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Sub-manifold distance based object recognition in clutter

Sub-manifold distance based object recognition in clutter

引用

International Conference on Natural Computation

作者： Zhou, Hua Cai, Chao Ding, Mingyue National Lab for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Imaging Processing and Intelligence Control Key Laboratory of Education Ministry School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424459612

Object recognition is challenging problem in computer vision due to appearance variation and presence of visual clutter and occlusions. Recently manifolds are thought to be fundamental for visual perception, and manifold learning algorithms are developed for discovering intrinsical features. Selective visual attention mechanism provides tools to reduce computation cost and avoid the influence of clutter background. In this paper, we described a new object recognition method, the submanifold distance (SMD) algorithm, which is induced by the visual attention mechanism to provide complex object recognition. Experiments with airport remote sensing images illustrated that our proposed algorithm can recognize complex objects accurately, robustly and quickly. ©2010 IEEE.

关键词： Object recognition

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Mining top-K Frequent and Flexible pattern from sequences

Mining top-K Frequent and Flexible Pattern from sequences

引用

2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Junyan, Zhang Fan, Min Information Science and Technology College Chengdu University China University Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781479916603

pattern Mining is a popular issue in biological sequence analysis. With the introduction of wildcard gaps, more interesting patterns can be mined. In this paper, we propose a new definition related to pattern frequency, under which the Apriori property holds. We define a pattern mining problem called Ming top-K Frequent patterns (MFP), where gaps are mined instead of specified. Compared with existing problems, MFP does not require any domain knowledge of the user. However, theoretical analysis and experimental results show that MFP favors inflexible patterns. We then define another problem where the flexibility threshold of each gap is specified by the user. The problem is called Mining top-K Frequent and Flexible patterns (MF2P). We develop algorithm with polynomial complexities for both problems. patterns can grow from both sides. Some interesting biological patterns mined by our algorithms are discussed. © 2013 IEEE.

关键词： Bioinformatics

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An Efficient Face recognition System Using a New Optimized Localization Method

An Efficient Face Recognition System Using a New Optimized L...

引用

International Conference on pattern recognition

作者： H.R. Kanan K. Faez M. Ezoji Image Processing and Pattern Recognition Laboratory Electrical Engineering Department Amirkabir University of Technology슠 Tehran Iran

In this paper a system is developed for face recognition processes. Preprocessing and face localization is necessary to obtain a high classification rate in face recognition tasks. In this study after preprocessing of face images, for omitting the redundant information such as background and hair, the oval shape of face is approximated by an ellipse using shape information. Then the parameters (orientation and center coordinates) of this ellipse are optimized using genetic algorithm (GA). High order pseudo Zernike moment invariant (PZMI) which has useful properties is utilized to produce feature vectors. Also radial basis function neural network (RBFNN) with HLA learning rule has been used as a classifier. Simulation results on ORL database indicate that the error rate of proposed system which uses genetic algorithm for optimizing the face localization step is lower than an older system which described in (H. Haddadnia et al., 2003)

关键词： Face recognition Optimization methods Face detection Shape Genetic algorithms pattern recognition Image processing Neural networks Feature extraction Hair

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An image-sequence compressing algorithm based on homography transformation for unmanned aerial vehicle

An image-sequence compressing algorithm based on homography ...

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International Symposium on Intelligence Information processing and Trusted Computing

作者： Gong, Junbin Zheng, Chenlin Tian, Jinwen Wu, Dingxue Institute for Pattern Recognition and Artificial Intelligence National Key Laboratory of Science and Technology on Multi-spectral Information Processing Huazhong University of Science and Technology Wuhan 430074 China College of Computer Science and Technology Huanggang Normal University Huanggang 438000 China

ISBN: (纸本)9780769541969

Focus on the image compressing problem of unmanned aerial vehicle with high compression ratio, fixed compressing ratio and low computational complexity requirement, a low-complexity image-sequence compressing algorithm based on homography transformation was proposed. The image sequences were dynamically divided into framegroups according the data from airborne inertial navigation systems, and the intermediate frames in the same frame-group was bi-directionally predicted by the first-frame and the end-frame with homography transformation. The homography matrix was got approximately by the airborne inertial navigation systems firstly and then was accurately computed by fast multiple sub-areas template matching. At the end the first frame and the residual images of the intermediate frames of the same frame-group was merged into a big image and coded by JPEG2000 to generate fixed-size code streams. The experiment results show that the proposed algorithm was with high compression performance, low computational complexity and excellent capacity for code-size control and will has good prospect in engineer. © 2010 IEEE.

关键词： Image compression

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Density-Aware Part-Based Object Detection with Positive Examples

Density-Aware Part-Based Object Detection with Positive Exam...

引用

International Conference on pattern recognition

作者： Ekaterina Riabchenko Joni-Kristian Kämäräinen Ke Chen Machine Vision and Pattern Recognition Laboratory Lappeenranta University of Technology Deparment of Signal Processing Tampere University of Technology

ISBN: (纸本)9781479952106

Part-based models have become the mainstream approach for visual object classification and detection. The key tools adopted by the most methods are interest point detectors and descriptors, shared codes for object parts (visual codebook) and discriminative learning using positive and negative class examples. Distinction of our method from the existing part-based methods for object detection is the use of sparse class-specific landmarks with semantic meaning. The landmarks are the additional distinguished information of object location in the proposed framework. Additionally, localising semantic and discriminative landmarks (object parts) is significant in other related applications of computer vision, such as facial expression recognition and pose/orientation estimation of objects. Therefore, we propose a model which deviates from the mainstream by the fact that the object parts' appearance and spatial variation, constellation, are explicitly modelled in a generative probabilistic manner. With using only positive examples our method can achieve object detection accuracy comparable to state-of-the-art discriminative method.

