Design Patterns: Elements of Reusable Object-Oriented Software (Addison-Wesley Professional Computing Series)

Amazon.com

Design Patterns is a modern classic in the literature of object-oriented development, offering timeless and elegant solutions to common problems in software design. It describes patterns for managing object creation, composing objects into larger structures, and coordinating control flow between objects. The book provides numerous examples where using composition rather than inheritance can improve the reusability and flexibility of code. Note, though, that it's not a tutorial but a catalog that you can use to find an object-oriented design pattern that's appropriate for the needs of your particular application--a selection for virtuoso programmers who appreciate (or require) consistent, well-engineered object-oriented designs.

Book Description

Now on CD, this internationally acclaimed bestseller is more valuable than ever!

Use the contents of the CD to create your own design documents and reusable components. The CD contains: 23 patterns you can cut and paste into your own design documents; sample code demonstrating pattern implementation; complete Design Patterns content in standard HTML format, with numerous hyperlinked cross-references; accessed through a standard web browser; Java-based dynamic search mechanism, enhancing online seach capabilities; graphical user environment, allowing ease of navigation.

First published in 1995, this landmark work on object-oriented software design presents a catalog of simple and succinct solutions to common design problems. Created by four experienced designers, the 23 patterns contained herein have become an essential resource for anyone developing reusable object-oriented software. In response to reader demand, the complete text and pattern catalog are now available on CD-ROM. This electronic version of Design Patterns enables programmers to install the book directly onto a computer or network for use as an online reference for creating reusable object-oriented software.

The authors first describe what patterns are and how they can help you in the design process. They then systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. All patterns are compiled from real-world examples and include code that demonstrates how they may be implemented in object-oriented programming languages such as C++ and Smalltalk. Readers who already own the book will want the CD to take advantage of its dynamic search mechanism and ready-to-install patterns.

Customer Reviews:

WOW!!!.......2007-09-13

Being a total noob to programming, my friend got me into learning about Objective-C and Cocoa. I have bought many books on the subject and I'd like to say that this book helped me with my understanding of objective-c programming language.

Thanks to the authors

Wonderful but it is difficult.......2007-09-05

The book is wonderful and samples are very exciting and meaningful.But book is difficult,if you don't have any idea about design patterns,you cannot learn easily from his book.But If you are not new about Design patterns,this book is very useful.

Fantastic Book.......2007-08-12

I would recommend this book to any software developer, architect, or designer. Fit for all levels of experience. It is clear why this is so often referenced in other development literature.

A Thorough Introduction.......2007-08-12

I have read this book many times over. I purchased
this book many years ago and I still reference it today. For those
who are interested in OO design this is the best book I have read,
followed by John Lakos "Large Scale C++ Design". This book will
have you thinking about how to design software, and if you are
diligent, eventually you will be designing your own patterns.

This book is a definite must for any language, since it will
change the way you view software.

Great book.......2007-08-05

I found the case study very helpfull. The patterns explained in a very clean way, so I recomand every developer that is interesting in design patterns, refactoring etc to read it.

Pattern Recognition and Machine Learning (Information Science and Statistics)

Average customer rating:

Perfect
Excellent book for pattern analysis and classification!
The book should change its title
Ok, but too much math destroys the intuition...
The best Pattern Recognition textbook I know

Pattern Recognition and Machine Learning (Information Science and Statistics)
Christopher M. Bishop
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Similar Items:

The Elements of Statistical Learning
Pattern Classification (2nd Edition)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Machine Learning

Accessories:

Pixelization Paradigm: Visual Information Expert Workshop, VIEW 2006, Paris, France, April 24-25, 2006, Revised Selected Papers (Lecture Notes in Computer Science)
Multimodal Technologies for Perception of Humans: First International Evaluation Workshop on Classification of Events, Activities and Relationships, CLEAR ... Papers (Lecture Notes in Computer Science)

ASIN: 0387310738

Book Description

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.

This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

Coming soon:

*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)

*For instructors, worked solutions to remaining exercises from the Springer web site

*Lecture slides to accompany each chapter

*Data sets available for download

Customer Reviews:

Perfect.......2007-10-17

This book is very useful as an introduction to Machine Learning as well as a reference book for more advanced topics.

