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Support vector description of clusters for content-based image annotation

Support vector description of clusters for content-based ima...

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作者： Sun, Liang Ge, Hongwei Yoshida, Shinichi Liang, Yanchun Tan, Guozhen College of Computer Science and Technology Dalian University of Technology Dalian 116024 China College of Computer Science and Technology Jilin University Changchun 130012 China School of Information Kochi University of Technology Kachi 782-8502 Japan

Continual progress in the fields of computer vision and machine learning has provided opportunities to develop automatic tools for tagging images;this facilitates searching and retrieving. However, due to the complexity of real-world image systems, effective and efficient image annotation is still a challenging problem. In this paper, we present an annotation technique based on the use of image content and word correlations. Clusters of images with manually tagged words are used as training instances. Images within each cluster are modeled using a kernel method, in which the image vectors are mapped to a higher-dimensional space and the vectors identified as support vectors are used to describe the cluster. To measure the extent of the association between an image and a model described by support vectors, the distance from the image to the model is computed. A closer distance indicates a stronger association. Moreover, word-to-word correlations are also considered in the annotation framework. To tag an image, the system predicts the annotation words by using the distances from the image to the models and the word-to-word correlations in a unified probabilistic framework. Simulated experiments were conducted on three benchmark image data sets. The results demonstrate the performance of the proposed technique, and compare it to the performance of other recently reported techniques. © 2013 Elsevier Ltd. All rights reserved.

关键词： computer vision

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A surveillance video analysis and storage scheme for scalable synopsis browsing

A surveillance video analysis and storage scheme for scalabl...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Shizheng Wang Jianwei Yang Yanyun Zhao Anni Cai Stan Z. Li CBSR & NLPR Institute of Automation Chinese Academy of Sciences China Multimedia Communication and Pattern Recognition Laboratories Beijing University of Posts and Telecommunications China

We propose a framework for efficient storing and scalable browsing of surveillance video based on video synopsis. Our framework employs a novel synopsis analysis scheme named Detail-based video synopsis to generate a set of object flags to store and browse surveillance video synopsis. The main contributions of our work are: 1) highlighting important contents of surveillance video; 2) improving the storage efficiency of original video and synopsis video; 3) realizing multi-scale scalable browsing of synopsis video while reserving essential information. The experiments of implementing the framework are shown compared with the previous independent storage method of original video and synopsis video.

关键词： Electron tubes Surveillance Streaming media Video coding Encoding Scalability Databases

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Illuminant color estimation for real-world mixed-illuminant scenes

Illuminant color estimation for real-world mixed-illuminant ...

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International Conference on computer vision Workshops (ICCV Workshops)

作者： Christian Riess Eva Eibenberger Elli Angelopoulou Pattern Recognition Lab University of Erlangen-Nuremberg Germany

We present a physics-based approach for illuminant color estimation of arbitrary images, which is explicitly designed for handling images with multiple illuminants. The majority of techniques that extract the illuminant color assume that the illumination is constant across the scene. This, however, is not often the case. We propose an illuminant-color estimation method which is based on robust local illuminant estimates. There are no assumptions on the number or type of illuminants. An illuminant color estimate is obtained independently from distinct image mini-regions. From these mini-regions a robust local illumination color is computed by consensus. These local estimates are then used in deriving the chromaticity of the dominant illuminants. Experiments on an established benchmark database of real-world images show that our technique performs comparably to uniform-illuminant estimation methods. Furthermore, extensive tests on real-world images show that we can reliably process mixed illuminant scenes.

关键词： Image color analysis Estimation Lighting Image segmentation Databases Robustness Cameras

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A fuzzy identification method of contours based on chain-code features

A fuzzy identification method of contours based on chain-cod...

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2011 3rd Chinese Conference on Intelligent Visual Surveillance, IVS 2011

作者： Dong, Qi Xu, Guangping Jing, Liu Xue, Yanbing Zhang, Hua Key Laboratory of Intelligence Computing and New Software Technology Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin 300384 China School of Computer and Communication Tianjin University of Technology Tianjin 300384 China

ISBN: (纸本)9781457718311

The contour analysis and identification are the important aspects in visual surveillance research. The paper proposes a fuzzy identification method of contours. First, according to the description of a contour based on the chain-code method, the proposed method utilizes the statistical features of contours including the chain-code entropy and chain-code space distribution entropies, from which the feature vector of a contour is composed. Then, the method generates the contour pattern from some contour samples and uses the approaching principle to identify a contour. Since our method integrates effectively multiple statistical feathers of its chain-code and employs a fuzzy pattern recognition technique, the experiments show quantitatively that it can achieve good results from various metrics. © 2011 IEEE.

关键词： Object recognition

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On experimenting with pedestrian classification using neural network

On experimenting with pedestrian classification using neural...

