HOOFR: An Enhanced Bio-Inspired Feature Extractor

Dai-Duong Nguyen (1) , Abdelhafid El Ouardi (1) , Emanuel Aldea (1) , Samir Bouaziz (1)

1 SATIE-MOSS - Méthodes et Outils pour les Signaux et Systèmes

—Feature matching plays an important role in many computer vision applications, such as object recognition, scene reconstruction or image mosaicing. In this paper, we propose an algorithm called Hessian ORB-Overlapped FREAK (HOOFR) which is based on the combination of the ORB detector and the FREAK bio-inspired descriptor. We address some modifications related to the detection and the description processes in order to enhance HOOFR reliability, speed and memory fingerprint. The experiments on a widely used dataset demonstrate the considerable performance of HOOFR compared to SIFT, SURF or ORB in terms of the execution time and the matching quality, in various matching contexts.

Mots clés: SIFT; SURF; ORB; FREAK; Hessian matrix; Feature matching

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Automatique / Robotique

Dai-Duong Nguyen, Abdelhafid El Ouardi, Emanuel Aldea, Samir Bouaziz. HOOFR: An Enhanced Bio-Inspired Feature Extractor. 2016 23rd International Conference on Pattern Recognition (ICPR), Dec 2016, Cancun, Mexico. ⟨10.1109/ICPR.2016.7900090⟩. ⟨hal-01691946⟩