Excellent book for pattern analysis and classification!.......2007-10-01

Excellent book for pattern analysis and classification! It begins with basic data curve fitting, linear classification models and ends with combining models (tree-based models, graphical models, etc.). Contains great number of examples and exercises. Very good introductory for beginners in pattern analysis, excellent companion for academics and researchers.

The book should change its title.......2007-09-25

This book (PRML) should be re-titled as "PRML: a bayesian approach". Yes, bayesian approach is very useful for machine learning, and sometimes the final goal of learning is to maximize some sort of posterior probability. However, if the author is such a huge fun of bayes statistics, please tell perspective readers in a clear way. Emphasize bayes aspects too much really hurt the quality of this book as a general-purpose textbook of machine learning.

For a better textbook of machine learning, I recommend:
1) The elements of statistical learning (perhaps this book a little hard for beginner in this field -- but as least better than PRML -- you can compare their chapters about linear regression to see which one is better).
2) Pattern classification (focus on classification, not regression. Also not very easy -- anyway, machine learning is not an easy field ^_^).
3) Machine Learning (a little old, but great for beginner.)

These three book also mention bayesian statistics, but in a proper way. If you have some experience in machine learning and have engineering-level math background, just choose the 1) or 2). If you are completely a beginner, first take a glance on 3), and then go to 1) or 2).

Finally, if you want a book that discusses machine learning purely from bayesian perspective, PRML is good.

Ok, but too much math destroys the intuition..........2007-09-09

This book is a fairly thorough overview of typical topics employed in a graduate machine learning course. However, from page 5 on, expect to see more equations on each page than paragraphs of text (with most of the remaining text explaining the context of the variables within the equations). Now, for someone such as myself who enjoys mathematics, this is not a problem. However, I would not recommend this book for someone with a mathematics background that is in any way weak. Furthermore, there is a more fundamental problem with the presentation of the material that warrants this book no more than a 3-star rating: the simple intuitiveness of the concepts is completely lost within the mathematics. Instead of explaining what variables represent and leaving it to the reader to figure out what is going on, this book could be made much more approachable by simply stating the intuition behind the equations. Take the sum rule, one of the first theorems in the book, for an example of how the author muddles what is effectively a basic and intuitive concept: the book has a fairly lengthy definition of several variables representing concepts such as "the number of observations in which x_ij appears" prior to presenting a summation over all y-variables (a notational convention that the author admits is "cumbersome" on the next page, and states that "there will be no need for such pedantry" as that which he proceeds to perpetrate throughout the book!), while he could have simply presented the simplified sum on the following page (p(X) = sum(p(X,Y), Y)) and it would be immediately clear to most readers what he was attempting to explain. He could also simply state the intuition behind the theorem in English, that summing over every event yields a probability of one, and therefore summing over all events in which a variable appears effectively marginalizes the variable (something he comes close to doing after the presentation of the equation, but by then, the reader's time has already been wasted). Similar examples abound throughout the book, becoming particularly bad during the middle sections, when the techniques begin to become less intuitive.

As another reader mentioned, the author also commits the serious mistake of using pi for a symbol other than the constant or the product operator, which muddles the equations on a skim and forces the reader to refer back to the variable definitions to determine the context.

Having done work in machine learning's applied cousin, data mining, and thus having used many of the techniques presented in the book in actual research, I can't help but think that the presentation of the book's content could be much clearer. When doing work in the field, we can look up the equations as-needed; it is the knowledge of *when* and *how* to apply or extend these techniques that is more important, and that is the area in which I feel this book is lacking.

The best Pattern Recognition textbook I know.......2007-07-17

This book brings the most updated research in this field. The writing stile combines common-sense intuitive explanations with precise mathematical formulations. A lot of colorful figures support the text and help the reader to understand and absorb the described ideas. Short biographies of scientists like Bayes, Laplace, Gauss etc. (which unfortunately substantially drop after the Ch. 2) provide a brief glancing on humans which are behind these great names. The author makes connections between the different chapters, which help the reader to see a wide picture. But don't expect for an easy work. As every deep scientific text it is sometimes fluent and fun, and sometimes demands an effort, rereading the same text again and again, and referring to other references. Personally I feel a great satisfaction when after such an effort the concept became clear to me.

The other useful feature is solved exercises which are available for download from the authors' web site [..]

