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

共 1569 条记录，以下是1291-1300 订阅

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Image Segmentation via Convolution of a Level-Set Function with a Rigaut Kernel

Image Segmentation via Convolution of a Level-Set Function w...

引用

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

作者： Ozlem N. Subakan Baba C. Vemuri Department of Computer and Information Science and Engineering University of Florida Gainesville FL USA

Image segmentation is a fundamental task in computer vision and there are numerous algorithms that have been successfully applied in various domains. there are still plenty of challenges to be met with. In this paper, we consider one such challenge, that of achieving segmentation while preserving complicated and detailed features present in the image, be it a gray level or a textured image. We present a novel approach that does not make use of any prior information about the objects in the image being segmented. Segmentation is achieved using local orientation information, which is obtained via the application of a steerable Gabor filter bank, in a statistical framework. this information is used to construct a spatially varying kernel called the Rigaut Kernel, which is then convolved with the signed distance function of an evolving contour (placed in the image) to achieve segmentation. We present numerous experimental results on real images, including a quantitative evaluation. Superior performance of our technique is depicted via comparison to the state-of-the-art algorithms in literature.

关键词： Image segmentation Convolution Kernel Gabor filters Active contours computer vision Context modeling Solid modeling Level set Layout

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Segmentation by transduction

Segmentation by transduction

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.2

作者： Olivier Duchenne Jean-Yves Audibert Renaud Keriven Jean Ponce Florent Segonne Willow-ENS/INRIA Paris France CERTIS-Ecole des Ponts Willow-ENS/INRIA France CERTIS-Ecole des Ponts France Willow-ENS/INRIA France

this paper addresses the problem of segmenting an image into regions consistent with user-supplied seeds (e.g., a sparse set of broad brush strokes). We view this task as a statistical transductive inference, in which some pixels are already associated with given zones and the remaining ones need to be classified. Our method relies on the Laplacian graph regularizer, a powerful manifold learning tool that is based on the estimation of variants of the Laplace-Beltrami operator and is tightly related to diffusion processes. Segmentation is modeled as the task of finding matting coefficients for unclassified pixels given known matting coefficients for seed pixels. the proposed algorithm essentially relies on a high margin assumption in the space of pixel characteristics. It is simple, fast, and accurate, as demonstrated by qualitative results on natural images and a quantitative comparison with state-of-the-art methods on the Microsoft GrabCut segmentation database.

关键词： Image segmentation Application software Object recognition Laplace equations Biomedical imaging Diffusion processes Image databases Partitioning algorithms computer vision Anatomical structure

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Evaluation of Color Descriptors for Object and Scene recognition

Evaluation of Color Descriptors for Object and Scene Recogni...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.8

作者： Koen E. A. van de Sande theo Gevers Cees G. M. Snoek University of Amsterdam Amsterdam Netherlands

Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used. To increase illumination invariance and discriminative power, color descriptors have been proposed only recently. As many descriptors exist, a structured overview of color invariant descriptors in the context of image category recognition is required. therefore, this paper studies the invariance properties and the distinctiveness of color descriptors in a structured way. the invariance properties of color descriptors are shown analytically using a taxonomy based on invariance properties with respect to photometric transformations. the distinctiveness of color descriptors is assessed experimentally using two benchmarks from the image domain and the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. the results reveal further that, for light intensity changes, the usefulness of invariance is category-specific.

关键词： Layout Lighting Image recognition Taxonomy Reflectivity Image color analysis Image retrieval Robustness Photometry Vegetation mapping

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Human-Assisted Motion Annotation

Human-Assisted Motion Annotation

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Ce Liu William T. Freeman Edward H. Adelson Yair Weiss CSAIL-MIT Israel Hebrew University of Jerusalem Israel

Obtaining ground-truth motion for arbitrary, real-world video sequences is a challenging but important task for both algorithm evaluation and model design. Existing ground-truth databases are either synthetic, such as the Yosemite sequence, or limited to indoor, experimental setups, such as the database developed in [5]. We propose a human-in-loop methodology to create a ground-truth motion database for the videos taken with ordinary cameras in both indoor and outdoor scenes, using the fact that human beings are experts at segmenting objects and inspecting the match between two frames. We designed an interactive computer vision system to allow a user to efficiently annotate motion. Our methodology is cross-validated by showing that human annotated motion is repeatable, consistent across annotators, and close to the ground truth obtained by [5]. Using our system, we collected and annotated 10 indoor and outdoor real-world videos to form a ground-truth motion database. the source code, annotation tool and database is online for public evaluation and benchmarking.

