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International Conference on Document Analysis and recognition

作者： Bing Zhang Ying Li Ching Y. Suen Xuemin Zhang CENPARMI (Center for Pattern Recognition and Machine Intelligence) Computer Science and Software Engineering Department Concordia University Montreal Canada School of Psychology Beijing Normal University Beijing China

ISBN: (纸本)9781457713507

More and more fonts have sprung up in recent years in digital publishing industry and reading devices. In this paper, we focus on methods of evaluating digital Chinese fonts and their typeface characteristics to seek a good way to enhance the character recognition rate. To accomplish this, we combined psychological analysis methods with statistical analysis. It involved an extensive survey of distinctive features of eighteen popular digital typefaces. Survey results were tabulated and analyzed statistically. Then another objective experiment was conducted using the best six fonts derived from the survey results. These experimental results reveal an effective way of choosing legible digital fonts most suitable for comfortable reading of books, magazines, newspapers, and for display of texts on cell-phones, e-books, and digital libraries, and finding out the features for improving character legibility of different Chinese typefaces. The relationships among legibility, eye-strain, and myopia, will be discussed.

关键词： Character recognition Educational institutions Periodic structures Text analysis Accuracy Electronic publishing Statistical analysis

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Fast k-NN Classifier for Documents Based on a Graph Structure

Fast k-NN Classifier for Documents Based on a Graph Structur...

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15th Iberoamerican Congress on pattern recognition, CIARP 2010

作者： Artigas-Fuentes, Fernando José Gil-García, Reynaldo Badía-Contelles, José Manuel Pons-Porrata, Aurora Center of Pattern Recognition and Data Mining Universidad de Oriente Santiago de Cuba Cuba Computer Science and Engineering Department Universitat Jaume I Castelló Spain

ISBN: (纸本)9783642166860

In this paper, a fast k nearest neighbors (k-NN) classifier for documents is presented. Documents are usually represented in a high-dimensional feature space, where their terms are treated as features and the weight of each term reflects its importance in the document. There are many approaches to find the vicinity of an object, but their performance drastically decreases as the number of dimensions grows. This problem prevents its application for documents. The proposed method is based on a graph index structure with a fast search algorithm. Its high selectivity permits to obtain a similar classification quality than the exhaustive classifier, with a few number of computed distances. Our experimental results show that our method can be applied to problems of very high dimensionality, such as Text Mining. © 2010, Springer-Verlag Berlin Heidelberg.

关键词： Nearest neighbor search

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Word Spotting in Gray Scale Handwritten Pashto Documents

Word Spotting in Gray Scale Handwritten Pashto Documents

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International Workshop on Frontiers in Handwriting recognition

作者： Muhammad Ismail Shah Ching Y. Suen CENPARMI (Center of Pattern Recognition and Machine Intelligence) Department of Computer Science and Software Engineering Concordia University Montreal QUE Canada

In this paper, we present an approach for word spotting in Gray-scale Pashto Documents, written in modified Arabic scripts. Various profile and transitional features are extracted from gray-scale word images. The gray-scale feature vectors are then converted into binary feature vectors by replacing each value within the gray-scale feature vectors with its binary equivalents. In this way, we have enabled the alignment of the gray-scale feature vectors via a faster binary pattern matching algorithm, i.e., Correlation Similarity Measure (CORR). The approach has effectively handled the handwriting variations of 200 different writers. The average precision rate achieved is 94.75 % for an average recall of 60.25%. The time taken for matching every set of two word images is 1.43 ms.

关键词： Feature extraction Gray-scale Image matching Correlation Heuristic algorithms Databases Hidden Markov models

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Early diagnosis of dementia based on intersubject whole-brain dissimilarities

Early diagnosis of dementia based on intersubject whole-brai...

