SHAPE AND COLOR FEATURES FOR OBJECT RECOGNITION SEARCH

In an object recognition search, in which objects are examined one at a time, a bio-inspired shape feature is introduced which emulates the integration of the saccadic eye movement and the horizontal layer in the retina of a vertebrate. An optimal computational model for shape extraction, which is based on Principal Component Analysis (PCA) and which reduces processing time and enables real time adaptive capability, is also developed. A color feature of the object, using color segmentation, is used to aid shape feature recognition to better solve object recognition problems in heterogeneous environments where a single technique, using either shape or color, may not be effective. To enable an effective recognition system, an adaptive architecture with an autonomous mechanism is introduced to recognize and adapt the shape and color features of moving objects. These preliminary results set a corner stone for further study of practical and effective discrete modeling of a 3-D object. The adaptive architecture and mechanism provide a major step toward enabling an optimal and practical bio-inspired visual system for the future. Additional work is planned to incorporate a bipolar layer which addresses the scaling issue.