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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231-240 订阅

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3D Visual Proxemics: Recognizing Human Interactions in 3D from a Single Image

3D Visual Proxemics: Recognizing Human Interactions in 3D fr...

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

作者： Chakraborty, Ishani Cheng, Hui Javed, Omar SRI Int Princeton NJ 08540 USA

ISBN: (纸本)9780769549897

We present a unified framework for detecting and classifying people interactions in unconstrained user generated images. 1 Unlike previous approaches that directly map people/face locations in 2D image space into features for classification, we first estimate camera viewpoint and people positions in 3D space and then extract spatial configuration features from explicit 3D people positions. this approach has several advantages. First, it can accurately estimate relative distances and orientations between people in 3D. Second, it encodes spatial arrangements of people into a richer set of shape descriptors than afforded in 2D. Our 3D shape descriptors are invariant to camera pose variations often seen in web images and videos. the proposed approach also estimates camera pose and uses it to capture the intent of the photo. To achieve accurate 3D people layout estimation, we develop an algorithm that robustly fuses semantic constraints about human interpositions into a linear camera model. this enables our model to handle large variations in people size, heights (e. g. age) and poses. An accurate 3D layout also allows us to construct features informed by Proxemics that improves our semantic classification. To characterize the human interaction space, we introduce visual proxemes;a set of prototypical patterns that represent commonly occurring social interactions in events. We train a discriminative classifier that classifies 3D arrangements of people into visual proxemes and quantitatively evaluate the performance on a large, challenging dataset.

关键词： feature extraction human computer interaction image classification image sensors object recognition pose estimation shape recognition

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Fusing Robust Face Region Descriptors via Multiple Metric Learning for Face recognition in the Wild

Fusing Robust Face Region Descriptors via Multiple Metric Le...

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

作者： Cui, Zhen Li, Wen Xu, Dong Shan, Shiguang Chen, Xilin Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing Peoples R China Nanyang Technol Univ Sch Comp Engn Singapore Singapore Huaqiao Univ Sch Comp Sci Xiamen Peoples R China

ISBN: (纸本)9780769549897

In many real-world face recognition scenarios, face images can hardly be aligned accurately due to complex appearance variations or low-quality images. To address this issue, we propose a new approach to extract robust face region descriptors. Specifically, we divide each image (resp. video) into several spatial blocks (resp. spatial-temporal volumes) and then represent each block (resp. volume) by sum-pooling the nonnegative sparse codes of position-free patches sampled within the block (resp. volume). Whitened Principal Component Analysis (WPCA) is further utilized to reduce the feature dimension, which leads to our Spatial Face Region Descriptor (SFRD) (resp. Spatial-Temporal Face Region Descriptor, STFRD) for images (resp. videos). Moreover, we develop a new distance metric learning method for face verification called Pairwise-constrained Multiple Metric Learning (PMML) to effectively integrate the face region descriptors of all blocks (resp. volumes) from an image (resp. a video). Our work achieves the state-of-the-art performances on two real-world datasets LFW and YouTube Faces (YTF) according to the restricted protocol.

关键词： face recognition learning (artificial intelligence) principal component analysis

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Recognize Human Activities from Partially Observed Videos

Recognize Human Activities from Partially Observed Videos

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

作者： Cao, Yu Barrett, Daniel Barbu, Andrei Narayanaswamy, Siddharth Yu, Haonan Michaux, Aaron Lin, Yuewei Dickinson, Sven Siskind, Jeffrey Mark Wang, Song Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Univ Toronto Dept Comp Sci Toronto ON Canada

ISBN: (纸本)9780769549897

Recognizing human activities in partially observed videos is a challenging problem and has many practical applications. When the unobserved subsequence is at the end of the video, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. However, in the general case, an unobserved subsequence may occur at any time by yielding a temporal gap in the video. In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case. Specifically, we formulate the problem into a probabilistic framework: 1) dividing each activity into multiple ordered temporal segments, 2) using spatiotemporal features of the training video samples in each segment as bases and applying sparse coding (SC) to derive the activity likelihood of the test video sample at each segment, and 3) finally combining the likelihood at each segment to achieve a global posterior for the activities. We further extend the proposed method to include more bases that correspond to a mixture of segments with different temporal lengths (MSSC), which can better represent the activities with large intra-class variations. We evaluate the proposed methods (SC and MSSC) on various real videos. We also evaluate the proposed methods on two special cases: 1) activity prediction where the unobserved subsequence is at the end of the video, and 2) human activity recognition on fully observed videos. Experimental results show that the proposed methods outperform existing state-of-the-art comparison methods.

