Hilman Nordin¹, Bushroa Abdul Razak^1,2, Norrima Mokhtar³, Mohd Fadzil Jamaludin^2
1Department of Mechanical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur Malaysia
²Centre of Advanced Manufacturing and Material Processing (AMMP Centre), Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur Malaysia
³Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur Malaysia
pp. 192–201
ABSTRACT
Paintings can be damaged by natural causes or accidents. One of the crucial natural damages was frequently caused by mold defects. The mold discovery is an important step in the restoration of damaged paintings. The procedure is usually tedious and depends heavily on the qualitative visual judgement of an expert restorer. The aim of this work is to assist the restoration process via an automatic mold defect detection technique based on derivative and image analysis. This new method, designated as Derivative Level Thresholding (DLT), combines binarization and detection algorithms to detect mold rapidly and accurately from scanned high-resolution images of a painting. This work also benchmarks the performance of the proposed method to existing binarization techniques of Otsu’s Thresholding Method, Minimum Error Thresholding (MET) and Contrast Adjusted Thresholding Method. Experimental results from the analysis of 20 samples from high-resolution scans of 2 mold-stained painting have shown that the DLT method is the most robust with the highest sensitivity rate of 84.73% and 68.40% accuracy.
Keywords: Image processing, Defect detection, Derivative oriented thresholding, Fine art

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd This is an open access article distributed under the CC BY-NC 4.0 license ( h ttp://creativecommons.org/licenses/by-nc/4.0/).

ARTICLE INFO
Article History
Received 04 December 2021
Accepted 19 July 2022

I-STAGE9214

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