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International Conference on Electronic computer Technology

作者： R. Rajesh K. Rajeev V. Gopakumar K. Suchithra V. P. Lekhesh TDC Network Systems and Technologies Private Limited Thiruvananthapuram India

Pedestrian classification is addressed by T. Watanabe et al. using SVM with 34704 CoHOG features. This paper addresses the pedestrian classification using neural network with 1344 CoHOG features (feature size is 25 ti... 详细信息

关键词： computer vision Intelligent vehicles pattern recognition Artificial neural networks Histograms Humans Conferences

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Unsupervised local color correction for coarsely registered images

Unsupervised local color correction for coarsely registered ...

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Conference on computer vision and pattern recognition (CVPR)

作者： Miguel Oliveira Angel D. Sappa Vitor Santos Department of Mechanical Engineering University of Aveiro Portugal Computer Vision Center Barcelona Spain

The current paper proposes a new parametric local color correction technique. Initially, several color transfer functions are computed from the output of the mean shift color segmentation algorithm. Secondly, color influence maps are calculated. Finally, the contribution of every color transfer function is merged using the weights from the color influence maps. The proposed approach is compared with both global and local color correction approaches. Results show that our method outperforms the technique ranked first in a recent performance evaluation on this topic. Moreover, the proposed approach is computed in about one tenth of the time.

关键词： Image color analysis Transfer functions Image segmentation Computational modeling Image registration Cameras Clocks

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Event recognition based on bag of local space-time interest points' features

Event recognition based on bag of local space-time interest ...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Chuan-Min Zhai Yi-Lan Guo Ji-xiang Du Department of Computer Science and Technology Huaqiao University Xiamen China

This paper proposes a novel method based on bag of local space-time interest points' features to recognize and retrieval complex events in real movies. In this method, an individual video sequence is represented as a bag of local space-time features then we integrate such bag-of-feature with SVM for recognition events. Local space-time features are introduced to capture the local events in video and can be adapted to size and velocity of the pattern of the event. To evaluate effectiveness of this method, this paper uses the public Hollywood dataset, in this dataset the shot sequences has collected from 32 different Hollywood movies and it includes 8 event classes. The presented result justify the proposed method explicitly improve the average accuracy and average precision compared to other relative approaches.

关键词： Visualization Video sequences Feature extraction Conferences Vocabulary computer vision pattern recognition

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μNect: On using a gaming RGBD camera in micro-metrology applications

μNect: On using a gaming RGBD camera in micro-metrology app...

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Matthias Rüther Martin Lenz Horst Bischof Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

Three-dimensional metrology problems are traditionally the domain of expensive devices like Laser-stripe sensors and custom-made projector-camera systems. The launch of Microsoft's Kinect sensor solved many of these problems which were deemed feasible only with several thousand-Dollar devices before. However, the sensor is highly tuned to its domain of application, which is the robust scanning of indoor environments at an accuracy range of few centimeters. In this work we investigate how the sensor can be modified to work at much higher accuracy, on a limited but scalable measurement range. By altering the sensor baseline and depth of field, we are able to reliably retrieve depth fields of objects at an accuracy in the sub-millimeter range. We further improve the sensor by adding multiple projectors, thus creating a low-cost multi-projector, single-camera system working at a frame rate of 30 Hz, at virtually no CPU consumption. We are hereby able to apply automated pattern intensity adaption and multi-view depth fusion in real-time and compete with a large range of sensors in terms of robustness, speed and accuracy, but at a fraction of the costs.

关键词： Cameras Sensors Hardware Robustness Three dimensional displays Image reconstruction Encoding

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Invited paper: Accelerating neuromorphic vision on FPGAs

Invited paper: Accelerating neuromorphic vision on FPGAs

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IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Sungho Park Srinidhi Kestur Kevin M. Irick Vijaykrishnan Narayanan Microsystems Design Laboratory (MDL) Department of Computer Science and Engineering Pennsylvania State University University Park PA USA

Reconfigurable hardware such as FPGAs are being increasingly employed for application acceleration due to their high degree of parallelism, flexibility and power efficiency - factors which are key in the rapidly evolving field of embedded real-time vision. While recent advances in technology have increased the capacity of FPGAs, lack of standard models for developing custom accelerators creates issues with scalability and compatibility. In this paper, we describe a model for designing streaming hardware accelerators with run-time configurability. This model provides a generic interface for each hardware module, a modular and hierarchical structure for parallelism at multiple levels and a run-time reconfiguration framework for increased flexibility. We present case studies to accelerate sample neu-romorphic vision algorithms which are inspired by models of the mammalian visual cortex. Experimental results show speedups of several factors over comparable CPU implementations and higher performance-per-watt over relevant GPU implementations.

关键词： Libraries Hardware Field programmable gate arrays Pipelines Streaming media Hardware design languages Aerospace electronics

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Face detection on partial and holistic LBP features

Face detection on partial and holistic LBP features

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International Conference on Electrical and Control Engineering (ICECE)

作者： Xiaorong Pu Yongtao Wang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Face detection in low resolution images is a huge challenge in computer vision due to its insufficient information. In this paper, an improved LBP for low resolution facial image feature extraction is proposed which considers both partial and holistic facial features. Extensive experiments show that the proposed method has an encouraging performance with good robustness and strong practicality in face detection.

关键词： Support vector machines Educational institutions Face detection pattern recognition Conferences Feature extraction

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