关键词： image recognition image sequences probability video signal processing

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Relative hidden markov models for evaluating motion skill

Relative hidden markov models for evaluating motion skill

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26th ieee conference on computer vision and pattern recognition, cvpr 2013

作者： Zhang, Qiang Li, Baoxin Computer Science and Engineering Arizona State Univerisity Tempe AZ 85281 United States

this paper is concerned with a novel problem: learning temporal models using only relative information. Such a problem arises naturally in many applications involving motion or video data. Our focus in this paper is on video-based surgical training, in which a key task is to rate the performance of a trainee based on a video capturing his motion. Compared with the conventional method of relying on ratings from senior surgeons, an automatic approach to this problem is desirable for its potential lower cost, better objectiveness, and real-time availability. To this end, we propose a novel formulation termed {Relative Hidden Markov Model}and develop an algorithm for obtaining a solution under this model. the proposed method utilizes only a relative ranking (based on an attribute of interest) between pairs of the inputs, which is easier to obtain and often more consistent, especially for the chosen application domain. the proposed algorithm effectively learns a model from the training data so that the attribute under consideration is linked to the likelihood of the inputs under the learned model. Hence the model can be used to compare new sequences. Synthetic data is first used to systematically evaluate the model and the algorithm, and then we experiment with real data from a surgical training system. the experimental results suggest that the proposed approach provides a promising solution to the real-world problem of motion skill evaluation from video. © 2013 ieee.

关键词： Hidden Markov models

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Tri-modal Person Re-identification with RGB, Depth and thermal Features

Tri-modal Person Re-identification with RGB, Depth and Therm...

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

作者： Mogelmose, Andreas Bahnsen, Chris Moeslund, thomas B. Clapes, Albert Escalera, Sergio Aalborg Univ Visual Anal People Lab Aalborg Denmark Univ Barcelona Dept Matematica Aplicada Anal E-08007 Barcelona Spain Autonomous Univ Barcelona Comp Vis Ctr E-08193 Barcelona Spain

ISBN: (纸本)9780769549903

Person re-identification is about recognizing people who have passed by a sensor earlier. Previous work is mainly based on RGB data, but in this work we for the first time present a system where we combine RGB, depth, and thermal data for re-identification purposes. First, from each of the three modalities, we obtain some particular features: from RGB data, we model color information from different regions of the body;from depth data, we compute different soft body biometrics;and from thermal data, we extract local structural information. then, the three information types are combined in a joined classifier. the tri-modal system is evaluated on a new RGB-D-T dataset, showing successful results in re-identification scenarios.

关键词： biometrics (access control) feature extraction image classification image colour analysis image recognition image sensors

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GPU-SHOT: parallel optimization for real-time 3D local description

GPU-SHOT: parallel optimization for real-time 3D local descr...

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

作者： Palossi, Daniele Tombari, Federico Salk, Samuele Ruggiero, Martino Di Stefano, Luigi Benini, Luca Univ Bologna DEI Bologna Italy Univ Bologna DISI Bologna Italy

ISBN: (纸本)9780769549903

the fields of 3D computer vision, 3D robotic perception and photogrammetry rely more and more heavily on matching 3D local descriptors, computed on a small neighborhood of a point cloud or a mesh, to carry out tasks such as point cloud registration, 3D object recognition and pose estimation in clutter, SLAM, 3D object retrieval. One major drawback of these applications is currently the high computational cost of processing 3D point clouds, with the 3D descriptor computation representing one of the main bottlenecks. In this paper we explore the optimization for parallel architectures of the recently proposed SHOT descriptor [22] and of its extension to RGB-D data [23]. Even though some steps of the original algorithm are not directly suitable for parallel optimization, we are able to obtain notable speed-ups with respect to the CPU implementation. We also show an application of our optimization to 3D object recognition in clutter, where the proposed parallel implementation allows for real-time 3D local description.

关键词： graphics processing units object recognition optimisation parallel architectures

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X-ray Testing by computer vision

X-ray Testing by Computer Vision

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

作者： Mery, Domingo Pontificia Univ Catolica Chile Dept Comp Sci Santiago Chile

ISBN: (纸本)9780769549903

X-ray imaging has been developed not only for its use in medical imaging for human beings, but also for materials or objects, where the aim is to analyze -nondestructively-those inner parts that are undetectable to the naked eye. thus, X-ray testing is used to determine if a test object deviates from a given set of specifications. Typical applications are analysis of food products, screening of baggage, inspection of automotive parts, and quality control of welds. In order to achieve efficient and effective X-ray testing, automated and semi-automated systems are being developed to execute this task. In this paper, we present a general overview of computer vision methodologies that have been used in X-ray testing. In addition, we review some techniques that have been applied in certain relevant applications;and we introduce a public database of X-ray images that can be used for testing and evaluation of image analysis and computer vision algorithms. Finally, we conclude that the following: that there are some areas -like casting inspection-where automated systems are very effective, and other application areas -such as baggage screening-where human inspection is still used;there are certain application areas -like weld and cargo inspections-where the process is semi-automatic;and there is some research in areas including food analysis-where processes are beginning to be characterized by the use of X-ray imaging.

