A Study of Measures for Contour-based Recognition and Localization of Known Objects in Digital Images

Abstract

Usually, the most important structures in an image are extracted by an edge detector. Once extracted edges are binarized, they represent the shape boundary information of an object. For the edge-based localization/matching process, the differences between a reference edge map and a candidate image are quantified by computing a performance measure. This study investigates supervised contour measures for determining the degree to which an object shape differs from a desired position. Therefore, several distance measures are evaluated for different shape alterations: translation, rotation and scale change. Experiments on both synthetic and real images exhibit which measures are accurate enough for an object pose or matching estimation, useful for robot task as to refine the object pose.