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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111-120 订阅

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Multi-view Photometric Stereo with Spatially Varying Isotropic Materials

Multi-view Photometric Stereo with Spatially Varying Isotrop...

引用

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

作者： Zhou, Zhenglong Wu, Zhe Tan, Ping Natl Univ Singapore Singapore 117548 Singapore

ISBN: (纸本)9780769549897

We present a method to capture both 3D shape and spatially varying reflectance with a multi-view photometric stereo technique that works for general isotropic materials. Our data capture setup is simple, which consists of only a digital camera and a handheld light source. From a single viewpoint, we use a set of photometric stereo images to identify surface points with the same distance to the camera. We collect this information from multiple viewpoints and combine it with structure-from-motion to obtain a precise reconstruction of the complete 3D shape. the spatially varying isotropic bidirectional reflectance distribution function (BRDF) is captured by simultaneously inferring a set of basis BRDFs and their mixing weights at each surface point. According to our experiments, the captured shapes are accurate to 0.3 millimeters. the captured reflectance has relative root-mean-square error (RMSE) of 9%.

关键词： cameras mean square error methods photometric light sources shape recognition stereo image processing

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Social Role Discovery in Human Events

Social Role Discovery in Human Events

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

作者： Ramanathan, Vignesh Yao, Bangpeng Li Fei-Fei Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9780769549897

We deal with the problem of recognizing social roles played by people in an event. Social roles are governed by human interactions, and form a fundamental component of human event description. We focus on a weakly supervised setting, where we are provided different videos belonging to an event class, without training role labels. Since social roles are described by the interaction between people in an event, we propose a Conditional Random Field to model the inter-role interactions, along with person specific social descriptors. We develop tractable variational inference to simultaneously infer model weights, as well as role assignment to all people in the videos. We also present a novel YouTube social roles dataset with ground truth role annotations, and introduce annotations on a subset of videos from the TRECVID-MED11 [1] event kits for evaluation purposes. the performance of the model is compared against different baseline methods on these datasets.

关键词： image motion analysis image recognition video signal processing

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A Critical Review of Action recognition Benchmarks

A Critical Review of Action Recognition Benchmarks

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

作者： Hassner, Tal Open Univ Israel Raanana Israel

ISBN: (纸本)9780769549903

Understanding human actions in videos has been a central research theme in computer vision for decades, and much progress has been achieved over the years. Much of this progress was demonstrated on standard benchmarks used to evaluate novel techniques. these benchmarks and their evolution, provide a unique perspective on the growing capabilities of computerized action recognition systems. they demonstrate just how far machine vision systems have come while also underscore the gap that still remains between existing state-of-the-art performance and the needs of real-world applications. In this paper we provide a comprehensive survey of these benchmarks: from early examples, such as the Weizmann set [1], to recently presented, contemporary benchmarks. this paper further provides a summary of the results obtained in the last couple of years on the recent ASLAN benchmark [12], which was designed to reflect the many challenges modern Action recognition systems are expected to overcome.

关键词： computer vision image recognition video signal processing

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Sketch Tokens: A Learned Mid-level Representation for Contour and Object Detection

Sketch Tokens: A Learned Mid-level Representation for Contou...

引用

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

作者： Lim, Joseph J. Zitnick, C. Lawrence Dollar, Piotr MIT Cambridge MA 02139 USA Microsoft Res Rensselaer NY USA

ISBN: (纸本)9780769549897

We propose a novel approach to both learning and detecting local contour-based representations for mid-level features. Our features, called sketch tokens, are learned using supervised mid-level information in the form of hand drawn contours in images. Patches of human generated contours are clustered to form sketch token classes and a random forest classifier is used for efficient detection in novel images. We demonstrate our approach on both top-down and bottom-up tasks. We show state-of-the-art results on the top-down task of contour detection while being over 200x faster than competing methods. We also achieve large improvements in detection accuracy for the bottom-up tasks of pedestrian and object detection as measured on INRIA [5] and PASCAL [10], respectively. these gains are due to the complementary information provided by sketch tokens to low-level features such as gradient histograms.

关键词： feature extraction image classification image representation learning (artificial intelligence) object detection pattern clustering pedestrians trees (mathematics)

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Information Consensus for Distributed Multi-Target Tracking

Information Consensus for Distributed Multi-Target Tracking

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

作者： Kamal, Ahmed T. Farrell, Jay A. Roy-Chowdhury, Amit K. Univ Calif Riverside Dept Elect Engn Riverside CA 92521 USA

ISBN: (纸本)9780769549897

Due to their high fault-tolerance, ease of installation and scalability to large networks, distributed algorithms have recently gained immense popularity in the sensor networks community, especially in computer vision. Multi-target tracking in a camera network is one of the fundamental problems in this domain. Distributed estimation algorithms work by exchanging information between sensors that are communication neighbors. Since most cameras are directional sensors, it is often the case that neighboring sensors may not be sensing the same target. Such sensors that do not have information about a target are termed as "naive" with respect to that target. In this paper, we propose consensus-based distributed multi-target tracking algorithms in a camera network that are designed to address this issue of naivety. the estimation errors in tracking and data association, as well as the effect of naivety, are jointly addressed leading to the development of an information-weighted consensus algorithm, which we term as the Multi-target Information Consensus (MTIC) algorithm. the incorporation of the probabilistic data association mechanism makes the MTIC algorithm very robust to false measurements/clutter. Experimental analysis is provided to support the theoretical results.

