Title :
An ultrasonic visual sensor using a neural network and its application for automatic object recognition
Author :
Watanabe, Sumio ; Yoneyama, Masahide
Author_Institution :
Ricoh Co Ltd., Yokohama, Japan
Abstract :
An ultrasonic visual sensor using a neural network is proposed and improved by reducing both the size of the neural network and the number of teaching samples. A 3-D image calculated by acoustic imaging is transformed into position and rotation invariant values, and then reorganized by a multilayered neural network. Many categories of metal or glass objects can easily be classified with this system, even when they are placed at unknown positions or rotation angles
Keywords :
acoustic imaging; acoustic signal processing; computer vision; feedforward neural nets; image recognition; ultrasonic applications; 3-D image; US robot eye; acoustic imaging; automatic object recognition; glass objects; metal object; multilayered neural network; position invariant values; robotic vision; rotation invariant values; ultrasonic visual sensor; Acoustic imaging; Acoustic sensors; Gas detectors; Glass; Multi-layer neural network; Neural networks; Optical imaging; Optical receivers; Research and development; Robot vision systems;
Conference_Titel :
Ultrasonics Symposium, 1991. Proceedings., IEEE 1991
Conference_Location :
Orlando, FL
DOI :
10.1109/ULTSYM.1991.234084
