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检索条件"任意字段=2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003"

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Structured feature similarity with explicit feature map

Structured feature similarity with explicit feature map

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Kobayashi, Takumi National Institute of Advanced Industrial Science and Technology Umezono 1-1-1 Tsukuba Japan

ISBN: (纸本)9781467388511

Feature matching is a fundamental process in a variety of computer vision tasks. Beyond the standard L2 metric, various methods to measure similarity between features have been proposed mainly on the assumption that the features are defined in a histogram form. On the other hand, in a field of image quality assessment, SSIM [27] produces effective similarity between images, taking the place of L2 metric. In this paper, we propose a feature similarity measurement method based on the SSIM. Unlike the previous methods, the proposed method is built on not a histogram form but a tensor structure of a feature array extracted such as on spatial grids, in order to construct effective SSIM-based similarity measure of high robustness which is a key requirement in feature matching. In addition, we provide the explicit feature map such that the proposed similarity metric is embedded as a dot product. It contributes to significant speedup in similarity measurement as well as to feature transformation toward an effective vector form to which linear classifiers are directly applicable. In the experiments on various tasks, the proposed method exhibits favorable performance in both feature matching and classification.

关键词： computer vision

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Person re-identification by multi-channel parts-based CNN with improved triplet loss function

Person re-identification by multi-channel parts-based CNN wi...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Cheng, De Gong, Yihong Zhou, Sanping Wang, Jinjun Zheng, Nanning Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China

ISBN: (纸本)9781467388511

Person re-identification across cameras remains a very challenging problem, especially when there are no overlapping fields of view between cameras. In this paper, we present a novel multi-channel parts-based convolutional neural network (CNN) model under the triplet framework for person re-identification. Specifically, the proposed CNN model consists of multiple channels to jointly learn both the global full-body and local body-parts features of the input persons. The CNN model is trained by an improved triplet loss function that serves to pull the instances of the same person closer, and at the same time push the instances belonging to different persons farther from each other in the learned feature space. Extensive comparative evaluations demonstrate that our proposed method significantly outperforms many state-of-the-art approaches, including both traditional and deep network-based ones, on the challenging i-LIDS, VIPeR, PRID2011 and CUHK01 datasets.

关键词： pattern recognition

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Actionness estimation using hybrid fully convolutional networks

Actionness estimation using hybrid fully convolutional netwo...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wang, Limin Qiao, Yu Tang, Xiaoou Van Gool, Luc Shenzhen Key Lab of Comp. Vis. and Pat. Rec. Shenzhen Institutes of Advanced Technology CAS China Department of Information Engineering Chinese University of Hong Kong Hong Kong Computer Vision Lab ETH Zurich Switzerland

ISBN: (纸本)9781467388511

Actionness [3] was introduced to quantify the likelihood of containing a generic action instance at a specific location. Accurate and efficient estimation of actionness is important in video analysis and may benefit other relevant tasks such as action recognition and action detection. This paper presents a new deep architecture for actionness estimation, called hybrid fully convolutional network (H-FCN), which is composed of appearance FCN (A-FCN) and motion FCN (M-FCN). These two FCNs leverage the strong capacity of deep models to estimate actionness maps from the perspectives of static appearance and dynamic motion, respectively. In addition, the fully convolutional nature of H-FCN allows it to efficiently process videos with arbitrary sizes. Experiments are conducted on the challenging datasets of Stanford40, UCF Sports, and JHMDB to verify the effectiveness of H-FCN on actionness estimation, which demonstrate that our method achieves superior performance to previous ones. Moreover, we apply the estimated actionness maps on action proposal generation and action detection. Our actionness maps advance the current state-of-the-art performance of these tasks substantially.

关键词： pattern recognition

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ASP vision: Optically computing the first layer of convolutional neural networks using angle sensitive pixels

ASP vision: Optically computing the first layer of convoluti...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Chen, Huaijin G. Jayasuriya, Suren Yang, Jiyue Stephen, Judy Sivaramakrishnan, Sriram Veeraraghavan, Ashok Molnar, Alyosha Rice University United States Cornell University United States

ISBN: (纸本)9781467388511

Deep learning using convolutional neural networks (CNNs) is quickly becoming the state-of-the-art for challenging computer vision applications. However, deep learning's power consumption and bandwidth requirements currently limit its application in embedded and mobile systems with tight energy budgets. In this paper, we explore the energy savings of optically computing the first layer of CNNs. To do so, we utilize bio-inspired Angle Sensitive Pixels (ASPs), custom CMOS diffractive image sensors which act similar to Gabor filter banks in the V1 layer of the human visual cortex. ASPs replace both image sensing and the first layer of a conventional CNN by directly performing optical edge filtering, saving sensing energy, data bandwidth, and CNN FLOPS to compute. Our experimental results (both on synthetic data and a hardware prototype) for a variety of vision tasks such as digit recognition, object recognition, and face identification demonstrate up to 90% reduction in image sensor power consumption and 90% reduction in data bandwidth from sensor to CPU, while achieving similar performance compared to traditional deep learning pipelines.

关键词： Deep learning

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WarpNet: Weakly supervised matching for single-view reconstruction

WarpNet: Weakly supervised matching for single-view reconstr...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Kanazawa, Angjoo Jacobs, David W. Chandraker, Manmohan University of Maryland College Park United States NEC Labs United States

ISBN: (纸本)9781467388511

We present an approach to matching images of objects in fine-grained datasets without using part annotations, with an application to the challenging problem of weakly supervised single-view reconstruction. This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone. We overcome this challenge through a novel deep learning architecture, WarpNet, that aligns an object in one image with a different object in another. We exploit the structure of the fine-grained dataset to create artificial data for training this network in an unsupervised-discriminative learning approach. The output of the network acts as a spatial prior that allows generalization at test time to match real images across variations in appearance, viewpoint and articulation. On the CUB-200-2011 dataset of bird categories, we improve the AP over an appearance-only network by 13.6%. We further demonstrate that our WarpNet matches, together with the structure of fine-grained datasets, allow single-view reconstructions with quality comparable to using annotated point correspondences.

关键词： computer vision

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Stereo matching with color and monochrome cameras in low-light conditions

Stereo matching with color and monochrome cameras in low-lig...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Jeon, Hae-Gon Lee, Joon-Young Im, Sunghoon Ha, Hyowon Kweon, In So Robotics and Computer Vision Lab. KAIST Korea Republic of Adobe Research United States

ISBN: (纸本)9781467388511

Consumer devices with stereo cameras have become popular because of their low-cost depth sensing capability. However, those systems usually suffer from low imaging quality and inaccurate depth acquisition under low-light conditions. To address the problem, we present a new stereo matching method with a color and monochrome camera pair. We focus on the fundamental trade-off that monochrome cameras have much better light-efficiency than color-filtered cameras. Our key ideas involve compensating for the radiometric difference between two crossspectral images and taking full advantage of complementary data. Consequently, our method produces both an accurate depth map and high-quality images, which are applicable for various depth-aware image processing. Our method is evaluated using various datasets and the performance of our depth estimation consistently outperforms state-of-the-art methods.

关键词： Stereo image processing

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Hedged deep tracking

Hedged deep tracking

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Qi, Yuankai Zhang, Shengping Qin, Lei Yao, Hongxun Huang, Qingming Lim, Jongwoo Yang, Ming-Hsuan Harbin Institute of Technology China Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Hanyang University China University of California at Merced United States

ISBN: (纸本)9781467388511

In recent years, several methods have been developed to utilize hierarchical features learned from a deep convolutional neural network (CNN) for visual tracking. However, as features from a certain CNN layer characterize an object of interest from only one aspect or one level, the performance of such trackers trained with features from one layer (usually the second to last layer) can be further improved. In this paper, we propose a novel CNN based tracking framework, which takes full advantage of features from different CNN layers and uses an adaptive Hedge method to hedge several CNN based trackers into a single stronger one. Extensive experiments on a benchmark dataset of 100 challenging image sequences demonstrate the effectiveness of the proposed algorithm compared to several state-of-theart trackers.

关键词： pattern recognition

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Beyond f-formations: Determining social involvement in free standing conversing groups from static images

Beyond f-formations: Determining social involvement in free ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhang, Lu Hung, Hayley Delft University of Technology Mekelweg 2 Delft Netherlands University of Twente Drienerlolaan 5 Enschede Netherlands

ISBN: (纸本)9781467388511

In this paper, we present the first attempt to analyse differing levels of social involvement in free standing conversing groups (or the so-called F-formations) from static images. In addition, we enrich state-of-the-art F-formation modelling by learning a frustum of attention that accounts for the spatial context. That is, F-formation configurations vary with respect to the arrangement of furniture and the non-uniform crowdedness in the space during mingling scenarios. The majority of prior works have considered the labelling of conversing group as an objective task, requiring only a single annotator. However, we show that by embracing the subjectivity of social involvement, we not only generate a richer model of the social interactions in a scene but also significantly improve F-formation detection. We carry out extensive experimental validation of our proposed approach by collecting a novel set of multi-annotator labels of involvement on the publicly available Idiap Poster Data; the only multi-annotator labelled database of free standing conversing groups that is currently available.

关键词： computer vision

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Structured prediction of unobserved voxels from a single depth image

Structured prediction of unobserved voxels from a single dep...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Firman, Michael Aodha, Oisin Mac Julier, Simon Brostow, Gabriel J. University College London United Kingdom

ISBN: (纸本)9781467388511

Building a complete 3D model of a scene, given only a single depth image, is underconstrained. To gain a full volumetric model, one needs either multiple views, or a single view together with a library of unambiguous 3D models that will fit the shape of each individual object in the scene. We hypothesize that objects of dissimilar semantic classes often share similar 3D shape components, enabling a limited dataset to model the shape of a wide range of objects, and hence estimate their hidden geometry. Exploring this hypothesis, we propose an algorithm that can complete the unobserved geometry of tabletop-sized objects, based on a supervised model trained on already available volumetric elements. Our model maps from a local observation in a single depth image to an estimate of the surface shape in the surrounding neighborhood. We validate our approach both qualitatively and quantitatively on a range of indoor object collections and challenging real scenes.

关键词： computer vision

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Discriminative multi-modal feature fusion for RGBD indoor scene recognition

Discriminative multi-modal feature fusion for RGBD indoor sc...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Zhu, Hongyuan Weibel, Jean-Baptiste Lu, Shijian I2R AStar Singapore Singapore Georgia Tech United States

ISBN: (纸本)9781467388511

RGBD scene recognition has attracted increasingly attention due to the rapid development of depth sensors and their wide application scenarios. While many research has been conducted, most work used hand-crafted features which are difficult to capture high-level semantic structures. Recently, the feature extracted from deep convolutional neural network has produced state-of-the-art results for various computer vision tasks, which inspire researchers to explore incorporating CNN learned features for RGBD scene understanding. On the other hand, most existing work combines rgb and depth features without adequately exploiting the consistency and complementary information between them. Inspired by some recent work on RGBD object recognition using multi-modal feature fusion, we introduce a novel discriminative multi-modal fusion framework for rgbd scene recognition for the first time which simultaneously considers the inter- and intra-modality correlation for all samples and meanwhile regularizing the learned features to be discriminative and compact. The results from the multimodal layer can be back-propagated to the lower CNN layers, hence the parameters of the CNN layers and multimodal layers are updated iteratively until convergence. Experiments on the recently proposed large scale SUN RGB-D datasets show that our method achieved the state-of-the-art without any image segmentation.

关键词： computer vision

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