Give your students a contemporary treatment of image processing that balances a broad coverage of major subject areas with in-depth examination of the most foundational topics. Birchfield’s IMAGE PROCESSING AND ANALYSIS offers a clear presentation that even your beginning students can follow along with higher-level discussions that will challenge your most advanced students. The book effectively balances key topics from the field of image processing in a format that gradually progresses from easy to more challenging material, while consistently reinforcing a fundamental understanding of the core concepts. The book’s hands-on learning approach and full-color presentation allow your students to begin working with images immediately. The book encourages programming as it incorporates algorithmic details and hints, using numerous full-color illustrations and detailed pseudocode to facilitate an understanding of algorithms and aid in implementation.

1. INTRODUCTION.
Image processing and analysis. History and related fields. Sample applications. Image basics. Looking forward. Further reading. Problems.
2. FUNDAMENTALS OF IMAGING.
Vision in nature. Image formation. Image acquisition. Other imaging modalities. A detailed look at electromagnetic radiation. Further reading. Problems.
3. POINT AND GEOMETRIC TRANSFORMATIONS.
Simple geometric transformations. Graylevel transformations. Graylevel histograms. Multi-spectral transformations. Multi-image transformations. Change detection. Compositing. Interpolation. Warping. Further reading. Problems.
4. BINARY IMAGE PROCESSING.
Morphological operations. Labeling regions. Computing distance in a digital image. Region properties. Skeletonization. Boundary representations.
5. SPATIAL-DOMAIN FILTERING.
Convolution. Smoothing by convolving with a Gaussian. Computing the first derivative. Computing the second derivative. Nonlinear filters. Grayscale morphological operators. Further reading. Problems.
6. FREQUENCY-DOMAIN PROCESSING.
Fourier transform. Discrete Fourier transform (DFT). Two-dimensional DFT. Frequency-domain filtering. Localizing frequencies in time. Discrete wavelet transform (DWT). Further reading. Problems.
7. EDGES AND FEATURES.
Multiresolution processing. Edge detection. Approximating intensity edges with polylines. Feature detectors. Feature descriptors. Further reading. Problems.
8. COMPRESSION.
Basics. Lossless compression. Lossy compression. Compression of videos. Further reading. Problems.
9. COLOR.
Physics and psychology of color. Trichromacy. Designating colors. Linear color transformations. Color spaces. Further reading. Problems.
10. SEGMENTATION.
Thresholding. Deformable models. Image segmentation. Graph-based methods. Further reading. Problems.
11. MODEL FITTING
Fitting curves. Fitting point cloud models. Robustness to noise. Fitting multiple models. Further reading. Problems.
12. CLASSIFICATION.
Fundamentals. Statistical pattern recognition. Generative methods. Discriminative methods. Further reading. Problems.
13. STEREO AND MOTION.
Human stereopsis. Matching stereo images. Computing optical flow. Projective geometry. Camera calibration. Geometry of multiple views. Further reading. Problems.

Give your students a contemporary treatment of image processing that balances a broad coverage of major subject areas with in-depth examination of the most foundational topics. Birchfield’s IMAGE PROCESSING AND ANALYSIS offers a clear presentation that even your beginning students can follow along with higher-level discussions that will challenge your most advanced students. The book effectively balances key topics from the field of image processing in a format that gradually progresses from easy to more challenging material, while consistently reinforcing a fundamental understanding of the core concepts. The book’s hands-on learning approach and full-color presentation allow your students to begin working with images immediately. The book encourages programming as it incorporates algorithmic details and hints, using numerous full-color illustrations and detailed pseudocode to facilitate an understanding of algorithms and aid in implementation. BOOK EMPHASIZES A PRACTICAL, WORKING APPROACH WITH DETAILED PSEUDOCODE. The author highlights pseudocode, complete with variables and data structures, to facilitate a working, practical understanding of the algorithms and aid students in implementation. ORGANIZED FROM SIMPLEST ALGORITHMS TO THE MORE COMPLEX. With this book’s unique approach, your students can start writing working code immediately while still developing the skills to tackle more challenging algorithms. SPECIFIC PSEUDOCODE ADDRESSES THE MOST COMMON ALGORITHMS. Your students examine basic image processing algorithms, such as floodfill, erosion, dilation, and Canny edge detection, before progressing to more advanced computer vision algorithms, such as Chan-Vese level sets, SIFT feature detection, and Lucas-Kanade feature tracking. THOROUGH DISCUSSION HIGHLIGHTS IMPORTANT IMPLEMENTATION DETAILS AND PITFALLS. The book carefully addresses key topics, such as the inefficiency and impracticability of the recursive version of floodfill, the need to use atan2 when computing the orientation of a binary region, how gamma compression renders the most common approach of RGB to grayscale conversion ineffective, and the inapplicability of using the Cholesky decomposition when enforcing Euclidean constraints in the Tomasi-Kanade structure-from-motion factorization method. ADVANCED MATHEMATICAL CONCEPTS ARE CLEARLY EXPLAINED AT A BASIC LEVEL. This book’s accessible approach introduces and clarifies critical mathematical concepts, such as principal components analysis, basis functions, projective geometry, graph cuts, and Bayesian decision theory. FOUNDATIONAL INSIGHTS EQUIP STUDENTS TO TACKLE THE MOST IMPORTANT CHALLENGES IN THE FIELD. Students leave your course prepared to handle issues, such as the deep connection among floodfill, region growing, the edge linking step of the Canny edge detector, and minimum-spanning-tree image segmentation. They also learn how to construct a Gaussian convolution kernel and distinguish between its continuous and discrete variance. EASY-TO-READ FORMAT IS IDEAL FOR UNDERGRADUATE SENIORS OR FIRST-YEAR GRADUATE STUDENTS. This introductory level book covers a vast range of topics regarding automated visual analysis to equip upper-level learners with the background and skills for further study. EVERY CHAPTER PROVIDES NUMEROUS EXAMPLES. Practical and memorable examples throughout as well as stepped-through solutions and meaningful commentary on alternate solutions prepare students to immediately apply what they’ve learned. AUTHOR EMPHASIZES THE RELEVANCE AND APPLICATION OF ALGORITHMS STUDENTS ARE LEARNING. This book consistently focuses attention upon the handful of classic algorithms that have stood the test of time, are well-cited in the literature, and form the basis for more recent developments. In addition, this edition reviews the techniques that have impacted the commercial world and are used on a daily basis by millions of people.