关键词： Image motion analysis Databases Video sequences Optical sensors Humans computer vision Motion analysis Optical noise Motion estimation Labeling

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View and Scale Invariant Action recognition Using Multiview Shape-Flow Models

View and Scale Invariant Action Recognition Using Multiview ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.9

作者： Pradeep Natarajan Ramakant Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles CA USA

Actions in real world applications typically take place in cluttered environments with large variations in the orientation and scale of the actor. We present an approach to simultaneously track and recognize known actions that is robust to such variations, starting from a person detection in the standing pose. In our approach we first render synthetic poses from multiple viewpoints using Mocap data for known actions and represent them in a Conditional Random Field (CRF) whose observation potentials are computed using shape similarity and the transition potentials are computed using optical flow. We enhance these basic potentials with terms to represent spatial and temporal constraints and call our enhanced model the Shape, Flow, Duration-Conditional Random Field (SFD-CRF). We find the best sequence of actions using Viterbi search in the SFD-CRF. We demonstrate our approach on videos from multiple viewpoints and in the presence of background clutter.

关键词： Optical computing Image motion analysis Humans pattern recognition Image recognition Hidden Markov models Shape Videos Intelligent robots Robustness

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Unsupervised Feature Selection via Distributed Coding for Multi-view Object recognition

Unsupervised Feature Selection via Distributed Coding for Mu...

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

作者： C. Mario Christoudias Raquel Urtasun Trevor Darrell UC Berkeley EECS & ICSI MIT CSAIL USA

Object recognition accuracy can be improved when information from multiple views is integrated, but information in each view can often be highly redundant. We consider the problem of distributed object recognition or indexing from multiple cameras, where the computational power available at each camera sensor is limited and communication between cameras is prohibitively expensive. In this scenario, it is desirable to avoid sending redundant visual features from multiple views. Traditional supervised feature selection approaches are inapplicable as the class label is unknown at each camera. In this paper we propose an unsupervised multi-view feature selection algorithm based on a distributed coding approach. With our method, a Gaussian Process model of the joint view statistics is used at the receiver to obtain a joint encoding of the views without directly sharing information across encoders. We demonstrate our approach on recognition and indexing tasks with multi-view image databases and show that our method compares favorably to an independent encoding of the features from each camera.

关键词： Object recognition Cameras Indexing Distributed computing Gaussian processes Statistical distributions Encoding Image recognition Image databases Image coding

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Performance Evaluation of State-of-the-Art Discrete Symmetry Detection Algorithms

Performance Evaluation of State-of-the-Art Discrete Symmetry...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Minwoo Park Seungkyu Lee Po-Chun Chen Somesh Kashyap Asad A. Butt Yanxi Liu Department of Computer Science and Engineering Department of Electrical Engineering The Pennsylvania State University USA

Symmetry is one of the important cues for human and machine perception of the world. For over three decades, automatic symmetry detection from images/patterns has been a standing topic in computer vision. We present a timely, systematic, and quantitative performance evaluation of three state of the art discrete symmetry detection algorithms. this evaluation scheme includes a set of carefully chosen synthetic and real images presenting justified, unambiguous single or multiple dominant symmetries, and a pair of well-defined success rates for validation. We make our 176 test images with associated hand-labeled ground truth publicly available with this paper. In addition, we explore the potential contribution of symmetry detection for object recognition by testing the symmetry detection algorithm on three publicly available object recognition image sets (PASCAL VOC'07, MSRC and Caltech-256). Our results indicate that even after several decades of effort, symmetry detection in real-world images remains a challenging, unsolved problem in computer vision. Meanwhile, we illustrate its future potential in object recognition.

