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International Joint conference on Neural Networks (IJCNN)

作者： K. Lalonde Institute of Atmospheric Sciences South Dakota School of Mines and Technology SD USA

The growing neural gas (GNG) pattern recognition algorithm is an unsupervised algorithm which inserts nodes into the state space of the training data. Observations of the behavior of the algorithm lead to the hypothesis that this method may be an efficient pre-classification clustering algorithm for data in highly discrete state spaces, as in satellite remote sensing images. The GNG algorithm was used to train a network using a Landsat image from Wyoming. The initial results of this investigation were extremely positive. The image derived from the trained GNG network is difficult to distinguish from the source image. Preliminary statistical results also indicate a high degree of correlation between the source and resultant images.

关键词： image analysis pattern analysis Algorithm design and analysis remote sensing pattern recognition Clustering algorithms State-space methods Satellites Space technology Cities and towns

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Fuzzy Models and Algorithms for pattern recognition and image processing 1

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丛书名： The handbooks of fuzzy sets series

2005年

作者： Keller, James Krisnapuram, Raghu Pal, Nikhil R.

ISBN: (纸本)9780387245157

Fuzzy Models and Algorithms for pattern recognition and image processing presents a comprehensive introduction of the use of fuzzy models in pattern recognition and selected topics in image processing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for pattern recognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, image processing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remote sensing, military image analysis, and industrial engineering.

关键词： Optical pattern recognition Fuzzy algorithms Cluster analysis image processing Computer vision

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The building recognition of high resolution satellite remote sensing image based on wavelet analysis

The building recognition of high resolution satellite remote...

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International conference on Machine Learning and Cybernetics (ICMLC)

作者： Qi-Ming Qin Si-Jin Chen Wen-Jun Wang De-Zhi Chen Lin Wang Institute of Remote Sensing and GIS Peking University Beijing China Department of Communication Engineering Yanshan University슠 Qinhuangdao Hebei China

This paper imported wavelet analysis and wavelet descriptor into the building recognition of high resolution satellite remote sensing image, and brought forward the building recognition method based on wavelet descriptor in theory: firstly used wavelet analysis to do image segmentation in order to wipe off the large blocks of vegetation area in the image, then used dyadic wavelet to extract the edge, and used coordinate wavelet descriptor to describe the detected building contour, after that, computed the affine invariants to recognize the buildings, at last employed shadow validating to ascertain if the detected edge was the real building edge. This paper also constructed the building recognition pattern database, and used a QuickBird RS image of Peking University to do the experiment and the experimental result proved the feasibility of the method.

关键词： image recognition image resolution Satellites remote sensing Wavelet analysis image analysis image edge detection Buildings image segmentation Vegetation mapping

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Skew estimation for scanned documents from "noises"

Skew estimation for scanned documents from "noises"

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8th International conference on Document Analysis and recognition

作者： Yuan, Bo Tan, Chew Lim Centre for Remote Imaging Sensing and Processing National University of Singapore Department of Computer Science School of Computing National University of Singapore

ISBN: (纸本)0769524206

The vast majority of the published skew estimation methods for scanned document images are for textual documents. These methods are based on the principle that the skew angles can be derived from the presence of the obvious text lines, The non-textual objects, such as line drawings, photographic inserts, scan artifacts including the dark bars around the borders and the center spine of bounded materials, and media contaminations are considered as "noises", thus are subject to elimination. Skew estimators that work in the presence of excessive noises are considered robust. This paper presents a skew estimation method that is based on the straight lines or edges. It uses the Muff Transform [15] with a probe-line mapping scheme for feature identification. Various strategies for optimized line probing are devised. This method is applicable to both textual and graphical documents scanned with ordinary scanners or copiers under normal conditions. Selected images from the University of Washington English Document image Database I (UWDB-I) are used for its usability evaluation. © 2005 IEEE.

关键词： Information retrieval

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Semantic discriminant mapping for classification and browsing of remote sensing textures and objects

Semantic discriminant mapping for classification and browsin...

