Digital Image Processing Gonzalez


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Digital Image Processing,2/e


Digital Image Processing,2/e

Author: Gonzalez

language: en

Publisher: Pearson Education India

Release Date: 2002


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Digital Image Processing


Digital Image Processing

Author: Rafael C. Gonzalez

language: en

Publisher: Prentice Hall

Release Date: 1992


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Completely self-contained and heavily illustrated, this introduction to basic concepts and methodologies for digital image processing is written at a level that is suitable for seniors and first-year graduate students in almost any technical discipline.

Digital Image Processing, Global Edition


Digital Image Processing, Global Edition

Author: Rafael C. Gonzalez

language: en

Publisher: Pearson UK

Release Date: 2018-06-21


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The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you will receive via email the code and instructions on how to access this product. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. For courses in Image Processing and Computer Vision. For years, 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 4th Edition is based on an extensive survey of faculty, students, and independent readers in 5 institutions from 3 countries. Their feedback led to expanded or new coverage of topics such as deep learning and deep neural networks, including convolutional neural nets, the scale-invariant feature transform (SIFT), MERS, graph cuts, k-means clustering and superpiels, active contours (snakes and level sets), and each 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 examples and homework exercises throughout the book.