Ant Colony Optimization for Estimating Pith Position on Images of Tree Log Ends

The pith location is one of the most important features to detect in order to determine the quality of wood. Indeed, it allows to extract other important features. In this paper, we address the problem of pith detection on images of wood cross-sections. Taking such images can be done at little cost and with a high resolution. However, contrary to computed tomographic images, digital images exhibit disturbances like sawing marks, dirt or ambient light variations which make difficult the image analysis. Few studies have focused on such images. Furthermore these studies do some prior segmentation or cropping before the detection. We propose an approach for estimating the pith location without any requirements. Our method is based on an ant colony optimization algorithm. It is a probabilistic approach for solving this task. We validate our algorithm on images of Douglas fir captured after harvesting. The efficiency of this algorithm has been demonstrated by performance comparisons with other approaches. Experiments show an accurate and fast estimation and the algorithm could be used in real time, at sawmill environment or in forest, with a smartphone.