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MuFeSaC: LEARNING WHEN TO USE WHICH FEATURE DETECTOR Sreenivas R. Sukumar, David L. Page, Hamparsum Bozdogan, Andreas F. Koschan, Mongi A. Abidi
 

Summary: MuFeSaC: LEARNING WHEN TO USE WHICH FEATURE DETECTOR
Sreenivas R. Sukumar, David L. Page, Hamparsum Bozdogan, Andreas F. Koschan, Mongi A. Abidi
The University of Tennessee, Knoxville, U.S.A
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.
Index Terms-- feature learning, RANSAC, interest point
detector evaluation
1. INTRODUCTION
There are two types of errors associated with image interest
points from low-level feature detectors: (a) classification

  

Source: Abidi, Mongi A. - Department of Electrical and Computer Engineering, University of Tennessee
Koschan, Andreas - Imaging, Robotics, and Intelligent Systems, University of Tennessee

 

Collections: Computer Technologies and Information Sciences; Engineering