关键词： Training Probabilistic logic Estimation Face Feature extraction Object detection Detectors

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Estimating the connection probability for 2-D mesh networks by Monte Carlo methods

Estimating the connection probability for 2-D mesh networks ...

引用

International Conference on Computer and Management

作者： Liang, Meilian Liang, Binmei Shao, Zehui Xu, Xiaodong School of Mathematics and Information Science Guangxi University Nanning 530004 China Key Laboratory of Pattern Recognition and Intelligent Information Processing School of Information Science and Technology Chengdu University Chengdu 610106 China Guangxi Academy of Sciences Nanning 530007 China

ISBN: (纸本)9781424492831

Fault tolerance is a central issue in the design and implementation of interconnection networks for large parallel systems. Connection probability of a network is a good network fault tolerance measure. For a mesh of given size and node failure probability, the gap between the known upper and lower bounds on the connection probability is often very large. In this paper we design algorithms to estimate the connection probability for 2-D meshes by Monte Carlo methods. The experiment is carefully designed and performed, and the simulation results give good estimates of the connection probability for 2-D meshes and can be used to evaluate the known lower and upper bounds on connection probability for 2-D meshes. ©2011 IEEE.

关键词： Monte Carlo methods

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Semi-supervised learning in nonstationary environments

Semi-supervised learning in nonstationary environments

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Gregory Ditzler Robi Polikar Signal Processing and Pattern Recognition Laboratory in the Electrical & Computer Engineering Department Rowan University Glassboro NJ USA

Learning in nonstationary environments, also called learning concept drift, has been receiving increasing attention due to increasingly large number of applications that generate data with drifting distributions. These applications are usually associated with streaming data, either online or in batches, and concept drift algorithms are trained to detect and track the drifting concepts. While concept drift itself is a significantly more complex problem than the traditional machine learning paradigm of data coming from a fixed distribution, the problem is further complicated when obtaining labeled data is expensive, and training must rely, in part, on unlabelled data. Independently from concept drift research, semi-supervised approaches have been developed for learning from (limited) labeled and (abundant) unlabeled data; however, such approaches have been largely absent in concept drift literature. In this contribution, we describe an ensemble of classifiers based approach that takes advantage of both labeled and unlabeled data in addressing concept drift: available labeled data are used to generate classifiers, whose voting weights are determined based on the distances between Gaussian mixture model components trained on both labeled and unlabeled data in a drifting environment.

关键词： Classification algorithms Data models Clustering algorithms Training data Training Testing Algorithm design and analysis

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A fast VLSI architecture for two-dimensional discrete wavelet transform based on lifting scheme

A fast VLSI architecture for two-dimensional discrete wavele...

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2004 7th International Conference on Solid-State and Integrated Circuits Technology Proceedings, ICSICT 2004

作者： Xiong, Chengyi Tian, Jinwen Liu, Jian Institute of Pattern Recognition and Artificial Intelligence Key Laboratory of Eduaction Ministry for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China College of Electronic Information Engineering South-Center University for Nationalities Wuhan 430074 China

This paper proposes a novel fast architecture for two-dimensional discrete wavelet transform by using lifting scheme. The parallel and embedded decimation techniques are employed to optimize the architecture, which is mainly composed of two horizontal filter modules and one vertical filter module, working in parallel and pipeline fashion with 100% hardware utilization. The architecture is designed to generate two outputs at one working clock cycle, with every two subbands coefficients alternately. The total time of computing J levels of decomposition for N×N image is approximately 2N2(I-4 J)/3 clock cycles. In comparison with the other devices reported in previous literature, the design has many advantages including lower hardware complexity and area and power efficient. The design is also fast, regular and simple, as well as well suited for VLSI implementation. © 2004 IEEE.

关键词： VLSI circuits

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Design and implementation of knowledge base for target recognition in remote sensing images

Design and implementation of knowledge base for target recog...

引用

2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010

作者： Chen, Shaobin Cai, Chao Li, Erlang Ding, Mingyue Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology State Key Laboratory for Multi-spectral Information Processing Technologies Wuhan 430074 China School of Life Science and Technology of Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781424459346

In this paper, the knowledge modeling, architecture design and detailed implementation of an ontology-based knowledge base for target recognition in remote sensing images is presented. Knowledge base is a critical component of a large ground target recognition system which is a hybrid system that combines knowledge base with remote sensing image processing module. The knowledge base provides necessary knowledge for remote sensing image processing module, and remote sensing image processing module can perform multiple tasks with the support of the knowledge base. The existence of the knowledge base makes the whole recognition system more flexible and more generic than those systems without knowledge base. The effectiveness of the presented knowledge base shows the good prospect of the application of knowledge base in remote sensing image processing domain. ©2010 IEEE.

关键词： Knowledge based systems

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A robust cell tracking framework by fusing global and local optimization algorithms

A robust cell tracking framework by fusing global and local ...

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第28届中国控制与决策会议

作者： Shu Meng Hong-Bin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

ISBN: (纸本)9781467397155

Automated cell tracking is an important branch of multi-object tracking,which can be used for quantitatively analyzing cell migration,proliferation and *** this paper,we proposed a hierarchical tracking method,fusing the global optimal method in consecutive frames assignment and local optimal approach in spatial trajectory *** the process,the detection errors were recognized and cell moving trajectories were completed *** also introduced the concept of clustering to measure the correlation between established short trajectories and reduce the tracking errors caused by fast *** rare information of cells was used in the linkage,the system can work well with *** experimental results show the effectiveness of our approach with cells having different density and activity.

关键词： Multi-object tracking Bipartite graph Optimization Hierarchical tracking

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