Series In Machine Perception And Artificial Intelligence

4 primary works

Active Robot Vision: Camera Heads, Model Based Navigation And Reactive Control

by Kevin Bowyer, Horst Bunke, and Henrik I Christensen

Published 1 May 1993

Experimental Environments for Computer Vision and Image Processing

by Henrik I Christensen

Published 1 January 1994

To fully appreciate new methods developed in the area of machine vision it is necessary to have facilities which allow experimental verification of such methods. Experimental research is typically a very expensive task in terms of manpower, and consequently it is desirable to adopt standard facilities/methods which allow more efficient experimental investigations. In this volume a range of different experimental environments which facilitate construction and integration of machine vision systems is described. The environments presented cover areas such as robotics, research in individual machine vision methods, system integration, knowledge representation, and distributed computing. The set of environments covered include commercial systems, public domain software and laboratory prototype, showing the diversity of the problem of experimental research in machine vision and providing the reader with an overview of the area.

Empirical Evaluation Methods In Computer Vision

by Henrik I Christensen and P Jonathon Phillips

Published 1 January 2002

This book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance.The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms.

Graph Classification and Clustering Based on Vector Space Embedding

by Kaspar Riesen and Horst Bunke

Published 1 January 2010

This book is concerned with a fundamentally novel approach to graph-based pattern recognition based on vector space embedding of graphs. It aims at condensing the high representational power of graphs into a computationally efficient and mathematically convenient feature vector.This volume utilizes the dissimilarity space representation originally proposed by Duin and Pekalska to embed graphs in real vector spaces. Such an embedding gives one access to all algorithms developed in the past for feature vectors, which has been the predominant representation formalism in pattern recognition and related areas for a long time.