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Technical Program
Paper Detail
| Paper: | MP-P1.10 |
| Session: | Edge Detection and Image Segmentation |
| Time: | Monday, October 9, 14:20 - 17:00 |
| Presentation: |
Poster
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| Title: |
INCREASING OBJECT RECOGNITION RATE USING REINFORCED SEGMENTATION |
| Authors: |
Farhang Sahba; University of Waterloo | | | | | Hamid R. Tizhoosh; University of Waterloo | | | | | Magdy M. A. Salama; University of Waterloo | | |
| Abstract: |
A new approach to object extraction and recognition based on reinforcement learning is presented. We use this novel idea as an effective way to optimally segment the image and increase recognition rate. This new approach is introduced to segment a specific object, which should be recognized. The proposed method controls the segmentation and the post-processing based on an intelligent scheme. The main purpose of this work is to demonstrate this potential that an ”object-dependent segmentation” via an intelligent technique outperforms the best thresholding algorithms with respect to final recognition rate. It is important to notice that we chose reinforcement learning, as an intelligent technique, since it does not need a huge training data and can be applied if no expert or a-priori knowledge is available. Promising results showed the effectiveness of proposed approach. |
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