A derivative oriented thresholding approach for feature extraction of mold defects on fine arts painting
Document Type
Conference Item
Publication Date
1-1-2022
Abstract
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).
Divisions
sch_ecs,mechanical
Funders
Universiti Malaya,IIRG034B-2019
Publication Title
Proceedings of International Conference on Artificial Life and Robotics
Publisher
ALife Robotics Corporation Ltd
Event Title
International Conference on Artificial Life and Robotics
Event Type
conference
Additional Information
Cited by: 0; Conference name: 27th International Conference on Artificial Life and Robotics, ICAROB 2022; Conference date: 20 January 2022 through 23 January 2022; Conference code: 271609