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检索条件"任意字段=27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014"

共 227 条记录，以下是121-130 订阅

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From Ego to Nos-vision: Detecting Social Relationships in First-Person Views

From Ego to Nos-vision: Detecting Social Relationships in Fi...

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

作者： Alletto, Stefano Serra, Giuseppe Calderara, Simone Solera, Francesco Cucchiara, Rita Univ Modena & Reggio Emilia I-41125 Modena Italy

ISBN: (纸本)9781479943098

In this paper we present a novel approach to detect groups in ego-vision scenarios. People in the scene are tracked through the video sequence and their head pose and 3D location are estimated. Based on the concept of f-formation, we define with the orientation and distance an inherently social pairwise feature that describes the affinity of a pair of people in the scene. We apply a correlation clustering algorithm that merges pairs of people into socially related groups. Due to the very shifting nature of social interactions and the different meanings that orientations and distances can assume in different contexts, we learn the weight vector of the correlation clustering using Structural SVMs. We extensively test our approach on two publicly available datasets showing encouraging results when detecting groups from first-person camera views.

关键词： Ego-vision f-formation social interaction

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Superpixel Estimation for Hyperspectral Imagery

Superpixel Estimation for Hyperspectral Imagery

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

作者： Massoudifar, Pegah Rangarajan, Anand Gader, Paul Univ Florida Dept CISE Gainesville FL 32611 USA

ISBN: (纸本)9781479943098

In the past decade, there has been a growing need for machine learning and computer vision components (segmentation, classification) in the hyperspectral imaging domain. Due to the complexity and size of hyperspectral imagery and the enormous number of wavelength channels, the need for combining compact representations with image segmentation and superpixel estimation has emerged in this area. Here, we present an approach to superpixel estimation in hyperspectral images by adapting the well known UCM approach to hyperspectral volumes. this approach benefits from the channel information at each pixel of the hyperspectral image while obtaining a compact representation of the hyperspectral volume using principal component analysis. Our experimental evaluation demonstrates that the additional information of spectral channels will substantially improve superpixel estimation from a single "monochromatic" channel. Furthermore, superpixel estimation performed on the compact hyperspectral representation outperforms the same when executed on the entire volume.

关键词： hyperspectral imagery information channels image segmentation Compact Compact pattern pixel extra message principal components analysis wavelengths

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Efficient Boosted Exemplar-based Face Detection 27

Efficient Boosted Exemplar-based Face Detection

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

作者： Li, Haoxiang Lin, Zhe Brandt, Jonathan Shen, Xiaohui Hua, Gang Stevens Inst Technol Hoboken NJ 07030 USA Adobe Res San Jose CA 95110 USA

ISBN: (纸本)9781479951178

Despite the fact that face detection has been studied intensively over the past several decades, the problem is still not completely solved. Challenging conditions, such as extreme pose, lighting, and occlusion, have historically hampered traditional, model-based methods. In contrast, exemplar-based face detection has been shown to be effective, even under these challenging conditions, primarily because a large exemplar database is leveraged to cover all possible visual variations. However, relying heavily on a large exemplar database to deal with the face appearance variations makes the detector impractical due to the high space and time complexity. We construct an efficient boosted exemplar-based face detector which overcomes the defect of the previous work by being faster, more memory efficient, and more accurate. In our method, exemplars as weak detectors are discriminatively trained and selectively assembled in the boosting framework which largely reduces the number of required exemplars. Notably, we propose to include non-face images as negative exemplars to actively suppress false detections to further improve the detection accuracy. We verify our approach over two public face detection benchmarks and one personal photo album, and achieve significant improvement over the state-of-the-art algorithms in terms of both accuracy and efficiency.

关键词： Face recognition

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Dense Semantic Image Segmentation with Objects and Attributes 27

Dense Semantic Image Segmentation with Objects and Attribute...

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

作者： Zheng, Shuai Cheng, Ming-Ming Warrell, Jonathan Sturgess, Paul Vineet, Vibhav Rother, Carsten Torr, Philip H. S. Univ Oxford Oxford OX1 2JD England Oxford Brookes Univ Oxford OX3 0BP England Tech Univ Dresden Dresden Germany

ISBN: (纸本)9781479951178

the concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. 'I see a shiny red chair'). In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU indoor scenes datasets show that the proposed approach is able to achieve state-of-the-art results.

