Stable recognition of specular objects by the eigenwindow method

This paper proposes a recognition method for partially occluded objects in bin-picking tasks using principal component analysis. Although it is well known to be effective in recognizing an isolated object, as was shown by Murase and Nayar, the current method cannot be applied to partially occluded objects that are typical in bin-picking tasks. The analysis also requires that the object be centered in an image before recognition. These limitations of eigenspace analysis are due to the fact that the whole appearance of an object is utilized as a template for the analysis. We propose a new method, referred to as the “eigenwindow” method, that stores multiple partial appearances of an object in an eigenspace. Such partial appearances require a large amount of memory space. Three measurements, detectability, uniqueness, and reliability, among windows are developed to eliminate redundant windows and thereby reduce memory requirements. Using a pose-clustering method among windows, the method determines the pose of an object and the object type of itself. We have implemented the method and verified its validity with recognition of multispecularity objects. © 1998 Scripta Technica, Syst Comp Jpn, 29(7): 12–20, 1998