Last edited by Fauk
Sunday, August 2, 2020 | History

5 edition of Soft Computing Approach to Pattern Recognition and Image Processing (Series in Machine Perception and Artificial Intelligence) found in the catalog.

Soft Computing Approach to Pattern Recognition and Image Processing (Series in Machine Perception and Artificial Intelligence)

  • 308 Want to read
  • 20 Currently reading

Published by World Scientific Pub Co Inc .
Written in English

    Subjects:
  • Image processing,
  • Pattern Recognition,
  • Artificial Intelligence,
  • Computers,
  • Artificial Intelligence - General,
  • Science/Mathematics,
  • Soft computing,
  • Pattern perception,
  • Data Processing - Optical Data Processing

  • Edition Notes

    ContributionsAshish Ghosh (Editor), Sankar K. Pal (Editor)
    The Physical Object
    FormatHardcover
    Number of Pages360
    ID Numbers
    Open LibraryOL9380437M
    ISBN 109812382518
    ISBN 109789812382511

      Unique Property of Soft computing • Learning from experimental data • Soft computing techniques derive their power of generalization from approximating or interpolating to produce outputs from previously unseen inputs by using outputs from previous learned inputs • Generalization is usually done in a high dimensional space. Using Image Pattern Recognition Algorithms for Processing Video Log Images to Enhance Roadway Infrastructure Data Collection IDEA Program Final Report for the period 1/ through 1/ Contract Number: NCHRP IDEA Prepared for the IDEA Program Transportation Research Board National Research Council Yichang (James) Tsai, Ph.D., P.E.

    Image segmentation is an important process and are used in in many image processing applications. Color images can increase the quality of segmentation but also increases the complexity of the problem. To reduce this complexity soft computing tools played promising role. This paper discussed the segmentation of image using soft computing. processing Outpu t Image restoration Segmentation are imag Knowledge base Image enhancement Representation & description e attribut e Image acquisition Object recognition Input Image s Yao Wang, NYU-poly EL Introduction 10 We will deal with mainly the light green boxes. Yellow boxes belong to “computer vision” and “pattern.

    techniques of soft computing with other disciplines, one can serve as a unifying platform that fosters comparisons, extensions, and new applications. This paper discussed about various existing work related to soft computing techniques. IndexTerms— Soft computing, pattern recognition, fuzzy logic. Reviewer in: IEEE Transaction in Image Processing, Signal Processing Letter, Transaction on fuzzy systems, IET Image processing, Elsevier- Pattern Recognition Letter, Fuzzy Sets and Systems, Micron, Computer Vision and Image Understanding, Applied Soft Computing, Information sciences, Image and vision computing.


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Soft Computing Approach to Pattern Recognition and Image Processing (Series in Machine Perception and Artificial Intelligence) Download PDF EPUB FB2

“This book addresses the use of soft computing techniques for pattern recognition problems, starting with an interesting overview of how the conventional approach to pattern recognition differs from the soft computing approach. The book achieves a well-balanced treatment of.

Relevance of fuzzy logic, artificial neural networks, genetic algorithms and rough sets to pattern recognition and image processing problems is described through examples. Different integrations of these soft computing tools are by: 3. This book is dedicated to object extraction, image segmentation, and edge detection using soft computing techniques with extensive real-life application to image and multimedia data.

The authors start with a comprehensive tutorial on the basics of brain structure and learning, and then the key soft computing techniques, including evolutionary. The book introduces the theory and concepts of digital image analysis and processing based on soft computing with real-world medical imaging applications.

Comparative studies for soft computing based medical imaging techniques and traditional approaches in medicine are addressed, providing flexible and sophisticated application-oriented solutions.

This book presents practical development experiences in different areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data.

Then different types of image-processing techniques are applied on them, to process those images, to get different and useful features needed for the purpose of analyzing later. Algorithm written below illustrated the step by step approach for the proposed image recognition and segmentation processes: (1)Cited by: Yegnanarayana, Suryakanth V.

