Matching method for mutated veneer sheet images using gray-level co-occurrence matrix features

Abstract

This paper studies the tracking of wooden veneer sheets by matching their respective wet and dry colour images. The tracking of veneer sheets has proved to be a challenging task due to random mutations during processing in terms of color changes, the emergence of defects, and, occasionally, lost pieces of the veneer surface. The proposed matching procedure involves image segmentation with five different sizes, followed by segment-wise extraction of Gray Level Co-occurrence Matrix (GLCM) textural feature arrays, and their similarity comparisons respectively. A voting mechanism is introduced that allocates the correct match based on the majority. An optional shifting procedure is applied to match candidates with missing areas. The method is demonstrated and benchmarked using a real-world dataset sourced from the industry, comprising 2579 high-quality images of spruce veneer pairs obtained from peeling and drying. In comparison to earlier studies that employed randomized 50 pair sampling on the same dataset, our approach yields a matching accuracy of 99.41%, outperforming the previously reported 84.93%. These findings have relevance for researchers in wood image analytics and carry practical implications for large-scale, automated veneer production facilities seeking innovative ways to optimize their raw material usage.