Edge based approach in object boundary detection on multiclass fruit images

Object boundary has been proven as a powerful shape representation in several approaches, since they stable to illumination and variations in object color and texture. Besides that, commonly, the objects in the image are recognized based on the outline of two dimensions of the objects. However, extracting the object boundary automatically using a computer with a result that is consistent with human perception is very difficult. Regarding to that, in this paper we present a quantitative evaluation of five edge-based contour detection algorithm on multiclass fruit images. The algorithms are evaluated using direct pixel comparison and chamfer matching with regard to ground-truth boundaries. The results reported here provide a useful benchmark for current and future research efforts in contour detection.