Article
Version 2
Preserved in Portico This version is not peer-reviewed
A New Principle Toward Robust Matching in Human-like Stereovision
Version 1
: Received: 19 June 2023 / Approved: 19 June 2023 / Online: 19 June 2023 (07:25:33 CEST)
Version 2 : Received: 19 June 2023 / Approved: 19 June 2023 / Online: 19 June 2023 (07:47:08 CEST)
Version 2 : Received: 19 June 2023 / Approved: 19 June 2023 / Online: 19 June 2023 (07:47:08 CEST)
A peer-reviewed article of this Preprint also exists.
Xie, M.; Lai, T.; Fang, Y. A New Principle toward Robust Matching in Human-like Stereovision. Biomimetics 2023, 8, 285. Xie, M.; Lai, T.; Fang, Y. A New Principle toward Robust Matching in Human-like Stereovision. Biomimetics 2023, 8, 285.
Abstract
Visual signals are the upmost important source for robots, vehicles or machines to achieve human-like intelligence. Human beings heavily depend on binocular vision to understand the dynamically changing world. Similarly, intelligent robots or machines must also have the innate capabilities of perceiving knowledge from visual signals. Until today, one of the biggest challenges faced by intelligent robots or machines is the matching in stereovision. In this paper, we present the details of a new principle toward achieving a robust matching solution which leverages on the use and integration of top-down image sampling strategy, hybrid feature extraction, and RCE neural network for incremental learning (i.e., cognition) as well as robust match-maker (i.e., recognition). A preliminary version of the proposed solution has been implemented and tested with data from Maritime RobotX Challenge (www.robotx.org). The contribution of this paper is to attract more research interest and effort toward this new direction which may eventually lead to the development of robust solutions expected by future stereovision systems in intelligent robots, vehicles and machines.
Keywords
Visual Signals; Stereovision; Image Sampling; Feature Extraction; Incremental Learning; Match-Maker; Cognition; Recognition; Possibility Function.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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