Efficient feedforward categorization of objects and human postures with address-event image sensors
Carrasco, Jose Antonio Perez
Date of Issue2012
School of Electrical and Electronic Engineering
This paper proposes an algorithm for feedforward categorization of objects and, in particular, human postures in real-time video sequences from address-event temporal-difference image sensors. The system employs an innovative combination of event-based hardware and bio-inspired software architecture. An event-based temporal difference image sensor is used to provide input video sequences, while a software module extracts size and position invariant line features inspired by models of the primate visual cortex. The detected line features are organized into vectorial segments. After feature extraction, a modified line segment Hausdorff-distance classifier combined with on-the-fly cluster-based size and position invariant categorization. The system can achieve about 90 percent average success rate in the categorization of human postures, while using only a small number of training samples. Compared to state-of-the-art bio-inspired categorization methods, the proposed algorithm requires less hardware resource, reduces the computation complexity by at least five times, and is an ideal candidate for hardware implementation with event-based circuits.
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
IEEE transactions on pattern analysis and machine intelligence
© 2012 IEEE