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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881-890 订阅

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Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop

Attribute-Based Detection of Unfamiliar Classes with Humans ...

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

作者： Wah, Catherine Belongie, Serge Univ Calif San Diego Dept Comp Sci & Engn San Diego CA 92103 USA

ISBN: (纸本)9780769549897

Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or defining characteristics of these unobserved classes are known, leveraging this information at test time to detect an unseen class. We address the more realistic problem of detecting categories that do not appear in the dataset in any form. We denote such a category as an unfamiliar class;it is neither observed at train time, nor do we possess any knowledge regarding its relationships to attributes. this problem is one that has received limited attention within the computer vision community. In this work, we propose a novel approach to the unfamiliar class detection task that builds on attribute-based classification methods, and we empirically demonstrate how classification accuracy is impacted by attribute noise and dataset "difficulty," as quantified by the separation of classes in the attribute space. We also present a method for incorporating human users to overcome deficiencies in attribute detection. We demonstrate results superior to existing methods on the challenging CUB-200-2011 dataset.

关键词： computer vision image classification learning (artificial intelligence) object detection

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POOF: Part-Based One-vs-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation

POOF: Part-Based One-vs-One Features for Fine-Grained Catego...

引用

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

作者： Berg, thomas Belhumeur, Peter N. Columbia Univ New York NY 10027 USA

ISBN: (纸本)9780769549897

From a set of images in a particular domain, labeled with part locations and class, we present a method to automatically learn a large and diverse set of highly discriminative intermediate features that we call Part-based One-vs-One Features (POOFs). Each of these features specializes in discrimination between two particular classes based on the appearance at a particular part. We demonstrate the particular usefulness of these features for fine-grained visual categorization with new state-of-the-art results on bird species identification using the Caltech UCSD Birds (CUB) dataset and parity with the best existing results in face verification on the Labeled Faces in the Wild (LFW) dataset. Finally, we demonstrate the particular advantage of POOFs when training data is scarce.

关键词： face recognition feature extraction

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Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object recognition

Efficient 2D-to-3D Correspondence Filtering for Scalable 3D ...

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

作者： Hao, Qiang Cai, Rui Li, Zhiwei Zhang, Lei Pang, Yanwei Wu, Feng Rui, Yong Tianjin Univ Tianjin 300072 Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China

ISBN: (纸本)9780769549897

3D model-based object recognition has been a noticeable research trend in recent years. Common methods find 2D-to-3D correspondences and make recognition decisions by pose estimation, whose efficiency usually suffers from noisy correspondences caused by the increasing number of target objects. To overcome this scalability bottleneck, we propose an efficient 2D-to-3D correspondence filtering approach, which combines a light-weight neighborhood-based step with a finer-grained pairwise step to remove spurious correspondences based on 2D/3D geometric cues. On a dataset of 300 3D objects, our solution achieves similar to 10 times speed improvement over the baseline, with a comparable recognition accuracy. A parallel implementation on a quad-core CPU can run at similar to 3fps for 1280 x 720 images.

关键词： filtering theory geometry object recognition pose estimation target tracking

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Decoding, Calibration and Rectification for Lenselet-Based Plenoptic Cameras

Decoding, Calibration and Rectification for Lenselet-Based P...

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

作者： Dansereau, Donald G. Pizarro, Oscar Williams, Stefan B. Univ Sydney Australian Ctr Field Robot Sch Aerosp Mech & Mechatron Engn Sydney NSW 2006 Australia

ISBN: (纸本)9780769549897

Plenoptic cameras are gaining attention for their unique light gathering and post-capture processing capabilities. We describe a decoding, calibration and rectification procedure for lenselet-based plenoptic cameras appropriate for a range of computer vision applications. We derive a novel physically based 4D intrinsic matrix relating each recorded pixel to its corresponding ray in 3D space. We further propose a radial distortion model and a practical objective function based on ray reprojection. Our 15-parameter camera model is of much lower dimensionality than camera array models, and more closely represents the physics of lenselet-based cameras. Results include calibration of a commercially available camera using three calibration grid sizes over five datasets. Typical RMS ray reprojection errors are 0.0628, 0.105 and 0.363 mm for 3.61, 7.22 and 35.1 mm calibration grids, respectively. Rectification examples include calibration targets and real-world imagery.

关键词： computer vision image sensors matrix algebra

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Recovering Line-networks in Images by Junction-Point Processes

Recovering Line-networks in Images by Junction-Point Process...

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

作者： Chai, Dengfeng Foerstner, Wolfgang Lafarge, Florent Zhejiang Univ Hangzhou Zhejiang Peoples R China Univ Bonn Bonn Germany INRIA Sophia Antipolis France

ISBN: (纸本)9780769549897

the automatic extraction of line-networks from images is a well-known computer vision issue. Appearance and shape considerations have been deeply explored in the literature to improve accuracy in presence of occlusions, shadows, and a wide variety of irrelevant objects. However most existing works have ignored the structural aspect of the problem. We present an original method which provides structurally-coherent solutions. Contrary to the pixel-based and object-based methods, our result is a graph in which each node represents either a connection or an ending in the line-network. Based on stochastic geometry, we develop a new family of point processes consisting in sampling junction-points in the input image by using a Monte Carlo mechanism. the quality of a configuration is measured by a probability density which takes into account both image consistency and shape priors. Our experiments on a variety of problems illustrate the potential of our approach in terms of accuracy, flexibility and efficiency.

