DocumentCode
358341
Title
The longitudinal motion stereo problem: a CNN approach
Author
Tarahlio, S. ; Zanela, Andrea ; Pellecchia, Antonio
Author_Institution
ENEA, Rome, Italy
fYear
2000
fDate
2000
Firstpage
375
Lastpage
379
Abstract
A cellular neural net (CNN) based approach to the resolution of the longitudinal motion stereo vision problem is presented. Because of the geometry of the system, through the use of a reference transform in the image sequence, the correlation of pixels between image frames can be performed with a static stereo vision algorithm, the Stereo-CNN. Results on real images are presented
Keywords
cellular neural nets; correlation methods; image motion analysis; image sequences; stereo image processing; Stereo-CNN; cellular neural net; image sequence; longitudinal motion stereo problem; longitudinal motion stereo vision; pixel correlation; static stereo vision algorithm; Cameras; Cellular neural networks; Image motion analysis; Image sequences; Layout; Navigation; Pixel; Robot kinematics; Robot vision systems; Stereo vision;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and Their Applications, 2000. (CNNA 2000). Proceedings of the 2000 6th IEEE International Workshop on
Conference_Location
Catania
Print_ISBN
0-7803-6344-2
Type
conf
DOI
10.1109/CNNA.2000.877358
Filename
877358
Link To Document