Abstract
This paper presents a new feature detector, with improved local contrast performance. The proposed method is based on an improved non-linear version of the classic Difference of Gaussians, which exhibits increased sensitivity to low contrast. Additionally, it does not employ computationally expensive or memory demanding routines. In order to evaluate the degree of illumination invariance that the proposed, as well as, other existing detectors exhibit, a new benchmark image database has been created. It features a greater variety of imaging conditions, compared to existing databases, containing real scenes under various degrees and combinations of uniform and non-uniform illumination. Experimental results show that the proposed detector extracts greater number of features, with high level of repeatability, compared to other existing ones. These results are evident for both uniform and non-uniform illumination, evincing a favorable usage of the proposed feature detector by robotic platforms working in outdoor working environments.
Original language | English |
---|---|
Title of host publication | Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST '12) |
Number of pages | 6 |
Publication date | 12 Jul 2012 |
Pages | 158 - 163 |
ISBN (Print) | 978-1-4577-1776-5 |
DOIs | |
Publication status | Published - 12 Jul 2012 |
Externally published | Yes |
Keywords
- Benchmark testing
- detecotr
- feature extractions
- image database
- lighting
- robustness