Object Identification in Dynamic Images Based on the Memory-Prediction Theory of Brain Function

Marek Bundzel, Shuji Hashimoto
DOI: 10.4236/jilsa.2010.24024                

Abstract

In 2004, Jeff Hawkins presented a memory-prediction theory of brain function, and later used it to create the Hierar-chical Temporal Memory model. Several of the concepts described in the theory are applied here in a computer vision system for a mobile robot application. The aim was to produce a system enabling a mobile robot to explore its envi-ronment and recognize different types of objects without human supervision. The operator has means to assign names to the identified objects of interest. The system presented here works with time ordered sequences of images. It utilizes a tree structure of connected computational nodes similar to Hierarchical Temporal Memory and memorizes frequent sequences of events. The structure of the proposed system and the algorithms involved are explained. A brief survey of the existing algorithms applicable in the system is provided and future applications are outlined. Problems that can arise when the robot’s velocity changes are listed, and a solution is proposed. The proposed system was tested on a sequence of images recorded by two parallel cameras moving in a real world environment. Results for mono- and ste-reo vision experiments are presented.

Keywords

Memory Prediction Framework, Mobile Robotics, Computer Vision, Unsupervised Learning

Conflicts of Interest

The authors declare no conflicts of interest.

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