Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab
This is an introductory to intermediate level text on the scienceof image processing, which employs the Matlab programming languageto illustrate some of the elementary, key concepts in modern imageprocessing and pattern recognition. The approach taken isessentially practical and the book offers a framework within whichthe concepts can be understood by a series of well chosen examples,exercises and computer experiments, drawing on specific examplesfrom within science, medicine and engineering. Clearly divided into eleven distinct chapters, the book beginswith a fast-start introduction to image processing toenhance the accessibility of later topics. Subsequent chaptersoffer increasingly advanced discussion of topics involving morechallenging concepts, with the final chapter looking atthe application of automated image classification (with Matlabexamples) . Matlab is frequently used in the book as a tool fordemonstrations, conducting experiments and for solving problems, asit is both ideally suited to this role and is widely available.Prior experience of Matlab is not required and those without accessto Matlab can still benefit from the independent presentation oftopics and numerous examples. Features a companion website www.wiley.com/go/solomon/fundamentalscontaining a Matlab fast-start primer, further exercises, examples, instructor resources and accessibility to allfiles corresponding to the examples and exercises within the bookitself. Includes numerous examples, graded exercises and computerexperiments to support both students and instructors alike.
Why Read This Book
You should read this book if you want a concise, hands-on introduction to image processing that teaches core principles through working MATLAB examples and exercises. It emphasizes practical algorithms and experiments so you can quickly move from theory to implementation and prototyping.
Who Will Benefit
Undergraduate students, engineers, and practitioners who need a practical, MATLAB-based introduction to image processing and basic pattern recognition for science, medicine, or engineering applications.
Level: Intermediate — Prerequisites: Basic calculus and linear algebra, familiarity with signals/images at a conceptual level, and introductory experience with MATLAB (or Octave) for running examples.
Key Takeaways
- Represent and manipulate digital images and color spaces, understanding sampling, quantization, and file formats.
- Implement spatial and frequency-domain filtering (including FFT-based convolution) to denoise and enhance images.
- Design and apply edge detection and segmentation algorithms to locate boundaries and partition images.
- Use morphological operations to process shapes, perform connected-component analysis, and extract region properties.
- Apply basic image restoration and compression concepts (noise models, inverse/ Wiener filtering, JPEG principles).
- Extract simple features and perform basic pattern recognition/classification workflows using MATLAB examples.
Topics Covered
- 1. Introduction and Image Representation (sampling, quantization, file formats)
- 2. Intensity Transformations and Spatial Filtering
- 3. Frequency Domain Processing and the 2-D Fourier Transform / FFT
- 4. Image Restoration and Noise Models
- 5. Color Image Processing and Color Models
- 6. Morphological Image Processing
- 7. Segmentation (thresholding, region growing, edge-based methods)
- 8. Feature Extraction and Descriptors
- 9. Pattern Recognition and Classification Basics
- 10. Image Compression Fundamentals
- 11. Applications, Case Studies and MATLAB Experiments
Languages, Platforms & Tools
How It Compares
More application- and MATLAB-focused and more concise than Gonzalez & Woods' Digital Image Processing (which is more comprehensive and theory-heavy); more tutorial and example-driven than large reference handbooks like The Image Processing Handbook.
