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共 2195 条记录，以下是1421-1430 订阅

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Visual saliency detection by spatially weighted dissimilarity

Visual saliency detection by spatially weighted dissimilarit...

引用

作者： Duan, Lijuan Wu, Chunpeng Miao, Jun Qing, Laiyun Fu, Yu College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Information Science and Engineering Graduate University Chinese Academy of Sciences Beijing 100049 China Department of Computing University of Surrey Guildford Surrey GU2 7XH United Kingdom

ISBN: (纸本)9781457703942

In this paper, a new visual saliency detection method is proposed based on the spatially weighted dissimilarity. We measured the saliency by integrating three elements as follows: the dissimilarities between image patches, which were evaluated in the reduced dimensional space, the spatial distance between image patches and the central bias. the dissimilarities were inversely weighted based on the corresponding spatial distance. A weighting mechanism, indicating a bias for human fixations to the center of the image, was employed. the principal component analysis (PCA) was the dimension reducing method used in our system. We extracted the principal components (PCs) by sampling the patches from the current image. Our method was compared with four saliency detection approaches using three image datasets. Experimental results show that our method outperforms current state-of-the-art methods on predicting human fixations. © 2011 IEEE.

关键词： Principal component analysis

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Image Classification Using Spatial Pyramid Coding and Visual Word Reweighting 1

引用

10th Asian conference on computer vision (ACCV)

作者： Zhang, Chunjie Liu, Jing Wang, Jinqiao Tian, Qi Xu, Changsheng Lu, Hanqing Ma, Songde Chinese Acad Sci Natl Lab Pattern Recognit Inst Automat POB 2728 Beijing Peoples R China Univ Texas San Antonio San Antonio TX 78249 USA

ISBN: (数字)9783642193187

ISBN: (纸本)9783642193170

the ignorance on spatial information and semantics of visual words becomes main obstacles in the bag-of-visual-words (BoW) method for image classification. To address the obstacles, we present an improved BoW representation using spatial pyramid coding (S PC) and visual word reweighting. In SPC procedure, we adopt the sparse coding technique to encode visual features with the spatial constraint. Visual features from the same spatial sub-region of images are collected to generate the visual vocabulary. Additionally, a relaxed but simple solution for semantic embedding into visual words is proposed. We relax the semantic embedding from ideal semantic correspondence to naive semantic purity of visual words, and reweight each visual word according to its semantic purity. Higher weights are given to semantically distinctive visual words, and lower weights to semantically general ones. Experiments on a public dataset demonstrate the effectiveness of the proposed method.

关键词： spatial pyramid coding bag-of-visual-words (BoW) reweighting image classification

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Automatic object segmentation from large scale 3D urban point clouds through manifold embedded mode seeking 11

Automatic object segmentation from large scale 3D urban poin...

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19th ACM International conference on Multimedia ACM Multimedia 2011, MM'11

作者： Yu, Zhiding Xu, Chunjing Liu, Jianzhuang Au, Oscar C. Tang, Xiaoou Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450306164

this paper presents a system that can automatically segment objects in large scale 3D point clouds obtained from urban ranging images. the system consists of three steps: the first one involves a ground detection process that can detect relatively complex terrain and separate it from other objects. the second step superpixelizes the remaining objects to speed up the segmentation process. In the final step, a manifold embedded mode seeking method is adopted to segment the point clouds. Even though the segmentation of urban objects is a challenging problem in terms of accuracy and problem scale, our system can efficiently generate very good segmentation results. the proposed manifold learning effectively improves the segmentation performance due to the fact that continuous artificial objects often have manifold-like structures. Copyright 2011 ACM.

关键词： Image segmentation

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Facial expression classification based on local spatiotemporal edge and texture descriptors

Facial expression classification based on local spatiotempor...

