Object Detection from a Range Image Using Sparse Keypoint Detector Technique

This proposal presents the object detection using Sparse Keypoint Detector technique, in which is computed the interesting points from directional surface. This surface is built with the normal vectors corresponding to the homogeneous surface, and this in turn, is obtained from range image. A probability dense function (pdf) analysis applied to the normal vectors contained in the directional surface, allows us to select the highlight points to obtain the contour of the scene. Furthermore, a probability mass function (pmf) analysis applied to the information contained in the surrounding of these points, aim to select a region of interest, in which is found the object 3D to be detected. Finally, it is applied a Chess distance to the interesting points contained in the region of interest to detect the object. The presented experimental tests involve a qualitative and quantitative analysis using the Middlebury and DSPLab dataset.