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Modern algorithms for image processing : computer imagery by example using C#

Author: Vladimir Kovalevsky
Publisher: [New York, New York : Apress, New York, NY : Distributed by Springer Science+Business Media New York [2019] ©2019
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Utilize modern methods for digital image processing and take advantage of the many time-saving templates provided for all of the projects included in this book. Modern Algorithms for Image Processing approaches the topic of image processing through teaching by example. Throughout the book, you will create projects that resolve typical problems that you might encounter in the world of digital image processing. Some  Read more...
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Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Vladimir Kovalevsky
ISBN: 9781484242377 1484242378
OCLC Number: 1085513943
Description: 1 online resource (1 volume) : illustrations
Contents: Intro; Table of Contents; About the Author; Acknowledgments; Introduction; Part I: Image Processing; Chapter 1: Introduction; Chapter 2: Noise Reduction; The Simplest Filter; The Simplest Averaging Filter; The Fast Averaging Filter; The Fast Gaussian Filter; The Median Filter; Sigma Filter: The Most Efficient One; Suppression of Impulse Noise; Chapter 3: Contrast Enhancement; Automatic Linear Contrast Enhancement; Histogram Equalization; Measuring the Lightness of Color Images; Contrast of Color Images; Manually Controlled Contrast Enhancement; Chapter 4: Shading Correction with Thresholding Thresholding the ImagesChapter 5: Project WFshadBinImpulse; Part II: Image Analysis; Chapter 6: Edge Detection; Laplacian Operator; The Method of Zero Crossing; Are Zero Crossings of Laplacian Closed Curves?; How to Eliminate Irrelevant Crossings; Noise Reduction Before Using the Laplacian; Blur During the Digitization and Extreme Value Filter; Fundamental Errors of the Method of Zero Crossing in the Laplacian; Chapter 7: A New Method of Edge Detection; Means for Encoding the Edges; The Idea of an Abstract Cell Complex; A Simple Method of Encoding Edges Improvements of the Method of Binarized GradientFurther Improvements of the Method of Binarized Gradient; The Edge Detector of Canny; Edges in Color Images; Conclusions; Chapter 8: A New Method of Image Compression; Using a Cell Complex for the Encoding of Boundaries; Description of the Project WFcompressPal; The Project WFrestoreLin; Chapter 9: Image Segmentation and Connected Components; Segmentation by Quantizing the Colors; Connected Components; The Graph Traversal Algorithm and Its Code; The Pseudo-Code of the Breadth-First Algorithm; The Approach of Equivalence Classes The Pseudo-Code of the Root AlgorithmThe Project WFsegmentAndComp; Conclusion; Chapter 10: Straightening Photos of Paintings; The Principle of Straightening; Codes of Most Important Methods; Conclusion; Chapter 11: Polygonal Approximation of Region Boundaries and Edges; The Problem of Polygonal Approximation; Schlesinger's Measure of Similarity of Curves; Statement of the Approximation Problem; Algorithms for Polygonal Approximation; The Split-and-Merge Method; The Sector Method; The Improvement of the Sector Method; Replacing Polygons by Sequences of Arcs and Straight Lines Definitions and the Problem StatementThe Approximate Solution; The Project WFpolyArc; Methods Used in the Project WFpolyArc; Precision of the Calculation of the Radii; Conclusion; Chapter 12: Recognition and Measurement of Circular Objects; Mathematical Foundation of the Method; The Project WFcircleReco; The Form of the Project WFcircleReco; Chapter 13: Recognition of Bicycles in Traffic; Mathematical Foundation of Ellipse Recognition; The Project WFellipseBike; Another Method of Recognizing the Direction; Chapter 14: A Computer Model of Cell Differentiation; Conclusion; References; Index
Responsibility: Vladimir Kovalevsky.

Abstract:

Utilize modern methods for digital image processing and take advantage of the many time-saving templates provided for all of the projects included in this book. Modern Algorithms for Image Processing approaches the topic of image processing through teaching by example. Throughout the book, you will create projects that resolve typical problems that you might encounter in the world of digital image processing. Some example projects teach you how to address the quality of images, such as reducing random errors or noise. Other methods will teach you how to correct inhomogeneous illumination, not by means of subtracting the mean illumination, but through division, which is a far more efficient method. Additional projects cover contrasting, edge detection, and edge detection in color images, which are important concepts to understand for image analysis. This book does not prove or disprove theorems, but instead details suggested methods to help you learn valuable concepts and how to customize your own image processing projects. What You'll Learn: Know the pros and cons of enlisting a particular method Use new methods for image compression and recognizing circles in photos Utilize a method for straightening photos of paintings taken at an oblique angle, a critical concept to understand when using flash at a right angle Understand the problem statement of polygonal approximation of boundaries or edges and its solution Access complete source code examples of all projects on GitHub The book is for C# developers who work with digital image processing or are interested in informatics. The reader should have programming experience and access to an integrated development environment (IDE), ideally .NET. Vladimir A. Kovalevsky holds a diploma in physics, a PhD in technical sciences, and a PhD in computer science. He has been a researcher, professor, and visiting professor at many esteemed universities worldwide, including the Central Institute of Cybernetics of the Academy of Sciences, University of Applied Sciences, and the Manukau Institute of Technology. Dr. Kovalevsky has been a plenary speaker at many conferences and his research interests include digital geometry, digital topology, computer vision, image processing, and pattern recognition. He has published four monographs and more than 180 journal and conference papers.

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