关键词： Attributes Image Segmentation Object recognition Scene Understanding

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FPGA-based fast response image analysis for autonomous or semi-autonomous indoor flight

FPGA-based fast response image analysis for autonomous or se...

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

作者： Ladig, Robert Shimonomura, Kazuhiro Ritsumeikan Univ Dept Robot Kusatsu Shiga 5258577 Japan

ISBN: (纸本)9781479943098

Small aerial vehicles, like quadrotor, have a high potential to be helpful tools in first response scenarios like earthquakes, landslides and fires. But even simple tasks like holding position and altitude can be challenging to accomplish by a human operator and even more challenging autonomously. When outdoors, using GPS and pressure sensors is feasible, but indoors or in GPS denied environments it is not. Until now, for indoor flight scenarios either a lot of energy consuming sensors and hardware or a perfectly defined surrounding is required. In this approach, the viability of an onboard FPGA based indoor flight navigation system with a pan-tilt camera mount and a single VGA camera is tested. It can be used to either support an operator performing a hold position and altitude task, or act completely autonomously to achieve this task.

关键词： Aerial robotics FPGA Monocular vision Onboard vision Quadrotor Monocular vision Field programmable gate arrays flight Global Positioning Systems Pressure Sensor Device Component FAST-RESPONSE INDOORS Outdoors

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Backscatter Compensated Photometric Stereo with 3 Sources 27

Backscatter Compensated Photometric Stereo with 3 Sources

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

作者： Tsiotsios, Chourmouzios Angelopoulou, Maria E. Kim, Tae-Kyun Davison, Andrew J. Univ London Imperial Coll Sci Technol & Med London SW7 2AZ England

ISBN: (纸本)9781479951178

Photometric stereo offers the possibility of object shape reconstruction via reasoning about the amount of light reflected from oriented surfaces. However, in murky media such as sea water, the illuminating light interacts with the medium and some of it is backscattered towards the camera. Due to this additive light component, the standard Photometric Stereo equations lead to poor quality shape estimation. Previous authors have attempted to reformulate the approach but have either neglected backscatter entirely or disregarded its non-uniformity on the sensor when camera and lights are close to each other. We show that by compensating effectively for the backscatter component, a linear formulation of Photometric Stereo is allowed which recovers an accurate normal map using only 3 lights. Our backscatter compensation method for point-sources can be used for estimating the uneven backscatter directly from single images without any prior knowledge about the characteristics of the medium or the scene. We compare our method with previous approaches through extensive experimental results, where a variety of objects are imaged in a big water tank whose turbidity is systematically increased, and show reconstruction quality which degrades little relative to clean water results even with a very significant scattering level.

关键词： backscatter photometric stereo restoration scattering underwater vision

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Multi-target Tracking with Motion Context in Tensor Power Iteration 27

Multi-target Tracking with Motion Context in Tensor Power It...

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

作者： Shi, Xinchu Ling, Haibin Hu, Weiming Yuan, Chunfeng Xing, Junliang Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

ISBN: (纸本)9781479951178

Interactions between moving targets often provide discriminative clues for multiple target tracking (MTT), though many existing approaches ignore such interactions due to difficulty in effectively handling them. In this paper, we model interactions between neighbor targets by pair-wise motion context, and further encode such context into the global association optimization. To solve the resulting global non-convex maximization, we propose an effective and efficient power iteration framework. this solution enjoys two advantages for MTT: First, it allows us to combine the global energy accumulated from individual trajectories and the between-trajectory interaction energy into a united optimization, which can be solved by the proposed power iteration algorithm. Second, the framework is flexible to accommodate various types of pairwise context models and we in fact studied two different context models in this paper. For evaluation, we apply the proposed methods to four public datasets involving different challenging scenarios such as dense aerial borne traffic tracking, dense point set tracking, and semi-crowded pedestrian tracking. In all the experiments, our approaches demonstrate very promising results in comparison with state-of-the-art trackers.

