Swietenia Mahagoni Wood Defects Segmentation Using YIQ Color Space and Thresholding

Abstract

Abstract The biggest income from Southeast Asian countries came from timber production export activities. The potential for timber exports in Indonesia continued to increase every year. This skyrocketing potential needed to be improved by maintaining quality so that trust and good cooperation continued to be established. The quality of wood has closely related to wood defects, the faster detection of wood defects would be the faster also determines the quality of wood. Current technology has being developing rapidly to help productive human activities, image processing has being a breakthrough to be able to detect wood defects. This study aims to detect wood defects by segmenting Swietenia Mahagoni wood images by using the YIQ color space and Thresholding has resulted in a fairly good segmentation that is successful in segmenting the types of bark grown wood defects on bontos and defects in healthy knot on the body of wood with each percentage of 83.3%.