Special Issue "Computer Vision and Pattern Recognition in the Era of Deep Learning"
Deadline for manuscript submissions: closed (31 December 2019).
Interests: image processing; computer vision; computer graphics; pattern recognition; virtual and augmented reality; multimedia systems and applications
Special Issues and Collections in MDPI journals
Deep learning has become a highly popular trend in the machine learning community in recent years, although the term was coined several decades ago. The idea behind deep learning is to try to imitate the function of the human brain by constructing an artificial neural network that has multiple hidden layers, in order to learn better features compared to a conventional shallow network. More precisely, deep learning introduces a hierarchical learning architecture that resembles the layered learning process that takes place in the primary sensory areas of the neocortex in the human brain. It has been shown that by increasing the size of the input dataset, the performance of deep networks increases at a much higher rate than that of shallow networks after a point. This has enabled the practical use of deep neural networks in recent years, since a vast amount of unlabeled multimedia information is now available, and the processing capability of modern computers has risen immensely.
Deep learning and related neural networks such as CNNs and RNNs have already been exploited in a great variety of applications such as automatic text translation, spoken language recognition, music composition, autonomous vehicles, robots, medical diagnosis, stock market prediction, and so on.
An especially popular field of deep learning applications has been that of computer vision and pattern recognition. Typical examples of areas where deep networks have been used are object detection, face detection and recognition, optical character recognition, and image classification. In this Special Issue, we welcome contributions from scholars in all related subjects, presenting either a deep learning solution to a novel application, or a deep learning enhancement to a preexisting application.
Prof. Athanasios Nikolaidis
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 papers will be 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 2000 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.
- Color restoration
- Face detection
- Pose estimation
- Sentiment recognition
- Behavior analysis
- Text image translation
- Automated lip reading
- Image synthesis
- Image classification
- Handwriting recognition
- Object detection
- Object classification