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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61-70 订阅

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Graph Matching with Anchor Nodes: A Learning Approach

Graph Matching with Anchor Nodes: A Learning Approach

引用

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

作者： Hu, Nan Rustamov, Raif M. Guibas, Leonidas Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9780769549897

In this paper, we consider the weighted graph matching problem with partially disclosed correspondences between a number of anchor nodes. Our construction exploits recently introduced node signatures based on graph Laplacians, namely the Laplacian family signature (LFS) on the nodes, and the pairwise heat kernel map on the edges. In this paper, without assuming an explicit form of parametric dependence nor a distance metric between node signatures, we formulate an optimization problem which incorporates the knowledge of anchor nodes. Solving this problem gives us an optimized proximity measure specific to the graphs under consideration. Using this as a first order compatibility term, we then set up an integer quadratic program (IQP) to solve for a near optimal graph matching. Our experiments demonstrate the superior performance of our approach on randomly generated graphs and on two widely-used image sequences, when compared with other existing signature and adjacency matrix based graph matching methods.

关键词： graph theory image sequences integer programming pattern matching quadratic programming

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Intrinsic Scene Properties from a Single RGB-D Image

Intrinsic Scene Properties from a Single RGB-D Image

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

作者： Barron, Jonathan T. Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9780769549897

In this paper we extend the "shape, illumination and reflectance from shading" (SIRFS) model [3, 4], which recovers intrinsic scene properties from a single image. though SIRFS performs well on images of segmented objects, it performs poorly on images of natural scenes, which contain occlusion and spatially-varying illumination. We therefore present Scene-SIRFS, a generalization of SIRFS in which we have a mixture of shapes and a mixture of illuminations, and those mixture components are embedded in a "soft" segmentation of the input image. We additionally use the noisy depth maps provided by RGB-D sensors (in this case, the Kinect) to improve shape estimation. Our model takes as input a single RGB-D image and produces as output an improved depth map, a set of surface normals, a reflectance image, a shading image, and a spatially varying model of illumination. the output of our model can be used for graphics applications, or for any application involving RGB-D images.

关键词： image segmentation object detection shape recognition

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Structured Face Hallucination

Structured Face Hallucination

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

作者： Yang, Chih-Yuan Liu, Sifei Yang, Ming-Hsuan Univ Calif Merced Merced CA 95343 USA

ISBN: (纸本)9780769549897

the goal of face hallucination is to generate high-resolution images with fidelity from low-resolution ones. In contrast to existing methods based on patch similarity or holistic constraints in the image space, we propose to exploit local image structures for face hallucination. Each face image is represented in terms of facial components, contours and smooth regions. the image structure is maintained via matching gradients in the reconstructed high-resolution output. For facial components, we align input images to generate accurate exemplars and transfer the high-frequency details for preserving structural consistency. For contours, we learn statistical priors to generate salient structures in the high-resolution images. A patch matching method is utilized on the smooth regions where the image gradients are preserved. Experimental results demonstrate that the proposed algorithm generates hallucinated face images with favorable quality and adaptability.

关键词： face recognition gradient methods image matching image reconstruction image resolution statistical analysis

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Fine-Grained Crowdsourcing for Fine-Grained recognition

Fine-Grained Crowdsourcing for Fine-Grained Recognition

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

作者： Jia Deng Krause, Jonathan Li Fei-Fei Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9780769549897

Fine-grained recognition concerns categorization at sub-ordinate levels, where the distinction between object classes is highly local. Compared to basic level recognition, fine-grained categorization can be more challenging as there are in general less data and fewer discriminative features. this necessitates the use of stronger prior for feature selection. In this work, we include humans in the loop to help computers select discriminative features. We introduce a novel online game called "Bubbles" that reveals discriminative features humans use. the player's goal is to identify the category of a heavily blurred image. During the game, the player can choose to reveal full details of circular regions ("bubbles"), with a certain penalty. With proper setup the game generates discriminative bubbles with assured quality. We next propose the "BubbleBank" algorithm that uses the human selected bubbles to improve machine recognition performance. Experiments demonstrate that our approach yields large improvements over the previous state of the art on challenging benchmarks.

关键词： computer games feature extraction image recognition

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POOF: Part-Based One-vs-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation

POOF: Part-Based One-vs-One Features for Fine-Grained Catego...

引用

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

作者： Berg, thomas Belhumeur, Peter N. Columbia Univ New York NY 10027 USA

ISBN: (纸本)9780769549897

From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.

关键词： face recognition feature extraction

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Scene Text recognition using Part-based Tree-structured Character Detection

Scene Text Recognition using Part-based Tree-structured Char...

