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Original Essays | September 17, 2014

Merritt Tierce: IMG Has My Husband Read It?



My first novel, Love Me Back, was published on September 16. Writing the book took seven years, and along the way three chapters were published in... Continue »
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    Love Me Back

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Evolutionary Synthesis of Pattern Recognition Systems (Monographs in Computer Science)

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Evolutionary Synthesis of Pattern Recognition Systems (Monographs in Computer Science) Cover

 

Synopses & Reviews

Publisher Comments:

Designing object detection and recognition systems that work in the real world is a challenging task due to various factors including the high complexity of the systems, the dynamically changing environment of the real world and factors such as occlusion, clutter, articulation, and various noise contributions that make the extraction of reliable features quite difficult. Evolutionary Synthesis of Pattern Recognition Systems presents novel effective approaches based on evolutionary computational techniques, such as genetic programming (GP), linear genetic programming (LGP), coevolutionary genetic programming (CGP) and genetic algorithms (GA) to automate the synthesis and analysis of object detection and recognition systems. The book's concepts, principles, and methodologies will enable readers to automatically build robust and flexible systems--in a systematic manner--that can provide human-competitive performance and reduce the cost of designing and maintaining these systems. Its content covers all key aspects of object recognition: object detection, feature selection, feature discovery, object recognition, domain knowledge. Basic knowledge of programming and data structures, and some calculus, is presupposed. Topics and Features: *Presents integrated coverage of object detection/recognition systems *Describes how new system features can be generated "on the fly," and how systems can be made flexible and applied to a variety of objects and images *Demonstrates how object detection and recognition systems can be automatically designed and maintained in a relatively inexpensive way *Explains automatic synthesis and creation of programs (which saves valuable human and economic resources) *Focuses on results using real-world imagery, thereby concretizing the book's novel ideas This accessible monograph provides the computational foundation for evolutionary synthesis involving pattern recognition and is an ideal overview of the latest concepts and technologies. Computer scientists, researchers, and electrical and computer engineers will find the book a comprehensive resource, and it can serve equally well as a text/reference for advanced students and professional self-study.

Synopsis:

Integrates computer vision, pattern recognition, and AI. Presents original research that will benefit researchers and professionals in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology

Synopsis:

 Evolutionary computation is becoming increasingly important for computer vision and pattern recognition and provides a systematic way of synthesis and analysis of object detection and recognition systems. Incorporating "learning" into recognition systems will enable these systems to automatically generate new features on the fly and cleverly select a good subset of features according to the type of objects and images to which they are applied.This unique monograph investigates evolutionary computational techniques---such as genetic programming, linear genetic programming, coevolutionary genetic programming and genetic algorithms---to automate the synthesis and analysis of object detection and recognition systems. The book achieves four aims:  *Shows the efficacy of genetic programming and coevolutionary genetic programming in synthesizing effective composite operators and composite features from domain-independent primitive image processing operations and primitive features (both elementary and complex) for object detection and recognition. *Integrates smart crossover, smart mutation and a new fitness function based on minimum description length (MDL) principle in a design to improve genetic programming's efficiency*Proposes a new MDL-based fitness function to improve the genetic algorithms performance on feature selection for object detection and recognition.*Synthesizes recognition systems by using adaptive coevolutionary linear genetic programming (LGP) in conjunction with general computer vision and image processing operatorsThe purpose of incorporating learning into the system design is to avoid the time-consuming process of feature generation and selection and to reduce the cost of building object detection and recognition systems.Researchers, professionals, engineers, and students working in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology will find this well-developed and organized volume an invaluable resource.

Table of Contents

List of Figures.- List of Tables.- Preface.- Introduction.- Feature Synthesis for Object Detection.- MDL-Based Efficient Genetic Programming for Object Detection.- Feature Selection for Object Detection.- Evolutionary Feature Synthesis for Object Recognition.- Linear Genetic Programming for Object Recognition Applications of Linear Genetic Programming for Object Recognition.- Summary and Future Work.- References.- Index.

Product Details

ISBN:
9780387212951
Author:
Bhanu, Bir
Publisher:
Springer
Author:
Lin, Yingqiang
Author:
Krawiec, Krzysztof
Author:
Krawiec, Chris
Subject:
Computer Science
Subject:
Computer Graphics - General
Subject:
Image processing
Subject:
Computer Vision
Subject:
Pattern recognition systems
Subject:
Genetic algorithms
Subject:
Pattern recognition.
Subject:
Genetic programming
Subject:
Artificial Intelligence - General
Subject:
Object detection
Subject:
Computer Graphics - Image Processing
Subject:
Intelligence (AI) & Semantics
Subject:
Image processing -- Digital techniques.
Subject:
Computer Imaging, Vision, Pattern Recognition and Graphics
Subject:
Image Processing and Computer Vision
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
User Interfaces and Human Computer Interaction
Subject:
Computation by Abstract Devices
Subject:
Personal Computers-General
Copyright:
Edition Number:
1
Edition Description:
Book
Series:
Monographs in Computer Science
Publication Date:
20050217
Binding:
HARDCOVER
Language:
English
Illustrations:
Y
Pages:
320
Dimensions:
235 x 155 mm 1380 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Artificial Intelligence » Image Processing
Computers and Internet » Computers Reference » General
Computers and Internet » Graphics » Image Processing
Computers and Internet » Networking » General
Computers and Internet » Personal Computers » General
Health and Self-Help » Health and Medicine » Medical Specialties
Science and Mathematics » Electricity » General Electronics
Science and Mathematics » Mathematics » General

Evolutionary Synthesis of Pattern Recognition Systems (Monographs in Computer Science) New Hardcover
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Product details 320 pages Springer - English 9780387212951 Reviews:
"Synopsis" by , Integrates computer vision, pattern recognition, and AI. Presents original research that will benefit researchers and professionals in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology
"Synopsis" by ,  Evolutionary computation is becoming increasingly important for computer vision and pattern recognition and provides a systematic way of synthesis and analysis of object detection and recognition systems. Incorporating "learning" into recognition systems will enable these systems to automatically generate new features on the fly and cleverly select a good subset of features according to the type of objects and images to which they are applied.This unique monograph investigates evolutionary computational techniques---such as genetic programming, linear genetic programming, coevolutionary genetic programming and genetic algorithms---to automate the synthesis and analysis of object detection and recognition systems. The book achieves four aims:  *Shows the efficacy of genetic programming and coevolutionary genetic programming in synthesizing effective composite operators and composite features from domain-independent primitive image processing operations and primitive features (both elementary and complex) for object detection and recognition. *Integrates smart crossover, smart mutation and a new fitness function based on minimum description length (MDL) principle in a design to improve genetic programming's efficiency*Proposes a new MDL-based fitness function to improve the genetic algorithms performance on feature selection for object detection and recognition.*Synthesizes recognition systems by using adaptive coevolutionary linear genetic programming (LGP) in conjunction with general computer vision and image processing operatorsThe purpose of incorporating learning into the system design is to avoid the time-consuming process of feature generation and selection and to reduce the cost of building object detection and recognition systems.Researchers, professionals, engineers, and students working in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology will find this well-developed and organized volume an invaluable resource.
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