关键词： Detection algorithms Reflection computer vision Testing Object detection Object recognition Image edge detection Detectors Image databases computer science

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Rotation Symmetry Group Detection Via Frequency Analysis of Frieze-Expansions

Rotation Symmetry Group Detection Via Frequency Analysis of ...

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26th ieee conference on computer vision and pattern recognition (cvpr 2008), vol.12

作者： Seungkyu Lee Robert T. Collins Yanxi Liu Department of Computer Science and Engineering Pennsylvania State University USA Dept. of Electrical Engineering The Pennsylvania State University USA

We present a novel and effective algorithm for rotation symmetry group detection from real-world images. We propose a frieze-expansion method that transforms rotation symmetry group detection into a simple translation symmetry detection problem. We define and construct a dense symmetry strength map from a given image, and search for potential rotational symmetry centers automatically. Frequency analysis, using Discrete Fourier Transform (DFT), is applied to the frieze-expansion patterns to uncover the types and the cardinality of multiple rotation symmetry groups in an image, concentric or otherwise. Furthermore, our detection algorithm can discriminate discrete versus continuous and cyclic versus dihedral symmetry groups, and identify the corresponding supporting regions in the image. Experimental results on over 80 synthetic and natural images demonstrate superior performance of our rotation detection algorithm in accuracy and in speed over the state of the art rotation detection algorithms.

关键词： Frequency Detection algorithms Reflection computer vision Image edge detection Image analysis Discrete Fourier transforms Algorithm design and analysis computer science Discrete transforms

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Object recognition and Segmentation by Non-Rigid Quasi-Dense Matching

Object Recognition and Segmentation by Non-Rigid Quasi-Dense...

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

作者： Juho Kannala Esa Rahtu Sami S. Brandt Janne Heikkila Machine Vision Group University of Oulu Finland

In this paper, we present a non-rigid quasi-dense matching method and its application to object recognition and segmentation. the matching method is based on the match propagation algorithm which is here extended by using local image gradients for adapting the propagation to smooth non-rigid deformations of the imaged surfaces. the adaptation is based entirely on the local properties of the images and the method can be hence used in non-rigid image registration where global geometric constraints are not available. Our approach for object recognition and segmentation is directly built on the quasi-dense matching. the quasi-dense pixel matches between the model and test images are grouped into geometrically consistent groups using a method which utilizes the local affine transformation estimates obtained during the propagation. the number and quality of geometrically consistent matches is used as a recognition criterion and the location of the matching pixels directly provides the segmentation. the experiments demonstrate that our approach is able to deal with extensive background clutter, partial occlusion, large scale and viewpoint changes, and notable geometric deformations.

关键词： Object recognition Testing Image recognition Image segmentation Detectors Image registration Large-scale systems Machine vision Pixel Solid modeling

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Robust Statistics on Riemannian Manifolds via the Geometric Median

Robust Statistics on Riemannian Manifolds via the Geometric ...

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

作者： P. thomas Fletcher Suresh Venkatasubramanian Sarang Joshi School of Computing University of Utah USA Biomedical Engineering University of Utah USA

the geometric median is a classic robust estimator of centrality for data in Euclidean spaces. In this paper we formulate the geometric median of data on a Riemannian manifold as the minimizer of the sum of geodesic distances to the data points. We prove existence and uniqueness of the geometric median on manifolds with non-positive sectional curvature and give sufficient conditions for uniqueness on positively curved manifolds. Generalizing the Weiszfeld procedure for finding the geometric median of Euclidean data, we present an algorithm for computing the geometric median on an arbitrary manifold. We show that this algorithm converges to the unique solution when it exists. this method produces a robust central point for data lying on a manifold, and should have use in a variety of vision applications involving manifolds. We give examples of the geometric median computation and demonstrate its robustness for three types of manifold data: the 3D rotation group, tensor manifolds, and shape spaces.

关键词： Robustness Statistics computer vision Electric breakdown Manifolds Biomedical computing Geophysics computing Tensile stress Shape measurement Application software

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