The main drawback of this book is a relative small amount of detailed examples. As an experienced educator, I know that "a single good example could worth a thousand explanations". It probably will be not an issue with appearance of the practical companion volume (Bishop and Nabney, 2008). The reference to the future (2008) still un-existed publication is unusual, fresh-thinking, and right idea.

With this book C. Bishop continues his "tradition" of writing deep and important scientific books which was started with the "Neural Networks for Pattern Recognition".

A short comment to the reviewer "lew lwndn123", who is deeply disappointed by the fact that this is a textbook. Yes, it is a textbook, and it is clearly written in the "Book Description". It is unfair to "kill" the book just because you didn't really check what you are going to buy, especially you admit that "as a textbook, this is very good text, and deserves 5 starts". I think it will be a decent step if you will correct your review.

Average customer rating:

Excellent!
Good presentation of both beginning and advanced material
Best Intro. Text I've Used
Excellent introduction guide

Computer Vision
Linda G. Shapiro , George C. Stockman , Linda G Shapiro , and George Stockman
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Algorithms for Image Processing and Computer Vision
Introductory Techniques for 3-D Computer Vision
Machine Vision : Theory, Algorithms, Practicalities
Digital Image Processing (3rd Edition)
Multiple View Geometry in Computer Vision

ASIN: 0130307963

Customer Reviews:

Excellent!.......2007-07-11

Pro:
This book is really good. In simple, the book is written in English. It seems to be aimed at an entry-level CV student. Having some prior Image Processing or Computer Vision background will help you run through the book faster, although it doesn't seem to be required.

Shapiro makes sure you understand the concept behind the algorithm and then provides you the pseudo code rather than typing up some complicated C/C++ code.

Con:
There are alot of exercises in the chapter and they really help in testing your understanding. I only wish the author provided solutions to the exercises.

Good presentation of both beginning and advanced material.......2005-10-07

Of the several computer vision textbooks that I haved owned and read, this book provides the best combination of introductory techniques with more advanced material in a very readable style.
The first two chapters are a very conversational overview of computer vision and image representation, but don't let this fool you. Starting in chapter three, the book becomes concise in presentation and in numerical examples. The authors starts out with the basics of binary image analysis which includes a very good discussion of image morphology. However, this is not an image processing book, so you should already be familiar with image processing on the same level as what is presented in Gonzales & Wood's "Digital Image Processing", which is my personal favorite among the various image processing texts. Next pattern recognition basics are discussed, including a section on neural networks that was clearer than anything I gleaned from Haykin's classic text on the subject. Next, the author moves into the realm of gray scale images by discussing the filtering and enhancing of images, which is similar to material in many image processing books. The basics of computer vision conclude with chapters on color, shading, and texture. Next, the book jumps into more advanced material that builds on the introductory material. For example, there are chapters on content-based image retrieval, a subject on which the author Linda Shapiro is conducting research at the University of Washington, and also on computing motion from 2D image sequences. Finally, the book tackles some 3D computer vision issues such as perceiving 3D from 2D images, object pose computation, and 3D models and matching using image "snakes". There are algorithms presented in pseudocode throughout this book, along with supporting mathematics, so the reader should have a good understanding of matrix algebra as well as calculus to really get the most from this book. The algorithms are concisely represented, and I had no trouble coding up a content-based image retrieval program based solely on the contents of this book. The pattern recognition chapter lacks a few details, and it might be helpful if the reader had a copy of Tom Mitchell's "Machine Learning", which parallels nicely with the pattern recognition chapter of Shapiro's book and is both complete and concise.

Best Intro. Text I've Used.......2003-11-17

This text is excellent as the basis for an introduction to CV, it treats a wide variety of topics in a clear and accessible manner. I particularly appreciated the books coverage of topics which aren't traditionally considered to be CV topics (like classification and some material on probabilistic inference). Highly recommended.

Excellent introduction guide.......2002-08-25

The book presents a nice complement to Image Processing, Analysis and Machine Vision (Image Processing, Analysis, and Machine Vision, 2nd ed., M. Sonka, V. Hlavac, and R. Boyle, 1998, IPAMV). As the difference in names implies, Computer Vision is not appropriate as an image processing textbook. It contains sufficient information on image processing to implement computer vision algorithms, but the focus of the book is on image analysis and high-level vision. The result is that the combination of IPAMV and Computer Vision cover the spectrum from intensive image processing and manipulation to high level analysis, object recognition and content-based image retrieval.

