Recursive Similarity-Based Learning algorithm (RSBL) follows the deep learning idea, exploiting similarity-based methodology to recursively generate new features. Each transformation layer is generated separately, using as inputs information from all previous layers, and as new features similarity to the k nearest neighbors scaled using Gaussian kernels. In the feature space created in this way...

Registration of 3D medical images consists in computing the “best” transformation between two acquisitions, or equivalently, determines the point to point correspondence between the images. Registration algorithms are usually based either on features extracted from the image (feature-based approaches) or on the optimization of a similarity measure of the images intensities (intensity-based or i...

In this paper we propose two mathematical methods for more thoroughly analysis of the electrical activity recordings of some human organs: electrocardiograms, electroencephalograms, electromyograms. The spectrograms obtained through short time Fourier transformation and wavelet transformation may be used to evaluate the degree of similarity and difference between two recordings of a healthy and...

We propose a geometric matching technique in which line segments and elliptical arcs are used as edge features. The use of these higher-order features renders feature representation efficient. We derive distance measures to evaluate the similarity between the features of the model and those of the image. The model transformation parameters are found by searching a 3-D transformation space using...

We consider a 1 + 1 dimensional field theory which contains both a complex fermion field and a real scalar field. We then construct a unitary operator that, by a similarity transformation, gives a continuum of equivalent theories which smoothly interpolate between the massive Thirring model and the sineGordon model. This provides an implementation of smooth bosonization proposed by Damgaard et ...

The success of many computer vision problems and machine learning algorithms critically depends on the quality of the chosen distance metrics or similarity functions. Due to the fact that the real-data at hand is inherently taskand data-dependent, learning an appropriate distance metric or similarity function from data for each specific task is usually superior to the default Euclidean distance...

We consider the anisotropic XY chain in a magnetic field with special boundary conditions described by a two-parameter Hamiltonian. It is shown that the exchange of the parameters corresponds to a similarity transformation, which reduces in a special limit to the Ising duality transformation. BONN HE-93-40 cond-mat/9310055 October 1993 In this paper we consider the anisotropic XY chain in a mag...

A new registration algorithm based on Newton-Raphson iteration is proposed to align images with rigid body transformation. A set of transformation parameters consisting of translation in x and y and rotation angle around z is calculated by optimizing a specified similarity metric using the Newton-Raphson method. This algorithm has been tested by registering and correlating pairs of topography m...

the paper presents the semi-numerical solution for the magnetohydrodynamic (mhd) viscous flow due to a stretching sheet caused by boundary layer of an incompressible viscous flow. the governing partial differential equations of momentum equations are reduced into a nonlinear ordinary differential equation (node) by using a classical similarity transformation along with appropriate boundary cond...

a method for solving the descriptor discrete-time linear system is focused. for easily, it is converted to a standard discrete-time linear system by the definition of a derivative state feedback. then partial eigenvalue assignment is used for obtaining state feedback and solving the standard system. in partial eigenvalue assignment, just a part of the open loop spectrum of the standard linear s...