Abstract
This paper introduces a novel adaptive texture feature selection algorithm for tracking. Specifically, we provide a statistical wavelet basis paradigm to maximally separate statistical characteristics of the object-in-interest and its background. The algorithm is based upon non-linearly selecting basis elements out of dual dictionaries in an iterative fashion to continually improve a cost function that is suitable for tracking. We demonstrate that such a selection is effective with several difficult sequences that are affected by lighting changes, occlusion and background motion.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE International Conference on Image Processing |
| Pages | 401-404 |
| Number of pages | 4 |
| Volume | 1 |
| State | Published - 2001 |
| Event | IEEE International Conference on Image Processing (ICIP) 2001 - Thessaloniki, Greece Duration: Oct 7 2001 → Oct 10 2001 |
Other
| Other | IEEE International Conference on Image Processing (ICIP) 2001 |
|---|---|
| Country/Territory | Greece |
| City | Thessaloniki |
| Period | 10/7/01 → 10/10/01 |
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering