Advances in Machine Vision: Strategies and Applications

Front Cover
World Scientific, 1992 - Computers - 370 pages
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This book describes recent strategies and applications for extracting useful information from sensor data. For example, the methods presented by Roth and Levine are becoming widely accepted as the ‘best’ way to segment range images, and the neural network methods for Alpha-numeric character recognition, presented by K Yamada, are believed to be the best yet presented. An applied system to analyze the images of dental imprints presented by J Côté, et al. is one of several examples of image processing systems that have already been proven to be practical, and can serve as a model for the image processing system designer. Important aspects of the automation of processes are presented in a practical way which can provide immediate new capabilities in fields as diverse as biomedical image processing, document processing, industrial automation, understanding human perception, and the defence industries. The book is organized into sections describing Model Driven Feature Extraction, Data Driven Feature Extraction, Neural Networks, Model Building, and Applications.
 

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Contents

Model Driven Feature Extraction
1
Robust High Breakdown Estimation and Consensus
23
On Circles and Circular Arcs Recognition
63
Modelbased Synthesis of Vision Routines
79
Data Driven Feature Extraction
99
Electromagnetic Models for Perceptual Grouping
129
Using Complex Gabor Filters To Detect and Localize Edges and Bars
151
A Morphological Skeleton Approach
171
Neural Networks for Image Analysis
211
A Neural Net Architecture for the Autoclassification
229
Perceptual and Geometric Model Building
257
From Slice Data to Suggestive Parts
289
Analysis of Retinal Images Using Mathematical Morphology
313
Behavior Based Robotic Assembly Using Vision
329
A Multioperator Approach for the Segmentation of 3D Images
343
Modelling Sonar Range Sensors
361

Representation of Visual Textural Properties
193

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