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Special Issue "Sensor Data Fusion Based on Deep Learning for Computer Vision and Medical Applications"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Dr. Rizwan Ali Naqvi
Website
Guest Editor
Department of Intelligent Mechatronics Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul, 05006, Korea
Interests: computer vision (gaze tracking); human–computer interaction; biometrics (iris and sclera segmentation); medical image processing and understanding; artificial intelligence; deep learning
Dr. Muhammad Arsalan
Website SciProfiles
Guest Editor
Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Korea
Interests: deep learning; semantic segmentation; image classification; medical image analysis; computer-aided diagnosis (CAD); biometrics (finger vein and iris segmentation)
Dr. Talha Qaiser
Website
Guest Editor
Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
Interests: medical image analysis; weakly supervised learning; reinforcement learning; computer aided diagnosis (CAD)
Dr. Tariq Mahmood Khan
Website
Guest Editor
School of Information Technology, Faculty of Science Engineering & Built Environment, Deakin University, Locked Bag 20000, Geelong, VIC 3220, Australia
Interests: image segmentation; image classification; medical image analysis; biometrics (fingerprints and iris segmentation); deep learning
Dr. Imran Razzak
Website
Guest Editor
School of Information Technology, Deakin University, Geelong, Australia
Interests: image analysis; machine learning; AI as service; medical image analysis; data analytics

Special Issue Information

Dear Colleagues,

It is our pleasure to invite submissions to this Special Issue on “Sensor Data Fusion Based on Deep Learning for Computer Vision and Medical Applications”.

Recent advancements have led to the extensive use of various sensors, such as visible light, near-infrared (NIR), thermal camera sensors, fundus cameras, H&E stains, endoscopy, OCT cameras, and magnetic resonance imaging sensors, in a variety of applications in computer vision, biometrics, video surveillance, image compression and image restoration, medical image analysis, computer-aided diagnosis, etc. Research related to sensor and data fusion, information processing and merging, and fusion architecture for the cooperative perception and risk assessment is needed for computer vision and medical applications. Indeed, prior to ensuring a high level of accuracy in the deployment of computer vision and deep learning applications, it is necessary to guarantee high-quality and real-time perception mechanisms. While computer vision technology has matured, its performance is still affected by various environmental factors, and recent approaches have been attempted to fuse data from various sensors based on deep learning techniques to guarantee higher accuracy. The objective of this Special Issue is to invite high-quality, state-of-the-art research papers that deal with challenging issues in deep-learning-based computer vision and medical applications. We solicit original papers of unpublished and completed research that are not currently under review by any other conference/magazine/journal. Topics of interest include, but are not limited to, the following:

  • Computer vision by various camera sensors;
  • Biometrics and spoof detection by various camera sensors;
  • Image classification using various, NIR, VL camera sensors;
  • Detection and localization by deep learning by various cameras;
  • Deep-learning-based object segmentation/instance segmentation by media sensors;
  • Medical image processing and analysis by various camera sensors;
  • Deep learning by various camera sensors;
  • Multiple-approach fusion that combines deep learning techniques and conventional methods on images obtained by various camera sensors.

Dr. Rizwan Ali Naqvi
Dr. Muhammad Arsalan
Dr. Talha Qaiser
Dr. Tariq Mahmood Khan
Dr. Imran Razzak
Guest Editors

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. Sensors 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.

Keywords

  • Sensor data fusion
  • Image processing
  • Deep feature fusion
  • Image/video-based classification
  • Semantic segmentation/instance segmentation
  • Medical image analysis
  • Computer-aided diagnosis
  • Computer vision
  • Fusion for biometrics
  • Fusion for medical applications
  • Fusion for semantic information
  • Smart sensors

Published Papers (1 paper)

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Research

Open AccessArticle
Recognition of Pashto Handwritten Characters Based on Deep Learning
Sensors 2020, 20(20), 5884; https://doi.org/10.3390/s20205884 - 17 Oct 2020
Abstract
Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to [...] Read more.
Handwritten character recognition is increasingly important in a variety of automation fields, for example, authentication of bank signatures, identification of ZIP codes on letter addresses, and forensic evidence. Despite improved object recognition technologies, Pashto’s hand-written character recognition (PHCR) remains largely unsolved due to the presence of many enigmatic hand-written characters, enormously cursive Pashto characters, and lack of research attention. We propose a convolutional neural network (CNN) model for recognition of Pashto hand-written characters for the first time in an unrestricted environment. Firstly, a novel Pashto handwritten character data set, “Poha”, for 44 characters is constructed. For preprocessing, deep fusion image processing techniques and noise reduction for text optimization are applied. A CNN model optimized in the number of convolutional layers and their parameters outperformed common deep models in terms of accuracy. Moreover, a set of benchmark popular CNN models applied to Poha is evaluated and compared with the proposed model. The obtained experimental results show that the proposed model is superior to other models with test accuracy of 99.64 percent for PHCR. The results indicate that our model may be a strong candidate for handwritten character recognition and automated PHCR applications. Full article
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