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J. Imaging
Special Issues
Modelling of Human Visual System in Image Processing
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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: 24 May 2024 | Viewed by 4694

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 1800 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 (2 papers)

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Research

28 pages, 1488 KiB  
Article
Measuring 3D Video Quality of Experience (QoE) Using A Hybrid Metric Based on Spatial Resolution and Depth Cues
by Sahin Coskun, Gokce Nur Yilmaz, Federica Battisti, Musaed Alhussein and Saiful Islam
J. Imaging 2023, 9(12), 281; https://doi.org/10.3390/jimaging9120281 - 18 Dec 2023
Viewed by 1688
Abstract
A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human [...] Read more.
A three-dimensional (3D) video is a special video representation with an artificial stereoscopic vision effect that increases the depth perception of the viewers. The quality of a 3D video is generally measured based on the similarity to stereoscopic vision obtained with the human vision system (HVS). The reason for the usage of these high-cost and time-consuming subjective tests is due to the lack of an objective video Quality of Experience (QoE) evaluation method that models the HVS. In this paper, we propose a hybrid 3D-video QoE evaluation method based on spatial resolution associated with depth cues (i.e., motion information, blurriness, retinal-image size, and convergence). The proposed method successfully models the HVS by considering the 3D video parameters that directly affect depth perception, which is the most important element of stereoscopic vision. Experimental results show that the measurement of the 3D-video QoE by the proposed hybrid method outperforms the widely used existing methods. It is also found that the proposed method has a high correlation with the HVS. Consequently, the results suggest that the proposed hybrid method can be conveniently utilized for the 3D-video QoE evaluation, especially in real-time applications. Full article
(This article belongs to the Special Issue Modelling of Human Visual System in Image Processing)
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16 pages, 828 KiB  
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
Cited by 5 | Viewed by 2291
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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