Comparative Study of Descriptors with Dense Key points

Hermine Chatoux (1, 2) , François Lecellier (1, 2) , Christine Fernandez-Maloigne (1, 2)

1 UP - Université de Poitiers = University of Poitiers
2 XLIM-ASALI - Synthèse et analyse d'images

A great deal of features detectors and descriptors are proposed every years for several computer vision applications. In this paper, we concentrate on dense detector applied to different descriptors. Eight descriptors are compared, three from gradient based family (SIFT, SURF, DAISY), others from binary category (BRIEF, ORB, BRISK, FREAK and LATCH). These descriptors are created and defined with certain invariance properties. We want to verify their invariances with various geometric and photometric transformations, varying one at a time. Deformations are computed from an original image. Descriptors are tested on five transformations: scale, rotation, viewpoint, illumination plus reflection. Overall, descriptors display the right invariances. This paper's objective is to establish a reproducible protocol to test descriptors invariances.

Mots clés: Segmentation; features and descriptors; Signal image and video processing; Image and video analysis and understanding

Domaines: Traitement du signal et de l'image [eess.SP]

Hermine Chatoux, François Lecellier, Christine Fernandez-Maloigne. Comparative Study of Descriptors with Dense Key points. 23rd International Conference on Pattern Recognition, Dec 2016, Cancun, Mexico. ⟨hal-01461562⟩