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检索条件"任意字段=7th Chinese Conference on Pattern Recognition and Computer Vision"

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Partial Occlusion Handling for Visual Tracking via Robust Part Matching 27

Partial Occlusion Handling for Visual Tracking via Robust Pa...

引用

27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Tianzhu Jia, Kui Xu, Changsheng Ma, Yi Ahuja, Narendra Chinese Acad Sci Inst Automat Beijing 100864 Peoples R China Adv Digital Sci Ctr Illinois Singapore Singapore ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781479951178

Part-based visual tracking is advantageous due to its robustness against partial occlusion. However, how to effectively exploit the confidence scores of individual parts to construct a robust tracker is still a challenging problem. In this paper, we address this problem by simultaneously matching parts in each of multiple frames, which is realized by a locality-constrained low-rank sparse learning method that establishes multi-frame part correspondences through optimization of partial permutation matrices. the proposed part matching tracker (PMT) has a number of attractive properties. (1) It exploits the spatial-temporal locality-constrained property for robust part matching. (2) It matches local parts from multiple frames jointly by considering their low-rank and sparse structure information, which can effectively handle part appearance variations due to occlusion or noise. (3) the proposed PMT model has the inbuilt mechanism of leveraging multi-mode target templates, so that the dilemma of template updating when encountering occlusion in tracking can be better handled. this contrasts with existing methods that only do part matching between a pair of frames. We evaluate PMT and compare with 10 popular state-of-the-art methods on challenging benchmarks. Experimental results show that PMT consistently outperform these existing trackers.

关键词： visual tracking

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Backscatter Compensated Photometric Stereo with 3 Sources 27

Backscatter Compensated Photometric Stereo with 3 Sources

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Tsiotsios, Chourmouzios Angelopoulou, Maria E. Kim, Tae-Kyun Davison, Andrew J. Univ London Imperial Coll Sci Technol & Med London SW7 2AZ England

ISBN: (纸本)9781479951178

Photometric stereo offers the possibility of object shape reconstruction via reasoning about the amount of light reflected from oriented surfaces. However, in murky media such as sea water, the illuminating light interacts with the medium and some of it is backscattered towards the camera. Due to this additive light component, the standard Photometric Stereo equations lead to poor quality shape estimation. Previous authors have attempted to reformulate the approach but have either neglected backscatter entirely or disregarded its non-uniformity on the sensor when camera and lights are close to each other. We show that by compensating effectively for the backscatter component, a linear formulation of Photometric Stereo is allowed which recovers an accurate normal map using only 3 lights. Our backscatter compensation method for point-sources can be used for estimating the uneven backscatter directly from single images without any prior knowledge about the characteristics of the medium or the scene. We compare our method with previous approaches through extensive experimental results, where a variety of objects are imaged in a big water tank whose turbidity is systematically increased, and show reconstruction quality which degrades little relative to clean water results even with a very significant scattering level.

关键词： backscatter photometric stereo restoration scattering underwater vision

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Generalized Autoencoder: A Neural Network Framework for Dimensionality Reduction

Generalized Autoencoder: A Neural Network Framework for Dime...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Wang, Wei Huang, Yan Wang, Yizhou Wang, Liang Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit CRIPAC Beijing Peoples R China Peking Univ Sch EECS Natl Eng Lab Video Technol Key Lab Machine Percep Beijing Peoples R China

ISBN: (纸本)9781479943098

the autoencoder algorithm and its deep version as traditional dimensionality reduction methods have achieved great success via the powerful representability of neural networks. However, they just use each instance to reconstruct itself and ignore to explicitly model the data relation so as to discover the underlying effective manifold structure. In this paper, we propose a dimensionality reduction method by manifold learning, which iteratively explores data relation and use the relation to pursue the manifold structure. the method is realized by a so called "generalized autoencoder" (GAE), which extends the traditional autoencoder in two aspects: (1) each instance x(i) is used to reconstruct a set of instances {x(j)} rather than itself. (2) the reconstruction error of each instance (|parallel to x(j) - x(i)'parallel to(2)) is weighted by a relational function of x(i) and x(j) defined on the learned manifold. Hence, the GAE captures the structure of the data space through minimizing the weighted distances between reconstructed instances and the original ones. the generalized autoencoder provides a general neural network framework for dimensionality reduction. In addition, we propose a multilayer architecture of the generalized autoencoder called deep generalized autoencoder to handle highly complex datasets. Finally, to evaluate the proposed methods, we perform extensive experiments on three datasets. the experiments demonstrate that the proposed methods achieve promising performance.

