Digital Image Processing: A Practical Introduction Using Java (With CD-ROM) Review

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Digital Image Processing: A Practical Introduction Using Java (With CD-ROM) ReviewIn general terms, this is a good book for intermediate and advanced Java programmers who need to work with images.
Just as the title reads, this is a Practical Introduction to digital image processing (DIP), that is, you will not find all the theory here, and the study of images will be mostly practical: with programs, what would be a good point or not, depending on you.
The CD is a very nice complement for the book, but a couple of programs just don't run as expected, and you might want to check the code to find the error or to use the code just as a reference to write your own Java classes. I have checked just part of the code and the problems do not seem a matter of deprecated classes or the like. You can find some errata searching in the Internet, but not much at time of this writing (June 2002).
You must read the book in front of your computer, to run the classes. The book sometimes doesn't tell you all about the classes available in the CD.
Always remember, the book tries to teach you DIP *using* Java, not DIP *and* Java. If you are not confident of your level of Java, try another book first. If you know about DIP and want to study further, try a book that is more specific on such subject.
This book is NOT for you if
- you are a beginner in Java
- you need to study DIP exhaustively
This book is for you if
- you have experience programming in Java
- you need basic and general concepts about DIPDigital Image Processing: A Practical Introduction Using Java (With CD-ROM) OverviewThis book features a breadth and depth that first explains IP concepts and then allows users to practice them by working with the accompanying CD-ROM. The material on this disc includes Java classes that can be used by programmers to build IP software, as well as a complete set of tools that will let them experiment with IP concepts.It provides a thorough introduction to current state-of-the-art IP technology while using the Java programming language. Computer scientists who want to learn more advanced topics in computer graphics.

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Algorithms for Image Processing and Computer Vision Review

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Algorithms for Image Processing and Computer Vision ReviewAt its best, this book is a wonderful cookbook of image processing techniques. For example the author's presentation of the Canny and Shen-Castan methods of edge detection is clear and detailed. Full C source is provided for this and all the other techniques discussed in the book. The book does not pretend to be a basic introduction to image processing -- if you do not have Castleman or Pratt or something similar on your bookshelf, you probably should not start by acquiring this book. A possible source of irritation for some readers will be the variation in depth of coverage of topics. For example, when the author discusses wavelets, the coverage is very scant and qualitative. It is well done, but quite different in kind from the aforementioned coverage of advanced edge detection techniques. Similarly, the author will cover specific aspects of image processing, such as motion blur for an image, in the context of a rather general discussion of image restoration. In short, the book seems to reflect the author's interests more than attempting to be an objective coverage of the current state of the art in image processing. Nonetheless, the high quality of the information that the author provides and the bibliography to further coverage of the given topics are well worth the price of the book. You just need to approach it on the author's terms rather than on a preconceived notion of what you should get out of the book.Algorithms for Image Processing and Computer Vision OverviewA cookbook of the hottest new algorithms and cutting-edge techniques in image processing and computer visionThis amazing book/CD package puts the power of all the hottest new image processing techniques and algorithms in your hands. Based on J. R. Parker's exhaustive survey of Internet newsgroups worldwide, Algorithms for Image Processing and Computer Vision answers the most frequently asked questions with practical solutions.Parker uses dozens of real-life examples taken from fields such as robotics, space exploration, forensic analysis, cartography, and medical diagnostics, to clearly describe the latest techniques for morphing, advanced edge detection, wavelets, texture classification, image restoration, symbol recognition, and genetic algorithms, to name just a few. And, best of all, he implements each method covered in C and provides all the source code on the CD.For the first time, you're rescued from the hours of mind-numbing mathematical calculations it would ordinarily take to program these state-of-the-art image processing capabilities into software. At last, nonmathematicians get all the shortcuts they need for sophisticated image recognition and processing applications.On the CD-ROM you'll find:* Complete code for examples in the book* A gallery of images illustrating the results of advanced techniques* A free GNU compiler that lets you run source code on any platform* A system for restoring damaged or blurred images* A genetic algorithms package

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Computational Intelligence Paradigms: Theory & Applications using MATLAB Review

