Application of Information Theory to Computer Vision and Image Processing III
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".
Deadline for manuscript submissions: 30 November 2025 | Viewed by 67
Special Issue Editors
Interests: machine vision; stereo vision; systems laser; scanner control; analogic and digital processing
Special Issues, Collections and Topics in MDPI journals
Interests: fourth industrial revolution; artificial intelligence; cybersystems
Special Issues, Collections and Topics in MDPI journals
Interests: machine vision; stereo vision; systems laser; scanner control; digital image processing
Special Issues, Collections and Topics in MDPI journals
Interests: automated metrology; 3D coordinate measurement; robotic navigation; machine vision; simulation of robotic swarm behavior
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We are pleased to announce that due to the great success of the first and second volume of “Application of Information Theory to Computer Vision and Image Processing”, a new Special Issue titled “Application of Information Theory to Computer Vision and Image Processing III” is now open for the submission of relevant papers of related topics.
The application of information theory to computer vision and image processing has significantly contributed to advancing our understanding and the capabilities of computer science. Mathematical methods are applied to signal and image processing for quantifying and obtaining accurate information with enhanced efficiency upon every innovation. Moreover, they provide valuable tools and techniques for the development of intelligent and adaptive machine vision systems for measuring and analyzing the amount of information contained within a signal and an image. These include the entropy theory, which is used to estimate the average amount of uncertainty or randomness in a dataset, where high entropy indicates a higher level of unpredictability, while low entropy suggests a more predictable and structured dataset.
This Special Issue aims to publish papers on information theory, measurement methods, data processing, tools, and techniques for the design and instrumentation used in machine vision systems via the application of computer vision and image processing for analyzing, processing, and understanding visual data based on the principles of information content, redundancy, and statistical properties.
Dr. Jesús Elías Miranda-Vega
Dr. Wendy Flores-Fuentes
Prof. Dr. Julio Cesar Rodríguez-Quiñonez
Dr. Oleg Sergiyenko
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. Entropy 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 2600 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
- information theory
- entropy and coding theory (data compression, watermark, minimizing data loss, visual information in a more compact form, transmission, storage)
- computer vision (identify relevant features and patterns)
- machine vision (data analysis and understanding, segmentation, registration, denoising and restoration, object recognition, classification and tracking)
- cyber-physical systems
- instrumentation
- signal and image processing
- measurements (3D spatial coordinates, redundancy, statistical properties)
- artificial intelligence
- applications (navigation, surveillance, facial recognition, medicine, robotics, entertainment, and more)
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