Deep Learning-Based Computer Vision Technology and Its Applications

Dear Colleagues,

Convolutional Neural Networks (CNNs) enable computer vision systems to learn visual data from large datasets in order to perform tasks like object detection, recognition, and localization, texture discrimination, facial recognition, and defect detection with high accuracy. For this Special Issue, we seek high-quality original research articles regarding all aspects of computer vision. We welcome both theoretical and practical studies of high technical quality across various disciplines, with the aim of highlighting methods employed in one area that may also apply to other areas.

Topics of interest include, but are not limited to, the following:

• Systems for facilitating medical diagnostics;
• Assisted surgery;
• Autonomous vehicles;
• Manufacturing (quality control);
• Security and surveillance (facial recognition, etc.);
• Agriculture (disease monitoring, crop yield assessment, etc.);
• Retail and logistics (facilitating inventory management by automating stock tracking and visual auditing within warehouses and stores);
• Moving-target indicators in synthetic aperture radar.

Dr. Carlos Lima
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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.