Range images incorporate 3-D surface coordinates of a scene and are well suited for a variety of vision tasks. For tasks, such as 3-D object recognition, a representation of the object(s) present in the image is derived. This representation is then matched with the stored models to determine the object identity. Surface based representation is the most widely used technique for range images. Surface based representation is generated by segmenting the object into a number of surfaces. The segmented surfaces are then classified into a number of primitives and the relations between these surfaces are defined. In this paper a number of techniques for the classification of range images of industrial parts are presented. Surface patches are classified into three primitives: planar, convex or concave. Four tests are presented two planarity tests, and two for classifying curved surface patches into convex, or concave. The first planarity test we is a variation of the Wald-Wolfowitz runs test. The second planarity test is based on the connected components formed after fitting a plane to surface patches. The nonplanar regions are then classified into convex or concave using multi-scale residual computation, and a goodness of fit test. The performance of the classification tests on a number of industrial parts range images is presented.
Ezzet H. Al-Hujazi,
Arun K. Sood,
"Classification of range images", Proc. SPIE 1293, Applications of Artificial Intelligence VIII, (1 January 1990); doi: 10.1117/12.21063; https://doi.org/10.1117/12.21063