Machine Learning and Image Interpretation

In this groundbreaking new volume, computer researchers discuss the development of technologies and specific systems that can interpret data with respect to domain knowledge. Although the chapters each illuminate different aspects of image interpretation, all utilize a common approach - one that asserts such interpretation must involve perceptual learning in terms of automated knowledge acquisition and application, as well as feedback and consistency checks between encoding, feature extraction, and the known knowledge structures in a given application domain. The text is profusely illustrated with numerous figures and tables to reinforce the concepts discussed.

Inhalt
1 An Overview and Perspective of Image Interpretation.- 2 Fuzzy Conditional Rule Generation for the Learning and Recognition of 3D Objects from 2D Images.- 3 Relational Evidence Theory and Interpreting Schematics.- 4 CiteScene Understanding and Object Recognition.- 5 See++: An Object Oriented Theory of Task Specific Vision.- 6 SOO-PIN: Picture Interpretation Networks.- 7 Invariance Signatures for Two-Dimensional Contours.- 8 ABC: Biologically Motivated Image Understanding.- References.- Author Index.