Analysis of Image Pattern Classification using Hopfield Network on Letters and Thai Banknotes via MATLAB Implementation
Authors: 
Ushik Shrestha Khwakhali, Amulya Bhattarai, Chinarom Hannarong, Siyu DengAuthors Info & Claims
Article No.: 70, Pages 1 - 6
Abstract
This paper presents the analysis of Image Pattern Detection using the Hopfield algorithm. Initially two simple letter patterns, L and T are used for mathematical and graphical illustration of pattern classification using Hopfield Algorithm. Mathematical analysis with simple and comprehensive elaboration helps reader to better understand and implement the algorithm in its applications. The analysis is further extended for the patterns L, T, C, U and Y. For each of the patterns, the analysis is done for matrix size of 3 × 3, 5 × 5, 10 × 10 and 28 × 28 with the noise ranging from 10% to 80%. The result of comparative analysis done for different patterns, matrix sizes, presence of noise for the algorithm presented in this paper shows that the convergence ratio decreases with the increase in noise percentage. Additionally, this paper explains the affect of Hebbian learning rule in the convergence ratio of patterns. Finally, Hopfield algorithm is applied for the classification of 20 Baht and 50 Baht Thai banknotes. With image processing in MATLAB and application of Hopfield algorithm, the classification of banknotes is successfully done in the presence of different noise levels.
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- Analysis of Image Pattern Classification using Hopfield Network on Letters and Thai Banknotes via MATLAB Implementation
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Published In
May 2023
711 pages
ISBN:9798400708237
DOI:10.1145/3604078
Copyright © 2023 ACM.
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Published: 26 October 2023
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ICDIP 2023
ICDIP 2023: The 15th International Conference on Digital Image Processing
May 19 - 22, 2023
Nanjing, China
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