Deep Learning for Computer Vision Application
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 February 2025 | Viewed by 8357
Special Issue Editor
Interests: computer vision; image processing; artificial intelligence; deep learning; medical imaging; thermal imaging; spectroscopy; virtual reality; data analytics and risk assessment; electronics/embedded systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) methods, and more specifically deep neural networks (also called deep learning models), have became the core technique for computer vision tasks across various applications. The advent of these powerful deep learning models allows state-of-the-art automation levels in autonomous pattern recognition from image data. In a general sense, the ultimate manifestation of these techniques can be seen in our daily life, from automatically sorting and retrieving photos in Google photos to autonomous cars. However, these powerful techniques still have not been utilized in all computer vision tasks. Future studies should seek to find more applications of AI in our life, e.g., via data acquisition and cleaning, as well as more model optimization, innovation, and research. In this Special Issue, we are particularly interested in new applications of deep learning in the computer vision field.
Topics of interest include but are not limited to:
- Image classification using deep learning;
- Object detection using deep learning;
- Semantic and instant segmentation using deep learning;
- Deep learning techniques for generating new images (generative adversarial networks);
- Employing reinforcement learning for computer vision tasks;
- Application of deep learning in the Internet of Things (IoT);
- Application of deep learning in embedded systems, sensor development, and electronics;
- Computer vision tasks using deep learning (medical image processing, remote sensing, hyperspectral imaging, thermal imaging, space and extraterrestrial observations);
- Image sequence analysis using deep learning;
- Deep learning and computer vision for smart and green building, smart industry, and smart devices.
Dr. Hamed Mozaffari
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. Electronics 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.
Keywords
- convolutional neural network
- deep learning
- computer vision
- artificial intelligence
- image processing
- medical image processing
- internet of things
- thermal imaging
- image technologies
- application of deep learning
- autonomous vehicles
- image classification
- object detection
- and object segmentation
