Image Processing and Analysis,
1st Edition

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.

• Stan Birchfield

  Dr. Stan Birchfield conducts research and development at Microsoft® Corporation, working on various aspects of robotics and computer vision. Previously, he was an assistant professor, then associate professor in the Electrical and Computer Engineering Department of Clemson University, where he spent a decade conducting research and teaching. He remains an adjunct faculty member at Clemson. He received a Ph.D. in Electrical Engineering with a minor in Computer Science from Stanford University in 1999, an M.S. from Stanford in 1996, and a B.S. from Clemson in 1993. While at Stanford, his research was supported by a National Science Foundation Graduate Research Fellowship, and he was part of the team that won first place at the AAAI Mobile Robotics Competition of 1994. After graduating from Stanford, he spent four years as a research engineer at a startup company in Palo Alto, California. At Clemson he co-founded a startup that uses computer vision to automatically collect aggregate traffic parameters from live video feeds. Over the years he has worked with or consulted for various companies, including Sun Microsystems, SRI International, Canon, Microsoft, and Autodesk. Dr. Birchfield has authored or co-authored more than 70 publications in the areas of computer vision, stereo correspondence, visual tracking, spatial acoustics, and mobile robotics, and his open-source software has been used by thousands of researchers around the world.

• FULL-COLOR PRESENTATION CONNECTS KEY CONCEPTS TO ACTUAL APPLICATIONS. Numerous photographs and detailed illustrations in every chapter bridge the gap between the concepts and equations underlying important concepts and the real-world application of those concepts.

• 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.

• UP-TO-DATE CONTENT HIGHLIGHTS THE MOST RECENT ADVANCEMENTS. This book helps students connect classic techniques with those that are on the cutting edge as it covers the latest breakthroughs and developments in the field.

• COMPREHENSIVE APPROACH ENSURES STUDENTS HAVE THOROUGH UNDERSTANDING. This book covers a broad array of the core topics in image processing and computer vision.

• READABLE PRESENTATION ENSURES STUDENTS UNDERSTAND THE “BIG PICTURE.” The author has carefully created a presentation that is as readable as possible with gentle introductions of every topic before the more detailed material and a consistently emphasis on “big picture” understanding that places all concepts and applications into a larger context.

• THE TEXT METICULOUSLY DETAILS THE EQUATIONS BEHIND THE TECHNIQUES. The author includes subtle details not found anywhere else that are necessary for your students to translate equations into working code. Detailed pseudocode of dozens of algorithms bridges the gap between otherwise difficult mathematics and the actual working code that they are learning to write.

• SYSTEMATIC ORGANIZATION ENSURES A LOGICAL FLOW. Due to their diverse blending of multiple fields, such as signal processing, artificial intelligence, computational algorithms, and psychology, as well as the lack of a single unifying mathematical basis, both image processing and computer vision are notoriously difficult fields to organize in a systematic fashion. However, this author has arranged the chapters in a logical and coherent order by seamlessly blending the fields of image processing and computer vision.

Cengage provides a range of supplements that are updated in coordination with the main title selection. For more information about these supplements, contact your Learning Consultant.

Companion Website for Birchfield's Image Processing and Analysis
9781305492868

Instructor's Solution Manual for Birchfield's Image Processing and Analysis, 1st
9781305884922

Cengage eBook: Image Processing and Analysis 12 Months
9788000030777