Digital image processing

著者

書誌事項

Digital image processing

Rafael C. Gonzalez, Richard E. Woods

Pearson, c2018

4th ed

大学図書館所蔵 件 / 全21件

この図書・雑誌をさがす

注記

Includes bibliographical references (p. [1143]-1155) and index

内容説明・目次

内容説明

Introduce your students to image processing with the industry's most prized text For 40 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, which celebrates the book's 40th anniversary, is based on an extensive survey of faculty, students, and independent readers in 150 institutions from 30 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), maximally-stable extremal regions (MSERs), graph cuts, k-means clustering and superpixels, active contours (snakes and level sets), and exact histogram matching. Major improvements were made in reorganizing 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. For the first time, we added MATLAB projects at the end of every chapter, and compiled support packages for you and your teacher containing, solutions, image databases, and sample code. The support materials for this title can be found at www.ImageProcessingPlace.com

目次

1. Introduction What Is Digital Image Processing? The Origins of Digital Image Processing Examples of Fields that Use Digital Image Processing Fundamental Steps in Digital Image Processing Components of an Image Processing System 2. Digital Image Fundamentals Elements of Visual Perception Light and the Electromagnetic Spectrum. Image Sensing and Acquisition Image Sampling and Quantization Some Basic Relationships Between Pixels An Introduction to the Mathematical Tools Used in Digital Image Processing 3. Intensity Transformations and Spatial Filtering Background Some Basic Intensity Transformation Functions Histogram Processing. Fundamentals of Spatial Filtering Smoothing Spatial Filters Sharpening Spatial Filters Combining Spatial Enhancement Methods Using Fuzzy Techniques for Intensity Transformations and Spatial Filtering 4. Filtering in the Frequency Domain Background Preliminary Concepts Sampling and the Fourier Transform of Sampled Functions The Discrete Fourier Transform (DFT) of One Variable Extension to Functions of Two Variables Some Properties of the 2-D Discrete Fourier Transform The Basics of Filtering in the Frequency Domain Image Smoothing Using Frequency Domain Filters Image Sharpening Using Frequency Domain Filters Selective Filtering Implementation 5. Image Restoration and Reconstruction A Model of the Image Degradation/Restoration Process Noise Models Restoration in the Presence of Noise Only-Spatial Filtering Periodic Noise Reduction by Frequency Domain Filtering Linear, Position-Invariant Degradations. Estimating the Degradation Function Inverse Filtering Minimum Mean Square Error (Wiener) Filtering Constrained Least Squares Filtering. Geometric Mean Filter Image Reconstruction from Projections. 6. Color Image Processing Color Fundamentals Color Models Pseudocolor Image Processing Basics of Full-Color Image Processing Color Transformations. Smoothing and Sharpening Image Segmentation Based on Color Noise in Color Images Color Image Compression 7. Wavelets and Multiresolution Processing Background Multiresolution Expansions Wavelet Transforms in One Dimension The Fast Wavelet Transform Wavelet Transforms in Two

「Nielsen BookData」 より

詳細情報

ページトップへ