Object Recognition Based on 2-D Orthogonal Expansion of Image by Marginal Eigenvectors.
Takeshi Kobayashi, Shun’ichi Kaneko, Satoru Igarashi · Journal of the Japan Society for Precision Engineering · 2000
This paper proposes a new method to recognize 3-D objects and their poses using the approximate representation of an image based on the 2-D orthogonal expansion by marginal eigenvecters of column- and row- covariance matrices. The proposed method considers 2-D structure of images, and executes learning process by treating low dimensional matrices, so it can learn a number of images with small computation cost compared with the methods based on the K-L expansion. The method can also recognize any pose of an object, which is not learned, using smooth interpolation between highly correlated discrete images obtained in learning stage of the object. In this paper, the learning and descriminating procedures are formulated and effectiveness of the method is shown through fundamental experiments with real objects.