Paper Info
Title
Recognition of the multi specularity objects for bin-picking task
Abstract
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
Year
DOI
Venue
1996
10.1109/IROS.1996.569004
Intelligent Robots and Systems '96, IROS 96, Proceedings of the 1996 IEEE/RSJ International Conference
Keywords
Field
DocType
computational complexity,eigenvalues and eigenfunctions,image recognition,object recognition,bin-picking task,eigenspace analysis,eigenwindow method,multiple partial appearances,multispecularity object recognition,partially occluded objects,piratically occluded objects,pose clustering method,similarity measure
Computer vision,Specularity,Bin picking,Control engineering,Artificial intelligence,Mathematics
Conference
Volume
ISBN
Citations 
3
0-7803-3213-X
10
PageRank 
References 
Authors
1.45
10
2
Name
Order
Citations
PageRank
kohtaro ohba131766.11
Katsushi Ikeuchi24651881.49