A multichannel filtering-based texture segmentation method is applied to a variety of document imageprocessing problems: text-graphics separation, address-block location, and bar code localization. In each of these s...
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A multichannel filtering-based texture segmentation method is applied to a variety of document imageprocessing problems: text-graphics separation, address-block location, and bar code localization. In each of these segmentation problems, the text context or bar code in the image is considered to define a unique texture. Thus, all three document analysis problems can be posed as texture segmentation problems. Two-dimensional Gabor filters are used to compute texture features. Both supervised and unsupervised methods are used to identify regions of text or bar code in the document images. The performance of the segmentation and classification scheme for a variety of document images demonstrates the generality and effectiveness of the approach.< >
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer re...
Based on the assumption that most probability densities in real life can be approximated by a mixture of Gaussian densities, we propose here a three-layer adaptive network with each neuron in the lower hidden layer representing a Gaussian basis function (covariance matrix equal to where I is a unit matrix) to estimate various probability densities and serve as a Bayes classifier. The width of the basis function may be the same for all neurons in this layer or it may vary from one neuron to another. This paper investigates the effectiveness of the network for both cases and presents a localized learning algorithm to adjust the network parameters. The network was trained with artificial data derived from known mixtures of memoryless Gaussian sources as well as exponential and Gamma densities. The performance of the network as a pattern density estimator was measured in terms of the relative difference between the target probability density function (p.d.f.) which generates the training and testing data and the network output representing the estimation. Samples from two mixtures corresponding to two classes were used to test the network capability as a classifier by comparing its error rate against that of a Bayes classifier. Both one- and two-dimensional cases were explored. The successfulness of the network depended on how well the target p.d.f.’s were represented by the training samples, the number of hidden neurons employed in the network and how thoroughly the network was trained. It was also found that allowing each basis function to have an independent width had a predominant effect on the network performance.
The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on...
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The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on a commercial CAD system and expressed in the industry-standard IGES form, and performs geometric inferencing to object a relational graph representation of the object which can be stored in a database of models of object recognition. Details of the IGES standard, the geometric inference engine, and some formal properties of 3-D models are discussed. In addition to the process of translation from one data format to another, the interference engine extracts higher-level information from the CAD model and stores it explicitly in the new data structure. The higher-level features will allow the search space explored during the object recognition stage to be pruned early.< >
作者：
Y. T. ChienTheodosios PavlidisGuest Editor
Professor and Head of the Department of Electrical Engineering and Computer Science. Guest Editor
member of the Association for Computing Machinery and Sigma Xi member of the editorial committee of the IEEE TRANSACTIONS OF PATTERN ANALYSIS AND MACHINE INTELLIGENCE Associate Editor of the Bulletin of Mathematical Biology Computer Graphics and Image Processing and Pattern Recognition.
This Special Issue is composed of the papers selected from the 1978 IEEE computer Society Workshop on patternrecognition (PR) and Artificial Intelligence (Al) held in Princeton, NJ, April 12-14, 1978. The Workshop wa...
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This Special Issue is composed of the papers selected from the 1978 IEEE computer Society Workshop on patternrecognition (PR) and Artificial Intelligence (Al) held in Princeton, NJ, April 12-14, 1978. The Workshop was sponsored by the Technical Committee on Machine Intelligence and pattern Analysis. Inevitably, the contributors to the Workshop determined, to a large degree, the tone and complexion of this Special Issue. For this reason, a brief account of the Workshop Proceedings, though now history, is given. About half of the papers presented at the Workshop were also submitted for the Special Issue, a total of 37. Those of high quality were far more than the number that could be accommodated within the available number of pages. We decided to choose three topics where the interaction between the methodologies of PR and Al was most prevelant: analysis of images, analysis of speech, and certain general algorithms. All the selected papers present either theoretical, or experimental results, or both. We felt that such results clearly demonstrate the progress achieved and can be seen as very impressive if measured against the difficult problem of emulating functions associated with human intelligence by machines. It is true that they often fall short from some of the earlier ambitious goals, but the time is probably ripe to reexamine such goals in view of the accumulated experience. The following is a brief scanning of the contents of this issue, especially as related to the integration and/or interaction of PR and Al methodologies.
This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in patternrecognition, GbRPR 2013, held in Vienna, Austria, in May 2013. The 24 papers presen...
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ISBN:
(数字)9783642382215
ISBN:
(纸本)9783642382208
This book constitutes the refereed proceedings of the 9th IAPR-TC-15 International Workshop on Graph-Based Representations in patternrecognition, GbRPR 2013, held in Vienna, Austria, in May 2013.
The 24 papers presented in this volume were carefully reviewed and selected from 27 submissions. They are organized in topical sections named: finding subregions in graphs; graph matching; classification; graph kernels; properties of graphs; topology; graph representations, segmentation and shape; and search in graphs.
computer Vision is a rapidly growing field of research investigating computational and algorithmic issues associated with image acquisition, processing, and understanding. It serves tasks like manipulation, recognitio...
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ISBN:
(数字)9783709165867
ISBN:
(纸本)9783211827307
computer Vision is a rapidly growing field of research investigating computational and algorithmic issues associated with image acquisition, processing, and understanding. It serves tasks like manipulation, recognition, mobility, and communication in diverse application areas such as manufacturing, robotics, medicine, security and virtual reality. This volume contains a selection of papers devoted to theoretical foundations of computer vision covering a broad range of fields, e.g. motion analysis, discrete geometry, computational aspects of vision processes, models, morphology, invariance, image compression, 3D reconstruction of shape. Several issues have been identified to be of essential interest to the community: non-linear operators; the transition between continuous to discrete representations; a new calculus of non-orthogonal partially dependent systems.
The Third International Workshop on Medical Imaging and Augmented Reality, MIAR 2006, was held in Shanghai, China at the Regal International East Asia Hotel during August 17-18, 2006. The goal of MIAR 2006 was to brin...
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ISBN:
(数字)9783540372219
ISBN:
(纸本)9783540372202
The Third International Workshop on Medical Imaging and Augmented Reality, MIAR 2006, was held in Shanghai, China at the Regal International East Asia Hotel during August 17-18, 2006. The goal of MIAR 2006 was to bring together researchers in medical image computing and intervention to present the state-of-the-art devel- ments in this ever-growing research area. The meeting consisted of a single track of oral/poster presentations, with each session led by an invited lecture from our dist- guished international faculty members. For MIAR 2006, we received 87 full subm- sions, which were subsequently reviewed by up to 5 reviewers, resulting in the acc- tance of 45 full papers included in this volume. For this workshop, we also included four papers from the invited speakers covering shape modeling, fMRI analysis, and study of cerebral connectivity and plasticity. Running such a workshop requires dedication, and we appreciate the commitment of the MIAR 2006 Programme Committee and reviewers who worked to a tight de- line in putting together this workshop. We would also like to thank members of the local Organizing Committee, who have been working so hard behind the scenes to make MIAR 2006 a great success. It was our great pleasure to welcome this year’s MIAR attendees to Shanghai, which is the largest base of Chinese industrial technology, an important seaport and China's largest commercial and financial center.
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