Scale Invariant Object Recognition Using Cortical Computational Models and a Robotic Platform

• Main Author: Voils, Danny
• Format: Others
• Published: PDXScholar 2012
• Subjects: Brain; Cortex; HMAX; Computational neuroscience; Robot vision; Pattern recognition systems; Image processing > Digital techniques > Computer programs; Optical character recognition devices; Artificial Intelligence and Robotics; Electrical and Computer Engineering
• Online Access: https://pdxscholar.library.pdx.edu/open_access_etds/632; https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1631&context=open_access_etds

This paper proposes an end-to-end, scale invariant, visual object recognition system, composed of computational components that mimic the cortex in the brain. The system uses a two stage process. The first stage is a filter that extracts scale invariant features from the visual field. The second stage uses inference based spacio-temporal analysis of these features to identify objects in the visual field. The proposed model combines Numenta's Hierarchical Temporal Memory (HTM), with HMAX developed by MIT's Brain and Cognitive Science Department. While these two biologically inspired paradigms are based on what is known about the visual cortex, HTM and HMAX tackle the overall object recognition problem from different directions. Image pyramid based methods like HMAX make explicit use of scale, but have no sense of time. HTM, on the other hand, only indirectly tackles scale, but makes explicit use of time. By combining HTM and HMAX, both scale and time are addressed. In this paper, I show that HTM and HMAX can be combined to make a com- plete cortex inspired object recognition model that explicitly uses both scale and time to recognize objects in temporal sequences of images. Additionally, through experimentation, I examine several variations of HMAX and its