January 31st, 2022 
Daniela Lopez de Luise Keywords: Resource-constrained Vision, Selective and Divided Attention Mechanisms,
Biologically Inspired Vision, Active Vision
Specialty Sections: Robot and Machine Vision; Bio-inspired Robotics, Humanoid Robotics
I. Background
Attention mechanisms are the fundamental processes in biological systems, responsible for prioritizing the elements of the visual scene to be attended, i.e., to control perceptual resources and cope with the brain computational limitations. Humans, for instance, rely on space-variant sensing (foveal vision), and on stimulus-driven (bottom-up) and goal-driven (top-down) information processing mechanisms to define where in the visual input the attentional foci should be oriented to. This way, information processing is constrained and directed towards salient or task-relevant stimuli. Likewise, an important issue in many computer vision applications requiring real-time performance, resides in the involved computational effort, especially in robotics where energy efficient, fast, and accurate perception is a fundamental requirement, e.g., in visual localization and servoing during grasping, manipulation and hand-over of tools to human or machine collaborators. In humanoid robotics real-time operation is conditioned by physical limitations on on-board computational and power resources, as well as sensory data transmission bandwidth.
II. Scope and information for Authors
This Research Topic aims to collect submissions which demonstrate how vision can be enhanced with novel and especially bio-inspired technologies. We are seeking contributions on the following topics of interest, but not limited to:
- Efficient perception with unconventional vision sensors, such as event cameras, focal-plane sensor-processor, software/hardware retinas, foveal vision, plenoptic cameras.
- Biologically plausible space and time variant visual attention and constrained resource allocation computational mechanisms for artificial systems with limited resources.
- Efficient neural network architectures and learning visual mechanisms for resource-constrained robots
- Biologically principled models for active vision
- Biologically motivated approaches to vision for embedded systems with hard real-time constraints.
III. Submission
Submission's deadline: 14 April 2022
Submitted papers will be reviewed as soon as they are received.
Median processing time for peer-review and a first decision in this journal in 2020 was about 14 days.
IV. Guest Editors
• Rui Pimentel de Figueiredo – Capra Robotics ApS, Aarhus, Denmark
• Lorenzo Jamone – Queen Mary University of London, London, United Kingdom
• Alexandre Bernardino – Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
We’re putting together a group of top researchers whose work we’d like to feature in this collection, and we thought you would be interested in participating.
Hosted by Frontiers in Robotics and AI, this is a unique opportunity for us to collaborate and to showcase your research.
I look forward to working together on this exciting project.
Kind Regards,
Rui Pimentel de Figueiredo
Topic Editor,
Robot and Machine Vision Section, Frontiers in Robotics and AI
On behalf of the Topic Editors.
__________
All submitted articles are peer reviewed.
Article processing charges are applied to all published articles.
• See if your institution has a payment plan with us. (see here: https://www.frontiersin.org/about/institutional-membership )
• Find out about applying for fee support (see here https://www.frontiersin.org/about/publishing-fees#feesupport )
About Frontiers in Robotics and AI
Leading research on robotics and artificial intelligence, bringing the latest technology to society. The journal is led by Field Chief Editor Kostas J Kyriakopoulos from the National Technical University of Athens.. CiteScore: 4.4 (as reported in Scopus by Elsevier). Frontiers is the world's third most-cited publisher with more than 2.2 million citations and 1.4 billion views and downloads from global research and innovation hubs.
Posted in