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Electronics
Special Issues
Real-Time Computer Vision
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Real-Time Computer Vision

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1019

Special Issue Editors

Dr. Hongbo Zhang

E-Mail Website
Guest Editor
Department of Engineering Technology, Middle Tennessee State University, Murfreesboro, TN 37132, USA
Interests: optical sensing; 3D imaging; robotics; computer vision
Dr. Wen-Jing Zhou

E-Mail Website
Guest Editor
Department of Precision Mechanical Engineering, Shanghai University, Shanghai 200444, China
Interests: digital holography
Special Issues, Collections and Topics in MDPI journals
Dr. Yuyin Zhou

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of California, Santa Cruz, CA 95064, USA
Interests: computer vision; machine learning; neural networks and artificial intelligence

Special Issue Information

Dear Colleagues,

This is an open opportunity for you to publish your work in a Special Issue on real-time computer vision. In this Special Issue, we call for publications in the following research areas:

  1. State-of-the-art computer vision methodology;
  2. Computer vision methods applicable to real-time applications, such as robotics, medicine, security, manufacturing, construction, etc.;
  3. Generative computer vision models;
  4. Other related emerging areas of computer vision research are also welcomed.

The success of the Special Issue will augment our capability to transform research into meaningful applications that can impact and model our societies. We look forward to working with our colleagues for the success of the Special Issue in impacting the computer vision community.

Dr. Hongbo Zhang
Dr. Wen-Jing Zhou
Dr. Yuyin Zhou
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 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

  • real-time computer vision
  • computer vision algorithms
  • computer vision applications

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Published Papers (1 paper)

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Research

11 pages, 1849 KiB  
Article
Improved Segmentation of Cellular Nuclei Using UNET Architectures for Enhanced Pathology Imaging
by Simão Castro, Vitor Pereira and Rui Silva
Electronics 2024, 13(16), 3335; https://doi.org/10.3390/electronics13163335 - 22 Aug 2024
Viewed by 625
Abstract
Medical imaging is essential for pathology diagnosis and treatment, enhancing decision making and reducing costs, but despite various computational methodologies proposed to improve imaging modalities, further optimization is needed for broader acceptance. This study explores deep learning (DL) methodologies for classifying and segmenting [...] Read more.
Medical imaging is essential for pathology diagnosis and treatment, enhancing decision making and reducing costs, but despite various computational methodologies proposed to improve imaging modalities, further optimization is needed for broader acceptance. This study explores deep learning (DL) methodologies for classifying and segmenting pathological imaging data, optimizing models to accurately predict and generalize from training to new data. Different CNN and U-Net architectures are implemented for segmentation tasks, with their performance evaluated on histological image datasets using enhanced pre-processing techniques such as resizing, normalization, and data augmentation. These are trained, parameterized, and optimized using metrics such as accuracy, the DICE coefficient, and intersection over union (IoU). The experimental results show that the proposed method improves the efficiency of cell segmentation compared to networks, such as U-NET and W-UNET. The results show that the proposed pre-processing has improved the IoU from 0.9077 to 0.9675, about 7% better results; also, the values of the DICE coefficient obtained improved from 0.9215 to 0.9916, about 7% better results, surpassing the results reported in the literature. Full article
(This article belongs to the Special Issue Real-Time Computer Vision)
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