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Foundations of Computer Vision: Computational Geometry, Visual Image Structures and Object Shape Detection

by James F. Peters
Save 35% Save 35%
Current price ₹8,005.00
Original price ₹12,314.00
Original price ₹12,314.00
Original price ₹12,314.00
(-35%)
₹8,005.00
Current price ₹8,005.00

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Book cover type: Hardcover
  • ISBN13: 9783319524818
  • Binding: Hardcover
  • Subject: N/A
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Publication Date:
  • Pages: 431
  • Original Price: EUR 109.99
  • Language: English
  • Edition: 2017
  • Item Weight: 808 grams
  • BISAC Subject(s): Software Development & Engineering / Computer Graphics, System Theory, and Engineering (General)

From the Back Cover

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.

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