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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671-680 订阅

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Image-based Synthesis and Re-Synthesis of Viewpoints Guided by 3D Models 27

Image-based Synthesis and Re-Synthesis of Viewpoints Guided ...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Rematas, Konstantinos Ritschel, Tobias Fritz, Mario Tuytelaars, Tinne Katholieke Univ Leuven IMinds Louvain Belgium Max Planck Inst Informat Munich Germany Univ Saarland D-66123 Saarbrucken Germany

ISBN: (纸本)9781479951178

We propose a technique to use the structural information extracted from a set of 3D models of an object class to improve novel-view synthesis for images showing unknown instances of this class. these novel views can be used to "amplify" training image collections that typically contain only a low number of views or lack certain classes of views entirely (e. g. top views). We extract the correlation of position, normal, reflectance and appearance from computer-generated images of a few exemplars and use this information to infer new appearance for new instances. We show that our approach can improve performance of state-of-the-art detectors using real-world training data. Additional applications include guided versions of inpainting, 2D-to-3D conversion, super-resolution and non-local smoothing.

关键词： computer graphics computer vision image-based rendering object detection synthetic training data

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Region-based Discriminative Feature Pooling for Scene Text recognition 27

Region-based Discriminative Feature Pooling for Scene Text R...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lee, Chen-Yu Bhardwaj, Anurag Di, Wei Jagadeesh, Vignesh Piramuthu, Robinson Univ Calif San Diego San Diego CA 92103 USA eBay Res Labs New York NY USA

ISBN: (纸本)9781479951178

We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition. Many existing methods rely on Histogram of Oriented Gradient (HOG) or part-based models, which do not span the feature space well for characters in natural scene images, especially given large variation in fonts with cluttered backgrounds. In this work, we propose a discriminative feature pooling method that automatically learns the most informative sub-regions of each scene character within a multi-class classification framework, whereas each sub-region seamlessly integrates a set of low-level image features through integral images. the proposed feature representation is compact, computationally efficient, and able to effectively model distinctive spatial structures of each individual character class. Extensive experiments conducted on challenging datasets (Chars74K, ICDAR'03, ICDAR'11, SVT) show that our method significantly outperforms existing methods on scene character classification and scene text recognition tasks.

关键词： computer vision object recognition optical character recognition text detection

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Predicting Failures of vision Systems 27

Predicting Failures of Vision Systems

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Peng Wang, Jiuling Farhadi, Ali Hebert, Martial Parikh, Devi Virginia Tech Blacksburg VA 24061 USA Univ Texas Austin Austin TX 78712 USA Univ Washington Seattle WA 98195 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781479951178

computer vision systems today fail frequently. they also fail abruptly without warning or explanation. Alleviating the former has been the primary focus of the community. In this work, we hope to draw the community's attention to the latter, which is arguably equally problematic for real applications. We promote two metrics to evaluate failure prediction. We show that a surprisingly straightforward and general approach, that we call ALERT, can predict the likely accuracy (or failure) of a variety of computer vision systems-semantic segmentation, vanishing point and camera parameter estimation, and image memorability prediction on individual input images. We also explore attribute prediction, where classifiers are typically meant to generalize to new unseen categories. We show that ALERT can be useful in predicting failures of this transfer. Finally, we leverage ALERT to improve the performance of a downstream application of attribute prediction: zero-shot learning. We show that ALERT can outperform several strong baselines for zero-shot learning on four datasets.

关键词： warning computer vision Systems computer vision failure prediction Classifiers

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Facial Expression recognition via a Boosted Deep Belief Network 27

Facial Expression Recognition via a Boosted Deep Belief Netw...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Ping Han, Shizhong Meng, Zibo Tong, Yan Univ S Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

ISBN: (纸本)9781479951178

A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. this paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance/shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis.

关键词： Face recognition

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Fast and Accurate Image Matching with Cascade Hashing for 3D Reconstruction 27

Fast and Accurate Image Matching with Cascade Hashing for 3D...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Cheng, Jian Leng, Cong Wu, Jiaxiang Cui, Hainan Lu, Hanqing Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Image matching is one of the most challenging stages in 3D reconstruction, which usually occupies half of computational cost and inaccurate matching may lead to failure of reconstruction. therefore, fast and accurate image matching is very crucial for 3D reconstruction. In this paper, we proposed a Cascade Hashing strategy to speed up the image matching. In order to accelerate the image matching, the proposed Cascade Hashing method is designed to be three-layer structure: hashing lookup, hashing remapping, and hashing ranking. Each layer adopts different measures and filtering strategies, which is demonstrated to be less sensitive to noise. Extensive experiments show that image matching can be accelerated by our approach in hundreds times than brute force matching, even achieves ten times or more than Kd-tree based matching while retaining comparable accuracy.

关键词： Image matching

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Dual Linear Regression Based Classification for Face Cluster recognition 27

Dual Linear Regression Based Classification for Face Cluster...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Chen, Liang Univ No British Columbia Prince George BC V2N 4Z9 Canada

ISBN: (纸本)9781479951178

We are dealing with the face cluster recognition problem where there are multiple images per subject in both gallery and probe sets. It is never guaranteed to have a clear spatio-temporal relation among the multiple images of each subject. Considering that the image vectors of each subject, either in gallery or in probe, span a subspace;an algorithm, Dual Linear Regression Classification (DLRC), for the face cluster recognition problem is developed where the distance between two subspaces is defined as the similarity value between a gallery subject and a probe subject. DLRC attempts to find a "virtual" face image located in the intersection of the subspaces spanning from both clusters of face images. the "distance" between the "virtual" face images reconstructed from both subspaces is then taken as the distance between these two subspaces. We further prove that such distance can be formulated under a single linear regression model where we indeed can find the "distance" without reconstructing the "virtual" face images. Extensive experimental evaluations demonstrated the effectiveness of DLRC algorithm compared to other algorithms.

关键词： Dual Linear Regression Face Cluster recognition Image Set based Face recognition Linear Regression

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Speeding Up Tracking by Ignoring Features 27

Speeding Up Tracking by Ignoring Features

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Lu Dibeklioglu, Hamdi van der Maaten, Laurens Delft Univ Technol Pattern Recognit & Bioinformat Grp NL-2628 CD Delft Netherlands

ISBN: (纸本)9781479951178

Most modern object trackers combine a motion prior with sliding-window detection, using binary classifiers that predict the presence of the target object based on histogram features. Although the accuracy of such trackers is generally very good, they are often impractical because of their high computational requirements. To resolve this problem, the paper presents a new approach that limits the computational costs of trackers by ignoring features in image regions that - after inspecting a few features - are unlikely to contain the target object. To this end, we derive an upper bound on the probability that a location is most likely to contain the target object, and we ignore (features in) locations for which this upper bound is small. We demonstrate the effectiveness of our new approach in experiments with model-free and model-based trackers that use linear models in combination with HOG features. the results of our experiments demonstrate that our approach allows us to reduce the average number of inspected features by up to 90% without affecting the accuracy of the tracker.

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Unsupervised One-Class Learning for Automatic Outlier Removal 27

Unsupervised One-Class Learning for Automatic Outlier Remova...

引用

27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Wei Hua, Gang Smith, John R. IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Stevens Inst Technol Hoboken NJ USA

ISBN: (纸本)9781479951178

Outliers are pervasive in many computer vision and pattern recognition problems. Automatically eliminating outliers scattering among practical data collections becomes increasingly important, especially for Internet inspired vision applications. In this paper, we propose a novel one-class learning approach which is robust to contamination of input training data and able to discover the outliers that corrupt one class of data source. Our approach works under a fully unsupervised manner, differing from traditional one-class learning supervised by known positive labels. By design, our approach optimizes a kernel-based max-margin objective which jointly learns a large margin one-class classifier and a soft label assignment for inliers and outliers. An alternating optimization algorithm is then designed to iteratively refine the classifier and the labeling, achieving a provably convergent solution in only a few iterations. Extensive experiments conducted on four image datasets in the presence of artificial and real-world outliers demonstrate that the proposed approach is considerably superior to the state-of-the-arts in obliterating outliers from contaminated one class of images, exhibiting strong robustness at a high outlier proportion up to 60%.

关键词： One-Class Learning Outlier Removal outliers images computer vision inliers pattern recognition problem Classifiers Data Collection Methods Data sources AUTOMATIC Optimization algorithms

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Turning Mobile Phones into 3D Scanners 27

Turning Mobile Phones into 3D Scanners

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Kolev, Kahn Tanskanen, Petri Speciale, Pablo Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781479951178

In this paper, we propose an efficient and accurate scheme for the integration of multiple stereo-based depth measurements. For each provided depth map a confidence-based weight is assigned to each depth estimate by evaluating local geometry orientation, underlying camera setting and photometric evidence. Subsequently, all hypotheses are fused together into a compact and consistent 3D model. thereby, visibility conflicts are identified and resolved, and fitting measurements are averaged with regard to their confidence scores. the individual stages of the proposed approach are validated by comparing it to two alternative techniques which rely on a conceptually different fusion scheme and a different confidence inference, respectively. Pursuing live 3D reconstruction on mobile devices as a primary goal, we demonstrate that the developed method can easily be integrated into a system for monocular interactive 3D modeling by substantially improving its accuracy while adding a negligible overhead to its performance and retaining its interactive potential.

关键词： three dimensional computer graphics

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Learning Scalable Discriminative Dictionary with Sample Relatedness 27

Learning Scalable Discriminative Dictionary with Sample Rela...

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27th ieee conference on computer vision and pattern recognition (cvpr)

作者： Feng, Jiashi Jegelka, Stefanie Yan, Shuicheng Darrell, Trevor Natl Univ Singapore Dept ECE Singapore 117548 Singapore Univ Calif Berkeley Dept EECS Berkeley CA USA Univ Calif Berkeley ICSI Berkeley CA USA

ISBN: (纸本)9781479951178

Attributes are widely used as mid-level descriptors of object properties in object recognition and retrieval. Mostly, such attributes are manually pre-defined based on domain knowledge, and their number is fixed. However, pre-defined attributes may fail to adapt to the properties of the data at hand, may not necessarily be discriminative, and/or may not generalize well. In this work, we propose a dictionary learning framework that flexibly adapts to the complexity of the given data set and reliably discovers the inherent discriminative middle-level binary features in the data. We use sample relatedness information to improve the generalization of the learned dictionary. We demonstrate that our framework is applicable to both object recognition and complex image retrieval tasks even with few training examples. Moreover, the learned dictionary also help classify novel object categories. Experimental results on the Animals with Attributes, ILSVRC2010 and PASCAL VOC2007 datasets indicate that using relatedness information leads to significant performance gains over established baselines.

关键词： Object recognition

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