"Version: 20241101"--Title page verso.

Includes bibliographical references.

1. Introduction -- 1.1. The scope and importance of digital image processing -- 1.2. Images -- 1.3. Digital images -- 1.4. Processes involved in image processing and recognition -- 1.5. Applications of image processing

2. Image enhancement in the spatial domain -- 2.1. Enhancement of contrast -- 2.2. Gray level transformations -- 2.3. Bit plane slicing -- 2.4. Histogram processing -- 2.5. Filtering in the spatial domain -- 2.6. Sharpening in the spatial domain

3. Filtering in the Fourier domain -- 3.1. From the Fourier series to the Fourier transform -- 3.2. Meaning of the Fourier transform -- 3.3. The impulse function -- 3.4. Fourier transform of a train of impulses -- 3.5. The convolution theorem -- 3.6. The discrete Fourier transform (DFT) -- 3.7. Additional properties of the DFT -- 3.8. Filtering in the Fourier domain -- 3.9. Low-pass filters -- 3.10. Other low-pass filters -- 3.11. High-pass filters -- 3.12. The FFT -- 3.13. Comparison of the FFT with convolution

4. Image compression -- 4.1. Basics of image compression -- 4.2. Basics of coding theory -- 4.3. Uniquely decodable codes (UDCs), instantaneously decodable codes (IDCs), and all that -- 4.4. Kraft's inequality -- 4.5. Efficiency of instantaneous codes -- 4.6. Information theory -- 4.7. Huffman coding : algorithm -- 4.8. Huffman coding : implementation -- 4.9. Nearly optimal codes -- 4.10. Reducing interpixel redundancy : run-length coding -- 4.11. LZW coding -- 4.12. Arithmetic coding -- 4.13. Transform coding

5. Image analysis and object recognition -- 5.1. Image analysis -- 5.2. Detection of points and lines -- 5.3. The Hough transform -- 5.4. Segmentation : edge detection -- 5.5. Thresholding -- 5.6. A global view of image analysis and pattern recognition -- 5.7. Representation of objects -- 5.8. Texture -- 5.9. Skeletonization or medial axis transformation (MAT) -- 5.10. Principal component analysis (PCA) -- 5.11. Pattern recognition

6. Image restoration -- 6.1. Analyzing motion blur -- 6.2. Inverse filtering -- 6.3. Noise -- 6.4. Removal of noise by morphological operations -- 6.5. Alternative method for extracting and labeling connected components -- 6.6. Image reconstruction from projections

7. Wavelets -- 7.1. Wavelets versus the Fourier transform -- 7.2. The Haar wavelet transform -- 7.3. An alternative view of wavelets

8. Color image processing -- 8.1. The RGB color model -- 8.2. The CMY and CMYK color models -- 8.3. The hue, saturation, and intensity (HSI) color model

9. Introduction to MATLABª -- 9.1. Introduction -- 9.2. Help with MATLABª -- 9.3. Variables -- 9.4. Mathematical operations -- 9.5. Loops and control statements -- 9.6. Built-in MATLABª functions -- 9.7. Some more useful MATLABª commands and programming practices -- 9.8. Functions

10. The image processing toolbox -- 10.1. Introduction -- 10.2. Reading from an image file and writing into an image file -- 10.3. Fourier domain processing -- 10.4. Calculation of entropy -- 10.5. Huffman code -- 10.6. Arithmetic code -- 10.7. Segmentation -- 10.8. Hough transform -- 10.9. Some common error messages in MATLAB -- 10.10. Using the MATLAB AI chat playground to generate MATLAB code -- 10.11. Exercises

11. Video processing -- 11.1. Introduction -- 11.2. Extracting frames from a video -- 11.3. Video compression -- 11.4. Detection and analysis of motion : optical flows -- 11.5. Exercises

12. Applications of machine learning -- 12.1. Linear discriminant analysis -- 12.2. Clustering using the k-means algorithm -- 12.3. K nearest neighbours -- 12.4. Artificial neural networks -- 12.5. Multilayer feedforward neural networks -- 12.6. Matrix formulation of ANNs -- 12.7. Back propagation for training neural networks

13. Error correcting codes and cryptography -- 13.1. Mutual information and channel capacity -- 13.2. Linear codes -- 13.3. Decoding linear codes -- 13.4. Cyclic codes -- 13.5. Galois fields -- 13.6. Cryptography -- 13.7. Encryption techniques -- 13.8. Public key cryptography -- 14. Solutions to selected exercises.

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Digital imaging is now ubiquitous, from the smart phone in your pocket to state-of-the-art medical imaging and satellite imagery. Raw imaging data can be manipulated using computer algorithms to perform a range of functions from reducing noise to complex image analysis and pattern and object recognition. The result is that digital image processing has applications in almost all areas of science and engineering. Designed for a one semester course, the aim of this book is to concentrate on the principles and techniques of image processing. This second edition includes important updates to the first edition, as well as two entirely new chapters, making the book ideal for advanced students in physics and engineering.

Professional and scholarly.

Also available in print.

Mode of access: World Wide Web.

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P.K. Thiruvikraman is a professor of physics at the Birla Institute of Technology & Science with a research interest in Digital Image Processing, Pattern Recognition / Machine Vision and Coding Theory. The author has been teaching at BITS Pilani for the last 20 years. He has taught Digital Image Processing for more than a decade.

Title from PDF title page (viewed on December 13, 2024).