Research on color image matching method based on feature point compensation in dark light environment

Image matching refers to the process of matching two or more images obtained at different time,different sensors or different conditions through a large number of feature points in the image.At present,image matching is widely used in target recognition and tracking,indoor positioning and navigation.Local features missing,however,often occurs in color images taken in dark light,making the extracted feature points greatly reduced in number,so as to affect image matching and even fail the target recognition.An unsharp masking(USM)based denoising model is established and a local adaptive enhancement algorithm is proposed to achieve feature point compensation by strengthening local features of the dark image in order to increase amount of image information effectively.Fast library for approximate nearest neighbors(FLANN)and random sample consensus(RANSAC)are image matching algorithms.Experimental results show that the number of effective feature points obtained by the proposed algorithm from images in dark light environment is increased,and the accuracy of image matching can be improved obviously.

An Ophthalmic Evaluation of Central Serous Chorioretinopathy

Nowadays in the medicalfield,imaging techniques such as Optical Coherence Tomography(OCT)are mainly used to identify retinal diseases.In this paper,the Central Serous Chorio Retinopathy(CSCR)image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods.Thefirst approach,which was focused on image quality,improves medical image accuracy.An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter(BADWUMF).The classifier used here is tofigure out whether the OCT image is a CSCR case or not.150 images are checked for this research work(75 abnormal from Optical Coherence Tomography Image Retinal Database,in-house clinical database,and 75 normal images).This article explicitly decides that the approaches suggested aid the ophthalmologist with the precise retinal analysis and hence the risk factors to be minimized.The total precision is 90 percent obtained from the Two Class Support Vector Machine(TCSVM)classifier and 93.3 percent is obtained from Shallow Neural Network with the Powell-Beale(SNNWPB)classifier using the MATLAB 2019a program.

基于Unsharp量子逻辑的自动机和文法理论

初步建立了具有某种分配律的扩展格序效应代数和格序QMV代数这两种unsharp量子结构上的自动机与文法理论的基本框架。引入了ε-值正则文法的概念,证明了任意ε-值自动机识别的语言等价于某种ε-值正则文法所生成的语言;反之,任意ε-值正则文法所生成的语言等价于某种ε-值自动机识别的语言。讨论了ε-值正则语言在和、连接及反转运算下的封闭性质。

Comparative analysis of different methods for image enhancement

Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima(WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean(μ),standard deviation(?), mean square error(MSE) and PSNR(peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement.

A comprehensive review of medical image enhancement technologies

Image enhancement plays an important role in many applications of medical imaging. Image enhancement technologies can improve the qualities of medical images with low contrast and high level noise by stretching contrast, suppressing noise and so on. Such images processed by image enhancement technologies are helpful to doctors in analyses and diagnoses. In this paper, we present a technical review of various existing image enhancement methodologies which are often emoloved.

Limits on sequential sharing of nonlocal advantage of quantum coherence

Sequential sharing of nonlocal correlation is inherently related to its application.We address the question as to how many observers can share the nonlocal advantage of quantum coherence(NAQC)in a(d×d)-dimensional state,where d is a prime or a power of a prime.We first analyze the trade-off between disturbance and information gain of the general d-dimensional unsharp measurements.Then in a scenario where multiple Alices perform unsharp measurements on one party of the state sequentially and independently and a single Bob measures coherence of the conditional states on the other party,we show that at most one Alice can demonstrate NAQC with Bob.This limit holds even when considering the weak measurements with optimal pointer states.These results may shed light on the interplay between nonlocal correlations and quantum measurements on high-dimensional systems and the hierarchy of different quantum correlations.

Sequential 3→1 quantum random access code utilizing unsharp measurements

Quantum random access codes(QRACs) are important communication tasks that are usually implemented in prepare-andmeasure scenarios. The receiver tries to retrieve one arbitrarily chosen bit of the original bit-string from the code qubit sent by the sender. In this Letter, we analyze in detail the sequential version of the 3 → 1 QRAC with two receivers. The average successful probability for the strategy of unsharp measurement is derived. The prepare-and-measure strategy within projective measurement is also discussed. It is found that sequential 3 → 1 QRAC with weak measurement cannot be always superior to the one with projective measurement, as the 2 → 1 version can be.