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J. Imaging
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Mathematical Models of Visual Perception and Biology with Applications to...
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Mathematical Models of Visual Perception and Biology with Applications to Images Processing and Computer Vision

A special issue of Journal of Imaging (ISSN 2313-433X).

Deadline for manuscript submissions: closed (25 December 2020) | Viewed by 13036

Special Issue Editor

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,

The comprehension of visual properties, both from a biological (microscopic) and a perceptual (macroscopic) point of view, is an active and fascinating field of research. Historically, the natural application fields of this research have been image processing and computer vision. More recently, the interest regarding human vision modeling has been renewed by the exponential growth of the research about artificial intelligence, where precise theoretical models can be intertwined with deep learning techniques to build artificial devices able to replicate visual features.

With this Special Issue, we want to provide a common setting to gather the most recent discoveries of scientists working in different disciplines related to vision and its applications.

This Special Issue is primarily focused, but not limited to, the following topics:

  • Biology and neuroscience of vision;
  • Vision-inspired image processing and computer vision;
  • Theoretical modeling of visual perception attributes;
  • Psycho-physical experiments in vision.

Prof. Dr. Edoardo Provenzi
Guest Editor

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

  • Biologically inspired models for image processing and computer vision
  • Cortical models for vision
  • Color modeling
  • Perception of visual attributes
  • Psychophysics of vision
  • Neuroscience of human vision

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Published Papers (4 papers)

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Research

18 pages, 743 KiB  
Article
A Cortical-Inspired Sub-Riemannian Model for Poggendorff-Type Visual Illusions
by Emre Baspinar, Luca Calatroni, Valentina Franceschi and Dario Prandi
J. Imaging 2021, 7(3), 41; https://doi.org/10.3390/jimaging7030041 - 24 Feb 2021
Cited by 4 | Viewed by 3003
Abstract
We consider Wilson-Cowan-type models for the mathematical description of orientation-dependent Poggendorff-like illusions. Our modelling improves two previously proposed cortical-inspired approaches, embedding the sub-Riemannian heat kernel into the neuronal interaction term, in agreement with the intrinsically anisotropic functional architecture of V1 based on both [...] Read more.
We consider Wilson-Cowan-type models for the mathematical description of orientation-dependent Poggendorff-like illusions. Our modelling improves two previously proposed cortical-inspired approaches, embedding the sub-Riemannian heat kernel into the neuronal interaction term, in agreement with the intrinsically anisotropic functional architecture of V1 based on both local and lateral connections. For the numerical realisation of both models, we consider standard gradient descent algorithms combined with Fourier-based approaches for the efficient computation of the sub-Laplacian evolution. Our numerical results show that the use of the sub-Riemannian kernel allows us to reproduce numerically visual misperceptions and inpainting-type biases in a stronger way in comparison with the previous approaches. Full article
(This article belongs to the Special Issue Mathematical Models of Visual Perception and Biology with Applications to Images Processing and Computer Vision)
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10 pages, 9038 KiB  
Article
Individualised Halo-Free Gradient-Domain Colour Image Daltonisation
by Ivar Farup
J. Imaging 2020, 6(11), 116; https://doi.org/10.3390/jimaging6110116 - 29 Oct 2020
Cited by 8 | Viewed by 2703
Abstract
Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously [...] Read more.
Daltonisation refers to the recolouring of images such that details normally lost by colour vision deficient observers become visible. This comes at the cost of introducing artificial colours. In a previous work, we presented a gradient-domain colour image daltonisation method that outperformed previously known methods both in behavioural and psychometric experiments. In the present paper, we improve the method by (i) finding a good first estimate of the daltonised image, thus reducing the computational time significantly, and (ii) introducing local linear anisotropic diffusion, thus effectively removing the halo artefacts. The method uses a colour vision deficiency simulation algorithm as an ingredient, and can thus be applied for any colour vision deficiency, and can even be individualised if the exact individual colour vision is known. Full article
(This article belongs to the Special Issue Mathematical Models of Visual Perception and Biology with Applications to Images Processing and Computer Vision)
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12 pages, 3014 KiB  
Article
On Computational Aspects of Krawtchouk Polynomials for High Orders
by Basheera M. Mahmmod, Alaa M. Abdul-Hadi, Sadiq H. Abdulhussain and Aseel Hussien
J. Imaging 2020, 6(8), 81; https://doi.org/10.3390/jimaging6080081 - 13 Aug 2020
Cited by 28 | Viewed by 3049
Abstract
Discrete Krawtchouk polynomials are widely utilized in different fields for their remarkable characteristics, specifically, the localization property. Discrete orthogonal moments are utilized as a feature descriptor for images and video frames in computer vision applications. In this paper, we present a new method [...] Read more.
Discrete Krawtchouk polynomials are widely utilized in different fields for their remarkable characteristics, specifically, the localization property. Discrete orthogonal moments are utilized as a feature descriptor for images and video frames in computer vision applications. In this paper, we present a new method for computing discrete Krawtchouk polynomial coefficients swiftly and efficiently. The presented method proposes a new initial value that does not tend to be zero as the polynomial size increases. In addition, a combination of the existing recurrence relations is presented which are in the n- and x-directions. The utilized recurrence relations are developed to reduce the computational cost. The proposed method computes approximately 12.5% of the polynomial coefficients, and then symmetry relations are employed to compute the rest of the polynomial coefficients. The proposed method is evaluated against existing methods in terms of computational cost and maximum size can be generated. In addition, a reconstruction error analysis for image is performed using the proposed method for large signal sizes. The evaluation shows that the proposed method outperforms other existing methods. Full article
(This article belongs to the Special Issue Mathematical Models of Visual Perception and Biology with Applications to Images Processing and Computer Vision)
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19 pages, 1033 KiB  
Article
Origins of Hyperbolicity in Color Perception
by Nicoletta Prencipe, Valérie Garcin and Edoardo Provenzi
J. Imaging 2020, 6(6), 42; https://doi.org/10.3390/jimaging6060042 - 4 Jun 2020
Cited by 6 | Viewed by 3315
Abstract
In 1962, H. Yilmaz published a very original paper in which he showed the striking analogy between Lorentz transformations and the effect of illuminant changes on color perception. As a consequence, he argued that a perceived color space endowed with the Minkowski metric [...] Read more.
In 1962, H. Yilmaz published a very original paper in which he showed the striking analogy between Lorentz transformations and the effect of illuminant changes on color perception. As a consequence, he argued that a perceived color space endowed with the Minkowski metric is a good approximation to model color vision. The contribution of this paper is twofold: firstly, we provide a mathematical formalization of Yilmaz’s argument about the relationship between Lorentz transformations and the perceptual effect of illuminant changes. Secondly, we show that, within Yilmaz’s model, the color space can be coherently endowed with the Minkowski metric only by imposing the Euclidean metric on the hue-chroma plane. This fact motivates the need of further investigation about both the proper definition and interrelationship among the color coordinates and also the geometry and metrics of perceptual color spaces. Full article
(This article belongs to the Special Issue Mathematical Models of Visual Perception and Biology with Applications to Images Processing and Computer Vision)
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