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J. Imaging
Special Issues
Modelling of Human Visual System in Image Processing
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Special Issue "Modelling of Human Visual System in Image Processing"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Visualization and Computer Graphics".

Deadline for manuscript submissions: 22 October 2023 | Viewed by 1209

Special Issue Editors

Dr. Alexey Mashtakov

E-Mail Website
Guest Editor
Program Systems Institute, Russian Academy of Sciences, Pereslavl-Zalessky, Moscow, Russia
Interests: sub-Riemannian geometry; invariant control systems on lie groups; optimal control; nonlinear geometric control theory; motion planning; applications to robotics; mechanics; image processing and modelling of human visual system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Edoardo Provenzi

E-Mail Website
Guest Editor
IMB Institute de Mathématiques de Bordeaux UMR 5251, Université de Bordeaux, 351, cours de la Libération, 33405 Talence, France
Interests: color image processing; variational principles; geometry of color spaces; high dynamic range imaging; statistics of natural images; contrast measures; color in art and science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding the human body's functioning and, in particular, the work of brain neurons is an urgent problem. The development and analysis of mathematical models of some aspects of brain functioning are important in extending our knowledge. This Special Issue is dedicated to the mathematical modeling of human vision and its applications to problems of image processing, such as image segmentation, enhancement, and inpainting.  The scope of the Special Issue is to expose some modern mathematical models of the processes involved in the perception of visual information by the human brain and to discuss the brain-inspired methods in image processing, which are based on these models.

Dr. Alexey Mashtakov
Prof. Dr. Edoardo Provenzi
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. Journal of Imaging is an international peer-reviewed open access monthly 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 1600 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

  • mathematical modelling
  • human vision
  • brain-inspired methods
  • image processing
  • computer vision
  • nonholonomic systems
  • geometric control
  • perceptual color space

Published Papers (1 paper)

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Research

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Article
Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images
by
Ahmad Alaiad
,
Aya Migdady
,
Ra’ed M. Al-Khatib
,
Omar Alzoubi
,
Raed Abu Zitar
and
Laith Abualigah
J. Imaging 2023, 9(3), 64; https://doi.org/10.3390/jimaging9030064 - 08 Mar 2023
Viewed by 927
Abstract
Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood [...] Read more.
Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models. Full article
(This article belongs to the Special Issue Modelling of Human Visual System in Image Processing)
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