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IEEE International Symposium on Biomedical Imaging

作者： S. Klein M. Loog F. van der Lijn T. den Heijer A. Hammers M. de Bruijne A. van der Lugt R.P.W. Duin M.M.B. Breteler W.J. Niessen Biomedical Imaging Group Rotterdam Departments of Radiology & Medical Informatics Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Pattern Recognition Laboratory Faculty of Electrical Engineering Mathematics Computer Science Delft University of Technnology Netherlands Department of Neurology Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Neurodis Foundation CERMEP PET Centre Lyon France MRC Clinical Sciences Centre and Division of Neuroscience and Mental Health Imperial College Hammersmith Hospital London UK Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Radiology Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Department of Epidemiology & Biostatistics Erasmus MC-University Medical Center Rotterdam Rotterdam Netherlands Imaging Science & Technology Department of Applied Sciences Delft University of Technnology Netherlands

ISBN: (纸本)9781424441259

This article studies the possibility of detecting dementia in an early stage, using nonrigid registration of MR brain scans in combination with dissimilarity-based pattern recognition techniques. Instead of focussing on the shape of a single brain structure, we take into account the shape differences within the entire brain. Imaging data was obtained from a longitudinal, population based study of the elderly. A set of 29 subjects was identified, who were asymptomatic at the time of scanning, but were diagnosed as having dementia within 0.7 to 5 years after the scan, and a set of 29 age and gender matched healthy controls were selected. Each subject was registered to all other subjects, using a nonrigid registration algorithm. Based on statistics of the deformation field in the brain, a dissimilarity measure was calculated between each pair of subjects, yielding a 58×58 dissimilarity matrix. A kNN classifier was trained on the dissimilarity matrix and the performance was tested in a leave-one-out experiment. A classification accuracy of 81% was attained (spec. 83%, sens. 79%). This demonstrates the potential of whole-brain intersubject dissimilarities to aid in early diagnosis of dementia.

关键词： Dementia Brain pattern recognition Shape Radiology computer science Biomedical imaging Senior citizens Diseases Biomarkers

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Multiresolution multiscale active mask segmentation of fluorescence microscope images

Multiresolution multiscale active mask segmentation of fluor...

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Wavelets XIII

作者： Srinivasa, Gowri Fickus, Matthew Kovacevic, Jelena Department of Mathematics and Statistics Air Force Institute of Technology Wright-Patterson AFB OH 45433 United States Center for Bioimage Informatics Departments of Biomedical and Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA 15213 United States Center for Pattern Recognition Department of Information Science and Engineering PES School of Engineering Bangalore India

ISBN: (纸本)9780819477361

We propose an active mask segmentation framework that combines the advantages of statistical modeling, smoothing, speed and flexibility offered by the traditional methods of region-growing, multiscale, multiresolution and active contours respectively. At the crux of this framework is a paradigm shift from evolving contours in the continuous domain to evolving multiple masks in the discrete domain. Thus, the active mask framework is particularly suited to segment digital images. We demonstrate the use of the framework in practice through the segmentation of punctate patterns in fluorescence microscope images. Experiments reveal that statistical modeling helps the multiple masks converge from a random initial configuration to a meaningful one. This obviates the need for an involved initialization procedure germane to most of the traditional methods used to segment fluorescence microscope images. While we provide the mathematical details of the functions used to segment fluorescence microscope images, this is only an instantiation of the active mask framework. We suggest some other instantiations of the framework to segment different types of images. © 2009 SPIE.

关键词： Fluorescence microscopy

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Text Segmentation from Complex Background Using Sparse Representations

Text Segmentation from Complex Background Using Sparse Repre...

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International Conference on Document Analysis and recognition

作者： W. Pan T. Bui C. Suen Center for Pattern Recognition and Machine Intelligence Concordia University USA Department Computer Science and Software Engineering Concordia University USA

A novel text segmentation method from complex background is presented in this paper. The idea is inspired by the recent development in searching for the sparse signal representation among a family of over-complete atoms, which is called a dictionary. We assume that the image under investigation is composed of two components: the foreground text and the complex background. We further assume that the latter can be modeled as a piece-wise smooth function. Then we choose two dictionaries, where the first one gives sparse representation to one component and non-sparse representation to another while the second one does the opposite. By looking for the sparse representations in each dictionary, we can decompose the image into the two composing components. After that, text segmentation can be easily achieved by applying simple thresholding to the text component. Preliminary experiments show some promising results.

关键词： Dictionaries Image segmentation pattern recognition Machine intelligence Signal representations Image analysis computer science Software engineering Image resolution Filtering algorithms

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A New Clustering Method for Improving Plasticity and Stability in Handwritten Character recognition Systems

A New Clustering Method for Improving Plasticity and Stabili...