Computer Vision contains sixteen chapters that fall into roughly four categories: overview, 2-D CV topics, 3D CV topics, and special CV topics. Since it was written with the intent of reaching a broader audience than IPAMV, this book is appropriate as a primary text or reference for a wider variety of courses. For example, it would be appropriate for courses ranging from an introduction to imaging for non-scientists to a sophomore-junior elective to a first-year graduate seminar.

The overview chapters (chapters 1-4) include a summary of problems in CV, imaging and image representations, simple binary image analysis and a survey of pattern recognition concepts. The 2-D processing topics (chapters 3, 5-7, and 11) include thresholding and binary image analysis, filtering and enhancement, edge detection, Fourier Transforms, color, texture, segmentation, and 2-D matching and pose calculation. The 3-D computer vision topics (chapters 9-10, and 12-14) include motion detection and analysis, range image analysis, stereo, calibration, intrinsic image analysis and line labeling, shape from X, and camera models. The special topics (chapters 6-8, 15-16) include color and shading, texture, content-based retrieval, virtual reality, and a set of case studies of CV systems. Different combinations of these are appropriate for different types of courses.

In comparison with other texts, the coverage of color and shading in Computer Vision is the best available without consulting a color reference such as Fairchild's Color Appearance Models (described below). However, it still does not contain adequate coverage of physical models of reflection or color appearance. The texture chapter is comparable to Sonka et. al., and the CBIR and VR chapters are unique. It is these latter two areas that give Computer Vision a nice high-level flavor and provides a reference for these growing areas of CV.

Like IPAMV, Computer Vision contains a large number of example images, diagrams, and algorithms. The writing is clear and the mathematics--when it is necessary to present it--is complete and accessible. Since the book is designed with multiple audiences in mind, the heavy mathematical sections are flagged and the book can be used effectively with or without them.

Of particular interest to CV practitioners and students dealing with issues of calibration, chapter 13 contains a nice description of Roger Tsai's camera calibration algorithm, complete with an example. Note that Trucco and Verri (see below) also cover Tsai's calibration algorithm.

Customer Reviews:

More of a reference book.......2007-08-23

I was a bit disappointed by this book. It is written more like a reference book than something you can read through to learn the material. It is full of various equations with little in the way of "plain english" explanations. If you are VERY comfortable with vector math and looking at lots of equations, then this book may be a good reference. If you are looking for understanding basic concepts, then you will need to look elsewhere.

Extra-ordinary book on Computer Vision!.......2006-04-29

I didn't know or hear about this book till one fine morning when I went to our IIT library looking for some good book on computer vision to supplement the knowledge imparted by Horn's book. I found this book and it contained most of the concepts covered thus far in my lectures. Still I was not convinced about the credibility of this book. Somehow, I started grazing through this book in leisure hours.

To my surprise, I found that it was simply an amazing book written so skillfully on computer vision from the basics. The Math fundae in the appendix were the ones I read first. They were very concise and helped me to grasp the concepts quickly. The chapters were also based on recent literature and very much coherent and self-explanatory.

This book has the potential to become a master-piece in computer vision. One unique feature of this book is the clear explanation of Math concepts in each chapter. For it to become more user-friendly, some real application oriented problems should be added.

But, on the whole, this book is an excellent book to be read along with Horn's book to fully understand the basics of computer vision. I strongly recommend this book to any novice to computer vision with little understanding of image processing concepts.

Some nice intuitions.......2005-12-23

In my humble opinion, mathematics is best explained through intuitions which motivate the rigor. That is, a general, high level, overall understanding of a particular problem and the "theory" behind a solution must be presented before a rigorious algebraic analysis. The particular mathematics should then read like a novel. In this sense, Trucco and Verri succeed, at various parts throughout the text, but certainly fail at others. I would say 4/5th's of the text is well written, and hence the 4 star review.

