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Image Processing. Principles and Applications Applications

Добавлено: 8-01-2014, 02:04

Автор: Acharya T., Ray A.K.
Название: Image Processing. Principles and Applications Applications
Издательство: John Wiley
Год: 2005
Формат: PDf
Размер: 31 Мб
Язык: Английский
Страниц: 451
Для сайта: MirKnig.com

There is a growing demand of image processing in diverse application areas, such as multimedia computing, secured image data communication, biomedical imaging, biometrics, remote sensing, texture understanding, pattern recognition, content-based image retrieval, compression, and so on. As a result, it has become extremely important to provide a fresh look at the contents of an introductory book on image processing. We attempted to introduce some of these recent developments, while retaining the classical ones.
The first chapter introduces the fundamentals of the image processing techniques, and also provides a window to the overall organization of the book. The second chapter deals with the principles of digital image formation and representation. The third chapter has been devoted to color and color imagery. In addition to the principles behind the perception of color and color space transforation, we have introduced the concept of color interpolation or demosaicing, which is today an integrated part of any color imaging device. We have described various image transformation techniques in Chapter

4. Wavelet transformation has become very popular in recent times for its many salient features. Chapter 5 has been devoted to wavelet transformation.
The importance of understanding the nature of noise prevalent in various types of images cannot be overemphasized. The issues of image enhancement and restoration including noise modeling and filtering have been detailed in Chapter
6. Image segmentation is an important task in image processing and pattern recognition. Various segmentation schemes have been elaborated in Chapter
7. Once an image is appropriately segmented, the next important task involves classification and recognition of the objects in the image. Various pattern classification and object recognition techniques have been presented in Chapter
8. Texture and shape play very important roles in image understanding. A number of texture and shape analysis techniques have been detailed in Chapter 9.
In sharp contrast with the classical crisp image analysis, fuzzy set theoretic approaches provide elegant methodologies for many image processing tasks. Chapter 10 deals with a number of fuzzy set theoretic approaches. We introduce content-based image retrieval and image mining in Chapter

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