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Computational Intelligence Paradigms: Theory & Applications using MATLAB ReviewWhile this book's technical content (e.g. neural net discussion) is quite good, I can't believe that the editor allowed such poor language to pass through the editing process. This book is probably the best example of the kind of linguistic degradation that George Orwell mentioned in "Politics and the English Language." Virtually every sentence contains pseudo-sophisticated words that appear to have been picked at random from a thesaurus. Synonyms usually convey a meaning similar, but not identical to, the original, but the authors never grasped this basic observation. Also, the authors seem not to understand the context in which a given word usually appears, thereby making pretty comical errors that distract the reader. Staying focused on the technical content while processing the linguistic gibberish is quite a challenge.Computational Intelligence Paradigms: Theory & Applications using MATLAB OverviewOffering a wide range of programming examples implemented in MATLAB, Computational Intelligence Paradigms: Theory and Applications Using MATLAB presents theoretical concepts and a general framework for computational intelligence (CI) approaches, including artificial neural networks, fuzzy systems, evolutionary computation, genetic algorithms and programming, and swarm intelligence. It covers numerous intelligent computing methodologies and algorithms used in CI research. The book first focuses on neural networks, including common artificial neural networks; neural networks based on data classification, data association, and data conceptualization; and real-world applications of neural networks. It then discusses fuzzy sets, fuzzy rules, applications of fuzzy systems, and different types of fused neuro-fuzzy systems, before providing MATLAB illustrations of ANFIS, classification and regression trees, fuzzy c-means clustering algorithms, fuzzy ART map, and Takagi-Sugeno inference systems. The authors also describe the history, advantages, and disadvantages of evolutionary computation and include solved MATLAB programs to illustrate the implementation of evolutionary computation in various problems. After exploring the operators and parameters of genetic algorithms, they cover the steps and MATLAB routines of genetic programming. The final chapter introduces swarm intelligence and its applications, particle swarm optimization, and ant colony optimization.Full of worked examples and end-of-chapter questions, this comprehensive book explains how to use MATLAB to implement CI techniques for the solution of biological problems. It will help readers with their work on evolution dynamics, self-organization, natural and artificial morphogenesis, emergent collective behaviors, swarm intelligence, evolutionary strategies, genetic programming, and the evolution of social behaviors.

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Neural Networks for Pattern Recognition Review

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Neural Networks for Pattern Recognition ReviewThis book came out at about the same time as Ripley's, which has almost the same title, but in reverse. At the time, I liked Ripley's better, because it covered more things that were totally new to me. Then a friend said he had chosen Bishop for a course he was teaching, and I went back and reconsidered the two books. I soon found that my friend was right: Bishop's book is better laid out for a course in that it starts at the beginning (well, not quite the beginning--you do need to be fairly sophisticated mathematically) and works up, while Ripley's is more a collection of insights all at the same level; confusing to learn from. Bishop is able to cover both theoretical and practical aspects well. There certainly are topics that aren't covered, but the ones that are there fit together nicely, are accurate and up to date, and are easy to understand. It has migrated from my bookcase to my desk, where it now stays, and I reach for it often.
To the reviewer who said "I was looking forward to a detailed insight into neural networks in this book. Instead, almost every page is plastered up with sigma notation", that's like saying about a book on music theory "Instead, almost every page is plastered with black-and-white ovals (some with sticks on the edge)." Or to the reviewer who complains this book is limited to the mathematical side of neural nets, that's like complaining about a cookbook on beef being limited to the carnivore side. If you want a non-technical overview, you can get that elsewhere (e.g. Michael Arbib's Handbook of Brain Theory and Neural Networks or Andy Clark's Connectionism in Context or Fausett's Fundamentals of Neural Networks), but if you want understanding of the techniques, you have to understand the math. Otherwise, there's no beef.Neural Networks for Pattern Recognition Overview

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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) Review

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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) ReviewWitten and Frank have generated a book that is readable without eliminating all technical (yes, even mathematical!) descriptions of the key data mining algorithms. And they are up-to-date, including support vector machines and boosting. There are sufficient examples of the techniques to provide readers with a good feel for what each technique can accomplish. For example, how many books can provide a readable explanation of support vector machines?
There are some quibbles, such as not including any discussion of neural networks (noted in Ch. 1 with another reference)--I believe it deserves some attention because of its widespread use. Additionally, future editions should include a least a brief summary of data preprocessing, input selection, feature creation, etc. But these are quibbles.
The Java portion of the book is not of as much interest to me, but for those wishing to implement the algorithms, it provides a nice blueprint (from the code I looked at).
For what they have undertaken, they have performed admirably, and I would highly recommend this book.Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) Overview

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Fundamentals of Speech Recognition Review

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Fundamentals of Speech Recognition ReviewThis book is a comprehensive and excellent introduction to the ever-expanding
field of Automatic Speech Recognition. Starting with models of speech
production, speech characterization, methods of analysis (transforms etc),
the authors go onto discuss pattern comparison, hidden Markov models (HMMs),
and design and implementation of speech recognition systems, right from
isolated word recognition to large vocabulary continuous speech recognition
systems. Neural networks and their use in speech recognition is also presented,
though somewhat briefly.
Rabiner was the author of the first widely-read tutorial on HMMs, so
naturally the presentation of HMMs is one of the strong points of this
textbook. The theory is developed in detail, but in an easy to follow
fashion, starting with the very basics and with plenty of helpful examples.
The implementation is discussed at great length as well, starting with
the simplest of tasks and progressing to the state-of-the-art (circa 1993).
That isn't to say that HMMs are the only good part of this book - indeed,
practically every topic, whether it be perception, transforms, vector quantization
or dynamic programming, is presented with great clarity. This book really is easy to
learn from, with numerous examples and illustrations.
The field of speech recognition is inherently multi-disciplinary in nature,
drawing upon various areas of study, including Physics, Physiology, Acoustics,
Signal Processing and Computer Science, to name but a few. The authors do a
great job of explaining all these facets, as well as the mathematics that
is an essential tool.