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IEEE International conference on image processing

作者： J. Fauqueur N. Kingsbury R. Anderson Signal Processing Group Department of Engineering University of Cambridge UK

We present a new approach based on discriminant analysis to map a high dimensional image feature space onto a subspace which has the following advantages: 1) each dimension corresponds to a semantic likelihood, 2) an efficient and simple multiclass classifier is proposed and 3) it is low dimensional. This mapping is learnt from a given set of labeled images with a class groundtruth. In the new space a classifier is naturally derived which performs as well as a linear SVM. We show that projecting images in this new space provides a database browsing tool which is meaningful to the user. Results are presented on a remote sensing database with eight classes, made available online. The output semantic space is a low dimensional feature space which opens perspectives for other recognition tasks.

关键词： remote sensing image databases Vegetation mapping Spatial databases Visual databases image recognition Signal processing image analysis image texture analysis Signal analysis

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Combining multiple evidence for video classification

Combining multiple evidence for video classification

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2nd International conference on Intelligent sensing and Information processing, ICISIP'05

作者： Suresh, Vakkalanka Mohan, C. Krishna Kumaraswamy, R. Yegnanarayana, B. Speech and Vision Laboratory Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai-600 036 India

ISBN: (纸本)0780388402

In this paper, we investigate the problem of video classification into predefined genre, by combining the evidence from multiple classifiers. It is well known in the pattern recognition community that the accuracy of classification obtained by combining decisions made by independent classifiers can be substantially higher than the accuracy of the individual classifiers. The conventional method for combining individual classifiers weighs each classifier equally (sum or vote rule fusion). In this paper, we study a method that estimates the performances of the individual classifiers and combines the individual classifiers by weighing them according to their estimated performance. We demonstrate the efficacy of the performance based fusion method by applying it to classification of short video clips (20 seconds) into six popular TV broadcast genre, namely cartoon, commercial, news, cricket, football, and tennis. The individual classifiers are trained using different spatial and temporal features derived from the video sequences, and two different classifier methodologies, namely Hidden Markov Models (HMMs) and Support Vector Machines (SVMs). The experiments were carried out on more than 3 hours of video data. A classification rate of 93.12% for all the six classes and 97.14% for sports category alone has been achieved, which is significantly higher than the performance of the individual classifiers. ©2005 IEEE.

关键词： image processing

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Encoding Methods of Spectral Vector in Hyperspectral Romote sensing image

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Journal of Shanghai University(English Edition) 2005年第1期9卷 52-57页

作者：杜培军方涛唐宏施鹏飞 Department of Remote Sensing and Geographical Information Science China University of Mining and Technology Xuzhou 221008 P.R. China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 P.R. China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 P.R. China

Taking into account the demands of hyperspectral remote sensing(RS) image retrieval and processing, some encoding methods of spectral vector including direct encoding, feature-based encoding and tree-based encoding methods are proposed and compared. In direct encoding, based on the analysis of binary encoding and quad-value encoding, decimal encoding is proposed. It is proved that quad-value encoding and decimal encoding are suitable to fast processing and retrieval. In absorption feature-based encoding method, five common metrics are compared. Because locations of reflection/absorption features are sensitive to noise, this method is not very effective in retrieval. In tree-based encoding methods, bitree, quadtree, octree and hextree are proposed and discussed. It is proved that 2-level octree and 2-level hextree are more effective than bitree and quadtree. Finally, quad-value encoding, decimal encoding, 2-level octree and 2-level hextree are proposed in spectral vectors encoding, similarity measure and hyperspectral RS image retrieval.

关键词： hyperspectral remote sensing spectral vector encoding similarity measure spectral angle.

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Automatic ports detection and recognition based on multi-features from satellite imagery

Automatic ports detection and recognition based on multi-fea...