关键词： X-ray imaging casting computer vision computerised instrumentation inspection

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Scene Parsing by Integrating Function, Geometry and Appearance Models

Scene Parsing by Integrating Function, Geometry and Appearan...

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

作者： Zhao, Yibiao Zhu, Song-Chun Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Comp Sci & Stat Los Angeles CA 90095 USA

ISBN: (纸本)9780769549897

Indoor functional objects exhibit large view and appearance variations, thus are difficult to be recognized by the traditional appearance-based classification paradigm. In this paper, we present an algorithm to parse indoor images based on two observations: i) the functionality is the most essential property to define an indoor object, e.g. "a chair to sit on";ii) the geometry (3D shape) of an object is designed to serve its function. We formulate the nature of the object function into a stochastic grammar model. this model characterizes a joint distribution over the function-geometry-appearance (FGA) hierarchy. the hierarchical structure includes a scene category, functional groups, functional objects, functional parts and 3D geometric shapes. We use a simulated annealing MCMC algorithm to find the maximum a posteriori (MAP) solution, i.e. a parse tree. We design four data-driven steps to accelerate the search in the FGA space: i) group the line segments into 3D primitive shapes, ii) assign functional labels to these 3D primitive shapes, iii) fill in missing objects/parts according to the functional labels, and iv) synthesize 2D segmentation maps and verify the current parse tree by the Metropolis-Hastings acceptance probability. the experimental results on several challenging indoor datasets demonstrate the proposed approach not only significantly widens the scope of indoor scene parsing algorithm from the segmentation and the 3D recovery to the functional object recognition, but also yields improved overall performance.

关键词： Markov processes Monte Carlo methods geometry image classification image segmentation maximum likelihood estimation object recognition simulated annealing trees (mathematics)

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

作者： Krishnan, Ravikiran Sarkar, Sudeep Univ S Florida Dept Comp Sci & Engn Tampa FL 33620 USA

ISBN: (纸本)9780769549903

One of the dominant approaches to gesture recognition, especially when we have one or few samples per class, is to compute the time-warped distance between the two sequences and perform nearest-neighbor classification. In this work, we show that we get much better results if instead we consider the similarity of the pattern of frame-wise distances of these two sequences with a third (anchor) sequence from the modelbase. We refer to these distance pattern vectors as the warp vectors. If these warp vectors are similar, then so are the sequences;if not, they are dissimilar. At the algorithmic core we have two dynamic time warping processes, one to compute the warp vectors with the anchor sequences and the other to compare these warp vectors. We select the anchor sequence to be the one that minimizes the overall distance, i.e. the sequence with respect to which these two sequences are the most similar. We present results on a large dataset of 1500 RGBD sequences spanning 150 gesture classes, such as traffic signals, sign language, and every day actions, extracted from the ChaLearn Gesture Challenge dataset. We experimented with three different feature types: difference of frames, HOG and relational distributions. We found that there were improvements of 5%, 15%, and 7%, respectively, at 20% false alarm rate, over traditional two-sequence based time-warped distance.

关键词： gesture recognition image classification vectors

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Dense Reconstruction Using 3D Object Shape Priors

Dense Reconstruction Using 3D Object Shape Priors

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

作者： Dame, Amaury Prisacariu, Victor A. Ren, Carl Y. Reid, Ian Univ Oxford Oxford OX1 2JD England Univ Adelaide Adelaide SA 5005 Australia

ISBN: (纸本)9780769549897

We propose a formulation of monocular SLAM which combines live dense reconstruction with shape priors-based 3D tracking and reconstruction. Current live dense SLAM approaches are limited to the reconstruction of visible surfaces. Moreover, most of them are based on the minimisation of a photo-consistency error, which usually makes them sensitive to specularities. In the 3D pose recovery literature, problems caused by imperfect and ambiguous image information have been dealt with by using prior shape knowledge. At the same time, the success of depth sensors has shown that combining joint image and depth information drastically increases the robustness of the classical monocular 3D tracking and 3D reconstruction approaches. In this work we link dense SLAM to 3D object pose and shape recovery. More specifically, we automatically augment our SLAM system with object specific identity, together with 6D pose and additional shape degrees of freedom for the object(s) of known class in the scene, combining image data and depth information for the pose and shape recovery. this leads to a system that allows for full scaled 3D reconstruction with the known object(s) segmented from the scene. the segmentation enhances the clarity, accuracy and completeness of the maps built by the dense SLAM system, while the dense 3D data aids the segmentation process, yielding faster and more reliable convergence than when using 2D image data alone.

关键词： SLAM (robots) image reconstruction image segmentation object tracking robot vision

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