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Computer Vision (Englisch) Taschenbuch – 20. April 2001


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Produktinformation

  • Taschenbuch: 608 Seiten
  • Verlag: Addison Wesley Pub Co Inc; Auflage: New. (20. April 2001)
  • Sprache: Englisch
  • ISBN-10: 0130307963
  • ISBN-13: 978-0130307965
  • Größe und/oder Gewicht: 17,5 x 3,3 x 23,6 cm
  • Durchschnittliche Kundenbewertung: 3.0 von 5 Sternen  Alle Rezensionen anzeigen (1 Kundenrezension)
  • Amazon Bestseller-Rang: Nr. 206.429 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
  • Komplettes Inhaltsverzeichnis ansehen

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Produktbeschreibungen

Synopsis

For upper level courses in Computer Vision and Image Analysis. Provides necessary theory and examples for students and practitioners who will work in fields where significant information must be extracted automatically from images. Appropriate for those interested in multimedia, art and design, geographic information systems, and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing and computer cartography. The text provides a basic set of fundamental concepts and algorithms for analyzing images, and discusses some of the exciting evolving application areas of computer vision.

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Scientists and science fiction writers have long been fascinated by the possibility of building intelligent machines and the capability of understanding the visual world is a prerequisite for such a machine. This book speaks to the notable research progress being conducted and brings together the important problem areas where computer vision is already providing solutions. Due to recent progress in the computer field, economical and flexible use of computer images is pervasive. Computing with images is no longer just for the realm of the sciences, but also for the arts and social sciences and even for hobbyists. This book should serve an established and growing audience including those interested in multimedia, art and design, geographic information systems, and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing, and computer cartography.

Computer Vision presents the necessary theory and techniques for students and practitioners who will work in fields where significant information must be extracted automatically from images. It will be a useful resource automatically from images. It will be a useful resource book for professionals and a core text for both undergraduate and beginning graduate computer vision and imaging courses.

Features

  • Topics include image databases an virtual and augmented reality in addition to classical topics.
  • Offers a complete view of two real-world systems that use computer vision.
  • Contains applications from industry, medicine, land use, multimedia, and computer graphics.
  • Includes over 250 exercises and programming projects, 48 separately defined algorithms, and 360 figures.
  • The companion website features include image archive, sample

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1 von 2 Kunden fanden die folgende Rezension hilfreich Von OkinawaDolphin am 21. September 2009
Format: Taschenbuch
In diesem Buch gibt es eine Einführung zu verschiedenen Themen der Bildverarbeitung wie z. B. Texturanalyse, Segmentierung und Bewegungsanalyse. Algorithmen werden mit Pseudocode dargestellt. Leider sind manche Abschnitte etwas oberflächlich, so dass man nicht alles selber implementieren kann.
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Amazon.com: 6 Rezensionen
36 von 37 Kunden fanden die folgende Rezension hilfreich
Excellent introduction guide 25. August 2002
Von "brucemaxwell531" - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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.
Overall, the choice between Computer Vision and IPAMV should be based on personal preference, the focus of your course, and the background of your students. IPAMV will be more accessible to engineers and contains more in-depth coverage of image processing techniques. Computer Vision is more accessible to computer scientists and covers a number of higher-level aspects of CV that are either not covered or briefly covered in IPAMV. In a number of areas--texture, stereo, motion, calibration, and segmentation--the two books are quite similar and the differences are mainly in style and emphasis.
28 von 30 Kunden fanden die folgende Rezension hilfreich
Great book but paperback version is a disappointment 23. Juni 2008
Von Jason - Veröffentlicht auf Amazon.com
Format: Taschenbuch
Shapiro's "Computer Vision" is an excellent book for someone looking for an introductory text in the field. The book is well structured and introduces fundamental concepts first, then uses these concepts to build on advanced approaches. The book assumes some knowledge of mathematics in linear algebra, calculus, and set theory, but does a good job of introducing the concepts before jumping into the math. Compared to other vision books this book is less focused on the math and more focused on conveying the concepts. Math intensive sections are noted up front in the table of contents.

The reason I rate this book 3 out of 5 stars is that the paperback is entirely in black and white. The original hardcover contained both color images and color plates. When these images are converted to B&W it defeats the purpose of having them as the reader cannot distinguish some of the effects occurring in the image which Shapiro is discussing (especially in sections discussing color image processing!). It is very disappointing that the publisher opted to go this route. If possible I recommend obtaining a hardcover which I give 5 out of 5 stars.
16 von 17 Kunden fanden die folgende Rezension hilfreich
Good presentation of both beginning and advanced material 7. Oktober 2005
Von calvinnme - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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.
6 von 6 Kunden fanden die folgende Rezension hilfreich
Low quality & Overpriced 28. Februar 2011
Von Hani - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This book has a nice and smooth approach to Computer Vision. It can be used as a textbook.
The material is older now. A revision seems to be necessary.

The paper back version is really disappointment, because all of the figures are in gray scale. For the chapters
that are about the color image processing and perception etc, this is unacceptable.
5 von 5 Kunden fanden die folgende Rezension hilfreich
Best Intro. Text I've Used 17. November 2003
Von Brendan Drew - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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.
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