检索结果-内蒙古大学图书馆

Novel method for point target detection in sea-clutter SAR image with fractal and ICA

Yuhang Xuebao/Journal of Astronautics 2007年第6期28卷 1709-1714+1723页

作者： Li, Yu-Tong Zhou, Yue Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China

This paper presents a novel approach for point target detection of sea-clutter SAR images. Traditional methods for this application can be classified into two aspects: threshold segmentation based on intensity difference and target extraction based on suitable denoising. However, they appear to be not effective enough especially when sea clutter is strong. Taking advantage of the essentials of both methods, a effective approach using space separation is developed based on fractal theory and independent component analysis. First, pointwise Holder exponent are computed and binary-fuzzy processing is used for enhancement;then, basis images and independent components of the processed image are respectively obtained by ICA technique. After that, according to separation criterion, the original space is separated into two subspaces called clean-space and noise-space with respective independent components and corresponding basis images. Finally, the recovery image is obtained after enhancing the independent components in clean-space. As the results show, the proposed method is validated and point target is extracted more efficiently compared with conventional ones.

关键词： Synthetic aperture radar

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Face recognition with Convolutional Neural Networks and subspace learning 2

Face Recognition with Convolutional Neural Networks and subs...

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2nd International Conference on image, Vision and Computing, ICIVC 2017

作者： Wan, Lihong Liu, Na Huo, Hong Tao, Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781509062379

Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture. Then, two types of subspace learning methods, namely, linear discriminate analysis (LDA) and whitening principal component analysis (WPCA), are respectively introduced to learn the subspace of the activation vectors for face recognition under multiple samples per subject and single sample per subject circumstances. The goals of applying subspace learning to the activation vectors are obtaining compact representation (dimensionality reduction) and performance improvement. Experiments on two face databases (CMU PIE and FERET) demonstrate the effectiveness of VGG-Face + LDA and VGG-Face + WPCA, compared with state-of-the-art methods. © 2017 IEEE.

关键词： Principal component analysis

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A Smart Mathematic Morphology Method for recognition and Understanding of Airfield

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Wuhan university Journal of Natural Sciences 2005年第5期10卷 867-872页

作者： WANG Zhi-cheng TIAN Yan TIAN Jin-wen LIU Jian LIANG Chao-sheng TAO Wen-bin State Education Commission Key Laboratory for Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan430074 Hubei China Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 Hubei China

A method used for recognition and understanding of airfield based on mathematical morphology is proposed in this paper. The new approach can he divided into three steps. First, to extract the typical geometric structure features of airfield, a segmentation method called recursive Otsu algorithm is employed on an airfield image. Second, thinning and shrinking algorithms are utilized to obtain the contour of airfield with single pixel and to remove diffused small particles. Finally, Radon transform is adopted to extract two typical and important components, primary and secondary runways of airfield exactly. At the same time, region growing algorithm is exploited to get the other components such as parking apron and garages. The experimental results demonstrate that the proposed method gives good performance.

关键词： mathematical morphology shrinking algorithm Radon transform Region growing

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Epitaxial growth of 2D gallium selenide flakes for strong nonlinear optical response and visible-light photodetection

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Frontiers of physics 2023年第5期18卷 237-244页

作者： Mengting Song Nan An Yuke Zou Yue Zhang Wenjuan Huang Huayi Hou Xiangbai Chen Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of TechnologyWuhan 430205China State Key Laboratory of Materials Processing and Die&Mould Technology School of Materials Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

As an emerging groupⅢ–Ⅵsemiconductor two-dimensional(2D)material,gallium selenide(GaSe)has attracted much attention due to its excellent optical and electrical *** this work,high-quality epitaxial growth of few-layer GaSe nanoflakes with different thickness is achieved via chemical vapor deposition(CVD)*** to the non-centrosymmetric structure,the grown GaSe nanoflakes exhibits excellent second harmonic generation(SHG).In addition,the constructed GaSe nanoflake-based photodetector exhibits stable and fast response under visible light excitation,with a rise time of 6 ms and decay time of 10 *** achievements clearly demonstrate the possibility of using GaSe nanoflake in the applications of nonlinear optics and(opto)-electronics.

关键词： 2D materials gallium selenide second harmonic generation chemical vapor deposition photodetector

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Weighted MLS-SVM for approximation of directional derivatives

Weighted MLS-SVM for approximation of directional derivative...

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MIPPR 2005: image Analysis Techniques

作者： Zheng, Sheng Tian, JinWen Liu, Jian China Three Gorges University Yichang 443002 China Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

Based on statistical learning theory, support vector machine (SVM) is a novel type of learning machine, and it contains polynomial, neural network and radial basis function (RBF) as special cases. The mapped least squares support vector machine (MLS-SVM) is a special least square SVM (LS-SVM), which extends the application of the SVM to the image processing. Based on the MLS-SVM, a family of filters for the approximation of partial derivatives of the digital image surface is designed. Prior information (e.g., local dominant orientation) are incorporated in a two dimension weighted function. The weighted MLS-SVM with the radial basis function kernel is applied to design the proposed filters. Exemplary application of the proposed filters to fingerprint image segmentation is also presented.

关键词： Learning systems

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Sparse slow feature analysis for enhanced control monitoring and fault isolation 1

Sparse slow feature analysis for enhanced control monitoring...

