| Paper: | WA-P4.1 |
| Session: | Object Recognition I / Interpolation and Superresolution |
| Time: | Wednesday, September 19, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
ELLIPSE DETECTION WITH HOUGH TRANSFORM IN ONE DIMENSIONAL PARAMETRIC SPACE |
| Authors: |
Alex Chia; Nanyang Technological University | | |
| | Maylor Leung; Nanyang Technological University | | |
| | How-Lung Eng; Institute for Infocomm Research | | |
| | Susanto Rahardja; Institute for Infocomm Research | | |
| Abstract: |
The main advantage of using the Hough Transform to detect ellipses is its robustness against missing data points. However, the storage and computational requirements of the Hough Transform preclude practical applications. Although there are many modifications to the Hough Transform, these modifications still demand significant storage requirement. In this paper, we present a novel ellipse detection algorithm which retains the original advantages of the Hough Transform while minimizing the storage and computation complexity. More specifically, we use an accumulator that is only one dimensional. As such, our algorithm is more effective in terms of storage requirement. In addition, our algorithm can be easily parallelized to achieve good execution time. Experimental results on both synthetic and real images demonstrate the robustness and effectiveness of our algorithm in which both complete and incomplete ellipses can be extracted. |
