With a point matching distance measure which is invariant under translation, rotation and permutation, we learn 2-D point-set objects , by clustering noisy point-set images. Unlike traditional clustering methods which use distance measures that operate on feature vectors { a representation common to most problem domains { this object-based clustering technique employs a distance measure spe-cii...

With a point matching distance measure which is invariant under translation, rotation and permutation, we learn 2-D point-set objects, by clustering noisy point-set images. Unlike traditional clustering methods which use distance measures that operate on feature vectors a representation common to most problem domains this object-based clustering technique employs a distance measure specific to ...

Targets and objects registration and tracking in a sequence of images play an important role in various areas. One of the methods in image registration is feature-based algorithm which is accomplished in two steps. The first step includes finding features of sensed and reference images. In this step, a scale space is used to reduce the sensitivity of detected features to the scale changes. Afterw...

In this paper, we introduce the notions of asymptotical strong σ 2 σ2 -equivalence, -statistical equivalence, lacunary -equivalence and equivalence in Wijsman sense for double set sequences. Also, investigate some relations between these new notions.

Outlier detection methods are fundamental to all of data analysis. They are desirably robust, affine invariant, and computationally easy in any dimension. The powerful projection pursuit approach yields the “projection outlyingness”, which is affine invariant and highly robust and does not impose ellipsoidal contours like the Mahalanobis distance approach. However, it is highly computationally ...