Visual saccades for object recognition

Abstract

This paper describes a method for rapid location and characterization of objects in 2D images. It derives optimizing parameters of a normalized Gaussian that best approximates the observed object, simultaneously finding the object location in the observed scene. A similarity measure to this optimized Gaussian is used to characterize the object. Optimization process has global and exponentially fast convergence, thus it can be used to implement saccadic motion for object recognition and scene analysis. This method was inspired by Perlovsky's work on neural dynamic logic used for fast location, characterization, and identification of objects. Developed method was tested and illustrated with an example of an object location and characterization.