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

作者： Shi, Cunzhao Wang, Chunheng Xiao, Baihua Zhang, Yang Gao, Song Zhang, Zhong CASIA State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9780769549897

Scene text recognition has inspired great interests from the computer vision community in recent years. In this paper, we propose a novel scene text recognition method using part-based tree-structured character detection. Different from conventional multi-scale sliding window character detection strategy, which does not make use of the character-specific structure information, we use part-based tree-structure to model each type of character so as to detect and recognize the characters at the same time. While for word recognition, we build a Conditional Random Field model on the potential character locations to incorporate the detection scores, spatial constraints and linguistic knowledge into one framework. the final word recognition result is obtained by minimizing the cost function defined on the random field. Experimental results on a range of challenging public datasets (ICDAR 2003, ICDAR 2011, SVT) demonstrate that the proposed method outperforms state-of-the-art methods significantly both for character detection and word recognition.

关键词： character recognition computer vision random processes trees (mathematics)

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Correlation Filters for Object Alignment

Correlation Filters for Object Alignment

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

作者： Boddeti, Vishnu Naresh Kanade, Takeo Kumar, B. V. K. Vijaya Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9780769549897

Alignment of 3D objects from 2D images is one of the most important and well studied problems in computer vision. A typical object alignment system consists of a landmark appearance model which is used to obtain an initial shape and a shape model which refines this initial shape by correcting the initialization errors. Since errors in landmark initialization from the appearance model propagate through the shape model, it is critical to have a robust landmark appearance model. While there has been much progress in designing sophisticated and robust shape models, there has been relatively less progress in designing robust landmark detection models. In this paper we present an efficient and robust landmark detection model which is designed specifically to minimize localization errors thereby leading to state-of-the-art object alignment performance. We demonstrate the efficacy and speed of the proposed approach on the challenging task of multi-view car alignment.

关键词： computer vision filtering theory object detection

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A Fast Semidefinite Approach to Solving Binary Quadratic Problems

A Fast Semidefinite Approach to Solving Binary Quadratic Pro...

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

作者： Wang, Peng Shen, Chunhua van den Hengel, Anton Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia

ISBN: (纸本)9780769549897

Many computer vision problems can be formulated as binary quadratic programs (BQPs). Two classic relaxation methods are widely used for solving BQPs, namely, spectral methods and semidefinite programming (SDP), each with their own advantages and disadvantages. Spectral relaxation is simple and easy to implement, but its bound is loose. Semidefinite relaxation has a tighter bound, but its computational complexity is high for large scale problems. We present a new SDP formulation for BQPs, with two desirable properties. First, it has a similar relaxation bound to conventional SDP formulations. Second, compared with conventional SDP methods, the new SDP formulation leads to a significantly more efficient and scalable dual optimization approach, which has the same degree of complexity as spectral methods. Extensive experiments on various applications including clustering, image segmentation, co-segmentation and registration demonstrate the usefulness of our SDP formulation for solving large-scale BQPs.

关键词： computational complexity computer vision image registration image segmentation quadratic programming

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Sampling Strategies for Real-time Action recognition

Sampling Strategies for Real-time Action Recognition

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

作者： Shi, Feng Petriu, Emil Laganiere, Robert Univ Ottawa Sch Elect Engn & Comp Scinece Ottawa ON Canada

ISBN: (纸本)9780769549897

Local spatio-temporal features and bag-of-features representations have become popular for action recognition. A recent trend is to use dense sampling for better performance. While many methods claimed to use dense feature sets, most of them are just denser than approaches based on sparse interest point detectors. In this paper, we explore sampling with high density on action recognition. We also investigate the impact of random sampling over dense grid for computational efficiency. We present a real-time action recognition system which integrates fast random sampling method with local spatio-temporal features extracted from a Local Part Model. A new method based on histogram intersection kernel is proposed to combine multiple channels of different descriptors. Our technique shows high accuracy on the simple Kth dataset, and achieves state-of-the-art on two very challenging real-world datasets, namely, 93% on Kth, 83.3% on UCF50 and 47.6% on HMDB51.

关键词： feature extraction image recognition image representation image sampling

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Evaluation of Color STIPs for Human Action recognition

Evaluation of Color STIPs for Human Action Recognition

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

作者： Everts, Ivo van Gemert, Jan C. Gevers, theo Univ Amsterdam Intelligent Syst Lab Amsterdam NL-1012 WX Amsterdam Netherlands

ISBN: (纸本)9780769549897

this paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. these issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. this enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.

关键词： image colour analysis image motion analysis image representation video signal processing

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