Model Based Recognition using 3D Line Sets and Multidimensional Hausdorff Distance

摘要

In this paper, we proposed a three dimensional (3D) line based matching algorithm using multi-dimensional Hausdorff distance. Classical line based recognition techniques using Hausdorff distance deals with two dimensional (2D) models and 2D images. In our proposed 3D line based matching technique, two sets of lines are extracted from a 3D model and 3D image (constructed by stereo imaging). For matching these line sets, we used multidimensional Hausdorff distance minimization technique which requires only to find the translation between the image and the model, whereas most of the model based recognition techniques require to find the rotation, scale and translation variations between the image and the models. A line based approach for model based recognition using four dimensional (4D) Hausdorff distance has been already proposed in Ref. [1]. However, our method requires a 4D Hausdorff distance calculation followed by a 3D Hausdorff distance calculation. In the proposed method, as the matching is performed using 3D line sets, it is more reliable and accurate.