Title :
Similarity and affine invariant distances between 2D point sets
Author :
Werman, Michael ; Weinshall, Daphna
Author_Institution :
Dept. of Comput. Sci., Hebrew Univ., Jerusalem, Israel
fDate :
8/1/1995 12:00:00 AM
Abstract :
We develop expressions for measuring the distance between 2D point sets, which are invariant to either 2D affine transformations or 2D similarity transformations of the sets, and assuming a known correspondence between the point sets. We discuss the image normalization to be applied to the images before their comparison so that the computed distance is symmetric with respect to the two images. We then give a general (metric) definition of the distance between images, which leads to the same expressions for the similarity and affine cases. This definition avoids ad hoc decisions about normalization. Moreover, it makes it possible to compute the distance between images under different conditions, including cases where the images are treated asymmetrically. We demonstrate these results with real and simulated images
Keywords :
image matching; 2D affine transformations; 2D point sets; 2D similarity transformations; affine invariant distances; image normalization; metric; similarity distances; Cameras; Computational modeling; Computer science; Covariance matrix; Euclidean distance; Pattern analysis; Pattern matching;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on