Last edited by Sharamar
Sunday, November 15, 2020 | History

1 edition of Soft Computing Approach to Pattern Classification and Object Recognition found in the catalog.

Soft Computing Approach to Pattern Classification and Object Recognition

A Unified Concept

by Kumar S. Ray

  • 160 Want to read
  • 32 Currently reading

Published by Springer New York, Imprint: Springer in New York, NY .
Written in English

    Subjects:
  • Image Processing and Computer Vision,
  • Computer science,
  • Image and Speech Processing Signal,
  • Computer vision,
  • Artificial intelligence,
  • Artificial Intelligence (incl. Robotics)

  • About the Edition

    Soft Computing Approach to Pattern Classification and Object Recognition establishes an innovative, unified approach to supervised pattern classification and model-based occluded object recognition. The book also surveys various soft computing tools, fuzzy relational calculus (FRC), genetic algorithm (GA) and multilayer perceptron (MLP) to provide a strong foundation for the reader. The supervised approach to pattern classification and model-based approach to occluded object recognition are treated in one framework , one based on either a conventional interpretation or a new interpretation of multidimensional fuzzy implication (MFI) and a novel notion of fuzzy pattern vector (FPV). By combining practice and theory, a completely independent design methodology was developed in conjunction with this supervised approach on a unified framework, and then tested thoroughly against both synthetic and real-life data. In the field of soft computing, such an application-oriented design study is unique in nature. The monograph essentially mimics the cognitive process of human decision making, and carries a message of perceptual integrity in representational diversity. Soft Computing Approach to Pattern Classification and Object Recognition is intended for researchers in the area of pattern classification and computer vision. Other academics and practitioners will also find the book valuable.

    Edition Notes

    Statementby Kumar S. Ray
    ContributionsSpringerLink (Online service)
    Classifications
    LC ClassificationsTA1637-1638
    The Physical Object
    Format[electronic resource] :
    PaginationXII, 173 p. 46 illus.
    Number of Pages173
    ID Numbers
    Open LibraryOL27088035M
    ISBN 109781461453482


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Soft Computing Approach to Pattern Classification and Object Recognition by Kumar S. Ray Download PDF EPUB FB2

Soft Computing Approach to Pattern Classification and Object Recognition establishes an innovative, unified approach to supervised pattern classification and model-based occluded object recognition.

The book also surveys various soft computing tools, fuzzy relational calculus (FRC), genetic algorithm (GA) and multilayer perceptron (MLP) to. Soft Computing Approach to Pattern Classification and Object Recognition is intended for researchers in the area of pattern classification and computer vision.

Other academics and practitioners will also find the book Computing Approach to Pattern Classification and Object Recognition: A Unified Concept (Hardcover)Brand: Kumar S Ray.

The basic aim of this research monograph is to develop a unified approach to supervised pattern classification (Tou and Gonzalez, Pattern Recognition Principles.

Addison-Wesley, Reading, ) and model based occluded object recognition (Koch and Kashyap, IEEE Trans Pattern Anal Machine lntell. 9(4)–, ; Ray and Dutta Mazumder Cited by: 1. The monograph essentially mimics the cognitive process of human decision making, and carries a message of perceptual integrity in representational diversity.

Soft Computing Approach to Pattern Classification and Object Recognition is intended for researchers in the area of pattern classification and computer vision.

Soft Computing Approach to Pattern Classification and Object Recognition establishes an innovative, unified approach to supervised pattern classification and model-based occluded object recognition.

The book also surveys various soft computing tools, fuzzy relational calculus (FRC), genetic algorithm (GA) and multilayer perceptron (MLP) to provide a strong. Soft Computing Approach to Pattern Classification and Object Recognition is intended for researchers in the area of pattern classification and computer vision.

Other academics and practitioners. 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 based reasoning, data mining and bio-informatics with a scope for future research.

This volume provides a collection of sixteen articles containing review and new material. In a unified way, they describe the recent development of theories and methodologies in pattern recognition, image processing and vision using fuzzy logic, artificial neural networks, genetic algorithms, rough sets and wavelets with significant real life book details the theory of.

We describe in this book, new methods for building intelligent systems for pattern recognition using type-2 fuzzy logic and soft computing techniques. Soft Computing (SC) consists of several. Neural Network Approaches for Speech Recognition. Weather Forecasting Using Soft Computing Technique.

An Object Oriented Fuzzy Programming Language. Soft ComputingA Fuzzy Logic Approach. Pattern Classification of ECG Signal Using Wavelet.

/5(2). This book presents practical development experiences in different Soft Computing Approach to Pattern Classification and Object Recognition book 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.

The book consists of three parts: (1) Pattern recognition methods and applications; (2) Computer vision and image processing; and (3) Systems, architecture and technology. This book is intended to capture the major developments in pattern recognition and computer vision though it is.

This book covers the entire gamut of soft computing, including fuzzy logic, rough sets, artificial neural networks, and various evolutionary algorithms. It offers a learner-centric approach where each new concept is introduced with carefully designed examples/instances to train the learner.

The objective of the present paper is to describe a pattern recognition approach for image segmentation using fuzzy clustering. Soft computing techniques have found wide applications. One of the most important applications is edge detection for image segmentation. Clustering analysis is one of the major techniques in pattern recognition.

These fuzzy clustering algorithms have been widely. Reviewer: Marc Roubens This book presents a fuzzy approach to the problems of pattern recognition, machine learning, and, especially, speech recognition.

