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Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2023 and IP&C 2023

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".

Deadline for manuscript submissions: closed (15 April 2024) | Viewed by 2715

Special Issue Editors


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Guest Editor
Faculty of Telecommunications, Computer Science and Electrical Engineering, UTP University of Science and Technology, 85-796 Bydgoszcz, Poland
Interests: pattern recognition; machine learning; AI; security; cybersecurity
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: machine learning; pattern recognition

E-Mail Website
Guest Editor
Institute of Telecommunications and Computer Science, University of Science and Technology (UTP) in Bydgoszcz, 85-796 Bydgoszcz, Poland
Interests: image processing; biometrics; machine learning
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Institute of Telecommunications and Computer Science, University of Science and Technology (UTP) in Bydgoszcz, 85-796 Bydgoszcz, Poland
Interests: telecommunication; networks; IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 13th International Conference on Computer Recognition Systems (CORES) and the 13th International Conference on Image Processing and  Communications (IP&C) will be held on June 28–29, 2023, in Wrocław, Poland or online.

The websites of the event are listed below:

http://ipc.pwr.edu.pl/

http://cores.pwr.edu.pl

The goal of both CORES 2023 and IP&C 2023 is to gather researchers and high-quality advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing, and machine learning (shallow and deep), including papers regarding the areas of modern telecommunications and cybersecurity.

Authors of selected papers from the conference will be invited to submit extended versions of their original papers and contributions under the conference topics (new papers closely related to the conference themes are also welcome).

The scope includes (but is not limited to) the following:

  • The classification and interpretation of text, video, and voice;
  • Statistical, soft, and structural methods of pattern recognition;
  • Image processing, analysis, and interpretation;
  • Feature extraction and selection;
  • Machine learning;
  • Trends and relations recognition and analysis;
  • Data and Web mining;
  • Machine-oriented knowledge representation and inference methods;
  • Knowledge-based decision support systems;
  • Advanced signal processing methods;
  • Special hardware architecture;
  • Applications;
  • Biometrics;
  • Cyber security;
  • Next-generation networks
  • Optical backbone and access networks;
  • Network reliability;
  • New services in IP networks;
  • QoS in IP networks;
  • Regular structures of communications networks;
  • Web applications;
  • IoT;
  • Cloud/Edge and Fog networks;
  • xAI;
  • Fake news detection.

Prof. Dr. Michal Choras
Dr. Mariusz Topolski
Dr. Agata Giełczyk
Prof. Dr. Rafal Kozik
Dr. Tomasz Marciniak
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. Entropy 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 2600 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 recognition
  • pattern recognition
  • mage processing
  • telecommunications
  • machine learning

Published Papers (2 papers)

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Research

24 pages, 16509 KiB  
Article
Research on Image Stitching Algorithm Based on Point-Line Consistency and Local Edge Feature Constraints
by Shaokang Ma, Xiuhong Li, Kangwei Liu, Tianchi Qiu and Yulong Liu
Entropy 2024, 26(1), 61; https://doi.org/10.3390/e26010061 - 10 Jan 2024
Viewed by 952
Abstract
Image stitching aims to synthesize a wider and more informative whole image, which has been widely used in various fields. This study focuses on improving the accuracy of image mosaic and proposes an image mosaic method based on local edge contour matching constraints. [...] Read more.
Image stitching aims to synthesize a wider and more informative whole image, which has been widely used in various fields. This study focuses on improving the accuracy of image mosaic and proposes an image mosaic method based on local edge contour matching constraints. Because the accuracy and quantity of feature matching have a direct influence on the stitching result, it often leads to wrong image warpage model estimation when feature points are difficult to detect and match errors are easy to occur. To address this issue, the geometric invariance is used to expand the number of feature matching points, thus enriching the matching information. Based on Canny edge detection, significant local edge contour features are constructed through operations such as structure separation and edge contour merging to improve the image registration effect. The method also introduces the spatial variation warping method to ensure the local alignment of the overlapping area, maintains the line structure in the image without bending by the constraints of short and long lines, and eliminates the distortion of the non-overlapping area by the global line-guided warping method. The method proposed in this paper is compared with other research through experimental comparisons on multiple datasets, and excellent stitching results are obtained. Full article
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18 pages, 26827 KiB  
Article
FEUSNet: Fourier Embedded U-Shaped Network for Image Denoising
by Xi Li, Jingwei Han, Quan Yuan, Yaozong Zhang, Zhongtao Fu, Miao Zou and Zhenghua Huang
Entropy 2023, 25(10), 1418; https://doi.org/10.3390/e25101418 - 5 Oct 2023
Cited by 1 | Viewed by 1143
Abstract
Deep convolution neural networks have proven their powerful ability in comparing many tasks of computer vision due to their strong data learning capacity. In this paper, we propose a novel end-to-end denoising network, termed Fourier embedded U-shaped network (FEUSNet). By analyzing the amplitude [...] Read more.
Deep convolution neural networks have proven their powerful ability in comparing many tasks of computer vision due to their strong data learning capacity. In this paper, we propose a novel end-to-end denoising network, termed Fourier embedded U-shaped network (FEUSNet). By analyzing the amplitude spectrum and phase spectrum of Fourier coefficients, we find that low-frequency features of an image are in the former while noise features are in the latter. To make full use of this characteristic, Fourier features are learned and are concatenated as a prior module that is embedded into a U-shaped network to reduce noise while preserving multi-scale fine details. In the experiments, we first present ablation studies on the Fourier coefficients’ learning networks and loss function. Then, we compare the proposed FEUSNet with the state-of-the-art denoising methods in quantization and qualification. The experimental results show that our FEUSNet performs well in noise suppression and preserves multi-scale enjoyable structures, even outperforming advanced denoising approaches. Full article
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