关键词： Multi-target tracking motion context tensor power iteration

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Towards Multi-view and Partially-occluded Face Alignment 27

Towards Multi-view and Partially-occluded Face Alignment

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

作者： Xing, Junliang Niu, Zhiheng Huang, Junshi Hu, Weiming Yan, Shuicheng Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore NUS Beijing Peoples R China

ISBN: (纸本)9781479951178

We present a robust model to locate facial landmarks under different views and possibly severe occlusions. To build reliable relationships between face appearance and shape with large view variations, we propose to formulate face alignment as an l(1)-induced Stagewise Relational Dictionary (SRD) learning problem. During each training stage, the SRD model learns a relational dictionary to capture consistent relationships between face appearance and shape, which are respectively modeled by the pose-indexed image features and the shape displacements for current estimated landmarks. During testing, the SRD model automatically selects a sparse set of the most related shape displacements for the testing face and uses them to refine its shape iteratively. To locate facial landmarks under occlusions, we further propose to learn an occlusion dictionary to model different kinds of partial face occlusions. By deploying the occlusion dictionary into the SRD model, the alignment performance for occluded faces can be further improved. Our algorithm is simple, effective, and easy to implement. Extensive experiments on two benchmark datasets and two newly built datasets have demonstrated its superior performances over the state-of-the-art methods, especially for faces with large view variations and/or occlusions.

关键词： Face alignment dictionary learning sparse coding

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Stacked Progressive Auto-Encoders (SPAE) for Face recognition Across Poses 27

Stacked Progressive Auto-Encoders (SPAE) for Face Recognitio...

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

作者： Kan, Meina Shan, Shiguang Chang, Hong Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China

ISBN: (纸本)9781479951178

Identifying subjects with variations caused by poses is one of the most challenging tasks in face recognition, since the difference in appearances caused by poses may be even larger than the difference due to identity. Inspired by the observation that pose variations change non-linearly but smoothly, we propose to learn pose-robust features by modeling the complex non-linear transform from the non-frontal face images to frontal ones through a deep network in a progressive way, termed as stacked progressive auto-encoders (SPAE). Specifically, each shallow progressive auto-encoder of the stacked network is designed to map the face images at large poses to a virtual view at smaller ones, and meanwhile keep those images already at smaller poses unchanged. then, stacking multiple these shallow auto-encoders can convert non-frontal face images to frontal ones progressively, which means the pose variations are narrowed down to zero step by step. As a result, the outputs of the topmost hidden layers of the stacked network contain very small pose variations, which can be used as the pose-robust features for face recognition. An additional attractiveness of the proposed method is that no pose estimation is needed for the test images. the proposed method is evaluated on two datasets with pose variations, i.e., MultiPIE and FERET datasets, and the experimental results demonstrate the superiority of our method to the existing works, especially to those 2D ones.

关键词： Deep network Stacked Progressive Auto-Encoders face recognition across pose

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Real-time Mobile Facial Expression recognition System - A Case Study

Real-time Mobile Facial Expression Recognition System - A Ca...

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

作者： Suk, Myunghoon Prabhakaran, Balakrishnan Univ Texas Dallas Dept Comp Engn Richardson TX 75080 USA

ISBN: (纸本)9781479943098

this paper presents a mobile application for real time facial expression recognition running on a smart phone with a camera. the proposed system uses a set of Support Vector Machines (SVMs) for classifying 6 basic emotions and neutral expression along with checking mouth status. the facial expression features for emotion recognition are extracted by Active Shape Model (ASM) fitting landmarks on a face and then dynamic features are generated by the displacement between neutral and expression features. We show experimental results with 86% of accuracy with 10 folds cross validation in 309 video samples of the extended Cohn-Kanade (CK+) dataset. Using the same SVM models, the mobile app is running on Samsung Galaxy S3 with 2.4 fps. the accuracy of real-time mobile emotion recognition is about 72% for 6 posed basic emotions and neutral expression by 7 subjects who are not professional actors.

关键词： active shape model emotion recognition facial expression recognition mobile vision support vector machine

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