关键词： Autoencoder Deep learning Dimensionality reduction

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A Study of Ancient Ceramics Verification Based on vision Methods 6

A Study of Ancient Ceramics Verification Based on Vision Met...

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6th chinese conference on pattern recognition (CCPR)

作者： Tang, Yunqi Ding, Jianwei Guo, Wei Peoples Publ Secur Univ China Dept Criminal Sci & Technol Beijing 100038 Peoples R China Peoples Publ Secur Univ China Dept Police Informat Engn Beijing 100038 Peoples R China

ISBN: (纸本)9783662456439;9783662456422

Ceramics appraisal is a hot topic in field of cultural relic collection. Traditionally, there are mainly two types of ceramics appraisal methods, which are experience-based methods and technology-based methods. In practice, the both methods would cause high cost and time consuming. In this paper, a novel vision based method, which is mainly inspired by the idea of biometrics recognition techniques, is proposed to achieve efficiently verification of the identity of a ceramics. In this method, the microscopic information of a ceramics captured by a digital microscope camera are used as the characteristics for verification. In technical detail, SURF(Speeded Up Robust Features) is first employed to align the probe image to the gallery images. LBP(Local Binary patterns) features are then extracted from the two aligned images. Finally, Chi-square distance is calculated to measure the similarity between probe and gallery. Experiments on the dataset constructed by this paper demonstrate the state-of-the-art performance of our method.

关键词： Ancient ceramics verification SURF Local binary patterns Biometric recognition

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Multiple Target Tracking Based on Undirected Hierarchical Relation Hypergraph 27

Multiple Target Tracking Based on Undirected Hierarchical Re...

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27th IEEE conference on computer vision and pattern recognition (CVPR)

作者： Wen, Longyin Li, Wenbo Yan, Junjie Lei, Zhen Yi, Dong Li, Stan Z. Chinese Acad Sci Ctr Biometr & Secur Res Beijing 100864 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China

ISBN: (纸本)9781479951178

Multi-target tracking is an interesting but challenging task in computer vision field. Most previous data association based methods merely consider the relationships (e.g. appearance and motion pattern similarities) between detections in local limited temporal domain, leading to their difficulties in handling long-term occlusion and distinguishing the spatially close targets with similar appearance in crowded scenes. In this paper, a novel data association approach based on undirected hierarchical relation hyper-graph is proposed, which formulates the tracking task as a hierarchical dense neighborhoods searching problem on the dynamically constructed undirected affinity graph. the relationships between different detections across the spatio-temporal domain are considered in a high-order way, which makes the tracker robust to the spatially close targets with similar appearance. Meanwhile, the hierarchical design of the optimization process fuels our tracker to long-term occlusion with more robustness. Extensive experiments on various challenging datasets (i.e. PETS2009 dataset, ParkingLot), including both low and high density sequences, demonstrate that the proposed method performs favorably against the state-of-the-art methods.

关键词： Multi-target tracking dense neighborhoods searching hypergraph undirected affinity graph

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STATISTICAL pattern recognition FOR REAL-TIME IMAGE EDGE DETECTION ON FPGA 12

STATISTICAL PATTERN RECOGNITION FOR REAL-TIME IMAGE EDGE DET...

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12th IEEE International conference on Signal Processing (ICSP)

作者： Liu, Ziyan Qi, Jia Feng, Liang Feng, Li Guizhou Univ Coll Elect & Informat Guiyang 550025 Peoples R China State Grid Chongqing Elect Power Co Chongqing 400014 Peoples R China

ISBN: (纸本)9781479921867

Image edge detection is a fundamental process in computer vision. Image edges represent the major fraction of information in an image. Traditional edge-detection techniques focus on the gradient calculation method. In this paper, for the first time, the statistical pattern recognition method is used to detect the edge after the real-time image was processed via the median filtering method and implemented on FPGA. In comparison to the Sobel algorithm, the proposed method has superior anti-noise capability.

关键词： Statistical pattern recognition Median Filtering FPGA Real-Time Image Edge Detection

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Hydraulic Excavators recognition Based on Inverse "V" Feature of Mechanical Arm 6

Hydraulic Excavators Recognition Based on Inverse "V" Featur...

