DocumentCode
2496750
Title
Delayed reinforcement learning for closed-loop object recognition
Author
Peng, Jing ; Bhanu, Bir
Author_Institution
Coll. of Eng., California Univ., Riverside, CA, USA
Volume
4
fYear
1996
fDate
25-29 Aug 1996
Firstpage
310
Abstract
Object recognition is a multi-level process requiring a sequence of algorithms at low, intermediate and high levels. Generally, such systems are open loop with no feedback between levels and assuring their robustness is a key challenge in computer vision research. A robust closed-loop system based on “delayed” reinforcement learning is introduced in this paper. The parameters of a multi-level system employed for model-based object recognition are learned. The method improves recognition results over time by using the output at the highest level as feedback for the learning system. It has been experimentally validated by learning the parameters of image segmentation and feature extraction and thereby recognizing 2D objects. The approach systematically controls feedback in a multi-level vision system and provides a potential solution to a long-standing problem in the field of computer vision
Keywords
closed loop systems; computer vision; feature extraction; feedback; image segmentation; learning (artificial intelligence); learning systems; object recognition; 2D object recognition; closed-loop system; computer vision; delayed reinforcement learning; feature extraction; feedback; image segmentation; learning system; model-based object recognition; Computer vision; Delay; Feature extraction; Feedback loop; Image recognition; Image segmentation; Learning systems; Object recognition; Output feedback; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location
Vienna
ISSN
1051-4651
Print_ISBN
0-8186-7282-X
Type
conf
DOI
10.1109/ICPR.1996.547436
Filename
547436
Link To Document