Authors:
Emilien Royer
;
Thibault Lelore
and
Frédéric Bouchara
Affiliation:
Université de Toulon, CNRS and LSIS UMR 7296, France
Keyword(s):
Keypoints Filtering, Computer Vision, Feature Matching, Kernel Density Estimator.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Geometry and Modeling
;
Image and Video Analysis
;
Image Registration
;
Image-Based Modeling
;
Pattern Recognition
;
Software Engineering
Abstract:
In computer vision, extracting keypoints and computing associated features is the first step for many applications such as object recognition, image indexation, super-resolution or stereo-vision. In many cases, in order to achieve good results, pre or post-processing are almost mandatory steps. In this paper, we propose a generic pre-filtering method for floating point based descriptors which address the confusion problem due to repetitive patterns. We sort keypoints by their unicity without taking into account any visual element but the feature vectors’s statistical properties thanks to a kernel density estimation approach. Even if highly reduced in number, results show that keypoints subsets extracted are still relevant and our algorithm can be combined with classical post-processing methods.