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7th International conference on Methods and Techniques in Behavioral Research, MB'10

作者： Gizatdinova, Yulia Surakka, Veikko Zhao, Guoying Mäkinen, Erno Raisamo, Roope Research Group for Emotions Sociality and Computing University of Tampere Finland Machine Vision Group University of Oulu Finland Multimodal Interaction Research Group University of Tampere Finland

ISBN: (纸本)9781605589268

Facial expressions are emotionally, socially and otherwise meaningful reflective signals in the face. Facial expressions play a critical role in human life, providing an important channel of nonverbal communication. Automation of the entire process of expression analysis can potentially facilitate human-computer interaction, making it to resemble mechanisms of human-human communication. In this paper, we present an ongoing research that aims at development of a novel spatiotemporal approach to expression classification in video. the novelty comes from a new facial representation that is based on local spatiotemporal feature descriptors. In particular, a combined dynamic edge and texture information is used for reliable description of both appearance and motion of the expression. Support vector machines are utilized to perform a final expression classification. the planned experiments will further systematically evaluate the performance of the developed method with several databases of complex facial expressions. © ACM 2010.

关键词： Local binary pattern

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Shape matching using points co-occurrence pattern

Shape matching using points co-occurrence pattern

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International conference on Image and Graphics

作者： Zhou, Yu Wang, Junwei Zhou, Quan Bai, Xiang Liu, Wenyu Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780769545417

Shape matching is a very critical problem in computer vision, and many smart features have been designed in recent literature for improving the similarity measure between pairs of shapes, and most of them consider either distribution of the sample contour points, or convexity/concavity property of the contour. In this paper, we design a novel shape feature to capture the Co-Occurrence pattern (COP) of the points sampled from any given shape contour, and each pattern is described by Self-Similarity which investigates the spatial cooccurrence relation among all the sample points. We test our feature on three famous shape databases: MPEG-7 CE-Shape- 1 part B, Tari1000, and Kimia99 data set for shape matching and retrieval. the experimental results show that the proposed descriptor achieves higher computational efficiency with no significant performance loss. © 2011 IEEE.

关键词： Computational efficiency

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thinning Based Multipurpose Camouflage pattern Design

Thinning Based Multipurpose Camouflage Pattern Design

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Iranian Machine vision and Image Processing (MVIP)

作者： Arash Abdi Hejrandoost Reza Safabakhsh Amirkabir university Tehran Iran

Traditional generations of camouflage pattern were an artistic and manually work. Most of current computer aided camouflage pattern designs focused on a single background to produce the pattern for. In our proposed method, a multi background camouflage pattern is designed using Fuzzy C Means (FCM) clustering method followed by a object thinning algorithm to combine clustered backgrounds with each other. the goal is to generate patterns with similarity to all backgrounds. Experimental results and their evaluations demonstrate generated patterns inherited color and texture characteristics of all backgrounds.

关键词： Image color analysis Histograms Clustering methods Merging Clustering algorithms Image segmentation computers

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Evaluation of Global Descriptors for Large Scale Image Retrieval

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16th International conference on Image Analysis and Processing (ICIAP)

作者： Wang, Hai Zhang, Shuwu Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China

ISBN: (纸本)9783642240850

In this paper, we evaluate the effectiveness and efficiency of the global image descriptors and their distance metric functions in the domain of object recognition and near duplicate detection. Recently, the global descriptor GIST has been compared with the bag-of-words local image representation, and has achieved satisfying results. We compare different global descriptors in two famous datasets against mean average precision (MAP) measure. the results show that Fuzzy Color and Texture Histogram (FCth) is outperforming GIST and several MPEG7 descriptors by a large margin. We apply different distance metrics to global features so as to see how the similarity measures can affect the retrieval performance. In order to achieve the goal of lower memory cost and shorter retrieval time, we use the Spectral Hashing algorithm to embed the FCth in the hamming space. Querying an image, from 1.26 million images database, takes 0.16 second on a common notebook computer without losing much searching accuracy.

关键词： Image representation

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Efficient Human Detection Based on Parallel Implementation of Gradient and Texture Feature Extraction Methods

Efficient Human Detection Based on Parallel Implementation o...

