MuFeSaC: Learning When to Use Which Feature Detector (original) (raw)

2007, 2007 IEEE International Conference on Image Processing

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.

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MuFeSaC: Learning When to Use Which Feature Detector (original) (raw)

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