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A novel unsupervised change detection algorithm for satellite images

Guangdianzi Jiguang/Journal of Optoelectronics Laser 2011年第3期22卷 461-464页

作者： Jia, Zhen-Hong Yu, Yin-Feng Yang, Jie Hu, Raphael College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

An unsupervised change detection algorithm for multi-temporal satellite images based on principal component analysis (PCA) and up-down-set fuzzy Kohonen clustering network (UDSFKCN) is proposed in this paper. This method generated eigenvector corresponding to every pixel combining itself with its neighbours using principal component analysis. At the same time, the detection of the changed pixel in a region was converted into the classification between two groups, changed group and unchanged group. Since every pixel was described as one eigenvector, to obtain a changed map of the changed region at pixel level, up-down-set fuzzy Kohonen clustering network was applied to classify all the eigenvectors into changed ones and unchanged ones. Experimental results demonstrate that the proposed method has higher accuracy and stability than traditional algorithms against Gaussian and speckle noise.

关键词： Principal component analysis

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A new Fuzzy C-means algorithm based on gray value compensation and spatial information for aeral image segmengtation

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Guangdianzi Jiguang/Journal of Optoelectronics Laser 2011年第3期22卷 469-473页

作者： Lu, Bin-Bin Jia, Zhen-Hong Yang, Jie Hu, Raphael College of Information Science and Engineering Xinjiang University Urumqi 830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland 1020 New Zealand

The fuzzy C-means (FCM) clustering algorithm has been proven to be effective for image segmentation. However, the standard FCM algorithm is sensitive to noise and gray inhomogeneity. An improved FCM-based algorithm is proposed in this paper, which firstly modeled the noise of an image as a slowly varying additive or multiplicative noise and iteratively approximate the gray inhomogeneity and noise areas by using the spatial neighborhood information. In this process, the threshold values of up and down cut-off were applied to adjust different memberships of pixel. The experimental results on the segmentation demonstrate that the algorithm performs more robust to noise than the standard FCM algorithm and MFCM algorithm.

关键词： image segmentation

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Interactive object extraction using hierarchical graph cuts

Interactive object extraction using hierarchical graph cuts

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2010 International Conference on Audio, Language and image processing, ICALIP 2010

作者： Zhang, Yan Song, Haohao Gu, Jian Yu, Shengyang Yang, Jie Third Research Institute of Ministry of Public Security Shanghai 200031 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 China

ISBN: (纸本)9781424458653

Foreground object extraction, which aims to accurately separate a foreground object from its background in still images, plays an important role in many computer vision applications. An interactive object extraction method by extending the graph cut approach is presented in this paper. The coarse-to-fine object segmentation method makes it efficiently to specify the main object and adjust the detail of object. The user interaction is simplified to drawing a rectangle around the desired object, followed by optional boundary editing. The coarse scale segmentation is performed on the basis of the initial interactive rectangle, the accurate boundary portrayal is performed at the finer scale. A pyramid structure provides a framework for the processing, and this hierarchical structure ensures rapid boundary mapping between pyramid levels. Experimental results on multiple kinds of color images show the effectiveness and convenience of the approach. ©2010 IEEE.

关键词： Extraction

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Discovering structural regularity in facade image

Discovering structural regularity in facade image

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IEEE International Conference on Systems, Man and Cybernetics

作者： Weiwei Li Xin Zheng Jie Chen Image Processing and Pattern Recognition College of Information Science and Technology Beijing Normal University Beijing China Information Center CITIC Guoan Group Beijing China

3D reconstruction from single view is a very difficult problem in computer vision and computer graphics. Few works have been done in this area. In this paper, we proposed a method to discover structural regularity in single facade images on the procedural modeling of buildings by using SIFT algorithm. Firstly, symmetry information is detected from the image using SIFT algorithm for image analysis. Next, the symmetry information will be removed from the facade image. Finally, get the SIFT feature of widows and doors. Experiments show that our method can successfully discover regular structures with a higher recognition rate than existing method especially amidst shadowing and the changing illumination environment.

