Recognition of the multi specularity objects for bin-picking task

This paper describes a method for recognizing partially occluded objects for bin-picking tasks using the eigen-space analysis. Although effective in recognizing an isolated object, as was shown by Murase and Nayar (1995), the current method can not be applied to piratically occluded objects that are typical in bin-picking tasks. The analysis also requires that the object is centered in an image before recognition. These limitations of the eigen-space 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 "eigen-window" method, that stores multiple partial appearances of an object in the eigen-space. Such partial appearances require a large number of memory space. To reduce the memory requirement by avoiding redundant windows and to select only effective windows to be stored, a similarity measure among windows is developed. 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 verify the validity of the method.