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检索条件"机构=the Image Processing and Pattern Recognition Laboratory"

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Patch-based Object Tracking via Locality-constrained Linear Coding

Patch-based Object Tracking via Locality-constrained Linear ...

引用

第35届中国控制会议

作者： Kunqi Gu Mingna Liu Tao Zhou Fanghui Liu Xiangjian He Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai Institute of Spaceflight Control Technology Infrared Detection Technology Research and Development Center China Aerospace Science and Technology Corporation School of Computing and Communications University of Technology The Key Laboratory of Ministry of Education for System Control and Information Processing

ISBN: (纸本)9781509009107

In this paper,the Locality-constrained Linear Coding(LLC) algorithm is incorporated into the object tracking ***,we extract local patches within a candidate and then utilize the LLC algorithm to encode these *** on these codes,we exploit pyramid max pooling strategy to generate a richer feature *** feature histogram which integrates holistic and part-based features can be more discriminative and ***,an occlusion handling strategy is utilized to make our tracker more ***,an efficient graph-based manifold ranking algorithm is exploited to capture the relevance between target templates and *** tracking,target templates are taken as labeled nodes while target candidates are taken as unlabeled nodes,and the goal of tracking is to search for the candidate that is the most relevant to existing labeled nodes by manifold ranking *** on challenging video sequences have demonstrated the superior accuracy and robustness of the proposed method in comparison to other state-of-the-art baselines.

关键词： object tracking LLC pyramid max-pooling local appearance model manifold ranking

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An improved algorithm of search for compressive sensing image recovery based on lp norm

An improved algorithm of search for compressive sensing imag...

引用

Chinese Automation Congress, CAC 2015

作者： Yuan, Jiang Pei, Shen Ping, Zhao Yang, Daiji Zhen, Chen Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition Nanchang330063 China College of Information Engineering Nanchang Hangkong University 330063 China

Compressed sensing theory by developing a signal sparse features, under the condition of far less than the Nyquist sampling rate, the correct signal is acquired with random sampling the discrete samples, and then thro... 详细信息

ISBN: (纸本)9781467371896

关键词： image reconstruction

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Small target detection using an optimization-based filter 40

Small target detection using an optimization-based filter

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40th IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2015

作者： Xie, Kai Fu, Keren Zhou, Tao Yang, Jie Wu, Qiang He, Xiangjian Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China School of Computing and Communications University of Technology Sydney Australia

ISBN: (纸本)9781467369978

Small target detection is a critical problem in the Infrared Search And Track (IRST) system. Although it has been studied for years, there are some challenges remained, e.g. cloud edges and horizontal lines are likely to cause false alarms. This paper proposes a novel method using an optimization-based filter to detect infrared small target in heavy clutter. First, we design a certain pixel area as active area. Second, a weighted quadratic cost function is performed in the active area. Finally, a filter based on statistics of active area is derived from the cost function. Our method could preserve heterogeneous area, meanwhile, remove target region. Experimental results show our method achieves satisfied performance in heavy clutter. © 2015 IEEE.

关键词： active area area statistics heavy clutter optimization-based filter small target detection

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Impulse noise removal in two-dimensional electrophoresis images based on dome recognition 8

Impulse noise removal in two-dimensional electrophoresis ima...

引用

8th International Conference on BioMedical Engineering and Informatics, BMEI 2015

作者： Ou, Qiaofeng Zhang, Huisheng Li, Lixin Xiong, Bangshu School of Electronic Information Northwestern Polytechnical University Xi'an710072 Hong Kong Key Laboratory of Image Processing and Pattern Recognition of Jiangxi Province Nanchang Hangkong University Nanchang330063 China

ISBN: (纸本)9781509000227

Two-dimensional gel electrophoresis (2DE) images are often corrupted by impulse noise in broad sense (including various artifacts, such as fingerprints, hairs, gel cracks, strips, water stains, dust and so on). In this paper, we propose a novel adaptive directional filter (ADF) to remove impulse noise completely and accurately. Firstly, we detect the impulse noise based on the observation that the directional derivative distributions around the protein spot centers and noise pixels are very different. Then, according to the bell-shaped three-dimensional appearance of protein spots, the greatest and smallest gradient directions were used to restore the noised pixels. Lastly, the pixels adjacent to the modified pixels are iteratively detected and restored until no impulse noise is detected. Experimental results on the synthetic and real 2DE images show that the proposed method performs much better than the previous approaches. © 2015 IEEE.

关键词： Pixels

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Comparison of linear dimensionality reduction methods in image annotation

Comparison of linear dimensionality reduction methods in ima...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Shiqiang Li Hussain Dawood Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Dimension reduction methods are often used to analyzing high dimensional data, linear dimension methods are commonly used due to their simple geometric interpretations and for effective computational cost. Dimension reduction plays an important role for feature selection. In this paper, we have given a detailed comparison of state-of-the-art linear dimension reduction methods like principal component analysis (PCA), random projections (RP), and locality preserving projections (LPP). We have determined which dimension reduction method performs better under the FastTag image annotation framework. Experiments are conducted on three standard bench mark image datasets such as CorelSk, IAPRTC-12 and ESP game to compare the efficiency, effectiveness and also memory usage. A detailed comparison among the aforementioned dimension reduction method is given.