关键词： Laboratories

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Interactive Object Extraction Using Hierarchical Graph Cuts

Interactive Object Extraction Using Hierarchical Graph Cuts

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2010 International Conference on Audio,Language and image processing(2010年音频、语言与图像处理国际会议 ICALIP 2010)

作者： Jian Gu Haohao Song Yan Zhang Jie Yang Shengyang Yu The Third Research Institute of Ministry of Public Security Shanghai 200031 China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240

ISBN: (纸本)9781424458653

Foreground object extraction, which aims to accurately separate a foreground object from its background in still images, plays an important role in many computer vision applications. An interactive object extraction method by extending the graph cut approach is presented in this paper. The coarse-tofine object segmentation method makes it efficiently to specify the main object and adjust the detail of object. The user interaction is simplified to drawing a rectangle around the desired object, followed by optional boundary editing. The coarse scale segmentation is performed on the basis of the initial interactive rectangle, the accurate boundary portrayal is performed at the finer scale. A pyramid structure provides a framework for the processing, and this hierarchical structure ensures rapid boundary mapping between pyramid levels. Experimental results on multiple kinds of color images show the effectiveness and convenience of the approach.

关键词： extraction of object Still images Interactive user-computer interaction color image Rectangle images computer vision Line graph

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Efficient Joint 2D and 3D Palmprint Matching with Alignment Refinement

Efficient Joint 2D and 3D Palmprint Matching with Alignment ...

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IEEE Conference on Computer Vision and pattern recognition

作者： Wei Li Lei Zhang David Zhang Guangming Lu Jingqi Yan Biometrics Research Center Dept. of Computing The Hong Kong Polytechnic University Biocomputing Research Center Shenzhen Graduate School Harbin Institute of Technology Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University

ISBN: (纸本)9781424469840;9781424469857

Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past decade, and recently three dimensional (3D) palmprint recognition techniques were also proposed. The 2D and 3D palmprint data can be captured simultaneously and they provide different and complementary information. 3D palmprint contains the depth information of the palm surface, while 2D palmprint contains plenty of textures. How to efficiently extract and fuse the 2D and 3D palmprint features to improve the recognition performance is a critical issue for practical palmprint systems. In this paper, an efficient joint 2D and 3D palmprint matching scheme is proposed. The principal line features and palm shape features are extracted and used to accurately align the palmprint, and a couple of matching rules are defined to efficiently use the 2D and 3D features for recognition. The experiments on a 2D+3D palmprint database which contains 8000 samples show that the proposed scheme can greatly improve the performance of palmprint verification.

关键词： Dermatoglyphics Three Dimensions

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Prediction of protein folding rates from structural topology and complex network properties

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IPSJ Transactions on Bioinformatics 2010年 3卷 40-53页

作者： Song, Jiangning Takemoto, Kazuhiro Shen, Hongbin Tan, Hao Gromiha, M. Michael Akutsu, Tatsuya Bioinformatics Center Institute for Chemical Research Kyoto University Japan Department of Biochemistry and Molecular Biology Monash University Australia PRESTO Japan Science and Technology Agency Japan Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China Computational Biology Research Center National Institute of Advanced Industrial Science and Technology Japan

As a fundamental biological problem, revealing the protein folding mechanism remains to be one of the most challenging problems in structural bioinformatics. Prediction of protein folding rate is an important step towards our further understanding of the protein folding mechanism and the complex sequencestructure-function relationship. In this article, we develop a novel approach to predict protein folding rates for two-state and multi-state protein folding kinetics, which combines a variety of structural topology and complex network properties that are calculated from protein three-dimensional structures. To take into account the specific correlations between network properties and protein folding rates, we define two different protein residue contact networks, based on two different scales Protein Contact Network (PCN) and Long-range Interaction Network (LIN) to characterize the corresponding network features. The leave-one-out cross-validation (LOOCV) tests indicate that this integrative strategy is more powerful in predicting the folding rates from 3D structures, with the Pearson's Correlation Coefficient (CC) of 0.88, 0.90 and 0.90 for two-state, multi-state and combined protein folding kinetics, which provides an improved performance compared with other prediction work. This study provides useful insights which shed light on the network organization of interacting residues underlying protein folding process for both two-state and multi-state folding kinetics. Moreover, our method also provides a complementary approach to the current folding rate prediction algorithms and can be used as a powerful tool for the characterization of the foldomics protein data. The implemented webserver (termed PRORATE) is freely accessible at http://***/~sjn/folding/. © 2010 Information processing Society of Japan.

