Special Issue "Signal, Image and Video Processing: Development and Applications"
Deadline for manuscript submissions: 15 October 2023 | Viewed by 1600
Interests: machine vision; deep learning; artificial intelligence; data-based industrial modeling and measurement
Interests: computer vision; pattern recognition; machine learning
Modern complex systems are required to have humanoid intelligence and ability, and vision is an indispensable technical means for complex systems to achieve automation and intelligence. Vision carries rich information regarding a system and its operating environment in the form of signals, images and videos, and through information mining, valuable information or knowledge can be obtained to support the implementation of the automation and intelligence of complex systems. Due to the complexity of the system, the variability and harshness of the operating environment, and the diversity of perception tasks, visual perception faces many challenges in data processing, model development, knowledge expression, etc. This prompts academic researchers and engineering practitioners to make unremitting efforts to promote the development of visual perception technology and its application in various fields.
This Special Issue aims to alleviate the contradiction between the increasing actual demand for visual perception and the backward visual perception technology and focuses on, but is not limited to, advanced machine-learning- and deep-learning-related signal, image, and video-processing technologies. The related topics are data acquisition, data quality enhancement, segmentation, representation and description, feature matching, motion tracking, etc., and technologies related to large-scale/lightweight deep neural networks, domain adaption, and transfer learning with industrial applications are preferred. This Special Issue provides a platform for researchers and practitioners to present original and innovative results regarding new models and methods and engineering solutions.
Prof. Dr. Xianqiang Yang
Prof. Dr. Changxing Ding
Dr. Zhihao Zhang
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 2200 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.
- machine-learning- and deep-learning-based signal, image, and video processing
- signal, image, and video acquisition
- data transform and filtering
- data quality enhancement
- image and video segmentation
- image and video representation and description
- feature extraction, compression, description, and matching based on image and video
- detection, recognition, classification, and measurement based on image and video
- motion analysis and object tracking based on image and video
- large-scale deep neural network development and applications
- lightweight deep neural network development and applications in embedded devices
- domain adaptation and transfer learning
- image- and video-processing-based industrial applications