DocumentCode
2857968
Title
Distortion-invariant object representation and discrimination using an FST neural net
Author
Casasent, David ; Sipe, Michael ; Talukder, Ashit
Author_Institution
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
Volume
3
fYear
1998
fDate
4-9 May 1998
Firstpage
1971
Abstract
A feature space trajectory (FST) neural net is used to represent distorted versions of an object. Its use in determining the class and pose of an object is addressed with attention to: which aspect views and how many are needed to represent an object, which viewpoint gives the best pose animate and the best classification confidence. New eigen features are also advanced to provide improved results
Keywords
CAD; active vision; image classification; image representation; neural nets; robot vision; aspect views; classification confidence; distortion-invariant object discrimination; distortion-invariant object representation; eigen features; feature space trajectory neural net; Adaptive algorithm; Infrared sensors; Inspection; Neural networks; Neurons; Robot sensing systems; Robot vision systems; Robotic assembly; Testing; Vehicles;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7576
Print_ISBN
0-7803-4859-1
Type
conf
DOI
10.1109/IJCNN.1998.687161
Filename
687161
Link To Document