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MIPPR 2005: image Analysis Techniques

作者： Yang, Yun Remote Sensing Information Engineering School Wuhan University Wuhan 430079

This paper presents a method for ports detection based on the framework of feature level fusion. Bearing in mind the fact that parallel lines and rectangular corners are main features in most ports, and ports are large scale man-made objects, these features are firstly extracted from high-to-moderate resolution optical satellite imagery. Taking account for the balance of data acquisition and spatial resolution, SPOT panchromatic image is used for such feature extraction. Considering the whether conditions in coastal area, which is characterized by rainy and cloudy climate, Radarsat image with the similar spatial resolution as SPOT panchromatic is used to extract linear features along coastal line. Since ships and boats are typical objects that can be easily detected in radar image, these are considered to be supplemented features for ports detection. All extracted features are associated under the framework of feature level fusion. The whole procedure can be described as follows: the first step is preprocessing the input images, mainly histogram stretching to SPOT image for visual quality improvement and filtering to radar image for denoising speckles. Then registration between SPOT and Radarsat image is carried out. Since Radarsat image is used mainly for coastal line extraction and ship detection, rigorous geometric processing is omitted since little attention will be paid to land area. Common polynomial model is used for co-registration with Ground Control Points manually selected from both images. Due to feature level fusion method is adopted, registration accuracy is not as a key factor as in pixel level fusion. The next step will be linear features and rectangular corners detection both in optical and radar image. The detected linear features are then fitted by least mean-square-error algorithm. All the detected features are associated by simply weighted mean algorithm, with different weights to features from optical and radar images. An automatic ports detect

关键词： pattern recognition

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A graph and PNN-based approach to image classification

A graph and PNN-based approach to image classification

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International conference on Machine Learning and Cybernetics (ICMLC)

作者： Jin Tang Chun-Yan Zhang Bin Luo Key Laboratory of Intelligent Computing and Signal Processing of Anhui University Hefei China

In this paper, a new image classification method is developed. This approach applies graph decomposition and probabilistic neural networks (PNN) to the task of supervised image classification. We use relational graphs to represent image. These graphs are constructed from the feature points of images. Spectra of these graphs are obtained as feature vectors for classification. PNN is adopted to classify image according to the feature vectors. Experimental results show that this method can achieve best result of images classification.

关键词： image classification Neural networks pattern recognition Eigenvalues and eigenfunctions remote sensing image sequences Equations Signal processing Electronic mail image processing

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Texture classification of aerial image based on PCA-NBC

Texture classification of aerial image based on PCA-NBC

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MIPPR 2005: SAR and Multispectral image processing

作者： Yu, Xin Zheng, Zhaobao Li, Linyi Ye, Zhiwei School of Remote Sensing Information Engineering Wuhan University 129 Luoyu Road Wuhan 430079 China

Bayesian Networks have emerged in recent years as a powerful data mining technique for handling uncertainty in complex domains. The Bayesian Network represents the joint probability distribution and domain (or expert) knowledge in a compact way and provides a comprehensive method of representing relationships and influences among nodes (variables) with a graphical diagram. Actually, however, in the classification domain it was not paid attention to by researchers until the simplest of form of Bayesian Networks, Naive Bayes Classifier, turned up. Naive Bayes Classifier is a simple and efficient probability classification method, and has shown surprising performance in some domains, which owes to the independence assumption that makes Naive Bayes Classifier fit the classification more easily. However, the independence assumption obviously does not hold in the real world. Therefore, in order to meet the "naive" (unreal) assumption, this paper proposes a new image texture classification method of aerial images, PCA-NBC, which combines the Principal Components Analysis (PCA) and Naive Bayes Classifier (NBC). The PCA transforms the highly correlated features into statistically independent and orthogonal "features", so it is suitable to solve that problem and can lay a solid theoretic foundation in the application. One hundred and thirteen aerial images are used to evaluate the classification performance in the experiment. The experimental results demonstrate that the proposed method can cut down the number of features and computational costs and improve the accuracy during classification. In one word, the new method, PCA-NBC, is an attractive and effective method, which outperforms the Naive Bayes Classifier.

关键词： pattern recognition

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