Gangashetty, and S. Palanivel, Autoassociative Neural Network Models for Pattern Recognition Tasks in Speech and Image, in Ashish Ghosh and Sankar K. Pal (Eds.), Soft Computing Approach to Pattern Recognition and Image Processing, World Scientific Publishing Co.

Pte. Ltd., Singapore,   This volume constitutes the first of three parts of the refereed proceedings of the First International Conference on Computer Science and Information Technology, CCSITheld in Bangalore, India, in January The 59 revised full papers presented in this volume were carefully reviewed and selected.

The papers are organized in topical sections on distributed and parallel Reviews: 1. Book Description. Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing. Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models.

In computer science, digital image processing is the use of a digital computer to process digital images through an algorithm. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and.

There has always been a quest to develop best OCR products which satisfy the user’s needs. Since past few decades soft computing techniques have come up as a promising candidate for the development of cost effective OCR systems.

Some important soft computing techniques for optical character recognition (OCR) systems are presented in this by: 2. Pattern recognition using soft computing (PRSC) solutions are unpredictable, uncertain and between 0 and 1.

Pattern recognition using soft computing became a formal area of study in Computer Science in the early s. Earlier computational approaches could. This practical handbook provides a broad overview of the major elements of pattern recognition and image processing (PRIP).

Currently the only handbook in the field, it is designed as a source of "quick answers" for those interested in the theoretical development and. The book details the theory of granular computing and the role of a rough-neuro approach as a way of computing with words and designing intelligent recognition systems.

It also demonstrates applications of the soft computing paradigm to case. Soft computing.; Image processing.; COMPUTERS / Enterprise Applications / Business Intelligence Tools. This practical handbook provides a broad overview of the major elements of pattern recognition and image processing (PRIP).

Currently the only handbook in the field, it is designed as a source of "quick answers" for those interested in the theoretical development and practical applications of PRIP years of research, development, and innovations in applications are documented.

An effective computational approach to objectively analyze image datasets is pattern recognition (PR, see Box 1). PR is a machine-learning approach where the machine finds relevant patterns that distinguish groups of objects after being trained Cited by: This book series addresses all aspects of machine perception and artificial intelligence.

Of particular interest are the areas of pattern recognition, image processing, computer vision, natural language understanding, speech processing, neural computing, machine learning, hardware architectures, software tools, and others.

Ghosh, R. Pal and R. Prasath (eds.) Mining Intelligence and Knowledge Exploration, LNAI vol.Springer, (CI-1) 9. Maji, A. Ghosh, M. Murty, K.

"I consider the fourth edition of the book Pattern Recognition, by S. Theodoridis and K. Koutroumbas as the Bible of Pattern Recognition." -- Simon Haykin, McMaster University, Canada "I have taught a graduate course on statistical pattern recognition for more than twenty five years during which I have used many books with different levels of.

Hands-On Pattern Recognition Challenges in Machine Learning, Volume 1 Isabelle Guyon, Gavin Cawley, ference, in the form of competitions or challenges.

This book opens the series Challenges in Machine Learning. It contains papers by the top ranking challenge participants, providing automated data processing, the search for better data.

CVonline: Vision Related Books including Online Books and Book Support Sites. We have tried to list all recent books that we know about that are relevant to computer vision and image processing.

The books are listed under: Online - if the full text is online; Online Subscription Sites - if the full text is online but you need a subscription fee.methods of soft computing methods for better accuracy and performance is the need of an hour.

A brief review is presented below. E. Fuzzy Logic in Pattern Recognition: Dealing with uncertainties is a .Image sensors ere w treated in Chapter 2. Chapter 3 discussed w ho to compute a b umer n of t di eren features from binary images of ob jects.

Computing color and texture features is treated in Chapters 6 and 7. The haracter c recognition example is again e. instructiv Characters written in a 30 x 20 w windo ould w result in Size: KB.