| Paper: | TA-P4.4 |
| Session: | Video Object Segmentation and Tracking I |
| Time: | Tuesday, September 18, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
TOPOLOGICAL-STABILIZATION BASED THRESHOLD QUANTIZATION FOR ROBUST CHANGE DETECTION |
| Authors: |
Chang Su; Concordia University | | |
| | Aishy Amer; Concordia University | | |
| Abstract: |
A threshold quantization algorithm for robust change detection is proposed in this paper. According to the threshold distribution of difference frames, a 4-level Lloyd-Max quantizer is designed, and then, based on the topological stabilization of video frames, the Lloyd-Max quantizer is refined by a linear adjusting function to form the proposed threshold quantizer. Objective and subjective experiments show that the proposed quantizer greatly improves the robustness of the thresholding methods for change detection thus significantly improves the quality of change masks without increasing computation loads. |
