Title: A vision sensor system with a real-time multi-scale filtering function
Authors: Shinsuke Yasukawa; Hirotsugu Okuno; Seiji Kameda; Tetsuya Yagi
Addresses: Department of Human Intelligence Systems, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Japan ' Department of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University, Japan ' Center for Advanced Medical Engineering and Informatics, Graduate School of Engineering, Osaka University, Japan ' Department of Electrical, Electronic and Information Engineering, Graduate School of Engineering, Osaka University, Japan
Abstract: We developed a compact and energy-efficient vision sensor system that separates an image into a set of spatial frequency bands at 50 fps. The vision sensor system comprises a photo-sensor array, a metal-oxide-semiconductor (MOS)-based resistive network, and a field-programmable gate array (FPGA). To apply multiple spatial filters efficiently, which is required for the separation of an image, we employed a MOS-based resistive network, whose strengths are instantaneous filtering, configurable filter size, and low power consumption. A digital circuit for controlling the filter size of the resistive network was programmed in the FPGA; this circuit changes the filter size four times in a single frame sampling period. This control scheme generates four filtered images from a single resistive network. This system was applied to edge extraction of a photograph or a movie of natural scenes, and it was successful in extracting edges and separating them by spatial frequencies in real time, e.g., the outline and stripe patterns of a zebra.
Keywords: robotic vision; vision sensors; multiple spatial filtering; multi-scale filtering; photo-sensor arrays; MOS-based resistive networks; field-programmable gate arrays; FPGA; filtered images; edge extraction; zebra stripes.
International Journal of Mechatronics and Automation, 2014 Vol.4 No.4, pp.248 - 258
Received: 05 Nov 2013
Accepted: 14 Apr 2014
Published online: 14 Jan 2015 *