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Iranian Machine vision and Image Processing (MVIP)

作者： Masoud Farhadi Seyed Ahmad Motamedi Saeed Sharifian Department of Electrical Engineering Amirkabir University of Technology (Tehran polytechnic) Tehran Iran

Pedestrian Detection is of interest in many computer vision applications such as intelligent transportation systems and human-robot interaction; among the existing methods, the combination of shape feature (i.e. Histogram of Oriented Gradients (HOG)) and texture features (i.e. Local Binary pattern (LBP)) has shown promising results in detection accuracy, but it is limited due to computation cost. In this paper, we introduce a new pedestrian detection algorithm with fast computation of these features on GPU. We propose a robust and rapid pedestrian detector by combining the HOG with LBP, as the feature set and corresponding Support Vector Machine (SVM) classifiers. Also, we use the integral image method and an efficient parallel implementation to reduce detection time. We can achieve a more than 10× speed up, and 7% increase in detection rate.

关键词： Feature extraction Humans Graphics processing unit Histograms Support vector machines computer vision Classification algorithms

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Digital Camera Identification by Modeling the Probability Distribution of Sensor pattern Noise

Digital Camera Identification by Modeling the Probability Di...

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Iranian Machine vision and Image Processing (MVIP)

作者： Fereshteh Gharibi Javad RavanJamjah Bahram ZahirAzami Fardin Akhlaghian Computer Engineering Department University of Kurdistan Sanandaj Kurdistan Iran

Sensor pattern noise (SPN) as the fingerprint of imaging devices, could be used as a reliable feature in digital source identification. In this paper, we introduce a new method which uses the probability model of SPN to identify the source. To achieve this goal, after extracting the SPN of some images, they are digitized and then for each value, the distribution of its neighbors is modeled separately ("Value-Model"). Finally by using the value-model, the quantized SPN is mapped to the probability domain. the average of probability matrix of some images of same camera forms camera model. this SPN model causes noticeable increases in the true detection rate of the source. To evaluate the efficiency of proposed approach, we do some benchmark on our hypothesis. the accuracy and performance of our model compared to similar works, proves high efficiency of the proposed theory.

关键词： Noise Correlation Semiconductor device modeling Earth Observing System Digital cameras Histograms

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A study of automatic recognition of three rodent species: Applying mathematic morphological characters of skull

A study of automatic recognition of three rodent species: Ap...

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International conference on Bioinformatics and Biomedical Engineering

作者： Yuan, Xiongfeng Gao, Lingwang Ma, Xiaoguang Zhang, Xin Guo, Tianyu IPMist Lab. College of Agriculture and Biotechnology China Agricultural University Beijing China Institute of Health Quarantine Chinese Academy of Inspection and Quarantine Sciences Beijing China College of Biological Sciences and Biotechnology Forestry University Beijing China Beijing Entry-Exit Inspection and Quarantine Bureau Beijing China

ISBN: (纸本)9781424450893

Based on the digital image processing technology, this study applied the mathematic morphological characterextracting software to get exterior shape feature from skull of three rodent species (Meriones unguiculatus, Microtus brandti and Rattus norvegicus). Via SPSS 13.0 for Windows, the data was explored for automatic recognition. 22 mathematic morphological characters were extracted and the results of univariate two-factor analysis of variance showed that all the features can provide a reliable foundation for a rapid automatic recognition, though failed to distinguish gender. Stepwise discriminatory method was applied and 9 parameters were selected for rodent identification: short axis(X1), perimeter(X2), eccentricity(X3), sphericity(X4), bump area(X5), paraxial area of enclosing rectangle(X6), hu1(X7), hu2(X8), hu3(X9). With the selected parameters, two standardized canonical discriminant functions were found: Y1 = 8.014X1 - 3.585X2 + 9.682X3 - 2.504X4 + 7.823X5 - 10.948X6 - 0.896X7 + 12.471X8 - 0.781X9;Y2 = 2.593X1 + 0.242X2 + 6.323X3 + 0.509X4 - 1.219X5 - 2.898X6 - 4.226X7 + 7.674X8 + 0.785X9. these two discriminant functions worked well for identification, the accuracy rate was 100%. © 2011 IEEE.

关键词： computer vision

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