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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261-270 订阅

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Efficient GPU-based Graph Cuts for Stereo Matching

Efficient GPU-based Graph Cuts for Stereo Matching

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

作者： Choi, Young-kyu Park, In Kyu Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA Inha Univ Sch Informat & Commun Engn Incheon South Korea

ISBN: (纸本)9780769549903

Although graph cuts (GC) is popularly used in many computer vision problems, slow execution time due to its high complexity hinders wide usage. Manycore solution using Graphics Processing Unit (GPU) may solve this problem. However, conventional GC implementation does not fully exploit GPU's computing power. To address this issue, a new GC algorithm which is suitable for GPU environment is presented in this paper. First, we present a novel graph construction method that accelerates the convergence speed of GC. Next, a repetitive block-based push and relabel method is used to increase the data transfer efficiency. Finally, we propose a low-overhead global relabeling algorithm to increase the GPU occupancy ratio. the experiments on Middlebury stereo dataset shows that 5.2X speedup can be achieved over the baseline implementation, with identical GPU platform and parameters.

关键词： computer vision graph theory graphics processing units image matching stereo image processing

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A multi-sensor traffic scene dataset with omnidirectional video

A multi-sensor traffic scene dataset with omnidirectional vi...

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

作者： Koschorrek, Philipp Piccini, Tommaso Oberg, Per Felsberg, Michael Nielsen, Lars Mester, Rudolf Linkoping Univ Dept Elect Engn Comp Vis Lab S-58183 Linkoping Sweden Linkoping Univ Dept Elect Engn Vehicular Syst Linkoping 58183 Sweden

ISBN: (纸本)9780769549903

the development of vehicles that perceive their environment, in particular those using computer vision, indispensably requires large databases of sensor recordings obtained from real cars driven in realistic traffic situations. these datasets should be time shaped for enabling synchronization of sensor data from different sources. Furthermore, full surround environment perception requires high frame rates of synchronized omnidirectional video data to prevent information loss at any speeds. this paper describes an experimental setup and software environment for recording such synchronized multi-sensor data streams and storing them in a new open source format. the dataset consists of sequences recorded in various environments from a car equipped with an omnidirectional multi-camera, height sensors, an IMU, a velocity sensor, and a GPS. the software environment for reading these data sets will be provided to the public, together with a collection of long multi-sensor and multi-camera data streams stored in the developed format.

关键词： computer vision public domain software software engineering traffic information systems very large databases video signal processing

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A Compensation Method of Motion Features with Regression for Deficient Depth Image

A Compensation Method of Motion Features with Regression for...

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

作者： Yumiba, Ryo Agata, Yoshiki Fujiyoshi, Hironobu Hitachi Ltd Hitachi Res Lab Tokyo Japan Chubu Univ Kasugai Aichi 487 Japan

ISBN: (纸本)9780769549903

In this paper, we propose a method for compensating for motion features that are outside a given viewing angle by using a regression estimate that is based on a correlation between the motion features from human bodies deficient visually, when recognizing the actions of people whose bodies are only partially within the given view. this compensation is good for use in situations where parts of a person's body are partially protruding outside the edges of the viewing angle, and contributes to enlarging the region coverage for action recognition. the motion features and position of the acting person in a depth image are calculated first in the proposed method. Second, the deficit length protruding outside the view angle is calculated, according to the position of the person. Finally, the motion features from the entire body are estimated using a regression estimate from the motion features by selecting the regression coefficients according to the deficit length. the method for improving the effectiveness of the F-measure is confirmed using three kinds of motion features in a fundamental laboratory experiment. We found from the experimental results that the F-measure was improved by more 12.5% when using motion feature compensation compared to without compensation when the person within the viewing angle cannot actually be seen from the floor to 630 mm above it.

关键词： correlation methods feature extraction gesture recognition image motion analysis regression analysis

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Nonparametric Facial Feature Localization

Nonparametric Facial Feature Localization

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

作者： Tamersoy, Birgi Hu, Changbo Aggarwal, J. K. Univ Texas Austin Comp & Vis Res Ctr Austin TX 78712 USA

ISBN: (纸本)9780769549903

Any facial feature localization algorithm needs to incorporate two sources of information: 1) prior shape knowledge, and 2) image observations. Existing methods have primarily focused on different ways of representing and incorporating the image observations into the problem solution. Prior shape knowledge, on the other hand, has been mostly modeled using parametrized shape models. Parametrized shape models have relatively few parameters to control the shape variations, and hence their representation power is limited with the examples provided in the training data. In this paper, we propose a novel method for modeling the prior shape knowledge. Rather than using a holistic approach, as in the case for parametrized shape models, we model the prior shape knowledge as a set of local compatibility potentials. this "distributed" approach provides a greater representation power as it allows for individual landmarks to move more freely. the prior shape knowledge is incorporated with local image observations in a probabilistic graphical model framework, where the inference is achieved through nonparametric belief propagation. through qualitative and quantitative experiments, the proposed approach is shown to outperform the state-of-the-art methods in terms of localization accuracy.

