Foundations of computer vision : computational geometry, visual image structures and object shape detection / James F. Peters.
2017
TA1634
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Online Access
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Unlimited
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Authorized users
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Can lend chapters, not whole ebooks
Details
Title
Foundations of computer vision : computational geometry, visual image structures and object shape detection / James F. Peters.
Author
Peters, James F., author.
ISBN
9783319524832 (electronic book)
3319524836 (electronic book)
9783319524818
331952481X
3319524836 (electronic book)
9783319524818
331952481X
Published
Cham, Switzerland : Springer, 2017.
Language
English
Description
1 online resource (xvii, 431 pages) : illustrations.
Other Standard Identifiers
10.1007/978-3-319-52483-2 doi
Call Number
TA1634
Dewey Decimal Classification
006.3/7
Summary
This book introduces the fundamentals of computer vision (CV), with a focus on extracting useful information from digital images and videos. Including a wealth of methods used in detecting and classifying image objects and their shapes, it is the first book to apply a trio of tools (computational geometry, topology and algorithms) in solving CV problems, shape tracking in image object recognition and detecting the repetition of shapes in single images and video frames. Computational geometry provides a visualization of topological structures such as neighborhoods of points embedded in images, while image topology supplies us with structures useful in the analysis and classification of image regions. Algorithms provide a practical, step-by-step means of viewing image structures. The implementations of CV methods in Matlab and Mathematica, classification of chapter problems with the symbols (easily solved) and (challenging) and its extensive glossary of key words, examples and connections with the fabric of CV make the book an invaluable resource for advanced undergraduate and first year graduate students in Engineering, Computer Science or Applied Mathematics. It offers insights into the design of CV experiments, inclusion of image processing methods in CV projects, as well as the reconstruction and interpretation of recorded natural scenes.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 27, 2017).
Series
Intelligent systems reference library ; v. 124.
Available in Other Form
Print version: 9783319524818
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Online Access
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Table of Contents
Basics Leading to Machine Vision
Working with Pixels
Visualising Pixel Intensity Distributions
Linear Filtering
Edges, Lines, Corners, Gaussian kernel and Voronoï Meshes
Delaunay Mesh Segmentation
Video Processing. An Introduction to Real-Time and Offline Video Analysis
Lowe Keypoints, Maximal Nucleus Clusters, Contours and Shapes
Postscript. Where Do Shapes fit into the Computer Vision Landscape?.
Working with Pixels
Visualising Pixel Intensity Distributions
Linear Filtering
Edges, Lines, Corners, Gaussian kernel and Voronoï Meshes
Delaunay Mesh Segmentation
Video Processing. An Introduction to Real-Time and Offline Video Analysis
Lowe Keypoints, Maximal Nucleus Clusters, Contours and Shapes
Postscript. Where Do Shapes fit into the Computer Vision Landscape?.