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International Conference on pattern recognition

作者： J. Sadri C.Y. Suen T.D. Bui CENPARMI Center for Pattern Recognition and Machine Intelligence Computer Science and Software Engineering Department Concordia University Montreal QUE Canada

This paper presents a new online clustering algorithm in order to improve plasticity and stability in handwritten character recognition systems. Our clustering algorithm is able to automatically determine the optimal number of clusters in the input data. An incremental learning technique similar to adaptive resonance theory (ART) is used to determine the best cluster for new data. Our technique also allows the previously learned clusters to be merged whenever the newly arrived data points push their centers close together. We also developed new features and similarity measures in order to describe and compare the shapes of handwritten digits to be used in our clustering algorithm. Results of our algorithm on clustering the shapes of the handwritten numerals from the CENPARMI isolated digit database are shown. Our method can incrementally learn new handwriting styles of digits, without forgetting the previous ones, therefore it can improve plasticity and stability

关键词： Clustering methods Stability Character recognition Shape measurement Clustering algorithms Prototypes Machine learning pattern recognition Subspace constraints Neural networks

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A Genetic Binary Particle Swarm Optimization Model

A Genetic Binary Particle Swarm Optimization Model

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Congress on Evolutionary Computation

作者： J. Sadri C.Y. Suen CENPARMI (Center for Pattern Recognition and Machine Intelligence) Computer Science and Software Enginneering Concordia University Montreal West QUE Canada Computer Science and Software Engineering Department Concordia University Montreal QUE Canada

In this paper, a genetic binary particle swarm optimization (GBPSO) model is proposed, and its performance is compared with the regular binary particle swarm optimizer (PSO), introduced by Kennedy and Eberhart. In the original model, the size of the swarm was fixed. In our model, we introduce birth and death operations in order to make the population very dynamic. Since birth and mortality rates change naturally with time, our model allows oscillations in the size of the population. Compared to the original PSO model, and genetic algorithms, our strategy proposes a more natural simulation of the social behavior of intelligent animals. The experimental results show that compared to original PSO, our GBPSO model can reach broader domains in the search space and converge faster in very high dimensional and complex environments.

关键词： Particle swarm optimization Multidimensional systems computer science Software engineering Java Genetic algorithms Animals Adaptive algorithm Birds Educational institutions

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Outlier detection by weighted mercer-kernel based fuzzy clustering algorithm

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Iranian Journal of Electrical and computer engineering 2005年第2期4卷 129-136页

作者： Shen, Hongbin Yang, Jie Dong, Yifei Wang, Shitong College of Computer and Information Engineering Hohai University Changzhou Jiangsu China Department of Image Processing and Pattern Recognition Jiaotong University Shanghai China School of Computer Science and Engineering University of New South Wales Australia School of Information Southern Yantze University Wuxi Jiangsu China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China IEEE Institute of National Automation Department of Information Southern Yangtse University Polytechnic University of Hongkong Advanced Computing Research Center Bristol University United Kingdom

Outliers are data values that lie away from the general cluster of other data values. Detecting the outliers of a dataset is an important research topic for data cleaning and finding new useful knowledge in many research areas, i.e. data mining, pattern recognition, etc. In the past decades, many useful algorithms were proposed in the literature. In this paper, a new fuzzy kernel-clustering algorithm with outliers (FKCO) is presented to locate critical areas that are often represented by only a few outliers. Theoretic analysis also shows that FKCO can converge to a local minimum of the objective function. Finally, based on the information theory, a new criterion for finding outliers is also proposed. Simulations of different types of datasets demonstrate the feasibility of this new method. © 2005 JD.

关键词： Fuzzy control

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Adaptive nonlinear auto-associative modeling through manifold learning

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9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2005

作者： Zhang, Junping Li, Stan Z. Intelligent Information Processing Laboratory Department of Computer Science and Engineering Fudan University Shanghai 200433 China Key Laboratory of Complex Systems and Intelligence Science Chinese Academy of Sciences China National Laboratory of Pattern Recognition Center for Biometrics and Security Research Institute of Automation Beijing 100080 China

ISBN: (纸本)3540260765

We propose adaptive nonlinear auto-associative modeling (ANAM) based on Locally Linear Embedding algorithm (LLE) for learning intrinsic principal features of each concept separately and recognition thereby. Unlike traditional supervised manifold learning algorithm, the proposed ANAM algorithm has several advantages: 1) it implicitly embodies discriminant information because the suboptimal parameters of ANAM are determined based on error rate of the validation set. 2) it avoids the curse of dimensionality without loss accuracy because recognition is completed in the original space. Experiments on character and digit databases show that the advantages of the proposed ANAM algorithm. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Learning systems

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