Showing posts with label pattern recognition. Show all posts
Showing posts with label pattern recognition. Show all posts

An Introduction to 3D Computer Vision Techniques and Algorithms Review

An Introduction to 3D Computer Vision Techniques and Algorithms
Average Reviews:

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Although Hartley's book offers a deeper perspective on stereo 3D reconstruction, it is also aimed at an audience with a higher knowledge on this area, both mathematically and conceptually.
This book on the other hand offers a more simplistic solution but it also offers a good review on disparity and point correspondence estimation, which on Hartley's book is barely covered.
As each of theese books offfers a slightly different point of view on the matter, I suggest having both as good references on the subject.

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Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model.

This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently:
present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms;
explain scale-space vision, as well as space reconstruction and multiview integration;
demonstrate a variety of practical applications for 3D surface imaging and analysis;
provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures.

An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

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Applied Multivariate Techniques Review

Applied Multivariate Techniques
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This book is provides a gentle introduction to the field of Multivariate analysis. The author provides a ground up introduction to each of the topic. Each chapter starts off with a relatively simple example to introduce the topic.
However, there are not many solved examples,which i feel is the basic shortcoming of this book. I would advice on obtaining a second book on this topic for additional examples and explanations.

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This book focuses on when to use the various analytic techniques and how to interpret the resulting output from the most widely used statistical packages (e.g., SAS, SPSS).

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