关键词： Monte Carlo methods computer vision graph theory image sampling probability

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Tracking People and their Objects

Tracking People and Their Objects

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

作者： Baumgartner, Tobias Mitzel, Dennis Leibe, Bastian Rhein Westfal TH Aachen Comp Vis Grp Aachen Germany

ISBN: (纸本)9780769549897

Current pedestrian tracking approaches ignore important aspects of human behavior. Humans are not moving independently, but they closely interact with their environment, which includes not only other persons, but also different scene objects. Typical everyday scenarios include people moving in groups, pushing child strollers, or pulling luggage. In this paper, we propose a probabilistic approach for classifying such person-object interactions, associating objects to persons, and predicting how the interaction will most likely continue. Our approach relies on stereo depth information in order to track all scene objects in 3D, while simultaneously building up their 3D shape models. these models and their relative spatial arrangement are then fed into a probabilistic graphical model which jointly infers pairwise interactions and object classes. the inferred interactions can then be used to support tracking by recovering lost object tracks. We evaluate our approach on a novel dataset containing more than 15,000 frames of person-object interactions in 325 video sequences and demonstrate good performance in challenging real-world scenarios.

关键词： image classification image motion analysis image sequences object tracking pedestrians probability solid modelling stereo image processing

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Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem

Rolling Riemannian Manifolds to Solve the Multi-class Classi...

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

作者： Caseiro, Rui Martins, Pedro Henriques, Joao F. Leite, Fatima Silva Batista, Jorge Univ Coimbra Inst Syst & Robot P-3000 Coimbra Portugal

ISBN: (纸本)9780769549897

In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [,]. A popular framework, valid over any Riemannian manifold, was proposed in [] for binary classification. Once moving from binary to multi-class classification this paradigm is not valid anymore, due to the spread of multiple positive classes on the manifold []. It is then natural to ask whether the multi-class paradigm could be extended to operate on a large class of Riemannian manifolds. We propose a mathematically well-founded classification paradigm that allows to extend the work in [] to multi-class models, taking into account the structure of the space. the idea is to project all the data from the manifold onto an affine tangent space at a particular point. To mitigate the distortion induced by local diffeomorphisms, we introduce for the first time in the computer vision community a well-founded mathematical concept, so-called Rolling map [,] the novelty in this alternate school of thought is that the manifold will be firstly rolled (without slipping or twisting) as a rigid body, then the given data is unwrapped onto the affine tangent space, where the classification is performed.

关键词： computer vision image classification learning (artificial intelligence)

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Action recognition by Hierarchical Sequence Summarization

Action Recognition by Hierarchical Sequence Summarization

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

作者： Song, Yale Morency, Louis-Philippe Davis, Randall MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA USC Inst Creative Technol Cambridge MA 02139 USA

ISBN: (纸本)9780769549897

Recent progress has shown that learning from hierarchical feature representations leads to improvements in various computer vision tasks. Motivated by the observation that human activity data contains information at various temporal resolutions, we present a hierarchical sequence summarization approach for action recognition that learns multiple layers of discriminative feature representations at different temporal granularities. We build up a hierarchy dynamically and recursively by alternating sequence learning and sequence summarization. For sequence learning we use CRFs with latent variables to learn hidden spatio-temporal dynamics;for sequence summarization we group observations that have similar semantic meaning in the latent space. For each layer we learn an abstract feature representation through non-linear gate functions. this procedure is repeated to obtain a hierarchical sequence summary representation. We develop an efficient learning method to train our model and show that its complexity grows sublinearly with the size of the hierarchy. Experimental results show the effectiveness of our approach, achieving the best published results on the ArmGesture and Canal9 datasets.

关键词： computer vision feature extraction image representation image resolution learning (artificial intelligence) object recognition

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Evaluation of Color STIPs for Human Action recognition

Evaluation of Color STIPs for Human Action Recognition

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

作者： Everts, Ivo van Gemert, Jan C. Gevers, theo Univ Amsterdam Intelligent Syst Lab Amsterdam NL-1012 WX Amsterdam Netherlands

ISBN: (纸本)9780769549897

this paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. these issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. this enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition.

关键词： image colour analysis image motion analysis image representation video signal processing

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Chinese Chess recognition Algorithm Based on computer vision

Chinese Chess Recognition Algorithm Based on Computer Vision

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第26届中国控制与决策会议

作者： Wu Gui Tao Jun Educational Administration office Jianghan University School of Mathematics and Computer Science Jianghan University

ISBN: (纸本)9781479937097

this paper introduces the Chinese chess recognition algorithm based on computer vision and image processing. In order to simplify processing and enhance efficiency, the images of chessboard and chessman need preprocessing in advance. the steps of preprocessing include of transformation from color images to gray images, images filtering with mean filter or median filter, and binaryzation of the gray images. the edges of chessboard and chessman are able to be extracted from the binarized images by image segmentation. then the location of center of chessman and the circle edge of chessman can be calculated with an advanced Hough transformation, which can ascertain the location of chessman in the chessboard and the size of each chessman. According to the features of chess images, main recognition method is to analyze the radial chess pixel statistical data with mathematical morphology. Because the values of pixel coordination in any angle of chessman can keep same and stable, the recognition algorithm should be with a good recognition rate from the experimental results. the advanced and modified recognition algorithm is proved to be practical and applicative by the experimentation of computer vision system in Chinese chess games provided in this paper.

关键词： Chinese Chess,Chess recognition,recognition Algorithm,computer vision

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