检索结果-内蒙古大学图书馆

SEGMENTATION OF RANGE image BASED ON KOHONEN NEURAL NETWORK

Journal of Electronics(China) 2001年第3期18卷 237-241页

作者： Zou Ning Liu Jian Zhou Manli Li Qing(state education commission Res. lab. for image processing & intelligent control. Electronic & Information Engineering Dept., Huazhong University of Science & Technology. Wuhan 430074) State Education Commission Res. Lab. for Image Processing & Intelligent Control Electronic & Information Engineering Dept. Huazhong University of Science & Technology Wuhan

This paper presents an unsupervised range image segmentation based on Kohonen neural network. At first, the derivative and partial derivative of each point are calculated and the normal in each points is gotten. With the character vectors including normal and range value, self-organization map is introduced to cluster. The normal analysis is used to eliminate over-segmentation and the last result is gotten. This method avoid selecting original seeds and uses fewer samples, moreover computes rapidly. The experiment shows the better performance.

关键词： Range image Segmentation Kohonen neural network Merge

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A SUPPORT VALUE TRANSFORM-BASED MULTI FOCUS image FUSION METHOD

A SUPPORT VALUE TRANSFORM-BASED MULTI FOCUS IMAGE FUSION MET...

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2006 8th International Conference on Signal processing

作者： Sheng ZHENG Jian Liu Guangxi Zhu Jinwen Tian College of Electrical Engineering & Information Technology China Three Gorges University Electronic & Information Engineering Department Huazhong University of Science and Technology(HUST) State Edu Commission Key Lab for Image Processing & Intelligent Control HUST Chinese Key lab of optic & electronics HUST State Edu Commission Key Lab for Image Processing & Intelligent Control

＜正＞The image fusion is an important approach to produce a single complete image which preserves all relevant information from different *** this paper,we proposed a support value transform-based multi focus image fusion method,where the fused saliency features are represented by support values. Based on the mapped least squares support vector machine,the support value transform is developed as a multi-scale analysis *** fusing results on the multi focus images demonstrate that the proposed image fusion method is effective and efficient.

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A no-reference ringing metrics for images deconvolution

A no-reference ringing metrics for images deconvolution

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International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)

作者： Bo-Xin Zuo Jin-Wen Tian De-Lie Ming State Key Laboratory of Education Commission for Image Processing and Intelligent Control Institute For Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

In this paper, we presented a ringing metric to evaluate the quality of images restored using iterative image restoration algorithms. A ringing metrics is used to assessment the restored images based on the Gabor filter. The experimental results validate the proposed method perform well over a wide range of restoration image ringing levels assessment. And the proposed model has given good agreement with observer ratings obtained in subjective experiments.

关键词： image restoration Gabor filters image edge detection Oscillators Deconvolution Maximum likelihood detection Band pass filters

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Multifeature distance map based detection of small infrared targets with low contrast in image sequences

Multifeature distance map based detection of small infrared ...

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International Conference on Space Information Technology

作者： Wang, Zhicheng Gao, Chenqiang Tian, Jinwen Liu, Jian Chen, Xiangping State Education Commission Key Lab for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei 430074 China Inst. for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan Hubei 430074 China Electronic and Engineering Department Huazhong University of Science and Technology Wuhan Hubei 430074 China

Target detection techniques play an important role in automatic target recognition (ATR) systems because overall ATR performance depends closely on detection results. In this paper, a novel method for fusion detection of infrared weak targets based on multifeature distance map (MFDM) in image sequences is proposed. As for small weak targets, there are many features, such as local entropy, average gradient strength. These features depict the characteristics of small infrared targets and can be extracted. Multifeature-based fusion techniques are applied to detect such weak targets. The problem of detecting small targets is converted to search peak values in specified feature space where multifeature vectors space (MFVS) is considered. Distance map (DM) can be derived according to feature vectors and target detection is performed in DM. In order to accumulate energy of targets deeply and suppress background and clutters to a great extent, five distance maps obtained by corresponding five consecutive frames are utilized to fuse with average weight, which results in the fact that the contrast between targets and background including clutters are enlarged and that the feature peaks of targets are obvious different from background and clutters. After these steps, a contrast segmentation method is used to extract targets from complicated background on the fused DM. Actual infrared image sequences in background of sea and sky are applied to validate the proposed approach. Experimental results demonstrate the robustness of the proposed method with high performance.

关键词： Automatic target recognition

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China

ISBN: (纸本)9781728162157

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools. © 2020 IEEE.

