G. Pajares et Jm. De La Cruz, A new learning strategy for stereo matching derived from a fuzzy clustering method, FUZ SET SYS, 110(3), 2000, pp. 413-427
This paper presents an approach to the local stereo correspondence problem.
The primitives or features used are groups of collinear connected edge poi
nts called segments. Each segment has several associated attributes or prop
erties. We have verified that the differences of the attributes for the tru
e matches cluster in a cloud around a center. Then for each current pair of
primitives we compute a distance between the difference of its attributes
and the cluster center. The correspondence is established in the basis of t
he minimum distance criterion (similarity constraint). We have designed an
image understanding system to learn the best representative cluster center.
For such purpose a new learning method is derived from the Fuzzy c-Means (
FcM) algorithm where the dispersion of the true samples in the cluster is t
aken into account through the Mahalanobis distance. This is the main contri
bution of this paper. A better performance of the proposed local stereo-mat
ching learning method is illustrated with a comparative analysis between cl
assical local methods without learning. (C) 2000 Elsevier Science B.V. All
rights reserved.