关键词： cameras clutter computer vision distributed algorithms estimation theory fault tolerance probability sensor fusion target tracking wireless sensor networks

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3D Interaction environment for free view point TV and games using multiple tablet computers

3D Interaction environment for free view point TV and games ...

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

作者： Dupre, Rob Acuna, Raul Herrera Argyriou, Vasileios Velastin, Sergio A.

ISBN: (纸本)9780769549903

the use of 3D technologies to represent elements and interact with them is an open and interesting research area. In this article we discuss a novel human computer interaction method that integrates mobile computing and 3D visualization techniques with applications on free viewpoint visualization and 3D rendering for interactive and realistic environments. Especially this approach is focused on augmented reality and home entertainment and it was developed and tested on mobiles and particularly on tablet computers. Finally, an evaluation mechanism on the accuracy of this interaction system is presented.

关键词： augmented reality human computer interaction interactive television mobile computing mobile television notebook computers

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3D Ground-Truth Systems for Object/Human recognition and Tracking

3D Ground-Truth Systems for Object/Human Recognition and Tra...

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

作者： Godil, Afzal Bostelman, Roger Saidi, Kamel Shackleford, Will Cheok, Geraldine Shneier, Michael Hong, Tsai NIST Gaithersburg MD 20899 USA

ISBN: (纸本)9780769549903

We have been researching three dimensional (3D) ground-truth systems for performance evaluation of vision and perception systems in the fields of smart manufacturing and robot safety. In this paper we first present an overview of different systems that have been used to provide ground-truth (GT) measurements and then we discuss the advantages of physically-sensed ground-truth systems for our applications. then we discuss in detail the three ground- truth systems that we have used in our experiments: ultra wide-band, indoor GPS, and a camera-based motion capture system. Finally, we discuss three different perception-evaluation experiments where we have used these GT systems

关键词： computer vision image motion analysis object recognition object tracking visual perception

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Optimizing 1-Nearest Prototype Classifiers

Optimizing 1-Nearest Prototype Classifiers

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

作者： Wohlhart, Paul Koestinger, Martin Donoser, Michael Roth, Peter M. Bischof, Horst Graz Univ Technol Inst Comp Vis & Graph A-8010 Graz Austria

ISBN: (纸本)9780769549897

the development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision. However, the trend towards large scale datasets revived the interest in simpler classifiers to reduce runtime. Simple nearest neighbor classifiers have several beneficial properties, such as low complexity and inherent multi-class handling, however, they have a runtime linear in the size of the database. Recent related work represents data samples by assigning them to a set of prototypes that partition the input feature space and afterwards applies linear classifiers on top of this representation to approximate decision boundaries locally linear. In this paper, we go a step beyond these approaches and purely focus on 1-nearest prototype classification, where we propose a novel algorithm for deriving optimal prototypes in a discriminative manner from the training samples. Our method is implicitly multi-class capable, parameter free, avoids noise overfitting and, since during testing only comparisons to the derived prototypes are required, highly efficient. Experiments demonstrate that we are able to outperform related locally linear methods, while even getting close to the results of more complex classifiers.

关键词： computational complexity computer vision image classification optimisation

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Efficient object detection and segmentation for fine-grained recognition

Efficient object detection and segmentation for fine-grained...

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

作者： Angelova, Anelia Zhu, Shenghuo NEC Labs Amer Cupertino CA 95014 USA

ISBN: (纸本)9780769549897

We propose a detection and segmentation algorithm for the purposes of fine-grained recognition. the algorithm first detects low-level regions that could potentially belong to the object and then performs a full-object segmentation through propagation. Apart from segmenting the object, we can also 'zoom in' on the object, i.e. center it, normalize it for scale, and thus discount the effects of the background. We then show that combining this with a state-of-the-art classification algorithm leads to significant improvements in performance especially for datasets which are considered particularly hard for recognition, e. g. birds species. the proposed algorithm is much more efficient than other known methods in similar scenarios [ 4, 21]. Our method is also simpler and we apply it here to different classes of objects, e. g. birds, flowers, cats and dogs. We tested the algorithm on a number of benchmark datasets for fine-grained categorization. It outperforms all the known state-of-the-art methods on these datasets, sometimes by as much as 11%. It improves the performance of our baseline algorithm by 3-4%, consistently on all datasets. We also observed more than a 4% improvement in the recognition performance on a challenging large-scale flower dataset, containing 578 species of flowers and 250,000 images.

关键词： image classification image segmentation object detection object recognition

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Generation of Ground Truth for Object Detection While Playing an Online Game: Productive Gaming or Recreational Working?

Generation of Ground Truth for Object Detection While Playin...

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

作者： Kavasidis, Isaak Spampinato, Concetto Giordano, Daniela Univ Catania Catania Italy

ISBN: (纸本)9780769549903

In this paper we present a flash game that aims at generating easily ground truth for testing object detection algorithms. Flash the Fish is an online game where the user is shown videos from underwater environments and has to take photos of fish by clicking on them. the initial ground truth is provided by object detection algorithms and, subsequent, cluster analysis and user evaluation techniques, allow for the generation of ground truth based on the weighted combination of these "photos". Evaluation of the platform and comparison of the obtained results against a hand drawn ground truth confirmed that reliable ground truth generation is not necessarily a cumbersome task both in terms of effort and time needed.

关键词： computer games object detection

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