关键词： image reconstruction

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An SVM-based small target segmentation and clustering approach

An SVM-based small target segmentation and clustering approa...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Sheng Zheng Jian Liu Jin-Wen Tian State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780384032

Segmentation and clustering of infrared small target images in a sky or sea-sky background is considered in this paper, which is the preprocessing part of the detection and recognition of the moving small targets in an infrared image sequence. The infrared image intensity surface is well fitted by the least squares support vector machines (LS-SVM), and then the maximum extremum points are detected on the well fitted intensity surface by convolving the image with the second order directional derivative operators deduced from the mapped LS-SVM with mixtures of kernels. With the coarse locations, the possible targets are extracted by the clustering analysis. The computer experiments are carried out for the real and simulated sky and sea-sky infrared images. The experimental results demonstrate the proposed approach is effective.

关键词： Infrared detectors Infrared imaging image segmentation Sea surface image recognition Target recognition image sequences Least squares methods Support vector machines Kernel

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One efficient facet-based small target detection technique

One efficient facet-based small target detection technique

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International Conference on Signal processing Proceedings (ICSP)

作者： Sheng Zheng ChengYi Xiong JinWen Tian Jian Liu State Education Commission Key Laboratory of Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

This paper presents a method for the detection of small objects from the infrared images. The detection is performed on the intensity surface well fitted by the cubic facet model. The small target energy distribution presents as a convex surface on the image intensity surface and the target center is the maximal extremum points of the convex surface. According to the extremum theory, the possible small target position is analytically determined by directly convolving the original image with the derivative operators deduced from the bivariate cubic function. With the available coarse target locations, the potential target is separated from the background by examining the intensity features of the target cluster. Experimental results on the sample infrared images demonstrate the proposed algorithm provides a robust and efficient performance.

关键词： Face detection Object detection Target tracking Infrared detectors Surface topography Infrared imaging Maintenance intelligent control Pattern recognition Artificial intelligence

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The Application of A Novel Target Region Extraction Model Based on Object-Accumulated Visual Attention Mechanism

The Application of A Novel Target Region Extraction Model Ba...

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International Conference on Natural Computation (ICNC)

作者： Jie Xiao Chao Cai Mingyue Ding Chengping Zhou Institute of Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Combining bottom-up and top-down attention influences, a novel region extraction model which based on object-accumulated visual attention mechanism is proposed in this paper. Compared with early research, the new approach brings in prior information at the proper time, updates scan path dynamically, needs less computational resources and reduces the probability to direct the attention to a less-meaning area. The application to search an airport target in remote sensing image was provided, through which the novel mechanism that how visual attention chose the area was described. Compared with another two region extraction models, experimental results confirm the effectiveness of the approach proposed in this paper.

关键词： Data mining Object detection Computer applications Chaos Pattern recognition Artificial intelligence laboratories image processing intelligent control Airports

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Cooperative path planning for multiple air vehicles using a co-evolutionary algorithm

Cooperative path planning for multiple air vehicles using a ...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chang-Wen Zheng Ming-Yue Ding Cheng-Ping Zhou State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattem Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China

The coordinated path-planning problem for multiple unmanned air vehicles is studied with the proposal of a co-evolving and cooperating path planner. In the new planner, potential paths of each vehicle form their own subpopulation, and evolve only in their own sub-population, while the interaction among all sub-problems is reflected by the definition of fitness function. Meanwhile, the individual candidates are evaluated with respect to the workspace so that the computation of the configuration space is avoided. By using a problem-specific representation of candidate solutions and genetic operators, our algorithm can take into account different kinds of mission constraints and generate the solutions in real-time.

关键词： Path planning Unmanned aerial vehicles Level measurement Terrain mapping Genetics Evolutionary computation laboratories image processing intelligent control image recognition

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Reflection invariant local binary patterns for image texture classification

Reflection invariant local binary patterns for image texture...

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Research in Adaptive and Convergent Systems, RACS 2015

作者： Song, Enmin Pan, Ning Hung, Chih-Cheng Li, Xiang Jin, Lianghai School of Computer Science and Technology Huazhong University of Science and Technology Key Laboratory of Education Ministry for Image Processing and Intelligent Control Wuhan430074 China College of Biomedical Engineering South-Central University for Nationalities Wuhan430074 China Sino-US Intelligent Information Processing Lab Anyang Normal University Kennesaw State University MariettaGA30060 United States

ISBN: (纸本)9781450337380

In this study we modified the local binary pattern operator (LBP) to obtain the robust invariant texture patterns for image texture classification. The modified method will be able to calculate patterns which are invariant for translation, scaling, rotation and reflection. Therefore, the modified LBP is called R-LBP. Although many variation of LBP have been proposed, most of them cannot detect and recognize the patterns of reflection. Both clockwise and counter clockwise coding is used in the proposed R-LBP operator in order to derive a minimum code to representing the pattern. Experimental results show that the proposed method is effective in determining the invariant patterns for image texture classification. © 2015 ACM.

关键词： Local binary pattern

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