关键词： Principal component analysis Benchmark testing Kernel Random access memory Customer relationship management

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Removal of random-valued impulse noise by Khalimsky grid

Removal of random-valued impulse noise by Khalimsky grid

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Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

作者： Hussain Dawood Hassan Dawood Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

In this paper, based on Khalimsky grid, a new Random-valued Impulse noise identification and removal method is proposed. Khalimsky grid can presents the neighborhood relationship among the pixels in the sliding window, effectively. The local statistics of Khalimsky grid is used to define an adaptive threshold range to identify the central pixel in current sliding window as noisy or noise free in an iterative way. The identified noisy pixel is replaced by local statistics of propose vertical direction based noise removal method. The performance of the propose method is evaluated on different test images and compared with state-of-the-art methods. Experimental results show that the propose method can identify the impulse noise, as well as can preserve the detailed information of an image, efficiently.

关键词： Noise Noise measurement Tin Multimedia communication Filtering algorithms Asia Digital multimedia broadcasting

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A Hybrid image Feature Descriptor for Classification

A Hybrid Image Feature Descriptor for Classification

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International Conference on Computational Intelligence and Security

作者： Hassan Dawood Hussian Dawood Ping Guo Image Processing and pattern Recognition Laboratory Beijing Normal University Beijing China Department of Computer Engineering University of Engineering and Technology Taxila Pakistan Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Feature extraction methods have an important role in image classification. In this paper, a hybrid texture feature descriptor is proposed by utilizing the attributes of two complementary features, PRICoLBP and LPQ. PRICoLBP performs well in the case of geometric and photometric variations however it does not properly express the local texture of an image, while LPQ method performs well for the local structure of an image. We propose to use the hybrid scheme by combining the properties of PRICoLBP and LPQ and name it as Pair wise Rotation Invariant Co-occurrence Local Phase Quantization (PRICLPQ). Standard texture and material datasets have been used to verify the robustness of proposed hybrid scheme. The experiments show that the proposed hybrid scheme outperforms the state-of-the-art feature extraction methods like LBP, LPQ, CLBP, LBPV, SIFT, MSLBP, Lazebnik and PRICoLBP in term of accuracy.

关键词： Feature extraction Histograms Robustness Training Quantization (signal) Support vector machines Yttrium

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A comparative study of different feature mapping methods for image annotation

A comparative study of different feature mapping methods for...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yiren Wang Hassan Dawood Qian Yin Ping Guo the Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China the Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Computer Science Department Beijing Normal University Beijing China

Automatic image annotation and tagging is necessary for indexing and searching of images using querying a text. It is widely used in search engines like Google, Yahoo, Baidu, etc. Fast image Tagging (FastTag) algorithm is proposed to accelerate image annotation process, while keeping the precision of automatic image annotation results. Feature mapping is used to map image features vectors onto higher dimensional feature space. Feature mapping methods plays an important role in automatic image annotation. In this paper, we have compared 6 kernels, among which four kernels are used in homogeneous feature mapping and two kernels are used in discriminative tree based feature mapping, to investigate which feature mapping performs better for automatic image annotation. The performance of these methods has been analyzed by conducting intensive experiments on three different datasets as used by FastTag algorithm in their experiments. We have found that the homogeneous feature mapping with χ 2 kernel is more suitable when used in FastTag algorithm in terms of precision, recall, FI score and N+ measures, and with a relatively acceptable performance.

关键词： Phase change materials Search engines Kernel Indexing Histograms

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Blind source separation with evolution based KICA

Blind source separation with evolution based KICA

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Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

作者： Peng Wu Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781479979684

Kernel independent component analysis (KICA) has been widely used in the field of blind source separation. The selection of kernel function and its parameters plays an important role in KICA algorithm performance. An optimal kernel model should be rich enough to well map the given samples. However, users usually use a singular kernel based model in their experiments, which leads to a suboptimal kernel model. In order to solve this problem, we propose the evolution based multiple kernel independent component analysis (EMKICA), in which a convex combination of multiple base kernels is used instead of single kernel of KICA. The combination weights are learned by particle swarm optimization algorithm. Firstly, we elaborate the basic theory of KICA and concept of EMKICA, also the combination form of the composition kernel used in EMKICA. Secondly, we describe the presentation of the individuals in the particle swarm optimization algorithm, the settings of the evaluation function and general algorithm. Finally, we evaluate the separation ability of EMKICA on three different data sets including one-dimensional mixed signals, composite images and images with reflection. The experimental results verify the effectiveness of EMKICA.

关键词： Kernel Independent component analysis Algorithm design and analysis Multimedia communication Correlation Optimization Asia

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A New Method for recognition Partially Occluded Curved Objects under Affine Transformation

A New Method for Recognition Partially Occluded Curved Objec...

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International Conference on Intelligent System and Knowledge Engineering, ISKE

作者： GuiMei Zhang JiYuan Xu JianXin Liu Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Jiangxi Province China School of Mechanical Engineering and Automation Xihua University Chengdu Sichuan China

A new method dealing with recognition of partially occluded and affine distortion objects is presented. The method is designed for objects with smooth curved boundary. It divides an object into affine-invariant parts based on the feature point. And a new approach for matching each part is presented in this paper. Robust Hausdorff distance (RHD) is introduced to measure the similarity between feature points set of model and that of target. In terms of the new RHD, the optimal affine transform can be estimated. And then the sub-curve match pairs are calculated based on the optimal affine transformation. The experimental results show proposed algorithm are capable of coping with partial occlusion and affine transformation.

关键词： Transforms Shape Robustness pattern recognition image recognition Mathematical model Distortion

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