The first three chapters are devoted to the mathematical framework of the theory of fuzzy sets, basic operations related to it, and the development of classification algorithms, mainly conventional algorithms known as “fuzzy clustering.

Pattern recognition encompasses two fundamental tasks: description and classification. Given an object to analyze, a pattern recognition system first generates a description of it (i.e., the pattern) and then classifies the object based on that description (i.e., the recognition).

Hybrid Intelligent Diagnosis Approach Based On Neural Pattern Recognition and Fuzzy Decision-Making: /ch Fault diagnosis is a complex and fuzzy cognitive process, and soft computing methods and technologies based.

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. approaches for pattern recognition are: 1) template matching, 2) statistical classification, 3) syntactic or structural match-ing, and 4) neural networks.

(Sergios Theodoridis,) Pattern recognition is a sci-entific discipline whose aim is the classification of the ob-jects into a lot of categories or classes. Pattern recognition is. Ray; Soft Computing Approach to Pattern Classification and Object Recognition, Springer,ISBN M.

Rezaei, R. Klette; Computer Vision for Driver Assistance, Springer,ISBN Soft computing based modeling approaches provide an alternative for identification of the system from the available data.

Emerging of soft computing began when Lotfi A. Zadeh was working on a fuzzy logic and soft analysis of data. This gave way to the intelligent systems such as neural network, fuzzy logic, genetic algorithm, etc. Applying emerging soft computing approaches to control chart pattern recognition for an SPC–EPC process.

and each object has its own corresponding measurable attributes. For example, U.S.A. His present research interests include statistical process control, data pattern recognition and classification, forecasting methods, and data mining.

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.

Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of. Soft computing, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth, and approximations.

In effect, the role model for soft computing is the human mind. The following topics are discussed: computer vision and image analysis; face and human analysis; face recognition; character recognition and document analysis; clustering algorithms; signal, speech and image processing; signal coding and compression; document image enhancement; visualization and restoration; systems, robotics and applications; biometrics; biomedical imaging; fingerprints.

A pattern recognition systems can be partitioned into are five typical components for various pattern recognition systems. These are as following: A Sensor: A sensor is a device used to measure a property, such as pressure, position, temperature, or.

Image processing-from basics to advanced applications Learn how to master image processing and compression with this outstanding state-of-the-art reference.

From fundamentals to sophisticated applications, Image Processing: Principles and Applications covers multiple topics and provides a fresh perspective on future directions and innovations in the field, including: * Image transformation.

Theories Template matching. Template matching theory describes the most basic approach to human pattern recognition. It is a theory that assumes every perceived object is stored as a "template" into long-term memory.

Incoming information is compared to these templates to find an exact match. In other words, all sensory input is compared to multiple representations of an object to form one. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. – Kim, D., Rho, S. and Hwang, E.

() Local feature-based multi-object recognition scheme for. Key words: Image processing, Artificial Neural Networks, Feature extraction, Pattern recognition, Insect classification, Soft Computing.

Introduction Farmers around the globe use pesticides as a main source to combat and eliminate insects from agricultural fields. The applications of pesticides lead. Stanford Libraries' official online search tool for books, media, journals, databases, government documents and more.

What is pattern recognition. • A pattern is an object, process or event that can be given a name. • A pattern class (or category) is a set of patterns sharing common attributes and usually originating from the same source.

• During recognition (or classification) given objects are assigned to prescribed classes. Pattern recognition solves classification problems; Pattern recognition solves the problem of fake bio metric detection.

It is useful for cloth pattern recognition for visually impaired blind people. It helps in speaker diarization. We can recognise particular object from different angle. Disadvantages: Syntactic Pattern recognition approach is.

SUSHMITA MITRA, PHD, is a Professor at Machine Intelligence Unit, Indian Statistical Institute, in Calcutta. She is a coauthor of Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing, also published by Wiley.

TINKU ACHARYA, PHD, Senior Executive vice president and Chief Science Officer of Avisere Inc., Tucson, Arizona, is involved in multimedia data mining applications. Object Recognition System Design in Computer Vision: a Universal Approach 3 contain separate strokes or letters, are fed into the classification system for recognition of alphabetical let-ters.

Our concept of object is essentially different. This concept comprises "all image fragments, which. To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy.

The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method. applications such as clustering, speech recognition, texture segmentation, vector coding etc [].

III. CLASSIFICATION Classification is one of the most frequently encountered decision making tasks of human activity. A classification problem occurs when an object needs to be assigned into a.

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.

Book Chapter Contributions. Xiaoxia Sun, Amirsina Torfi, and Nasser Nasrabadi, “Deep Siamese Convolutional Neural Networks for Identical Twins and look-alike Identification”, Deep Learning in Biometrics, Mayank Vatsa, Richa Singh and Angshul Majumdar (editors), CRC Press, Soheil Bahrampour, Nasser M.

Nasrabadi, and Asok Ray, “ Sparse Representation for Time-Series Classification. A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property ([email protected], [email protected]) 2Departments of Electrical and Computer Engineering, Center of Excellence on Soft Computing and Intelligent pattern recognition, classification and prediction.

In particular, it is tested on twelve UCI. Search Tips. Phrase Searching You can use double quotes to search for a series of words in a particular order. For example, "World war II" (with quotes) will give more precise results than World war II (without quotes). Wildcard Searching If you want to search for multiple variations of a word, you can substitute a special symbol (called a "wildcard") for one or more letters.