Special Issue "Evolutionary Image Processing"

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 31 May 2021.

Special Issue Editors

Prof. Dr. Jose Santamaria Lopez
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Guest Editor
Department of Informática, University de Jaén, Jaén 23071, Spain
Interests: evolutionary computation; computer vision and computer graphics; applications of soft computing to real-world problems
Special Issues and Collections in MDPI journals
Prof. Dr. Erik Valdemar Cuevas
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Guest Editor
Department of Electronics, Universidad de Guadalajara, Av.Revolucion 1500, Guadalajara, Mexico
Interests: computer vision; evolutionary computation; artificial intelligence; bio-inspired computation
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The integration of evolutionary computation (EC) and image processing (IP) has become an important topic in Computer Engineering and has attracted the interest of many researchers. IP covers novel theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals for a variety of applications. On the other hand, in the field of EC, including swarm intelligence (SI) and other similar approaches, the process of natural evolution is used as a role model for a strategy for finding optimal or near-optimal solutions for a given problem, and this approach is now playing an increasingly important role in the community. Specifically, this Special Issue is focused on research that addresses IP problems using novel approaches of EC. Therefore, the purpose is to broadly engage the communities of IP and EC together and provide a forum for the researchers and practitioners related to this rapidly developing field to share their novel and original research regarding the topic of evolutionary image processing (EIP). Additionally, survey papers addressing relevant topics of EC and IP are also welcome.

Topics of interest include, but are not limited to the following:

  • Object detection and classification
  • Gesture identification and recognition
  • Automatic feature extraction and construction in complex images
  • Image Registration
  • Image Segmentation
  • Edge detection
  • Real-time IP and Analysis

Prof. Dr. Jose Santamaria Lopez
Prof. Dr. Erik Valdemar Cuevas
Guest Editors

Manuscript Submission Information

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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. Mathematics 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 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.

Published Papers (2 papers)

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Research

Open AccessArticle
An Evolutionary Approach to Improve the Halftoning Process
Mathematics 2020, 8(9), 1636; https://doi.org/10.3390/math8091636 - 22 Sep 2020
Abstract
The techniques of halftoning are widely used in marketing because they reduce the cost of impression and maintain the quality of graphics. Halftoning converts a digital image into a binary image conformed by dots. The output of the halftoning contains less visual information; [...] Read more.
The techniques of halftoning are widely used in marketing because they reduce the cost of impression and maintain the quality of graphics. Halftoning converts a digital image into a binary image conformed by dots. The output of the halftoning contains less visual information; a possible benefit of this task is the reduction of ink when graphics are printed. The human eye is not able to detect the absence of information, but the printed image stills have good quality. The most used method for halftoning is called Floyd-Steinberger, and it defines a specific matrix for the halftoning conversion. However, most of the proposed techniques in halftoning use predefined kernels that do not permit adaptation to different images. This article introduces the use of the harmony search algorithm (HSA) for halftoning. The HSA is a popular evolutionary algorithm inspired by the musical improvisation. The different operators of the HSA permit an efficient exploration of the search space. The HSA is applied to find the best configuration of the kernel in halftoning; meanwhile, as an objective function, the use of the structural similarity index (SSIM) is proposed. A set of rules are also introduced to reduce the regular patterns that could be created by non-appropriate kernels. The SSIM is used due to the fact that it is a perception model used as a metric that permits comparing images to interpret the differences between them numerically. The aim of combining the HSA with the SSIM for halftoning is to generate an adaptive method that permits estimating the best kernel for each image based on its intrinsic attributes. The graphical quality of the proposed algorithm has been compared with classical halftoning methodologies. Experimental results and comparisons provide evidence regarding the quality of the images obtained by the proposed optimization-based approach. In this context, classical algorithms have a lower graphical quality in comparison with our proposal. The results have been validated by a statistical analysis based on independent experiments over the set of benchmark images by using the mean and standard deviation. Full article
(This article belongs to the Special Issue Evolutionary Image Processing)
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Open AccessArticle
Comparison of Circular Symmetric Low-Pass Digital IIR Filter Design Using Evolutionary Computation Techniques
Mathematics 2020, 8(8), 1226; https://doi.org/10.3390/math8081226 - 26 Jul 2020
Cited by 1
Abstract
The design of two-dimensional Infinite Impulse Response (2D-IIR) filters has recently attracted attention in several areas of engineering because of their wide range of applications. Synthesizing a user-defined filter in a 2D-IIR structure can be interpreted as an optimization problem. However, since 2D-IIR [...] Read more.
The design of two-dimensional Infinite Impulse Response (2D-IIR) filters has recently attracted attention in several areas of engineering because of their wide range of applications. Synthesizing a user-defined filter in a 2D-IIR structure can be interpreted as an optimization problem. However, since 2D-IIR filters can easily produce unstable transfer functions, they tend to compose multimodal error surfaces, which are computationally difficult to optimize. On the other hand, Evolutionary Computation (EC) algorithms are well-known global optimization methods with the capacity to explore complex search spaces for a suitable solution. Every EC technique holds distinctive attributes to properly satisfy particular requirements of specific problems. Hence, a particular EC algorithm is not able to solve all problems adequately. To determine the advantages and flaws of EC techniques, their correct evaluation is a critical task in the computational intelligence community. Furthermore, EC algorithms are stochastic processes with random operations. Under such conditions, for obtaining significant conclusions, appropriate statistical methods must be considered. Although several comparisons among EC methods have been reported in the literature, their conclusions are based on a set of synthetic functions, without considering the context of the problem or appropriate statistical treatment. This paper presents a comparative study of various EC techniques currently in use employed for designing 2D-IIR digital filters. The results of several experiments are presented and statistically analyzed. Full article
(This article belongs to the Special Issue Evolutionary Image Processing)
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