Image processing comprises a broad variety of methods that operate on images to produce another image. A unique textbook, Introduction to Image Processing and Analysis establishes the programming involved in image processing and analysis by utilizing skills in C compiler and both Windows and MacOS programming environments. The provided mathematical background illustrates the workings of algorithms and emphasizes the practical reasons for using certain methods, their effects on images, and their appropriate applications. The text concentrates on image processing and measurement and details the implementation of many of the most widely used and most important image processing and analysis algorithms. Homework problems are included in every chapter with solutions available for download from the CRC Press website The chapters work together to combine image processing with image analysis. The book begins with an explanation of familiar pixel array and goes on to describe the use of frequency space. Chapters 1 and 2 deal with the algorithms used in processing steps that are usually accomplished by a combination of measurement and processing operations, as described in chapters 3 and 4. The authors present each concept using a mixture of three mutually supportive tools: a description of the procedure with example images, the relevant mathematical equations behind each concept, and the simple source code (in C), which illustrates basic operations. In particularly, the source code provides a starting point to develop further modifications. Written by John Russ, author of esteemed Image Processing Handbook now in its fifth edition, this book demonstrates functions to improve an image's of features and detail visibility, improve images for printing or transmission, and facilitate subsequent analysis.

I. The past. the present . . . and the future It is possible to take the view that ever since it began, the "ancient" branch of physics known as Optics has been concerned with process ing images. But since the Nineteen-Thirties increasingly close ties have been forming between Optics, which until then had been largely based on instruments, and the sciences of communication and infor mation arising out of mathematics and electronics. Such developments follow naturally, since communication systems and image-forming systems are all designed to receive or transmit information. Further more the same mathematical forms are used for describing the beha viour of electrical and optical systems. It is a question of systems theory, particularly linear systems, and of Fourier's analysis methods, which together constitute an important part of Signal Theory. In the case of communication systems carrying signals of an electrical nature, information is time-related or temporal. Transmitted signals are one-dimensional and functions of a single variable, time t. In the case of optical systems information is spatial in nature. Signals are distributions of light intensity in space. In general they are treated as two-dimensional signals, being functions of two spatia! variables written as x and y. In the early Fifties the way forward became clearer still when some scientists at the Institut d'Optique in Paris began using optical filtering techniques in coherent light in order to enhance the quality of photographs.

Written as an introduction for undergraduate students, this textbook covers the most important methods in digital image processing. Formal and mathematical aspects are discussed at a fundamental level and various practical examples and exercises supplement the text. The book uses the image processing environment ImageJ, freely distributed by the National Institute of Health. A comprehensive website supports the book, and contains full source code for all examples in the book, a question and answer forum, slides for instructors, etc. Digital Image Processing in Java is the definitive textbook for computer science students studying image processing and digital processing.

This textbook presents the fundamental concepts and methods for understanding and working with images and video in an unique, easy-to-read style which ensures the material is accessible to a wide audience. Exploring more than just the basics of image processing, the text provides a specific focus on the practical design and implementation of real systems for processing video data. Features: includes more than 100 exercises, as well as C-code snippets of the key algorithms; covers topics on image acquisition, color images, point processing, neighborhood processing, morphology, BLOB analysis, segmentation in video, tracking, geometric transformation, and visual effects; requires only a minimal understanding of mathematics; presents two chapters dedicated to applications; provides a guide to defining suitable values for parameters in video and image processing systems, and to conversion between the RGB color representation and the HIS, HSV and YUV/YCbCr color representations.

This book explores image processing from several perspectives: the creative, the theoretical (mainly mathematical), and the programmatical. It explains the basic principles of image processing, drawing on key concepts and techniques from mathematics, psychology of perception, computer science, and art, and introduces computer programming as a way to get more control over image processing operations. It does so without requiring college-level mathematics or prior programming experience. The content is supported by PixelMath, a freely available software program that helps the reader understand images as both visual and mathematical objects. The first part of the book covers such topics as digital image representation, sampling, brightness and contrast, color models, geometric transformations, synthesizing images, stereograms, photomosaics, and fractals. The second part of the book introduces computer programming using an open-source version of the easy-to-learn Python language. It covers the basics of image analysis and pattern recognition, including edge detection, convolution, thresholding, contour representation, and K-nearest-neighbor classification. A chapter on computational photography explores such subjects as high-dynamic-range imaging, autofocusing, and methods for automatically inpainting to fill gaps or remove unwanted objects in a scene. Applications described include the design and implementation of an image-based game. The PixelMath software provides a "transparent" view of digital images by allowing the user to view the RGB values of pixels by zooming in on an image. PixelMath provides three interfaces: the pixel calculator; the formula page, an advanced extension of the calculator; and the Python window.

This book introduces the statistical software R to the image processing community in an intuitive and practical manner. R brings interesting statistical and graphical tools which are important and necessary for image processing techniques. Furthermore, it has been proved in the literature that R is among the most reliable, accurate and portable statistical software available. Both the theory and practice of R code concepts and techniques are presented and explained, and the reader is encouraged to try their own implementation to develop faster, optimized programs. Those who are new to the field of image processing and to R software will find this work a useful introduction. By reading the book alongside an active R session, the reader will experience an exciting journey of learning and programming.

Image recognition has become an increasingly dynamic field with new and emerging civil and military applications in security, exploration, and robotics. Written by experts in fractal-based image and video compression, A Concise Introduction to Image Processing using C++ strengthens your knowledge of fundamentals principles in image acquisition, con

The subject of digital image processing has migrated from a graduate to a junior or senior level course as students become more proficient in mathematical background earlier in their college education. With that in mind, Introduction to Digital Image Processing is simpler in terms of mathematical derivations and eliminates derivations of advanced s

For junior/graduate-level courses in Remote Sensing in Geography, Geology, Forestry, and Biology. This revision of Introductory Digital Image Processing: A Remote Sensing Perspective continues to focus on digital image processing of aircraft- and satellite-derived, remotely sensed data for Earth resource management applications. Extensively illustrated, it explains how to extract biophysical information from remote sensor data for almost all multidisciplinary land-based environmental projects. Part of the Prentice Hall Series Geographic Information Science.