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Robot and Machine Vision publishes ambitious scientific and technological proposals in this ever-growing field of artificial vision systems. This section welcomes challenging papers covering all aspects of robot and machine vision systems from the low-level processes of early vision to the high-level processes of recognition and interpretation to be applied to robotics and industry
Robot and Machine Vision publishes ambitious scientific and technological proposals in this ever-growing field of artificial vision systems. This section welcomes challenging papers covering all aspects of robot and machine vision systems from the low-level processes of early vision to the high-level processes of recognition and interpretation to be applied to robotics and industry.
Foundational and novel as well as visionary theoretical research aspects of robot and machine vision systems are at the core of this section, including a strong mathematical, physical and/or computational focus.
The topics covered at the core of this section should address all relevant aspects of robot and machine vision including early vision, perception and cognition, computational geometry, shape analysis, range analysis, motion analysis, image processing, signal processing, image matching, pattern recognition, real-time systems, 2D/3D visual systems, and multisensory fusion, among others.
The specialty section especially welcomes the submission of manuscripts describing robot vision techniques and algorithms as well as machine vision applications in the area of Robotics and Artificial Intelligence, particularly where the application domain is cutting-edge and meant to be smart. This would be the case of Robot Vision for Ambient Intelligence, Smart Environments, Affective Computing, Human-Machine Interaction, Social Robotics, Brain Modeling, and so on. Machine Vision should face novel approaches to industrial applications as part of Industry 4.0, Internet of Things and Cyber Physical Systems. Lastly, new findings relating the bio-inspiration of the natural (human and animal) vision into the artificial (robot and machine) vision will render this section unique.
Indexed in: Scopus, Google Scholar, DOAJ, CrossRef, dblp, Ulrich's Periodicals Directory, ESCI, Emerging Sources Citation Index, CLOCKSS
Robot and Machine Vision welcomes submissions of the following article types: Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Mini Review, Opinion, Original Research, Perspective and Review.
All manuscripts must be submitted directly to the section Robot and Machine Vision, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Robot and Machine Vision will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Robotics and AI.
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