Excellent intermediate textbook on Computer Vision.......2005-09-17

If you already understand image processing and the basics of computer vision, this book is a very good at concisely presenting more advanced algorithms to the reader. Also, because this book is so well organized, you can read it from beginning to end. Rest assured if you are looking at an algorithm on page 84, you will not need to skip ahead to later sections in the book to understand it. From the beginning, algorithms are named and presented in numbered steps for clarity of presentation. The book starts out with introductory material such as basic optics and the geometry of camera models. It continues with image denoising, as well as two full chapters devoted to image features and their detection. Finally, the more basic material concludes with a chapter on the mathematics of camera calibration. One aspect of vision that is often neglected in other computer vision books that is treated well here is that of motion. For those working in video processing, this might make this book a good selection. Also, the book gives one of the best discussions of eigenspaces that I have seen in print in chapter ten of the book, where the subject is recognition of 3D objects. I was able to code up the eigenfaces face recognition algorithm based almost entirely on the information found in chapter ten of this book. If you need an introduction to computer vision before tackling the more advanced material in this text, try Shapiro's book "Computer Vision" ISBN 0130307963. A good knowledge of linear algebra is necessary prior to understanding the algorithms in this book such as is found in Schaum's outline of Matrix Operations. Given the specific subject matter of this book, it would probably be an excellent choice for an engineer or scientist that is interested in computer vision as it relates to robotics.

Not for the beginner.......2005-03-22

Book uses a very analytical approach. Concepts are very poorly explained and derviations are not explained. As a text this book is well suited as a review for graduate level students. Using the word introductory in the title is very misleading. Do not recommend this book to those looking for an introduction to the world of computer vision.

Average customer rating:

Broad, but a mess.
Computer vision
A good up-to-date reference. Comes with solid introductions to (multi-)camera geometries.
Not a good book to begin with
Somewhat opaque

Computer Vision: A Modern Approach
David A. Forsyth , and Jean Ponce
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Hardcover

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Introductory Techniques for 3-D Computer Vision
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Computer Vision
An Invitation to 3-D Vision
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ASIN: 0130851981

Book Description

The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Customer Reviews:

Broad, but a mess........2006-10-24

This is, from what I can tell, just about the most complete up-to-date text in the field of computer vision as of late 2006.

But it's a mess.

I'm a PhD student, and have worked my way through more than my fair share of high-level computer science textbooks. This one makes me really appreciate many of them. It reads like a first draft -- overly wordy at times, skipping over important issues, poorly organized... Some concepts that ought to be really simple are made very painful due to what seems to be laziness on the part of the editor. It's like the only people that critiqued this book prior to publication already knew all there is to know about computer vision.

A particularly nasty aspect of this book is that the authors have a horrible habit of including some term in some complex mathematical formula, with no reference whatsoever to that term in the surrounding text! In an explanation of how to use Expectation Maximization in line-fitting, they include a standard-deviation term, with no mention of how you're supposed to choose a value for it other than "...for sigma as before". The only "before" in which the SD (sigma) is mentioned in a similar context that I can find is IN THE PREVIOUS CHAPTER!!!

Anyway, if you want to try to teach yourself vision, don't bother. If you need the book for a class, I'm sorry it's so expensive. Either way, don't expect much.

Computer vision.......2006-07-29

I think this book is the most complete computer vision arguments. In fact it start to speaks from radiometry to steriovision passing by filter uses!!
Good very good

A good up-to-date reference. Comes with solid introductions to (multi-)camera geometries........2006-06-06

First of all, I got a 2003 reprint, and somehow, every single fraction bar (the division sign) is missing in the math expressions for the entire book! Very annoying. I had expected more from Prentice Hall. I advise one to open the book before the return period elapses.

Secondly, I agree to the reviews that say this book being not intuitive. I assume the authors tried to make it concise and came up with a hard read. For example, it explains 2D Fourier analysis in just 3 pages. Of course it's not for first learners. But I get the feeling they could've done a little better in presenting ideas.

The upside is that this book covers quite recent topics. Hence this book may be used as a good survey of the field today.

Personally, I get to grasp rough ideas of the topics I usually don't have time to hit the papers on. Which I appreciate. I knock 2 stars off for overpricing and the misprints in my copy.

[Added in July 2007] I find the chapters on multi-camera geometry, stereopsis, and structure-from-motion are very well written. I'd give it another star for it but the "edit" page won't allow it.

So here it is. It's * * * * -- a four star!

Not a good book to begin with.......2006-02-07

The book fails to explain simple concepts in a clear way. Trying other literature such as 'Shapiro and Stockman's' book is enough to convince yourself that the same concepts can be explained clearly without losing insight.

