Document Type
Article
Publication Date
12-1-1998
Journal / Book Title
IEEE Transactions on Pattern Analysis and Machine Intelligence
Abstract
Current computer vision systems whose basic methodology is open-loop or fi'/tertype typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation parameters. This is accomplished by using the confidence level of model matching as a reinforcement signal for a team of learning automata to search for segmentation parameters during training. The use of the recognition algorithm as part of the evaluation function for image segmentation gives rise to significant improvement of the system performance by automatic generation of recognition strategies. The system is verified through experiments on sequences of indoor and outdoor color images with varying external conditions.
DOI
10.1109/34.659932
Montclair State University Digital Commons Citation
Peng, Jing and Bhanu, Bir, "Closed-Loop Object Recognition using Reinforcement Learning" (1998). Department of Computer Science Faculty Scholarship and Creative Works. 163.
https://digitalcommons.montclair.edu/compusci-facpubs/163
Published Citation
Peng, J., & Bhanu, B. (1998). Closed-loop object recognition using reinforcement learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(2), 139-154.