In the present work a similarity metric based on difference image entropy has been investigated for medical images nonrigid registration using block-matching algorithm. The used image histogram size has shown to affect registration accuracy, allowing registration improvements for difference entropy metric, which has shown best results when compared to others two well known metrics in literature...

Many applications in machine learning handle bags of features or histograms rather than simple vectors. In that context, defining a proper geometry to compare histograms can be crucial for many machine learning algorithms. While one might be tempted to use a default metric such as the Euclidean metric, empirical evidence shows this may not be the best choice when dealing with observations that ...

The performance of many supervised and unsupervised learning algorithms is very sensitive to the choice of an appropriate distance metric. Previous work in metric learning and adaptation has mostly been focused on classification tasks by making use of class label information. In standard clustering tasks, however, class label information is not available. In order to adapt the metric to improve...

Fighter agility metrics determined from measured or calculated data can be sensitive to the coordinate system used. Although simple and intuitive, agility metrics expressed in Cartesian coordinates are not always robust to variations in initial conditions and uncertainties in physical characteristics. This paper seeks to establish the relationship between form of agility equations and the measu...

Outlier Detection is a critical and cardinal research task due its array of applications in variety of domains ranging from data mining, clustering, statistical analysis, fraud detection, network intrusion detection and diagnosis of diseases etc. Over the last few decades, distance-based outlier detection algorithms have gained significant reputation as a viable alternative to the more traditio...

Person re-identification across disjoint cameras has attracted increasing interest in computer vision due to its wide potential applications in visual surveillance. In this paper, we propose a new regularized Bayesian metric learning (RBML) method for person re-identification. While numerous metric learning methods have been proposed for person re-identification in recent years, most of them su...

introduction: appropriate definition of the distance measure between diffusion tensors has a deep impact on diffusion tensor image (dti) segmentation results. the geodesic metric is the best distance measure since it yields high-quality segmentation results. however, the important problem with the geodesic metric is a high computational cost of the algorithms based on it. the main goal of this ...

Addressing a long-standing problem, we show that every van Stockum dust can be matched to 1-parametric family of non-static Papapetrou vacuum metrics, and the converse. The boundary, if existing, is determined by vanishing certain first-order invariant on side. Moreover, establish relation Ehlers Kramer--Neugebauer transformations, which allows us look for clouds with prescribed boundary. Expli...

In this paper we explore possible phases arising from doping neutral excitons into a Mott insulator in the context of moir\'e bilayers. We consider two layers coupled together only through inter-layer repulsion and there is U(2)$\times$ U(2) symmetry. The densities can be tuned to $n_t=x,n_b=1-x$ with $n_t+n_b=1$. $x=0$ limit layer polarized small $x$ regime reached by at density $x$. Charge ga...

The possible amplification of gauge invariant metric fluctuations in the infrared sector are very important during reheating stage of inflation. In this stage the inflaton field oscillates around the minimum of the scalar potential. The evolution for the super Hubble scales gauge invariant metric fluctuations can be studied by means of the Bardeen parameter. For a massive scalar field with pote...