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.
By:
James F. Peters
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: Softcover reprint of the original 1st ed. 2017
Volume: 124
Dimensions:
Height: 235mm,
Width: 155mm,
Weight: 688g
ISBN: 9783319849126
ISBN 10: 3319849123
Series: Intelligent Systems Reference Library
Pages: 431
Publication Date: 19 July 2018
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
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?.