Somewhat opaque.......2005-12-03

Good point: probably most recent vision book
Bad point: hard to read, missing obvious computer vision topics

Mind Hacks: Tips & Tricks for Using Your Brain (Hacks)

Average customer rating:

Interesting but disappointed
Mind hacks
Not really a "hacks" book
Mind Hacks: Tips & Tools for Using Your Brain
A fun book

Mind Hacks: Tips & Tricks for Using Your Brain (Hacks)
Tom Stafford , and Matt Webb
Manufacturer: O'Reilly Media, Inc.
ProductGroup: Book
Binding: Paperback

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Similar Items:

Mind Performance Hacks: Tips & Tools for Overclocking Your Brain (Hacks)
On Intelligence
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Lifehacker: 88 Tech Tricks to Turbocharge Your Day

ASIN: 0596007795

Book Description

The brain is a fearsomely complex information-processing environment--one that often eludes our ability to understand it. At any given time, the brain is collecting, filtering, and analyzing information and, in response, performing countless intricate processes, some of which are automatic, some voluntary, some conscious, and some unconscious. Cognitive neuroscience is one of the ways we have to understand the workings of our minds. It's the study of the brain biology behind our mental functions: a collection of methods--like brain scanning and computational modeling--combined with a way of looking at psychological phenomena and discovering where, why, and how the brain makes them happen. Want to know more? Mind Hacks is a collection of probes into the moment-by-moment works of the brain. Using cognitive neuroscience, these experiments, tricks, and tips related to vision, motor skills, attention, cognition, subliminal perception, and more throw light on how the human brain works. Each hack examines specific operations of the brain. By seeing how the brain responds, we pick up clues about the architecture and design of the brain, learning a little bit more about how the brain is put together. Mind Hacks begins your exploration of the mind with a look inside the brain itself, using hacks such as "Transcranial Magnetic Stimulation: Turn On and Off Bits of the Brain" and "Tour the Cortex and the Four Lobes." Also among the 100 hacks in this book, you'll find:

Release Eye Fixations for Faster Reactions
See Movement When All is Still
Feel the Presence and Loss of Attention
Detect Sounds on the Margins of Certainty
Mold Your Body Schema
Test Your Handedness
See a Person in Moving Lights
Make Events Understandable as Cause-and-Effect
Boost Memory by Using Context
Understand Detail and the Limits of Attention

Steven Johnson, author of "Mind Wide Open" writes in his foreword to the book, "These hacks amaze because they reveal the brain's hidden logic; they shed light on the cheats and shortcuts and latent assumptions our brains make about the world." If you want to know more about what's going on in your head, then Mind Hacks is the key--let yourself play with the interface between you and the world.

Customer Reviews:

Interesting but disappointed.......2007-07-02

Learning about the mind is okay, but I didn't learn anything that I hadn't seen in Psych 101, and a few interesting articles later on.

Calling it a "hacks" book is false advertising, at best. Should it make it to another printing, I would hope O'Reilly would rename it.

The information seemed accurate, so I'll give it that.

Mind hacks.......2007-02-13

This is a really great book. More novelty than improvement, but still great. If you're looking for something that will be useful as well as attention grabbing then look for the second edition; "Mind Performance Hacks"

Not really a "hacks" book.......2006-11-02

I was sorely disappointed in this book--I checked it out from the local library and hoped that it would actually live up to the subtitle of including tips and tools for using my brain. It didn't; the few "tips" included were those that are found in virtually every other discussion of how to improve memory.

This book is more geared toward folks who are designing user interfaces--it talks about how the brain processes information (and thus describes ways to improve information conveyance).

Mind Hacks: Tips & Tools for Using Your Brain.......2006-07-25

It's a book very interested about language in mind and speech comprehension. I read a short comment in BBC Focus magazine and I wish to read because it's very easy to learn the misteries of mind in cognitive science applied to language and psichology.

A fun book.......2006-04-30

It is not a typical "hacks" book in that it does not tell you how to utilize you brain more effectively or do neat things. It *does* have a lot of exercises that show you cool things on how your brain works with sections describing how your brain works - and a number of experiments (blind spot, Magnet interaction with the brain, word parsing in the the mind, and so on). This book goes very well with a recent title called _Mind Wide Open_ by Steven Johnson.

If you want traditional "hacks" the book "Mind Performance Hacks" just came out, and is chock full of those sorts of experiments, while less informative, does do things like memory tricks, meath calculation, creativity enhancement and so on.

