For years, Digital Image Processing has been the foundational text for the study of digital image processing. The book is suited for students at the college senior and first-year graduate level with prior background in mathematical analysis, vectors, matrices, probability, statistics, linear systems, and computer programming. As in all earlier editions, the focus of this edition of the book is on fundamentals.

The Fourth Edition, which celebrates the book-s 20th anniversary, is based on an etensive survey of faculty, students, and independent readers in 5 institutions from 3 countries. Their feedback led to epanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform SIFT, maimally-stable etremal regions MSERs, graph cuts, k-means clustering and superpiels, active contours snakes and level sets, and eact histogram matching. Major improvements were made in reorganising the material on image transforms into a more cohesive presentation, and in the discussion of spatial kernels and spatial filtering. Major revisions and additions were made to eamples and homework eercises throughout the book. For the first time, we added MATLAB projects at the end of every chapter, and compiled support packages for students and faculty containing, solutions, image databases, and sample code.

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