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

共 656 条记录，以下是221-230 订阅

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Robust Estimation of Nonrigid Transformation for Point Set Registration

Robust Estimation of Nonrigid Transformation for Point Set R...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Ma, Jiayi Zhao, Ji Tian, Jinwen Tu, Zhuowen Yuille, Alan L. Huazhong Univ Sci & Technol Wuhan Peoples R China Univ Calif Los Angeles Dept Stat Los Angeles CA USA CMU Inst Robot Pittsburgh PA USA Univ Calif Los Angeles Lab Neuro Imaging Los Angeles CA 90024 USA

ISBN: (纸本)9780769549897

We present a new point matching algorithm for robust nonrigid registration. the method iteratively recovers the point correspondence and estimates the transformation between two point sets. In the first step of the iteration, feature descriptors such as shape context are used to establish rough correspondence. In the second step, we estimate the transformation using a robust estimator called L2E. this is the main novelty of our approach and it enables us to deal with the noise and outliers which arise in the correspondence step. the transformation is specified in a functional space, more specifically a reproducing kernel Hilbert space. We apply our method to nonrigid sparse image feature correspondence on 2D images and 3D surfaces. Our results quantitatively show that our approach outperforms state-of-the-art methods, particularly when there are a large number of outliers. Moreover, our method of robustly estimating transformations from correspondences is general and has many other applications.

关键词： Hilbert spaces image matching image registration shape recognition

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Not Everybody's Special: Using Neighbors in Referring Expressions with Uncertain Attributes

Not Everybody's Special: Using Neighbors in Referring Expres...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sadovnik, Amir Gallagher, Andrew Chen, Tsuhan Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

ISBN: (纸本)9780769549903

Referring expression generation is widely considered a basic building block of any natural language generation system. Generating these phrases, which can point out a single object from a group of objects, has been studied extensively in that community. However, to build systems which can discuss images in an intelligent way, it is necessary to consider additional factors unique to the visual domain. In this paper we consider the use of neighbors as anchors to create a referring expression for a person in a group image. We describe a target person using the people around him, when we cannot find a reliable set of attributes to describe the target himself. We first present a method for including neighbors in a referring expression, and discuss several ways of presenting this data to a user. We show through experiments that using descriptions with neighbors can significantly improve the probability of conveying the correct information to a user.

关键词： gesture recognition natural language processing probability

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Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems

Multi-resolution Shape Analysis via Non-Euclidean Wavelets: ...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kim, Won Hwa Chung, Moo K. Singh, Vikas Univ Wisconsin Dept Comp Sci 1210 W Dayton St Madison WI 53706 USA Univ Wisconsin Dept Biostat & Med Informat Madison WI USA

ISBN: (纸本)9780769549897

the analysis of 3-D shape meshes is a fundamental problem in computer vision, graphics, and medical imaging. Frequently, the needs of the application require that our analysis take a multi-resolution view of the shape's local and global topology, and that the solution is consistent across multiple scales. Unfortunately, the preferred mathematical construct which offers this behavior in classical image/signal processing, Wavelets, is no longer applicable in this general setting (data with non-uniform topology). In particular, the traditional definition does not allow writing out an expansion for graphs that do not correspond to the uniformly sampled lattice (e.g., images). In this paper, we adapt recent results in harmonic analysis, to derive Non-Euclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging. We show how descriptors derived from the dual domain representation offer native multi-resolution behavior for characterizing local/global topology around vertices. With only minor modifications, the framework yields a method for extracting interest/key points from shapes, a surprisingly simple algorithm for 3-D shape segmentation (competitive with state of the art), and a method for surface alignment (without landmarks). We give an extensive set of comparison results on a large shape segmentation benchmark and derive a uniqueness theorem for the surface alignment problem.

关键词： computer vision feature extraction image representation image resolution image segmentation mesh generation wavelet transforms

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Ground Truth For Pedestrian Analysis and Application to Camera Calibration

Ground Truth For Pedestrian Analysis and Application to Came...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Creusot, Clement Courty, Nicolas Toshiba R&D Ctr Kawasaki Kanagawa Japan UBS IRISA Vannes France

ISBN: (纸本)9780769549903

this paper investigates the use of synthetic 3D scenes to generate ground truth of pedestrian segmentation in 2D crowd video data. Manual segmentation of objects in videos is indeed one of the most time-consuming type of assisted labeling. A big gap in computer vision research can not be filled due to this lack of temporally dense and precise segmentation ground truth on large video samples. Such data is indeed essential to introduce machine learning techniques for automatic pedestrian segmentation, as well as many other applications involving occluded people. We present a new dataset of 1.8 million pedestrian silhouettes presenting human-to-human occlusion patterns likely to be seen in real crowd video data. To our knowledge, it is the first publicly available large dataset of pedestrian in crowd silhouettes. Solutions to generate and represent this data are detailed. We discuss ideas of how this ground truth can be used for a large number of computer vision applications and demonstrate it on a camera calibration toy problem.

