Computer Vision: Algorithms and Applications (Texts in Computer Science) Review

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(More customer reviews)I have been reading the drafts of this book posted on Richard Szeliski's website, [...] , for about an year now. This book is written to cover almost all state-of-the-art research areas in computer vision and provides a solid introduction and reference. Unlike other books on vision, this book is about applications. The chapters are arranged keeping in mind the different key research areas which should be learned by a computer vision student. Apart from providing an overview, every chapter has abundant key references which direct the student for in-depth understanding of a particular area. This book is a welcome addition as literary resource for the computer vision community. Even though Szeliski has kept the digital version freely accessible in his site, this book as a hardbound version with color figures is definitely indispensable for every computer vision student and researcher. After Horn's landmark book, this book is here to stay as the premier computer vision book for years to come. I have started recommending this book for all the undergraduate and graduate students in my lab and I am planning to order a hardbound version for my personal bookshelf.
I strongly recommend this book for every computer vision enthusiast and I definitely feel that this book has the best content to interest people working in different areas of computer vision either in industry or academia. This book is surely the best book to learn computer vision at this point of time.

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Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing Review

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(More customer reviews)The book starts with a prologue of an under-determined linear system and how sparsity constraints help to solve it with the use of a Langrangian. Next the authors introduce the key idea of how certain norms promote sparsity. There are some good diagrams that really help the geometric intuition (though not as good as the ones by Donoho et al. in connection with Lasso). I really love the way they motivate and frame the entire field but still appeal to concept that most people who have studied linear algebra can relate to.
The first 6 chapters are a master piece in pedagogy. Except for the not so-standard usage of Spark as the measurement of coherence among elements of a dictionary. Mutual coherence is common and easier to grasp since it directly address the size of inner products. This leads to a rather jarring switch when RIP is introduced.
I am still puzzled why the authors do not appeal to frame theory. That leads to strange looking reference to self-dual frames and tight frames when the book never talked about frames.
I also wonder why the authors did not cite Boyd's great book. The treatment of log-barrier was sort of just another penalty function. The term log-barrier was never used in the book.
Overall I cannot put the book down and was especially grateful to the authors for introducing iterative shrinkage as a central theme to link many modern numerical algorithms to solve the basic sparse optimization problem.

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This textbook introduces sparse and redundant representations with a focus on applications in signal and image processing. The theoretical and numerical foundations are tackled before the applications are discussed. Mathematical modeling for signal sources is discussed along with how to use the proper model for tasks such as denoising, restoration, separation, interpolation and extrapolation, compression, sampling, analysis and synthesis, detection, recognition, and more. The presentation is elegant and engaging.Sparse and Redundant Representations is intended for graduate students in applied mathematics and electrical engineering, as well as applied mathematicians, engineers, and researchers who are active in the fields of signal and image processing.

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Numerical Geometry of Non-Rigid Shapes (Monographs in Computer Science) Review

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(More customer reviews)The book is accompanied by a website [...] that hosts additional materials such as lecture slides, exercises, code tutorials and examples, data, links to relevant software, and will host much more stuff as it grows. Materials from the site are intended for students, as an enhancement to the text, as well as for teachers preparing courses on relevant subjects.

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Deformable objects are ubiquitous in the world surrounding us, on all levels from micro to macro. The need to study such shapes and model their behavior arises in a wide spectrum of applications, ranging from medicine to security. In recent years, non-rigid shapes have attracted growing interest, which has led to rapid development of the field, where state-of-the-art results from very different sciences - theoretical and numerical geometry, optimization, linear algebra, graph theory, machine learning and computer graphics, to mention several - are applied to find solutions.This book gives an overview of the current state of science in analysis and synthesis of non-rigid shapes. Everyday examples are used to explain concepts and to illustrate different techniques. The presentation unfolds systematically and numerous figures enrich the engaging exposition. Practice problems follow at the end of each chapter, with detailed solutions to selected problems in the appendix. A gallery of colored images enhances the text.This book will be of interest to graduate students, researchers and professionals in different fields of mathematics, computer science and engineering. It may be used for courses in computer vision, numerical geometry and geometric modeling and computer graphics or for self-study.

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Mathematical Methods in Image Reconstruction (Monographs on Mathematical Modeling and Computation) Review

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(More customer reviews)By way of introduction, I do research in tomographic methods and have written a complete system for electron microscope tomography. This system has been in use for about three years, by about a dozen tomographers.
During the software development process I consulted this book extensively for new ideas, pointers to the literature, and basic mathematical understanding of the inverse problems behind tomographic reconstruction. Admittedly, there are no handy numerical algorithms to crib from, but if one is doing serious research in the area, this monograph can be taken as an extended review of a wide swath of applied mathematics. Personally, I think this is the best available resource on the subject.

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Since the advent of computerized tomography in radiology, many imaging techniques have been introduced in medicine, science, and technology. This book describes the state of the art of the mathematical theory and numerical analysis of imaging. The authors survey and provide a unified view of imaging techniques, provide the necessary mathematical background and common framework, and give a detailed analysis of the numerical algorithms. This book not only reflects the theoretical progress and the growth of the field in the last 10 years but also serves as an excellent reference. It will provide readers with a superior understanding of the mathematical principles behind imaging and will enable them to write state-of-the-art software as a result. Some of the applications covered in the book include computerized tomography, magnetic resonance imaging, emission tomography, electron microscopy, ultrasound transmission tomography, industrial tomography, seismic tomography, impedance tomography, and NIR imaging.

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Image-Based Modeling Review

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(More customer reviews)I am very happy to see this book by my colleague now in print. Many of the results have been already familiar to me. It summarizes years' efforts by Prof Long Quan and his students on 3d reconstruction and modeling, a fundamental topic in vision, graphics and visualization.
The book is comprehensive in that it is well structured into three parts:
the first fundamental vision geometry, the computational part, and the final application of vision methods to many of modeling and reconstruction topics.
In particular, I very much appreciated the first geometry part, as a more graphics and non-vision researcher myself, it's hard to read too many publications in the vision geometry area that has been well searched in the past two decades. With about 40 pages in a single chapter, chapter 3, we could have an overview of the vision geometry and quickly grasp the most essential algorithms exposed to a larger audience in a concise manner. The part 3 is mostly a collection of recent publications of the author and his students.
Given the potential of Google earth and Virtual Earth's efforts of turning everything into three-dimensional, the techniques systematically described in this book will likely guide its development in the future. I strongly recommend this book as an excellent text for graduate students and also a very useful resource for researchers in this field.

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