关键词： belief networks face recognition feature extraction graph theory inference mechanisms message passing probability shape recognition

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Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action recognition

Bio-inspired Dynamic 3D Discriminative Skeletal Features for...

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

作者： Chaudhry, Rizwan Ofli, Ferda Kurillo, Gregorij Bajcsy, Ruzena Vidal, Rene Johns Hopkins Univ Ctr Imaging Sci Baltimore MD 21218 USA Univ Calif Berkeley Tele Immers Lah Berkeley CA 94720 USA

ISBN: (纸本)9780769549903

Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been proposed that extract representative features from 3D depth data, a reconstructed 3D surface mesh or more commonly from the recovered estimate of the human skeleton. Recent advances in neuroscience have discovered a neural encoding of static 3D shapes in primate infero-temporal cortex that can be represented as a hierarchy of medial axis and surface features. We hypothesize a similar neural encoding might also exist for 3D shapes in motion and propose a hierarchy of dynamic medial axis structures at several spatio-temporal scales that can be modeled using a set of Linear Dynamical Systems (LDSs). We then propose novel discriminative metrics for comparing these sets of LDSs for the task of human activity recognition. Combined with simple classification frameworks, our proposed features and corresponding hierarchical dynamical models provide the highest human activity recognition rates as compared to state-of-the-art methods on several skeletal datasets.

关键词： gesture recognition mesh generation neural nets pattern classification shape recognition solid modelling surface fitting

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Video Demo: An Egocentric vision based Assistive Co-robot

Video Demo: An Egocentric Vision based Assistive Co-robot

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

作者： Zhang, Jingzhe Zhuang, Lishuo Wang, Yang Zhou, Yameng Meng, Yan Hua, Gang Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA Stevens Inst Technol Dept Comp Sci Hoboken NJ 07030 USA

ISBN: (纸本)9780769549903

We present the video demo of the prototype of an egocentric vision based assistive co-robot system. In this co-robot system, the user is wearing a pair of glasses with a forward looking camera, and is actively engaged in the control loop of the robot in navigational tasks. the egocentric vision glasses serve for two purposes. First, it serves as a source of visual input to request the robot to find a certain object in the environment. Second, the motion patterns computed from the egocentric video associated with a specific set of head movements are exploited to guide the robot to find the object. these are especially helpful for quadriplegic individuals who do not have the needed hand functionality for control with other modalities (e.g., joystick). In our co-robot system, when the robot does not fulfill the object finding task in a pre-specified time window, it would actively solicit user controls for guidance. then the users can use the egocentric vision based gesture interface to orient the robot towards the direction of the object. After that the robot will automatically navigate towards the object until it finds it. Our experiments validated the efficacy of the closed-loop design to engage the human in the loop.

关键词： closed loop systems gesture recognition handicapped aids human-robot interaction image motion analysis medical robotics mobile robots path planning robot vision video signal processing

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Recognising Team Activities from Noisy Data

Recognising Team Activities from Noisy Data

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

作者： Bialkowski, Alina Lucey, Patrick Carr, Peter Denman, Simon Matthews, Iain Sridharan, Sridha Disney Res Pittburgh PA 94565 USA Queensland Univ Technol Image & Video Lab Brisbane Qld 4072 Australia

ISBN: (纸本)9780769549903

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e. g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e. g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

关键词： computer vision image sensors object recognition object tracking sport

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Real-time Mobile Food recognition System

