by R. Jain, R. Kasturi, B. G. Schunck
Publisher: McGraw-Hill 1995
Number of pages: 549
This text is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithm in practical application are provided, and engineering aspects of techniques are emphasized. This text intentionally omits theories of machine vision that do not have sufficient practical applications at the time.
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by David Marshall - Cardiff School of Computer Science
From the table of contents: Image Acquisition: 2D Image Input, 3D imaging; Image processing: Fourier Methods, Smoothing Noise; Edge Detection; Edge Linking; Segmentation; Line Labelling; Relaxation Labelling; Optical Flow; Object Recognition.
by Jean Gallier - arXiv
These are notes on the method of normalized graph cuts and its applications to graph clustering. I provide a thorough treatment of this deeply original method, including complete proofs. The main thrust of this paper is the method of normalized cuts.
by Joachim Weickert - Teubner
Many recent techniques for digital image enhancement and multiscale image representations are based on nonlinear PDEs. This book gives an introduction to the main ideas behind these methods, and it describes in a systematic way their foundations.
by Peng-Yeng Yin - IN-TECH
The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms.