Author :
Bock, Peter ; Klinnert, Roland ; Kober, Rudolf ; Rovner, Richard M. ; Schmidt, Hauke
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., George Washington Univ., DC, USA
fDate :
4/1/1992 12:00:00 AM
Abstract :
Based on the paradigm of collective learning systems, ALIAS (adaptive learning image analysis system) is an adaptive image-processing engine specifically designed to detect anomalies in otherwise normal images and signals. To accomplish this, ALIAS requires only one pass through a training set, which typically consists of less than 100 samples. The original version of ALIAS (1.0) was limited to an input domain of binary images. A gray-scale version of ALIAS (2.3) was completed in Apr. 1991. The authors present the theoretical background and technical design of ALIAS and describe two experiments with the gray-scale capability
Keywords :
computerised picture processing; learning systems; adaptive image-processing engine; binary images; collective learning systems; gray-scale version; training set; Adaptive signal detection; Adaptive systems; Computer architecture; Engines; Gray-scale; Humans; Image color analysis; Image processing; Machine learning; Signal design;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
