Advances, Challenges, and Applications of Deep Learning Models in Computer Vision and Image Processing and Analysis

Dear Colleagues,

Deep learning (DL) models have gained crucial importance in the field of machine learning, especially in the research areas of computer vision and image processing and analysis. The widespread use of DL models is attributed to a strong mathematical foundation, innovation in theoretical and technological research, and broad applications in diverse domains such as healthcare and medicine, industry and commerce, robotics and autonomous vehicles, cybersecurity and surveillance, agriculture and forestry, and remote sensing and geospatial analysis, to name a few.

Despite numerous obstacles facing DL models, such as the need for massive amounts of labeled data, high training costs, scale, translation and rotation invariance in object recognition, adversarial attacks, and their increasing mathematical complexity, recent advances continue to push DL models forward, addressing one or more of the aforementioned challenges. These advances include more robust convolutional neural networks, recurrent neural networks, generative adversarial networks, capsule neural networks, novel deep transfer learning techniques, vision transformers, and hybrid DL architectures, among others.

This Special Issue calls for original research articles and reviews that contribute to the latest advances and applications of DL models in computer vision and image processing and analysis, particularly those focused on real-world problems, as well as articles that explore the challenges of DL models along with trends and ways to address them.

Dr. Carlos Soubervielle-Montalvo
Dr. Cesar Puente
Guest Editors

Manuscript Submission Information

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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. Mathematics 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 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.

• deep learning
• computer vision
• image processing
• image analysis
• convolutional neural networks
• recurrent neural networks
• generative adversarial networks
• capsule neural networks
• deep transfer learning
• vision transformers
• hybrid deep learning architecture
• image classification
• image segmentation
• object recognition
• video tracking
• adversarial attacks
• healthcare and medicine
• autonomous vehicles
• cybersecurity
• surveillance
• artistic-style classification
• agriculture and forestry
• remote sensing and geospatial analysis