Skip to Main content Skip to Navigation
Journal articles

COnfusion REduction (CORE) algorithm for local descriptors, floating-point and binary cases

Abstract : In this paper, we propose a generic pre-filtering method of point descriptors which addresses the confusion problem due to repetitive patterns. This confusion often leads to wrong descriptor matches and prevents further processes such as object recognition, image indexation, super-resolution or stereo-vision. Our method sorts 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. Both binary descriptors and floating point based descriptors are studied, regardless of their dimensions. 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.
Complete list of metadata

Cited literature [40 references]  Display  Hide  Download
Contributor : Frederic Bouchara Connect in order to contact the contributor
Submitted on : Wednesday, September 12, 2018 - 9:20:53 PM
Last modification on : Thursday, July 14, 2022 - 4:08:20 AM
Long-term archiving on: : Thursday, December 13, 2018 - 3:58:35 PM


Files produced by the author(s)




Frederic Bouchara, Emilien Royer, Thibault Lelore. COnfusion REduction (CORE) algorithm for local descriptors, floating-point and binary cases. Computer Vision and Image Understanding, Elsevier, 2017, 158, pp.115 - 125. ⟨10.1016/j.cviu.2017.01.005⟩. ⟨hal-01873102⟩



Record views


Files downloads