework. In this paper, we present an algorithm to learn class-specific similarity functions. Mapping our problem in a Conditional Random Fields (CRF) framework enables us to pose the task of learning affinities as parameter learning in undirected graphical models. there are two significant advances over previous work. First, we learn the affinity between a pair of data-points as a function of a pairwise feature and (in contrast with previous approaches) the classes to which these two data-points were mapped, allowing us to work with a richer class of affinities. Second, our formulation provides a principled probabilistic interpretation for learning all of the parameters that define these affinities. Using ground truth segmentations and labellings for training, we learn the parameters with the greatest discriminative power (in an MLE sense) on the training data. We demonstrate the power of this learning algorithm in the setting of joint segmentation and recognition of object classes. Specifically, even with very simple appearance features, the proposed method achieves state-of-the-art performance on standard datasets.

关键词： Labeling Image segmentation Graphical models Image recognition Maximum likelihood estimation Birds computer vision Cost function Clustering algorithms Training data

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Multiplicative Kernels: Object Detection, Segmentation and Pose Estimation

Multiplicative Kernels: Object Detection, Segmentation and P...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.10

作者： Quan Yuan Ashwin thangali Vitaly Ablavsky Stan Sclaroff Computer Science Department Boston University USA

Object detection is challenging when the object class exhibits large within-class variations. In this work, we show that foreground-background classification (detection) and within-class classification of the foreground class (pose estimation) can be jointly learned in a multiplicative form of two kernel functions. One kernel measures similarity for foreground-background classification. the other kernel accounts for latent factors that control within-class variation and implicitly enables feature sharing among foreground training samples. Detector training can be accomplished via standard SVM learning. the resulting detectors are tuned to specific variations in the foreground class. they also serve to evaluate hypotheses of the foreground state. When the foreground parameters are provided in training, the detectors can also produce parameter estimate. When the foreground object masks are provided in training, the detectors can also produce object segmentation. the advantages of our method over past methods are demonstrated on data sets of human hands and vehicles.

关键词： Kernel Object detection Detectors Humans Parameter estimation Support vector machines Biological system modeling Image recognition Vehicle detection Training data

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Unsupervised estimation of segmentation quality using nonnegative factorization

Unsupervised estimation of segmentation quality using nonneg...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.3

作者： Roman Sandler Michael Lindenbaum Computer Science Department Technion-Israel Institute of Technology Haifa Israel

We propose an unsupervised method for evaluating image segmentation. Common methods are typically based on evaluating smoothness within segments and contrast between them, and the measure they provide is not explicitly related to segmentation errors. the proposed approach differs from these methods on several important points and has several advantages over them. First, it provides a meaningful, quantitative assessment of segmentation quality, in precision/recall terms, which were applicable so far only for supervised evaluation. Second, it builds on a new image model, which characterizes the segments as a mixture of basic feature distributions. the precision/recall estimates are then obtained by a nonnegative matrix factorization (NMF) process. A third important advantage is that the estimates, which are based on intrinsic properties of the specific image being evaluated and not on a comparison to typical images (learning), are relatively robust to context factors such as image quality or the presence of texture. Experimental results demonstrate the accuracy of the precision/recall estimates in comparison to ground truth based on human judgment. Moreover, it is shown that tuning a segmentation algorithm using the unsupervised measure improves the algorithm's quality (as measured by a supervised method).

关键词： Image segmentation Partitioning algorithms Robustness Humans Image quality computer science computer errors Image texture analysis Layout Active contours

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Randomized Trees for Human Pose Detection

Randomized Trees for Human Pose Detection

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

作者： Gregory Rogez Jonathan Rihan Srikumar Ramalingam Carlos Orrite Philip H. S. Torr Computer Vision Lab-I3A University of Zaragoza Spain Department of Computing Oxford Brookes University UK

this paper addresses human pose recognition from video sequences by formulating it as a classification problem. Unlike much previous work we do not make any assumptions on the availability of clean segmentation. the first step of this work consists in a novel method of aligning the training images using 3D Mocap data. Next we define classes by discretizing a 2D manifold whose two dimensions are camera viewpoint and actions. Our main contribution is a pose detection algorithm based on random forests. A bottom-up approach is followed to build a decision tree by recursively clustering and merging the classes at each level. For each node of the decision tree we build a list of potentially discriminative features using the alignment of training images;in this paper we consider Histograms of Orientated Gradient (HOG). We finally grow an ensemble of trees by randomly sampling one of the selected HOG blocks at each node. Our proposed approach gives promising results with both fixed and moving cameras.

关键词： Humans computer vision Cameras Detection algorithms Decision trees Histograms Detectors Shape Motion detection Video sequences

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Discovering Class Specific Composite Features through Discriminative Sampling with Swendsen-Wang Cut

Discovering Class Specific Composite Features through Discri...

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

作者： Feng Han Ying Shan Harpreet S. Sawhney Rakesh Kumar Sarnoff Corporation Princeton NJ USA One Microsoft Way Redmond Microsoft Corporation Washington D.C. DC USA

this paper proposes a novel approach to discover a set of class specific "composite features" as the feature pool for the detection and classification of complex objects using AdaBoost. Each composite feature is constructed from the combination of multiple individual features. Unlike previous works that design features manually or with certain restrictions, the class specific features are selected from the space of all combinations of a set of individual features. To achieve this, we first establish an analogue between the problem of discriminative feature selection and generative image segmentation, and then draw discriminative samples from the combinatory space with a novel algorithm called Discriminative Generalized Swendsen-Wang Cut. these samples form the initial pool of features, where AdaBoost is applied to learn a strong classifier combining the most discriminative composite features. We demonstrate the efficacy of our approach by comparing with existing detection algorithms for finding people in general pose.

关键词： Sampling methods Object detection Face detection computer vision Image sampling Image segmentation Voting Image generation Detection algorithms Image converters

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