DocumentCode :
53000
Title :
A Real-Time Motion-Feature-Extraction VLSI Employing Digital-Pixel-Sensor-Based Parallel Architecture
Author :
Hongbo Zhu ; Shibata, Takuma
Author_Institution :
VLSI Design & Educ. Center, Univ. of Tokyo, Tokyo, Japan
Volume :
24
Issue :
10
fYear :
2014
fDate :
Oct. 2014
Firstpage :
1787
Lastpage :
1799
Abstract :
A very-large-scale integration capable of extracting motion features from moving images in real time has been developed employing row-parallel and pixel-parallel architectures based on the digital pixel sensor technology. Directional edge filtering of input images is carried out in row-parallel processing to minimize the chip real estate. To achieve a real-time response of the system, a fully pixel-parallel architecture has been explored in adaptive binarization of filtered images for essential feature extraction as well as in their temporal integration and derivative operations. As a result, self-speed-adaptive motion feature extraction has been established. The chip was designed and fabricated in a 65-nm CMOS technology and used to build an object detection system. Motion-sensitive target image localization was demonstrated as an illustrative example.
Keywords :
CMOS image sensors; VLSI; edge detection; feature extraction; image motion analysis; object detection; CMOS technology; adaptive binarization; digital pixel sensor technology; directional edge filtering; motion feature extraction VLSI; motion-sensitive target image localization; moving images; object detection system; pixel-parallel architectures; row-parallel processing; self-speed-adaptive motion feature extraction; size 65 nm; temporal integration; very-large-scale integration; Feature extraction; Image edge detection; Image sensors; System-on-chip; Very large scale integration; Block-readout scheme; digital pixel sensor (DPS); motion feature extraction (MFE); parallel architecture; vision chip;
fLanguage :
English
Journal_Title :
Circuits and Systems for Video Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
1051-8215
Type :
jour
DOI :
10.1109/TCSVT.2014.2313899
Filename :
6778799
Link To Document :
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