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6th chinese conference on pattern recognition (CCPR)

作者： Yang, Wenming Li, Dedi Sun, Daren Liao, Qingmin Tsinghua Univ Grad Sch Shenzhen Dept Elect Engn Shenzhen Key Lab Informat Sci & TechShenzhen Eng Shenzhen Peoples R China

ISBN: (纸本)9783662456439;9783662456422

Detecting hydraulic excavators in videos can increase the confidence coefficient of illegal construction in nationalized land. Hydraulic Excavators have multifarious working postures making them a difficult target using even state of the art object recognition algorithms. the contribution of this paper is to propose an inverse "V" model for hydraulic excavator detection. In this paper, we describe an hydraulic excavator detection system based on inverse "V" feature of mechanical arm which is formed by boom and dipper and show a detection system. then a real-time video processing method is presented which is used for monitoring illegal construction activities on a land of state-ownership.

关键词： computer vision Object recognition Hydraulic Excavator Inverse "V" feature Processing method for video

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Multi-feature Deep Learning for Face Gender recognition 7

Multi-feature Deep Learning for Face Gender Recognition

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IEEE 7th Joint International Information Technology and Artificial Intelligence conference (ITAIC)

作者： Jiang, Yuxin Li, Songbin Liu, Peng Dai, Qiongxing Chinese Acad Sci Inst Acoust Haikou Lab Haikou Peoples R China

ISBN: (纸本)9781479944194

Face gender recognition is a challenging problem in the traditional field of pattern recognition. In this paper, we propose a deep learning model that can learn the joint high-level and low-level features of human face to address this problem. Our deep neural networks apply convolution and subsampling in extracting the local and abstract features of human face, and reconstruct the raw input images to learn global and effective features as supplementary information at the same time. We also add a trainable weight in the networks when combining the two kinds of features to realize the final gender classification. Experiment results show that our method achieves the highest accuracy compared with existing methods, when test on the mixed face dataset. Further, in the generalization test, the average classification rate on 3 public datasets of our method is 5% higher than the joint Local Binary pattern (LBP) and Support Vector Machine (SVM) method, and is nearly 1% higher than the SVM with face pixels method. this proves our method outperforms the traditional methods in both learning ability and generalization ability.

关键词： face gender recognition deep learning multi-feature learning generalization ability

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Path recognition for agricultural robot vision navigation under weed environment 7

Path recognition for agricultural robot vision navigation un...

引用

7th IFIP WG 5.14 International conference on computer and Computing Technologies in Agriculture, CCTA 2013

作者： Wang, Peidong Meng, Zhijun Luo, Changhai Mei, Hebo Beijing Research Center for Intelligent Agricultural Equipment Beijing100097 China

ISBN: (纸本)9783642543432

In this paper, a path recognition method for agricultural robot vision navigation under weed environment is proposed. the vision navigation is based on color images sampled by a high speed camera. First, the crop and weed information is extracted using an appropriate color feature model to separate the green crop from background;then the image is thresholded, and the noise caused by weed is filtered by deleting small-area objects in the image;the navigation path is extracted through Hough transformation. Experiments are carried out in corn seedling field, and results show that the method can recognize navigation path correctly under weed environment. © IFIP International Federation for Information Processing 2014.

关键词： Crops

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A Fast Straight-Line Growing Algorithm for Sheet-Counting with Stacked-Paper Images 6

A Fast Straight-Line Growing Algorithm for Sheet-Counting wi...

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6th chinese conference on pattern recognition (CCPR)

作者： Gang, ZhenXiao Shuo, Yang Xiao, Changyan Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9783662456460;9783662456453

the measurement of stacked-sheet quantity is an essential step in packaging and printing production, and its counting accuracy has a direct impact on economic efficiency of related companies. With its noncontact, non-destructivity and real-time measurement merits, the machine vision method has been widely applied to quality control for high-end printing products. In this paper, we aim to circumvent the fringe detection problem in stacked-sheet images by introducing a level line guided line-segment growing algorithm. then, a high-accuracy measurement of stack quantity can be realized with the improvement of precision and completeness on fringe identification. Our work mainly consists of three parts: 1) A unidirectional gradient operator is adopted to eliminate multiple responses on a single fringe. 2) the gradient magnitude and level-line direction are combined to improve the growth of line support regions in noisy environment. 3) To completely identify each sheet fringe, a connected component analysis algorithm is integrated to remedy the local gap in line detection. the performance of our algorithm has been verified in experiments using various kinds of printed-papers with a large number. It is shown that the long-term measurement error is less than 0.75% and is sufficient to meet the requirement of factory applications.

关键词： Machine vision Stacked sheet counting Line segment detection LSD

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