A THINNING-BASED ALGORITHM TO CHARACTERIZE FRUIT STEMS FROM PROFILE IMAGES

In this paper we present a thinning-based approach to detect protrusio ns from binary patterns, and its use to characterize fruit stems from fruit profile images. The method to detect protrusions in binary patte rns is based on the property of iterative thinning algorithms which ca uses protrusions to become skeletons during the first iterations of th e thinning process. A modified thinning method, and a restoration of t he thinned parts using criteria based on some topological properties o f digital images, allow to identify, locate and characterize in size a nd length these protrusions from binary patterns. The method has been applied to characterize fruit stems, allowing the successful detection of a wide range of stem shapes, with high effectiveness, 99% of stems being correctly identified.