Real-time Mobile Food Recognition System

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

作者： Kawano, Yoshiyuki Yanai, Keiji Univ Electrocommun Chofu Tokyo 1828585 Japan

ISBN: (纸本)9780769549903

We propose a mobile food recognition system the purposes of which are estimating calorie and nutritious of foods and recording a user's eating habits. Since all the processes on image recognition performed on a smartphone, the system does not need to send images to a server and runs on an ordinary smartphone in a real-time way. To recognize food items, a user draws bounding boxes by touching the screen first, and then the system starts food item recognition within the indicated bounding boxes. To recognize them more accurately, we segment each food item region by GrubCut, extract a color histogram and SURF-based bag-of-features, and finally classify it into one of the fifty food categories with linear SVM and fast chi(2) kernel. In addition, the system estimates the direction of food regions where the higher SVM output score is expected to be obtained, show it as an arrow on the screen in order to ask a user to move a smartphone camera. this recognition process is performed repeatedly about once a second. We implemented this system as an Android smartphone application so as to use multiple CPU cores effectively for real-time recognition. In the experiments, we have achieved the 81.55% classification rate for the top 5 category candidates when the ground-truth bounding boxes are given. In addition, we obtained positive evaluation by user study compared to the food recording system without object recognition.

关键词： behavioural sciences computing feature extraction mobile computing object recognition operating systems (computers) support vector machines

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PatchMatch Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation

PatchMatch Filter: Efficient Edge-Aware Filtering Meets Rand...

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

作者： Lu, Jiangbo Yang, Hongsheng Min, Dongbo Do, Minh N. Adv Digital Sci Ctr Singapore Singapore Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9780769549897

though many tasks in computer vision can be formulated elegantly as pixel-labeling problems, a typical challenge discouraging such a discrete formulation is often due to computational efficiency. Recent studies on fast cost volume filtering based on efficient edge-aware filters have provided a fast alternative to solve discrete labeling problems, with the complexity independent of the support window size. However, these methods still have to step through the entire cost volume exhaustively, which makes the solution speed scale linearly with the label space size. When the label space is huge, which is often the case for (subpixel-accurate) stereo and optical flow estimation, their computational complexity becomes quickly unacceptable. Developed to search approximate nearest neighbors rapidly, the PatchMatch method can significantly reduce the complexity dependency on the search space size. But, its pixel-wise randomized search and fragmented data access within the 3D cost volume seriously hinder the application of efficient cost slice filtering. this paper presents a generic and fast computational framework for general multi-labeling problems called PatchMatch Filter (PMF). For the very first time, we explore effective and efficient strategies to weave together these two fundamental techniques developed in isolation, i.e., PatchMatch-based randomized search and efficient edge-aware image filtering. By decompositing an image into compact superpixels, we also propose superpixel-based novel search strategies that generalize and improve the original PatchMatch method. Focusing on dense correspondence field estimation in this paper, we demonstrate PMF's applications in stereo and optical flow. Our PMF methods achieve state-of-the-art correspondence accuracy but run much faster than other competing methods, often giving over 10-times speedup for large label space cases.

关键词： computational complexity computer vision edge detection estimation theory filtering theory image matching image retrieval image sequences query formulation

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A Divide-and-conquer Method for Scalable Low-rank Latent Matrix Pursuit

A Divide-and-conquer Method for Scalable Low-rank Latent Mat...

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

作者： Pan, Yan Lai, Hanjiang Liu, Cong Yan, Shuicheng Sun Yat Sen Univ Guangzhou 510275 Guangdong Peoples R China Natl Univ Singapore Singapore 117548 Singapore

ISBN: (纸本)9780769549897

Data fusion, which effectively fuses multiple prediction lists from different kinds of features to obtain an accurate model, is a crucial component in various computer vision applications. Robust late fusion (RLF) is a recent proposed method that fuses multiple output score lists from different models via pursuing a shared low-rank latent matrix. Despite showing promising performance, the repeated full Singular Value Decomposition operations in RLF's optimization algorithm limits its scalability in real world vision datasets which usually have large number of test examples. To address this issue, we provide a scalable solution for large-scale low-rank latent matrix pursuit by a divide-and-conquer method. the proposed method divides the original low-rank latent matrix learning problem into two size-reduced subproblems, which may be solved via any base algorithm, and combines the results from the subproblems to obtain the final solution. Our theoretical analysis shows that with fixed probability, the proposed divide-and-conquer method has recovery guarantees comparable to those of its base algorithm. Moreover we develop an efficient base algorithm for the corresponding subproblems by factorizing a large matrix into the product of two size-reduced matrices. We also provide high probability recovery guarantees of the base algorithm. the proposed method is evaluated on various fusion problems in object categorization and video event detection. Under comparable accuracy, the proposed method performs more than 180 times faster than the state-of-the-art baselines on the CCV dataset with about 4,500 test examples for video event detection.

关键词： computer vision divide and conquer methods matrix decomposition probability sensor fusion

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