Symmetry/Asymmetry in Image Processing and Computer Vision Using Embedded Systems
A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".
Deadline for manuscript submissions: 31 July 2025 | Viewed by 656
Special Issue Editors
Interests: computer vision; mobile robotics; evolutionary algorithms; embedded systems; bio-inspired algorithms
Interests: pattern recognition; combinatorial optimization; logistics and supply chain management; design of distribution networks; strategic planning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue examines the essential functions of symmetry and asymmetry in image processing and computer vision, especially in relation to embedded systems. It seeks to compile pioneering research that demonstrates how these notions might improve algorithm efficiency, accuracy, and performance in real-time applications.
Contributions may encompass theoretical frameworks, algorithmic innovations, and practical applications that utilize symmetry for tasks including object identification, image segmentation, and feature extraction. Conversely, research examining the advantages of asymmetrical strategies in dynamic and complex situations is also advocated.
For this reason, this Special Issue encourages researchers and practitioners from different fields to interact, focusing on how hardware and software solutions can work together in embedded systems.
We invite contributions that address a wide range of topics, including, but not limited to, the following:
- Symmetry detection algorithms: Innovative methods for detecting and leveraging symmetrical features in images to improve recognition and classification tasks.
- Asymmetry analysis: Approaches to analyze asymmetrical patterns and their implications for object detection and scene understanding.
- Embedded system architectures: Designs and implementations of hardware and software frameworks that optimize the processing of symmetrical and asymmetrical image data.
- Applications: Case studies showcasing practical applications in areas such as robotics, surveillance, medical imaging, and autonomous vehicles, highlighting how symmetry/asymmetry influences system performance.
- Machine learning techniques: The integration of machine learning methods that utilize symmetry and asymmetry for enhanced image analysis and feature extraction.
Dr. Felipe Trujillo-Romero
Prof. Dr. Santiago Omar Caballero-Morales
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- image processing
- computer vision
- embedded systems
- object recognition
- image segmentation
- feature extraction
- dynamic environments
- hardware integration
- real-time applications
- machine learning
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