SIFT-ONN: SIFT Feature Detection Algorithm Employing ONNs for Edge Detection
| Autor: | Madeleine Abernot, Sylvain Gauthier, Theophile Gonos, Aida Todri-Sanial |
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| Přispěvatelé: | Smart Integrated Electronic Systems (SmartIES), Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), A.I.Mergence [Paris], Eindhoven University of Technology [Eindhoven] (TU/e), European Project: 871501,H2020-EU.2.1.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT),H2020-ICT-2019-2,NeurONN(2020), EAISI, Center for Quantum Materials and Technology Eindhoven, NanoComputing Research Lab, Electronic Systems |
| Rok vydání: | 2023 |
| Předmět: |
Feature Detection
Oscillatory Neural Networks SIFT [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Image Edge Detection [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] |
| Zdroj: | NICE 2023-Neuro-Inspired Computational Elements Workshop NICE 2023-Neuro-Inspired Computational Elements Workshop, Apr 2023, San Antonio, TX, United States. ⟨10.1145/3584954.3584999⟩ Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference, NICE 2023, 100-107 STARTPAGE=100;ENDPAGE=107;TITLE=Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference, NICE 2023 |
| DOI: | 10.1145/3584954.3584999 |
| Popis: | International audience; Mobile robot navigation tasks can be applied in various domains, such as in space, underwater, and transportation industries, among others. In navigation, robots analyze their environment from sensors and navigate safely up to target points by avoiding obstacles. Numerous methods exist to perform each navigation task. In this work, we focus on robot localization based on feature extraction algorithms using images as sensory data. ORB, and SURF are state-of-the-art algorithms for featurebased robot localization thanks to their fast computation time, even if ORB lacks precision. SIFT is state-of-the-art for high precision feature detection but it is slow and not compatible with realtime robotic applications. Thus, in our work, we explore how to speed up SIFT algorithm for realtime robot localization by employing an unconventional computing paradigm with oscillatory neural networks (ONNs). We present a hybrid SIFT-ONN algorithm that replaces the computation of Difference of Gaussian in SIFT with ONNs by performing image edge detection. We report on SIFT-ONN algorithm performances, which are similar to the state-of-the-art ORB algorithm. |
| Databáze: | OpenAIRE |
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