A variation pixels identification method based on kernel spatial attraction model and local entropy for robust endmember extraction

A variation pixels identification method was proposed aiming at depressing the effect of variation pixels, which dilates the theoretical hyperspectral data simplex and misguides volume evaluation of the simplex. With integration of both spatial and spectral information, this method quantitatively defines a variation index for every pixel. The variation index is proportional to pixels local entropy but inversely proportional to pixels kernel spatial attraction. The number of pixels removed was modulated by an artificial threshold factor α. Two real hyperspectral data sets were employed to examine the endmember extraction results. The reconstruction errors of preprocessing data as opposed to the result of original data were compared. The experimental results show that the number of distinct endmembers extracted has increased and the reconstruction error is greatly reduced. 100% is an optional value for the threshold factor α when dealing with no prior knowledge hyperspectral data.

Novel detection method for infrared small targets using weighted information entropy

This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy （LMWIE）, is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection.

Introduction to Thermo-Photo-Electronics

Building the foundations of Thermo-Photo-Electronics became possible only after the correction of thermodynamic errors in the traditional theory of semiconductor Electronics and Photo-Electronics.It is these errors that determined the output of the asymptotics of the operating parameters of semiconductor electronic devices,in particular,both the saturation of the limiting clock frequency of processors,and the saturation of the efficiency of both thermoelectric and photoelectric converters.But in semiconductors,although these thermodynamic errors manifested themselves not only in the instrumental,but also in the technological aspect,they could not prohibit semiconductor Electronics itself,unlike Electronics based on other materials.It’s just that a number of qualitative mistakes were made in the theory of semiconductor devices and photo devices.In this work,it is shown that the energy band diagram of semiconductor contacts itself was constructed with a significant omission-without taking into account the temperature force on the contact.At the same time,because of the incorrect calculation of currents according to the outdated formulas of Richardson-Langmuir-Deshman,there were also PROHIBITIONS.So the practitioners compensated for the errors of the theory with“empirical corrections”.So electronics engineers often made devices not according to a strict theory(which simply did not exist until now),but on a hunch and according to empirical local laws.And only the correction of the historical mistakes made it possible to expand the phenomenology of the description of processes in a Solid Body,on the basis of which it is possible to make calculations of highly efficient elements of Photo-Thermo-Electronics.

Terrain Simplification Research in Augmented Scene Modeling

Augmented reality is the merging of synthetic sensory information into a user's perception of a real environment. As one of the most important tasks in augmented scene modeling, terrain simplification research has gained more and more attention. In this paper, we mainly focus on point selection problem in terrain simplification using triangulated irregular network. Based on the analysis and comparison of traditional importance measures for each input point, we put forward a new importance measure based on local entropy. The results demonstrate that the local entropy criterion has a better performance than any traditional methods. In addition, it can effectively conquer the 'short-sight' problem associated with the traditional methods.

Local Unstable Entropy and Local Unstable Pressure for Partially Hyperbolic Endomorphisms

In this paper,local unstable metric entropy,local unstable topological entropy and local unstable pressure for partially hyperbolic endomorphisms are introduced and investigated.Specially,two variational principles concerning relationships among the above mentioned numbers are formulated.

Polynomial Entropy of Amenable Group Actions for Noncompact Sets

In this paper, we define and study polynomial entropy on an arbitrary subset and local measure theoretic polynomial entropy for any Borel probability measure on a compact metric space,and investigate the relation between local measure-theoretic polynomial entropy of Borel probability measures and polynomial entropy on an arbitrary subset. Also, we establish a variational principle for polynomial entropy on compact subsets in the context of amenable group actions.

Two Notes on Measure-Theoretic Entropy of Random Dynamical Systems

In this paper, Brin-Katok local entropy formula and Katok＇s definition of the measuretheoretic entropy using spanning set are established for the random dynamical system over an invertible ergodic system.

Relative volume comparison of Ricci flow

In this paper we derive a relative volume comparison of Ricci flow under a certain local curvature condition.It is a refinement of Perelman’s no local collapsing theorem in Perelman(2002).

ACTIVE CONTOUR BASED ON 3D STRUCTURE TENSOR APPLIED IN MEDICAL IMAGE SEGMENTATION

The paper presents an improved tensor-based active contour model in a variational level set formulation for medical image segmentation. In it, a new energy function is defined with a local intensity fitting term in intensity inhomogeneity of the image, and with a global intensity fitting term in intensity homogeneity domain. Weighting factor is chosen to balance these two intensity fitting terms, which can be calculated automatically by local entropy. The level set regularization term is to replace contour curve to find the minimum of the energy function. Particularly, structure tensor is applied to describe the image, which overcomes the disadvantage of image feature without structure information.The experimental results show that our proposed method can segment image efficiently whether it presents intensity inhomogeneity or not and wherever the initial contour is. Moreover, compared with the Chan-Vese model and local binary fitting model, our proposed model not only handles better intensity inhomogeneity, but also is less sensitive to the location of initial contour.