The analysis of time-dependent data is often guided by the question of how dominant structures develop over time. It is important to understand how patterns or structures identified for one time step evolve over time, by changing or moving in the domain. To gain insight into such evolving structural change it is crucial to effectively compare different time steps. This paper proposes a comparis...

Moment invariants are popular descriptors for real valued functions. Their independence from certain transformations makes them a powerful tool for the recognition of patterns and shapes. It has recently been demonstrated that the basic ideas can also be transferred to vector valued functions. Vector moment invariants can be used to define and search for interesting flow structures. A generaliz...

We study the class of logarithmic skew-normal (LSN) distributions. They have heavy tails; however, all their moments of positive integer orders are finite. We are interested in the problem of moments for such distributions. We show that the LSN distributions are all nonunique (moment-indeterminate). Moreover, we explicitly describe Stieltjes classes for some LSN distributions; they are families...

Past few years, a lot of research on moment functions have been explored in pattern recognition. Several new techniques have been investigated to improve conventional regular moment by proposing the scaling factor of geometrical function. In this paper, integrated scaling formulations of Aspect Invariant Moment and Higher Order Scaling Invariant with United Moment Invariant are presented in Wri...

This paper proposes a method for matching images based on their higher order moments without knowing the point correspondences. It is assumed that the disparity between the images can be explained by an affine transformation. The second order statistics is used to transform the image points into canonical form, which reduces the affine matching problem for determining an orthonormal transformat...