The purpose of the thesis, Biologically Inspired Object Categorization system, is to provide an automatic system to classify the real-world images into categories. Generally, computer algorithms classify objects with much lower efficiency than human. Furthermore, some images with complex features such as cat and dog faces are difficult to be classified by ordinary computer algorithms. Therefore, the simulation of the structure and process of a mammalian’s visual cortex is created, which functions similarly to a human’s visual cortex, by using a computer. In this paper, I am presenting a biologically inspired neural network system which processes the images in a hierarchical order, starting from emulation of the retina cells to the virtual cortex. The goal of the network is to recognize objects in images which serve to answer the “what” objects that are in the scene. “What” is one of the pathways the brain recognizes of an object, aside from the ‘where’ pathway. The system can be used in many applications such as categorizing cat and dog faces individually or clustering automobiles in an urban scene.
Library of Congress Subject Headings
Computer vision; Pattern recognition systems; Image processing--Digital techniques; Neural networks (Computer science)
Department, Program, or Center
Computer Science (GCCIS)
Peerasathien, Theparit, "Biologically inspired object categorization in cluttered scenes" (2008). Thesis. Rochester Institute of Technology. Accessed from
RIT – Main Campus