I view "Mind Hacks" as more informative, though, so would recommend this as the first one to get, though the next purchase in this should be the "Mind Performance Hacks."

Average customer rating:

Very solid on math, very expensive in price.
One of the best for Learning the Material
a good book with a broad coverage in the image processing

Digital Image Processing
Kenneth R. Castleman
Manufacturer: Prentice Hall
ProductGroup: Book
Binding: Paperback

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Digital Image Processing (3rd Edition)
Digital Image Processing Using MATLAB
Algorithms for Image Processing and Computer Vision
Radar System Analysis And Modeling (Artech House Radar Library)
The Pocket Handbook of Image Processing Algorithms In C

ASIN: 0132114674

Book Description

This broad introduction to the fundamental concepts of digital imaging shows how the various techniques can be applied to solve real-world problems (e.g., in biology, astronomy, forensics, etc.). It helps readers develop the insight required to use the tools of digital imaging to solve new problems. Discusses color, image compression, user interfaces, software development project management, 2-D graphs of Fourier Transforms, analysis of digital imaging systems performance, optics, pattern recognition, image recording and display, CCD cameras.

Customer Reviews:

Very solid on math, very expensive in price........2004-02-26

This book covers most of the elements you will ever need or want to know about image processing. It presents the algorithms, mathematics, and logic behind all major techniques. It has enough math to scare off any casual reader, in enough detail to satisfy anyone seriously interested in image processing. The only bad thing about the book is the cost. If it were priced slashed to $60 or less, I'd have given it 5 stars. If you plan on doing any serious image processing in your career, it is worth the money. If you only need it for a class and not your career, then you are probably better off reading it with a formerly-wealthy classmate.

One of the best for Learning the Material.......2000-08-24

I used Ken Castleman's first edition in 1979 that same year to get a thorough and fast mastery of digital image processing. The book is highly recommended, because I feel that if a book is easy to read and gets the job done of educating the reader as quickly as possible, and it is actually a pleasure to go through, then it is a winner. This book is one of the rare technical books I can recommend this way. The price tag of 105 is hefty, but many books today are outlandishly priced. Even so, if the budget allows only one or two Digital Imaging texts, this would be one I would definitely pick.

a good book with a broad coverage in the image processing.......2000-06-20

This book helped me gain the basic knowledge in digital image processing. Although it doesn't have any sample programming code, the description and the math functions in the book are good enough for me to understand the different imaging processing techniques and finish my image processing programming projects at work.

Average customer rating:

Theory of Remote Image Formation
Richard E. Blahut
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Hardcover

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Fundamentals of Radar Signal Processing

ASIN: 0521553733

Book Description

Richard Blahut presents a unified analysis of the mathematical methods that underlie the various algorithms used in remote image formation. In many applications, images, such as ultrasonic or X-ray signals, are recorded and then analyzed with digital or optical processors in order to extract information. Such processing requires the development of algorithms of great precision and sophistication. Key features of the text include practical applications that illustrate theoretical concepts and many homework problems that enhance the learning of graduate students of electrical engineering and computer science, as well as professionals.

Algorithms for Image Processing and Computer Vision

Average customer rating:

An unusually clear book -- and great for OCR researchers.
Outstanding source of implementations of image processing algorithms
I like this book
Muy buena referencia
A great Scope

Algorithms for Image Processing and Computer Vision
J. R. Parker
Manufacturer: Wiley
ProductGroup: Book
Binding: Paperback

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Computer Vision
Digital Image Processing (3rd Edition)
Feature Extraction in Computer Vision and Image Processing
Machine Vision : Theory, Algorithms, Practicalities
Digital Image Processing Using MATLAB

ASIN: 0471140562

Book Description

A cookbook of the hottest new algorithms and cutting-edge techniques in image processing and computer vision

This 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

Customer Reviews:

An unusually clear book -- and great for OCR researchers........2006-05-07

This nifty book is unusual in several respects: it is written in an extremely clear and simple style; it presents good clear code, both in print and on the accompanying CD; and it presents a variety of different methods for achieving each given goal. For example, the book presents a half-dozen different algorithms (usually with code) for edge-detection, for thresholding, and for skeletonization. For the most part, the book does a good job of explaining the relative merits or suitabilities of the different algorithms.

