Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Computer Graphics and Artificial Intelligence
share announcement

Computer Graphics and Artificial Intelligence

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 2955

Special Issue Editors

Dr. Andres Iglesias

E-Mail Website
Guest Editor
1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, C.P. 39005 Santander, Spain
2. Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, Funabashi 274-8510, Japan
Interests: swarm intelligence and swarm robotics; bio-inspired optimization; computer graphics; geometric modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Akemi Galvez Tomida

E-Mail Website
Guest Editor
1. Department of Applied Mathematics and Computational Sciences, University of Cantabria, C.P. 39005 Santander, Spain
2. Department of Information Science, Faculty of Sciences, Toho University, 2-2-1 Miyama, Funabashi 274-8510, Japan
Interests: artificial Intelligence; soft computing for optimization; evolutionary computation; computational intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Lihua You

E-Mail Website
Guest Editor
National Centre for Computer Animation, Bournemouth University, Talbot Campus, Poole BH12 5BB, UK
Interests: geometric modelling; computer animation; computer graphics; applications of ODEs and PDEs in geometric modelling and computer animation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer graphics and artificial intelligence are two active, important and exciting fields of research in computer science, applied sciences, and engineering. These areas have seen impressive growth during the last few years, with an increasingly broad range of applications in many different fields, and exciting new developments continue to arise each year. This Special Issue aims to provide a forum for discussion of new techniques, algorithms, methods, and technologies in these areas, as well as their applications to science, engineering, industry, education, health, and entertainment. The interplay between these areas is also of interest.

We invite prospective authors to submit their contributions for fruitful interdisciplinary cooperation and an exchange of new ideas and experiences, as well as to identify new issues and challenges and shape future directions and trends for research in computer graphics and/or artificial intelligence.

Potential topics include (but are not limited to):

  • Geometric and solid modelling;
  • Geometric and solid processing;
  • CAD/CAM/CAE;
  • Curve/surface reconstruction;
  • Computer graphic techniques, algorithms, software, and hardware;
  • Computer animation, video games;
  • Virtual/augmented reality, virtual environments, autonomous agents;
  • Computer graphics applications (science, engineering, education, health, industry, entertainment);
  • Evolutionary and nature-inspired algorithms (evolutionary programming, genetic algorithms);
  • Neural networks, machine learning, deep learning, and data mining;
  • Swarm intelligence and swarm robotics;
  • Bio-informatics and bio-engineering;
  • Natural computing, soft computing, and evolutionary computing;
  • Artificial intelligence theory and applications;
  • Artificial Intelligence-based optimization;
  • Artificial intelligence for science, engineering, arts, industry, education, health, and entertainment;
  • Interplay between computer graphics and artificial intelligence.

Prof. Dr. Andres Iglesias Prieto
Prof. Dr. Akemi Galvez Tomida
Prof. Dr. Lihua You
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. Applied Sciences 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

  • computer graphics
  • geometric modelling and processing
  • curves and surfaces
  • visualisation
  • artificial intelligence
  • machine learning
  • deep learning
  • bio-inspired and evolutionary computation
  • swarm intelligence
  • cognitive sciences

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

13 pages, 14823 KiB  
Article
Quadratic Curve Fitting-Based Image Edge Line Segment Detection: A Novel Methodology
by Rui Qiao, Guili Xu, Ping Wang, Yuehua Cheng and Wende Dong
Appl. Sci. 2023, 13(15), 8654; https://doi.org/10.3390/app13158654 - 27 Jul 2023
Cited by 1 | Viewed by 779
Abstract
In the field of computer vision, edge line segment detection in images is widely used in tasks such as 3D reconstruction and simultaneous localization and mapping. Currently, there are many algorithms that primarily focus on detecting straight line segments in undistorted images, but [...] Read more.
In the field of computer vision, edge line segment detection in images is widely used in tasks such as 3D reconstruction and simultaneous localization and mapping. Currently, there are many algorithms that primarily focus on detecting straight line segments in undistorted images, but they do not perform well in detecting edge line segments in distorted images. To address this quandary, the present study introduces a novel method of line segment identification founded on the principles of quadratic fitting. The method proposed utilizes the inherent property of a linear projection in a three-dimensional space, whereby it appears as a quadratic curve in a distorted two-dimensional image. This approach applies an iterative estimation process to ascertain the optimal parameters of the quadratic form that aligns with the edge contour. This process is facilitated by implementing an assumption and validation mechanism. Upon deriving the optimal model, it is then employed to identify the line segments that are encompassed within the edge contour. The experimental assessment of this novel method incorporates its application to both distorted and distortion-free image datasets. The method eliminates the necessity for preliminary processing to discarding distortions, thereby making it universally applicable to both distorted and non-distorted images. In addition to this, the experimental results based on the dataset indicate that the proposed algorithm in this paper achieves an average computational efficiency that is 27 times faster than traditional ones. Thus, this research will contribute to line segment detection in computer vision. Full article
(This article belongs to the Special Issue Computer Graphics and Artificial Intelligence)
►▼ Show Figures

Figure 1

14 pages, 4282 KiB  
Article
Chinese Character Component Deformation Based on AHP
by Tian Chen, Fang Yang and Xiang Gao
Appl. Sci. 2022, 12(19), 10059; https://doi.org/10.3390/app121910059 - 06 Oct 2022
Viewed by 1427
Abstract
Since Chinese characters are composed of components, deforming the components in a small number of existing calligraphy characters to generate new characters is an effective method to produce a Chinese character library in the same style. Usually, the component deformation is achieved by [...] Read more.
Since Chinese characters are composed of components, deforming the components in a small number of existing calligraphy characters to generate new characters is an effective method to produce a Chinese character library in the same style. Usually, the component deformation is achieved by affine transformation. However, when calculating the parameters in affine transformation, existing methods usually have the problems of a large amount of manual participation or complicated calculation. In this paper, we proposed an Analytic Hierarchy Process (AHP)-based Chinese character component deformation method, which is simple in calculation and can effectively realize the deformation of Chinese character components on the basis of reducing manual intervention. We first determined the factors that affect the selection of control points in affine transformation, then used AHP to calculate the weights of feature points and select the control points according to the weights. As a prerequisite for affine transformation, a matching method of Chinese character feature points based on the Chinese character skeleton map and neighborhood information is also proposed, which helps to achieve more efficient deformation. Experimental results on different fonts demonstrate the effectiveness and generality of our method. Full article
(This article belongs to the Special Issue Computer Graphics and Artificial Intelligence)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop