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Издательство Springer, 2015, -430 pp.We, humans, generally called as Homo sapiens, are the most superior species in the globe. Probably the most complex part of human body is the vision system which is the combination of the two eyes, the brain, and perception. Our vision system is probably the most accurate sensor among the other five, to create an illustration of the nature around us onto our brain in terms of perception. Hence the eye rather the entire vision system is not only one image-capturing device like a camera, but also a complex processing device of the captured image. The processing includes detection, identification, recognition, analysis, synthesis, and modeling.

In today’s digital world, everything including some abstract concepts like emotions is expressed in terms of digital logic. The basic rule of digitization is finiteness and discreteness in the expression of any kind of data. The natural image can have infinite number of shades against each of the infinite number of colors. The variation of color along the space is also continuous and the precision of change of intensity in natural image might be infinitesimally small. Hence, in digital representation of image we must have to go for some kind of approximation in terms of primary colors, spatial samples, and quantized intensities.

Any system, whether a computing system or psycho-visual system, infers images in terms of a highly correlated signal. The aforementioned correlation is not necessarily neighborhood spatial correlation always. There can be a correlation in an image in terms of a repetitive texture, frequency component, shape, template, and many more. For any kind of object recognition from captured image we generally see that image from two different angles namely, local texture and holistic pattern.

This book illustrates the subject digital image processing from two different viewpoints. The first viewpoint is the signal processing perspective where each of the separation (primary color) of the image can be expressed as a two-dimensional (2D) matrix of discrete points representing intensities. These intensities must follow the aforementioned rule of digitization having finite upper limit, lower limit, and minimum step between adjacent levels. The second viewpoint is the pattern perspective where we discuss identification/selection, extraction, and reduction of suitable features from images, based on different applications and domains. Hence, the primary motivation of this book is to illustrate the wonderful subject of digital image processing from two different perspectives namely, signal processing and pattern recognition. The guide to the journey through the discipline of image processing would be signals and patterns. Wayne Wheeler, Sr. Editor of Springer, UK has proposed the title of this book as Guides to Signals and Patterns in Image Processing—Foundations, Methods and Applications. I believe this is the most appropriate title depicting the motivation of this text.

Even though the subject digital image processing has been evolved through years and requires background of mathematics, physics, computer science, statistics, and information theory, I have tried to describe the concepts in a lucid way. Wherever signal processing or background of statistics or information theory is required, I have mentioned the prerequisites briefly at the beginning of the chapter or as an appendix. Like all my other books, specially the last two books published by Springer, I started the chapters considering almost zero prerequisite and discussed the application levels and research scope in that particular area in depth after introducing the chapter of interest formally. Hence I believe this book would not only be a good text and reference book on image processing, but also a good teaching material for the teachers.Introduction to Digital Image

Image Enhancement in Spatial Domain

Interpretation and Processing of Image in Frequency Domain

Color Science and Color Technology

Wavelets: Multiresolution Image Processing

Compression and Encoding of Image: Image Formats

Morphology-Based Image Processing

Patterns in Images and Their Applications

Psycho-visual pattern recognition: Computer Vision

A: Digital Differentiation and Edge Detection

B: Elementary Probability Theory

C: Frequently Used MATLAB Functions

In today’s digital world, everything including some abstract concepts like emotions is expressed in terms of digital logic. The basic rule of digitization is finiteness and discreteness in the expression of any kind of data. The natural image can have infinite number of shades against each of the infinite number of colors. The variation of color along the space is also continuous and the precision of change of intensity in natural image might be infinitesimally small. Hence, in digital representation of image we must have to go for some kind of approximation in terms of primary colors, spatial samples, and quantized intensities.

Any system, whether a computing system or psycho-visual system, infers images in terms of a highly correlated signal. The aforementioned correlation is not necessarily neighborhood spatial correlation always. There can be a correlation in an image in terms of a repetitive texture, frequency component, shape, template, and many more. For any kind of object recognition from captured image we generally see that image from two different angles namely, local texture and holistic pattern.

This book illustrates the subject digital image processing from two different viewpoints. The first viewpoint is the signal processing perspective where each of the separation (primary color) of the image can be expressed as a two-dimensional (2D) matrix of discrete points representing intensities. These intensities must follow the aforementioned rule of digitization having finite upper limit, lower limit, and minimum step between adjacent levels. The second viewpoint is the pattern perspective where we discuss identification/selection, extraction, and reduction of suitable features from images, based on different applications and domains. Hence, the primary motivation of this book is to illustrate the wonderful subject of digital image processing from two different perspectives namely, signal processing and pattern recognition. The guide to the journey through the discipline of image processing would be signals and patterns. Wayne Wheeler, Sr. Editor of Springer, UK has proposed the title of this book as Guides to Signals and Patterns in Image Processing—Foundations, Methods and Applications. I believe this is the most appropriate title depicting the motivation of this text.

Even though the subject digital image processing has been evolved through years and requires background of mathematics, physics, computer science, statistics, and information theory, I have tried to describe the concepts in a lucid way. Wherever signal processing or background of statistics or information theory is required, I have mentioned the prerequisites briefly at the beginning of the chapter or as an appendix. Like all my other books, specially the last two books published by Springer, I started the chapters considering almost zero prerequisite and discussed the application levels and research scope in that particular area in depth after introducing the chapter of interest formally. Hence I believe this book would not only be a good text and reference book on image processing, but also a good teaching material for the teachers.Introduction to Digital Image

Image Enhancement in Spatial Domain

Interpretation and Processing of Image in Frequency Domain

Color Science and Color Technology

Wavelets: Multiresolution Image Processing

Compression and Encoding of Image: Image Formats

Morphology-Based Image Processing

Patterns in Images and Their Applications

Psycho-visual pattern recognition: Computer Vision

A: Digital Differentiation and Edge Detection

B: Elementary Probability Theory

C: Frequently Used MATLAB Functions

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Издательство CRC Press, 2002, -493 pp.
Image recognition and classification is one of the most actively pursued areas in the broad field of imaging sciences and engineering. The reason is evident: the ability to replace human visual capabilities with a machine is very important and there are diverse applications. The main idea is to inspect an image scene by processing data...

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