关键词： calibration cameras computer vision image segmentation learning (artificial intelligence) video signal processing

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Face recognition Across Poses Using A Single 3D Reference Model

Face Recognition Across Poses Using A Single 3D Reference Mo...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Hsu, Gee-Sern Peng, Hsiao-Chia Natl Taiwan Univ Sci & Technol Taipei 106 Taiwan

ISBN: (纸本)9780769549903

Approaches for cross-pose face recognition can be split into 2D image based and 3D model based. Many 2D based methods are reported with promising performance but can only work for poses same as those in the training set. Although 3D based methods can handle arbitrary poses, only a small number of approaches are available. Extended from a latest face reconstruction method using a single 3D reference model, this study focuses on using the reconstructed 3D face for recognition. the reconstructed 3D face allows the generation of multi-pose samples for recognition. the recognition performance varies with poses, the closer the pose to the frontal, the better the performance attained. Several ways to improve the performance are attempted, including different numbers of fiducial points for alignment, multiple reference models considered in the reconstruction phase, and both frontal and profile poses available in the gallery. these attempts make this approach competitive to the state-of-the-art methods.

关键词： face recognition image reconstruction pose estimation solid modelling

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Detecting and Aligning Faces by Image Retrieval

Detecting and Aligning Faces by Image Retrieval

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Shen, Xiaohui Lin, Zhe Brandt, Jonathan Wu, Ying Northwestern Univ 2145 Sheridan Rd Evanston IL 60208 USA Adobe Res San Jose CA 95110 USA

ISBN: (纸本)9780769549897

Detecting faces in uncontrolled environments continues to be a challenge to traditional face detection methods[24] due to the large variation in facial appearances, as well as occlusion and clutter. In order to overcome these challenges, we present a novel and robust exemplar-based face detector that integrates image retrieval and discriminative learning. A large database of faces with bounding rectangles and facial landmark locations is collected, and simple discriminative classifiers are learned from each of them. A voting-based method is then proposed to let these classifiers cast votes on the test image through an efficient image retrieval technique. As a result, faces can be very efficiently detected by selecting the modes from the voting maps, without resorting to exhaustive sliding window-style scanning. Moreover, due to the exemplar-based framework, our approach can detect faces under challenging conditions without explicitly modeling their variations. Evaluation on two public benchmark datasets shows that our new face detection approach is accurate and efficient, and achieves the state-of-the-art performance. We further propose to use image retrieval for face validation (in order to remove false positives) and for face alignment/landmark localization. the same methodology can also be easily generalized to other face-related tasks, such as attribute recognition, as well as general object detection.

关键词： face recognition image retrieval learning (artificial intelligence) object detection

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Incorporating Structural Alternatives and Sharing into Hierarchy for Multiclass Object recognition and Detection

Incorporating Structural Alternatives and Sharing into Hiera...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xiaolong Lin, Liang Huang, Lichao Yan, Shuicheng Sun Yat Sen Univ Guangzhou Guangdong Peoples R China Natl Univ Singapore Dept ECE Singapore Singapore

ISBN: (纸本)9780769549897

this paper proposes a reconfigurable model to recognize and detect multiclass (or multiview) objects with large variation in appearance. Compared with well acknowledged hierarchical models, we study two advanced capabilities in hierarchy for object modeling: (i)"switch" variables(i.e. or-nodes) for specifying alternative compositions, and (ii) making local classifiers (i. e. leaf-nodes) shared among different classes. these capabilities enable us to account well for structural variabilities while preserving the model compact. Our model, in the form of an And-Or Graph, comprises four layers: a batch of leaf-nodes with collaborative edges in bottom for localizing object parts;the or-nodes over bottom to activate their children leaf-nodes;the and-nodes to classify objects as a whole;one root-node on the top for switching multiclass classification, which is also an or-node. For model training, we present an EM-type algorithm, namely dynamical structural optimization (DSO), to iteratively determine the structural configuration, (e.g., leaf-node generation associated with their parent or-nodes and shared across other classes), along with optimizing multi-layer parameters. the proposed method is valid on challenging databases, e.g., PASCAL VOC 2007 and UIUC-People, and it achieves state-of-the-arts performance.

