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

Image Intrepretation. Fuzzy Conditional Rule Generation. Relational evidence Theory and Schematic Interpretation. Cite-Scene Understanding and Object Recognition. See++: An object Oriented Theory of Task Specific Vision. SOO-PIN. Invariance Signatures. ABC: Biologically Motivated Image Understanding. 184 Figures. 21 Tables. Author Index. Subject Index.