A derivative oriented thresholding approach for feature extraction of mold defects on fine arts painting

• Main Authors: Nordin, Hilman, Abdul Razak, Bushroa, Mokhtar, Norrima, Jamaludin, Mohd Fadzil
• Format: Conference or Workshop Item
• Published: ALife Robotics Corporation Ltd 2022
• Subjects: T Technology (General); TJ Mechanical engineering and machinery
• Online Access: http://eprints.um.edu.my/43251/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125148938&partnerID=40&md5=77b10f62016489364c3430dca85da22e

Identification of mold defects is an important step in the restoration of damaged paintings. The process is usually lengthy and depends heavily on the qualitative visual judgement of an expert restorer. This study proposes an automatic mold defect detection technique based on derivative and image analysis to assist in the restoration process. 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. The performance of the proposed method is compared 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. © The 2022 International Conference on Artificial Life and Robotics (ICAROB2022).