关键词： Forecasting

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Evaluating deformation patterns of the thoracic aorta in gated CTA sequences

Evaluating deformation patterns of the thoracic aorta in gat...

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IEEE International Symposium on Biomedical Imaging

作者： Ernst Schwartz Roman Gottardi Johannes Holfeld Christian Loewe Martin Czerny Georg Langs Computational Image Analysis and Radiology Lab (CIR) Medical University of Vienna Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Division of Cardiothoracic Surgery Department of Surgery Medical University of Vienna Austria Division of Interventional Radiology Department of Radiology Medical University of Vienna Austria Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA

ISBN: (纸本)9781424441259;9781424441266

Cardiovascular interventions in the region of the aortic isthmus such as stent-grafting and vessel transposition introduce substantial changes in the deformation properties of the affected vessels. The changes play a fundamental role in the long-term prognosis for any such treatment, but are only poorly understood to date. We explore a fully automated method to quantify the deformation patterns of the thoracic aorta in gated computed tomography sequences. The aorta is segmented by a level set approach that accurately identifies the vessel lumen in each frame of the sequence. Consequently, landmarks on the vessel wall in each frame are registered using a probabilistic method. This allows for the measurement of global and local deformation properties. We evaluate our method on synthetic datasets and report first results of its application on real world data.

关键词： Biomedical imaging image segmentation Radiology Cardiology Surgery Computed tomography Level set Electrocardiography Pathology Deformable models

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People detection in complex scene using a cascade of Boosted classifiers based on Haar-like-features

People detection in complex scene using a cascade of Boosted...

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IEEE Intelligent Vehicles Symposium

作者： Siala, A. Khlifa, N. Bremond, F. Hamrouni, K. Research Unit in Signal Processing Image Processing and Pattern Recognition ENIT Tunisia PULSAR Team INRIA Sophia Antipolis Nice France

ISBN: (纸本)9781424435036

Pedestrian detection in a real scene is an interesting application for video surveillance systems. This paper presents our contribution to improve the work of Viola and Jones, originally designed to detect faces. This work uses a cascade of classifiers based on Adaboost using Haar features. It improves the learning step by including a decision tree presenting the different poses and possible occlusions. The method has been tested on real and complex sequences and has given a good detection despite occlusions and poses variation.

关键词： Adaptive boosting

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Spatial and spectral based segmentation of text in multispectral images of ancient documents

Spatial and spectral based segmentation of text in multispec...

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ICDAR2009 - 10th International Conference on Document Analysis and recognition

作者： Lettner, Martin Sablatnig, Robert Pattern Recognition and Image Processing Group Institute of Computer Aided Automation Vienna University of Technology Austria

ISBN: (纸本)9780769537252

In this paper we propose a character segmentation method for multispectral images of ancient documents. Due to the low quality of the images the main idea of this study is to combine the multispectral behavior and contextual spatial information. Therefore we utilize a Markov Random Field model using the spectral information of the images and stroke properties to include spatial dependencies of the characters. Since the stroke properties and the Gaussian parameters for the imaging model are evaluated automatically the proposed segmentation method requires no training phase. We compared the method to state of the art character segmentation methods and demonstrate the effectiveness of combining spectral and spatial features for the segmentation of characters in multispectral images. © 2009 IEEE.

关键词： image segmentation

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