关键词： graph theory image classification object detection object recognition optimisation

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thETIS: three Dimensional Tennis Shots A human action dataset

THETIS: THree Dimensional Tennis Shots A human action datase...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gourgari, Sofia Goudelis, Georgios Karpouzis, Konstantinos Kollias, Stefanos Natl Tech Univ Athens Image Video & Multimedia Syst Lab GR-10682 Athens Greece

ISBN: (纸本)9780769549903

the detection and classification of human movements, as a joint field of computer vision and pattern recognition, is used with an increasing rate in applications designed to describe human activity. Such applications require efficient methods and tools for the automatic analysis and classification of motion capture data, which constitute an active field of research. To facilitate the development and the benchmarking of methods for action recognition, several video collections have previously been proposed. In this paper, we present a new video database that can be used for an objective comparison and evaluation of different motion analysis and classification methods. the database contains video clips that capture the 3D motion of individuals. To be more specific, the set consists of 8374 video clips, which contain 12 different types of tennis actions performed by 55 individuals, captured by Kinect. Kinect provides the depth map of motion data and helps to extract the 3D skeletal joint connections. Performing experiments using state of the art algorithms, the database shows to be very challenging. It contains very similar to each other actions, offering the opportunity to algorithms dedicated to gaming and athletics, to be developed and tested. the database is freely available for research purposes.

关键词： computer vision feature extraction image classification image motion analysis interactive devices object detection object recognition sport video databases

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A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems

A Comparative Study of Modern Inference Techniques for Discr...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kappes, Joerg H. Andres, Bjoern Hamprecht, Fred A. Schnoerr, Christoph Nowozin, Sebastian Batra, Dhruv Kim, Sungwoong Kausler, Bernhard X. Lellmann, Jan Komodakis, Nikos Rother, Carsten Heidelberg Univ Bergheimer Str 58 D-69115 Heidelberg Germany Harvard Univ Cambridge MA 02138 USA Microsoft Res Cambridge Cambridge England

ISBN: (纸本)9780769549897

Seven years ago, Szeliski et al. published an influential study on energy minimization methods for Markov random fields (MRF). this study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenominal success of random field models means that the kinds of inference problems we solve have changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of 24 state-of-art techniques on a corpus of 2,300 energy minimization instances from 20 diverse computer vision applications. To ensure reproducibility, we evaluate all methods in the OpenGM2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.

关键词： Markov processes computer vision inference mechanisms integer programming minimisation random processes

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Principal Observation Ray Calibration for Tiled-Lens-Array Integral Imaging Display

Principal Observation Ray Calibration for Tiled-Lens-Array I...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Li, Weiming Wang, Haitao Zhou, Mingcai Wang, Shandong Jiao, Shaohui Mei, Xing Hong, Tao Lee, Hoyoung Kim, Jiyeun Samsung Adv Inst Technol China SAIT China Beijing Peoples R China SAIT Yongin South Korea CASIA NLPR Beijing Peoples R China

ISBN: (纸本)9780769549897

Integral imaging display (IID) is a promising technology to provide realistic 3D image without glasses. To achieve a large screen IID with a reasonable fabrication cost, a potential solution is a tiled-lens-array IID (TLA-IID). However, TLA-IIDs are subject to 3D image artifacts when there are even slight misalignments between the lens arrays. this work aims at compensating these artifacts by calibrating the lens array poses with a camera and including them in a ray model used for rendering the 3D image. Since the lens arrays are transparent, this task is challenging for traditional calibration methods. In this paper, we propose a novel calibration method based on defining a set of principle observation rays that pass lens centers of the TLA and the camera's optical center. the method is able to determine the lens array poses with only one camera at an arbitrary unknown position without using any additional markers. the principle observation rays are automatically extracted using a structured light based method from a dense correspondence map between the displayed and captured pixels. Experiments show that lens array misalignments can be estimated with a standard deviation smaller than 0.4 pixels. Based on this, 3D image artifacts are shown to be effectively removed in a test TLA-IID with challenging misalignments.

关键词： calibration cameras lenses liquid crystal displays pose estimation ray tracing realistic images rendering (computer graphics) screens (display) three-dimensional displays

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