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1st International Conference on Industrial Artificial Intelligence, IAI 2019

作者： Shang, Chao Huang, Xiaolin Yang, Fan Huang, Dexian Tsinghua University Beijing National Research Center for Information Science and Technology Beijing100084 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200400 China

ISBN: (纸本)9781728135939

This paper presents sparse slow feature analysis (SFA) for efficient process monitoring and fault isolation, which is a new latent variable model for time series data. We first recast sparse SFA in terms of a novel regression-Type problem, and further incorporate l-{1}-norm penalty into the objective in order to promote sparsity. To solve the induced nonconvex optimization problem, a tailored iterative algorithm is developed. With the sparse representation, process variables with insignificant contributions can be completely omitted, and each latent variable relates only to a fraction of crucial process variables in comparison with the generic SFA. A new process monitoring and fault isolation approach is developed based on the sparse SFA, which results in improved monitoring performance, easy-To-interpret diagnostics, and meaningful process knowledge discovery. Case studies on the Tennessee Eastman process are carried out to address the applicability of the proposed approach. © 2019 IEEE.

关键词： Process monitoring

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An adaptive multiple model approach for fast content-based skin detection in on-line videos

An adaptive multiple model approach for fast content-based s...

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1st ACM Workshop on Analysis and Retrieval of Events, Actions and Workflows in Video Streams, ACM AREA 2008, Co-located with the 2008 ACM International Conference on Multimedia, MM'08

作者： Khan, Rehanullah Stöttinger, Julian Kampel, Martin Vienna University of Technology Institute of Computer Aided Automation Pattern Recognition and Image Processing Group Favoritenstr. 9/1832 A-1040 Vienna Austria

ISBN: (纸本)9781605583181

We propose a straightforward skin detection method for online videos. To overcome varying illumination circumstances and a variety of skin colors, we introduce a multiple model approach which can be carried out independently per model. The color models are initiated by skin detection based on face detection and adapted in real time. Our approach outperforms static approaches both in precision and runtime. If we detect a face in a scene, the number of false positives can be diminished significantly. Evaluation is carried out on publicly available on-line videos showing that adaptive multiple model outperforms static methods in classification precision and suppression of false positives. Copyright 2008 ACM.

关键词： Face recognition

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ChromTR:chromosome detection in raw metaphase cell images via deformable transformers

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Frontiers of Medicine 2024年第6期18卷 1100-1114页

作者： Chao Xia Jiyue Wang Xin You Yaling Fan Bing Chen Saijuan Chen Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong UniversityShanghai200240China Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational Medicine at ShanghaiRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200025China

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases,of which chromosome detection in raw metaphase cell images is the most critical and challenging *** this work,focusing on the joint optimization of chromosome localization and classification,we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell *** incorporates semantic feature learning and class distribution learning into a unified DETR-based detection ***,we first propose a Semantic Feature Learning Network(SFLN)for semantic feature extraction and chromosome foreground region segmentation with object-wise ***,we construct a Semantic-Aware Transformer(SAT)with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic *** provide a prediction with a precise chromosome number and category distribution,a Category Distribution Reasoning Module(CDRM)is built for foreground-background objects and chromosome class distribution *** evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset *** proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56%in R-band chromosome detection,surpassing the baseline method by 3.02%.In a clinical test,ChromTR is also confident in tackling normal and numerically abnormal *** extended to the chromosome enumeration task,ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image *** these superior performances to other methods,our proposed method has been applied to assist clinical karyotype diagnosis.

关键词： chromosome detection deformable transformer metaphase cell image distribution reasoning

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Profile-based Pottery Reconstruction

Profile-based Pottery Reconstruction

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Conference on Computer Vision and pattern recognition Workshop, CVPRW 2003

作者： Kampel, M. Sablatnig, R. Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Vienna University of Technology Favoritenstrasse 9 183-2 ViennaA-1040 Austria

ISBN: (纸本)0769519008

A major obstacle to the broader use of 3D object reconstruction and modeling is the extent of manual intervention needed. Such interventions are currently extensive and exist throughout every phase of a 3D reconstruction project: collection of images, image management, establishment of sensor position and image orientation, extracting the geometric information describing an object, and merging geometric, texture and semantic data. We present a fully automated approach to pottery reconstruction based on the fragment profile, which is the cross-section of the fragment in the direction of the rotational axis of symmetry. We demonstrate the method and give results on synthetic and real data. © 2003 IEEE.

关键词： Semantics

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A velocity-separation difference model for car-following theory and simulation tests

A velocity-separation difference model for car-following the...

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2006 IEEE International Conference on Networking, Sensing and Control, ICNSC'06

作者： Zhipeng, Li Xiaobo, Gong Yuncai, Liu IEEE Institute of Image Processing and Pattern Recognition Shanghai JiaoTong University Shanghai MN 200240 China Department of Control Science and Technology Shanghai JiaoTong University Shanghai MN 200240 China

ISBN: (纸本)1424400651

This paper attempts to introduce a velocity -separation difference model that modifies the previous models in the literature. The improvement of this new model over the previous ones lies in that it performs more realistically than others in the dynamical evolution of congestion. Furthermore, the proposed model is investigated with analytic and numerical method, with the finding that this model can demonstrate some complex physical features observed in real traffic such as the existence of three phases: free flow, coexisting flow, and jam flow;sudden flow drop;traffic hysteresis in transition between the free and the coexisting flow. © 2006 IEEE.

关键词： Control theory

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