The only caveat is that it's now a little old (published 1993), since the
field has been growing by leaps and bounds - so while the basics remain
the same, things have changed and hence what's said here should not be
taken as the last word on the subject.
Perhaps a new edition is due, and would certainly be most welcome.
However, for an excellent, accessible introduction to this exciting field,
this is still a great choice.Fundamentals of Speech Recognition Overview Provides a theoretically sound,technically accurate, and complete description of the basicknowledge and ideas that constitute a modern system forspeech recognition by machine. Coversproduction, perception, and acoustic-phoneticcharacterization of the speech signal; signal processing andanalysis methods for speech recognition; pattern comparisontechniques; speech recognition system design andimplementation; theory and implementation of hidden Markovmodels; speech recognition based on connected word models;large vocabulary continuous speech recognition; and task-oriented application of automatic speech recognition. For practicing engineers, scientists,linguists, and programmers interested in speech recognition.

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Advanced Computer Architecture: Parallelism, Scalability, Programmability Review

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Advanced Computer Architecture: Parallelism, Scalability, Programmability ReviewThis is an excellent book especially for students who want to do Masters in Computer Science & Engineering. Concept of Parallel processing, multistage Unix Kernel etc. are excellent. More detailed discussion on SuperComputer Architecture with diagrams are required.Advanced Computer Architecture: Parallelism, Scalability, Programmability OverviewThis book deals with advanced computer architecture and parallel programming techniques. The material is suitable for use as a textbook in a one-semester graduate or senior course, offered by Computer Science, Computer Engineering, Electrical Engineering, or Industrial Engineering programs.

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Spoken Language Processing: A Guide to Theory, Algorithm and System Development Review

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Spoken Language Processing: A Guide to Theory, Algorithm and System Development ReviewThis book is a comprehensive overview of most of the major topics associated with speech processing. Divided into five main sections, the book is well structured with a clear division of concerns. The title, "Spoken Language Processing", may be misleading to some as language processing topics only accounts for one section of the book.
The first two sections cover the fundamental theories that should be understood before embarking in-depth into a study of speech processing. This may seem an obvious approach but many texts do not follow this pattern making their use as reference tomes limited. Separating background theory from its use is also useful in that it allows a rigorous approach to its description. Too often texts give a hurried imprecise overview of theories used before launching into a long and complex use of the theory; losing the reader instantly in a quagmire of formulae.
The first two sections of the book deals with background material, material that the reader should at least understand the key concepts of. The first section concentrates on speech in general (including production and perception), probability and statistics, and pattern classification. These last two topics mentioned are both important parts of the book and are dealt with in their own chapters. Both are well written with the right amount of explanation and background. Much of the remainder of the book expects at least some familiarity with the material presented here. These chapters, like all chapters in the book finish with a section entitled, "Historical Perspective and Further Reading". The inclusion of recommended further reading, in addition to the vast number of references appearing in each chapter, make the book as a whole a very good starting point for any work in speech processing.
The second section concerns itself with the DSP topics which relate to speech processing. In this section the reader will find everything from FFTs to multi-rate signal processing and speech signal representations to speech coding. Again the section is well written and the reader is not forced to refer to other texts to understand what is written. If a topic is not expanded upon here then it is an indication that is not dealt further in any great depth in the remainder of the book.
The third section of the book covers speech recognition and is probably the section which will find most use with many readers. This section is very thorough in its treatment of the subject. It starts immediately with a discussion of Hidden Markov Models which is almost exclusively the method employed in the pattern matching stage of speech recognition. Any algorithms that are mentioned are also detailed which really make the book useful. In fact algorithms are presented throughout the book making it a practical reference as much as a theoretical one. This is important because there is a big jump from understanding theory to being able to implement an algorithm to exploit that theory. Other topics covered include an excellent chapter on environmental robustness with one of the best discussions of microphones I have seen. Language modelling and search algorithms are given a thorough treatment. I would like to have seen more detailed information on front-end processing and endpoint detection, as this remains a critical stage of the recognition process. Perhaps the level of detail reflects the fact that this is currently a hot research topic with potential for significant advancement.
Section four, on text-to-speech processing, is a good overview of the field and better than any book I've seen on the subject. It shows numerous block diagrams of what you need to build such a system and gives numerous algorithms in pseudocode. It also dedicates a subsection to each block of the text-to-speech system block diagram, discussing in detail what you would need to do to implement that particular block. Since much of the individual blocks have been discussed earlier in the book, it refers you back to specific earlier sections for details.
The fifth section is a short one on entire systems and shows some case studies, concentrating on what Microsoft was doing at the time this book was published, since that is where the authors' research came from. I would highly recommend that anyone anticipating getting into speech processing have a copy of this classic nearby.Spoken Language Processing: A Guide to Theory, Algorithm and System Development OverviewThis will be the definitive book on spoken language systems written by the people at Microsoft Research who have developed the voic-activated technologies that will be imbedded in Windows 2000 and other key Microsoft products of the future. This is not a Microsoft book, however, this is a book on the science and linguistics of this technology and how to use it in developing and building hardware and software products.

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