It's important to say, though, that this book is not a comprehensive introduction to image processing issues and algorithms (such as Gonzales&Woods), nor is it a soup-to-nuts cookbook of code (such as Myler&Weeks' admittedly dated "Pocket Handbook"). Although the book is written in a style that students could understand, I wouldn't recommend it as a first book for novice students. It would be better suited to a non-specialist who nevertheless needs to accomplish something specific -- like thresholding or edge-detection -- in short order, and with an algorithm that's tuned to their particular application.

One last thing: this book will be VERY MUCH appreciated by anyone seeking a foundation in optical character recognition.

Outstanding source of implementations of image processing algorithms.......2006-03-11

This book is a great source of code for the implementations of common and not so common algorithms used in image processing and computer vision. It is not meant to be a textbook on either subject. For that I suggest you turn to "Digital Image Processing" by Gonzalez and Woods for the subject of image processing and to "Computer Vision" by Linda Shapiro for computer vision.
The book starts out with the treatment of a common subject - edge detection - and provides code for various edge detectors including Canny, Shen-Casten, and Marr-Hildreth.
Chapter 2 does a pretty good job of explaining digital morphology, the various operations, and their uses.
Chapter 3 has a very brief introduction to gray-level segmentation, and then jumps into more advanced topics such as the method of iterative selection, entropy, fuzzy logic methods, and the method of Chow and Kaneko, among others. Most of the methods here are somewhat mathematically involved, and the author does a pretty good job of explaining the mathematics and showing resulting images that show the strengths of each method.
Chapter 4 is about the calculation and use of texture in image processing and computer vision. Again, the mathematics here can get involved and equations and illustrations are included to help make the concepts clear. Even fractal geometry and its use in texture is very briefly mentioned.
Chapter 5 is one of my favorites, since it covers the topic of skeletonization, which is not commonly covered in most vision or image processing books. The most common method is mentioned first - that of the medial axis transform. This is followed by a discussion of iterative morphological methods and also the use of contours. Finally, the chapter is rounded out with an explanation of the Zhang-Suen/Stentiford/Holt combined method and accompanying code. This method usually produces the best combination of good visual results and computational efficiency.
In Chapter 6, on image restoration, the author moves to the frequency domain and discusses the Fourier Transform, the FFT, and their uses in image restoration. The author provides his own image restoration system, the Eagle Restoration System, complete with code.
Chapter 7, on wavelets, is a very brief and not too satisfying introduction to the topic. The author does provide some mathematics and some code for some basic transforms, but does not provide much in the way of motivation.
Chapters 8 and 9, on optical character recognition and symbol recognition respectively, are two very good chapters on this subject. It provides the simple introductory information on recognizing printed alphabetic characters. It also talks about skew detection in the case of slanted lines of text. Chapter 9 expands the concepts with a very good section on neural networks and character recognition concluding with a system that recognizes printed music.
Chapter 10, the final chapter of the book, was an OK introduction to genetic algorithms and evolutionary computing. However, like the chapter on wavelets, I found it a bit thin.
The appendix contains a listing of all of the C code included on the CD and each program's purpose. Going through the individual programs was, for me, almost as educational as reading the book, since the code is very well laid out and commented. I highly recommend this book to individuals looking for implementations and discussions of certain image processing and computer vision algorithms.

I like this book.......2004-04-20

It has source C code available, which I found is very helpful. It would be better if its code is written in C++.

Muy buena referencia.......2001-03-26

Recomiendo este libro a todos aquellos que estén trabajando en procesamiento digital de imágenes. Requiere conocimiento previo dado que el análisis matemático asociado no está descrito con suficiente profundidad en todos los temas, sin embargo describe muy bien las técnicas y alcances de las las diferentes teorías relacionadas con el procesamiento de señales. El CD que lo acompaña es de gran valor y permite fijar con mucha rapidez los logros descritos en el libro.

A great Scope.......1999-12-09

This book is very helpfull. Not only for image processing. An unsuspected use that I do to this book was use an algorithm for correct blurred images to revert effect of movil mean that produce a strain gage when one sense an compresion wave traveling throw an iron bar, and work very well. This book has an "honor place" in my library.

2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cvpr 2003)

Average customer rating:

2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cvpr 2003)
IEEE
Manufacturer: Institute of Electrical & Electronics Enginee
ProductGroup: Book
Binding: Paperback

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ASIN: 0769519008

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