Journal of
Systemics, Cybernetics and Informatics
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ISSN: 1690-4524 (Online)


Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.

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Published by
The International Institute of Informatics and Cybernetics


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Academia.edu
(A Community of about 40.000.000 Academics)


Honorary Editorial Advisory Board's Chair
William Lesso (1931-2015)

Editor-in-Chief
Nagib C. Callaos


Sponsored by
The International Institute of
Informatics and Systemics

www.iiis.org
 

Editorial Advisory Board

Quality Assurance

Editors

Journal's Reviewers
Call for Special Articles
 

Description and Aims

Submission of Articles

Areas and Subareas

Information to Contributors

Editorial Peer Review Methodology

Integrating Reviewing Processes


How Does Logical Dynamics Assist Interdisciplinary Education and Research in Addressing Cognitive Challenges?
Mengqin Ning, Jiahong Guo
(pages: 1-6)

Inter-Corrective Meta-Dialogue on Constructive Impact of Trans-disciplinary Communication in Modern Education
Vinod Kumar Verma
(pages: 7-9)

Intergenerational Learning for Older and Younger Employees: What Should Be Done and Should Not?
Gita Aulia Nurani, Ya-Hui Lee
(pages: 10-15)

On the Ontological Notion of Education
Jeremy Horne
(pages: 16-24)

Research-Based Learning in Intergenerational Dialogue and Its Relationship to Education
Sonja Ehret
(pages: 25-29)

Role-Playing in Education: An Experiential Learning Framework for Collaborative Co-design
Cristo Leon, James Lipuma, Sirimuvva Pathikonda, Rafael Arturo Llaca Reyes
(pages: 30-38)

The Emergent Role of Artificial Intelligence as Tool in Conducting Academic Research
Bilquis Ferdousi
(pages: 39-46)

The Impact of Cybernetic Relationships Between Education and Work-Based Learning
Birgit Oberer, Alptekin Erkollar
(pages: 47-51)

The Notions of Education and Research
Nagib Callaos, Jeremy Horne
(pages: 52-62)

Towards Sustainable Legal Education Reform: Interdisciplinary and Transdisciplinary Approaches in Albania's Justice System
Adrian Leka, Brunilda Haxhiu
(pages: 63-67)

Transdisciplinary Research and the Gift Economy
Teresa Henkle Langness
(pages: 68-75)


 

Abstracts

 


ABSTRACT


Adaptive Image Restoration and Segmentation Method Using Different Neighborhood Sizes

Chengcheng Li, William J. B. Oldham


The image restoration methods based on the Bayesian’s framework and Markov random fields (MRF) have been widely used in the image-processing field. The basic idea of all these methods is to use calculus of variation and mathematical statistics to average or estimate a pixel value by the values of its neighbors. After applying this averaging process to the whole image a number of times, the noisy pixels, which are abnormal values, are filtered out. Based on the Tea-trade model, which states that the closer the neighbor, more contribution it makes, almost all of these methods use only the nearest four neighbors for calculation. In our previous research [1, 2], we extended the research on CLRS (image restoration and segmentation by using competitive learning) algorithm to enlarge the neighborhood size. The results showed that the longer neighborhood range could improve or worsen the restoration results. We also found that the autocorrelation coefficient was an important factor to determine the proper neighborhood size. We then further realized that the computational complexity increased dramatically along with the enlargement of the neighborhood size. This paper is to further the previous research and to discuss the tradeoff between the computational complexity and the restoration improvement by using longer neighborhood range. We used a couple of methods to construct the synthetic images with the exact correlation coefficients we want and to determine the corresponding neighborhood size. We constructed an image with a range of correlation coefficients by blending some synthetic images. Then an adaptive method to find the correlation coefficients of this image was constructed. We restored the image by applying different neighborhood CLRS algorithm to different parts of the image according to its correlation coefficient. Finally, we applied this adaptive method to some real-world images to get improved restoration results than by using single neighborhood size. This method can be extended virtually on all the methods based on MRF framework and result in improved algorithms.

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