| Paper: | WP-L5.6 |
| Session: | Motion Detection and Estimation III |
| Time: | Wednesday, September 19, 16:30 - 16:50 |
| Presentation: |
Lecture
|
| Title: |
MUFESAC: LEARNING WHEN TO USE WHICH FEATURE DETECTOR |
| Authors: |
Sreenivas Sukumar; University of Tennessee, Knoxville | | |
| | David Page; University of Tennessee, Knoxville | | |
| | Hamparsum Bozdogan; University of Tennessee, Knoxville | | |
| | Andreas Koschan; University of Tennessee, Knoxville | | |
| | Mongi Abidi; University of Tennessee, Knoxville | | |
| Abstract: |
Interest point detectors are the starting point in image analysis for depth estimation using epipolar geometry and camera ego-motion estimation. With several detectors defined in the literature, some of them outperforming others in a specific application context, we introduce Multi-Feature Sample Consensus (MuFeSaC) as an adaptive and automatic procedure to choose a reliable feature detector among competing ones. Our approach is derived based on model selection criteria that we demonstrate for mobile robot self-localization in outdoor environments consisting of both man-made structures and natural vegetation. |
