Automatic object detection employing viewing angle histogram for range images

In this paper, a general scheme for automatic object detection is presented. Classification of three dimensional (3-D) objects using range images remains to be one of the most challenging problems in 3-D computer vision due to its noisy and cluttered scene characteristics. The key breakthroughs for this problem lie mainly in defining unique features that distinguish the similarity among various 3-D objects and developing robust segmentation algorithms that can effectively utilize these defined similarity features. In our approach, the object detection scheme can identify inspecting targets automatically in the range images using an initial process of object segmentation to subdivide all possible objects in the scenes and then applying a process of object classification based on geometric constrains (dimension, point density and surface types) and viewing angle histogram for object classification. The methodology computes the surface normal vector distribution of object model at each viewing angle and aggregates the features into histograms over mesh neighborhoods. These histograms are stored in the database for object searching. The classified objects are finally labeled with the consistent labels by finding the highest histogram matching coefficient according to the object list. The method was verified through some experimental tests for its feasibility confirmation.