MuFeSaC: Learning When to Use Which Feature Detector

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.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Sreenivas R. Sukumar, David L. Page, Hamparsum Bozdogan, Andreas Koschan, Mongi A. Abidi
• Keywords: Camera Ego-motion Estimation, Depth Estimation, ICIP 2007, Image Processing, Interest Point Detectors, Model Selection Criteria, Reliable Feature Detector