Quantum watermarking based on threshold segmentation using quantum informational entropy

We propose a new quantum watermarking scheme based on threshold selection using informational entropy of quantum image.The core idea of this scheme is to embed information into object and background of cover image in different ways.First,a threshold method adopting the quantum informational entropy is employed to determine a threshold value.The threshold value can then be further used for segmenting the cover image to a binary image,which is an authentication key for embedding and extraction information.By a careful analysis of the quantum circuits of the scheme,that is,translating into the basic gate sequences which show the low complexity of the scheme.One of the simulation-based experimental results is entropy difference which measures the similarity of two images by calculating the difference in quantum image informational entropy between watermarked image and cover image.Furthermore,the analyses of peak signal-to-noise ratio,histogram and capacity of the scheme are also provided.

Determination of multi-component content and construction of digital cores based on CT grey thresholds of altered igneous rocks

Altered igneous reservoirs have low porosity and permeability,compact structure and certain heterogeneity.A simple digital core with certain generality and multi-parameter constraints can be con-structed to characterize the microscopic pore structure and mineral composition.In this paper,based on core X-ray,CT images and whole-rock mineral analysis,threshold segmentation of mass content and grayscale distribution of various minerals in different lithologies of igneous rocks in the buried hill of Huizhou depression is carried out to construct digital core of altered igneous rocks.The results show that after converting the mineral mass content into volume content,the minerals of altered igneous rocks in Huizhou depression can be classified into components.According to the range of grayscale value,components can be divided into six parts.Due to the difference of the content of components in different lithologies of igneous rocks,differentiated grayscale threshold segmentation is needed to obtain the digital core for a single lithology.The final digital core generation process includes two steps:building a single component digital core,and stacking and combining.This kind of universal digital core model can support the subsequent pore scale numerical simulation and comprehensive rock physics research.

A New Adaptive Image Segmentation Method

Aim Researching the optimal thieshold of image segmentation. M^ethods An adaptiveimages segmentation method based on the entropy of histogram of gray-level picture and genetic. algorithm (GA) was presental. Results In our approach, the segmentation problem was formulated as an optimization problem and the fitness of GA which can efficiently search the segmentation parameter space was regarded as the quality criterion. Conclusion The methodcan be adapted for optimal behold segmentation.

A New Regularized Minimum Error Thresholding Method

To overcome the shortcoming that the traditional minimum error threshold method can obtain satisfactory image segmentation results only when the object and background of the image strictly obey a certain type of probability distribution,one proposes the regularized minimum error threshold method and treats the traditional minimum error threshold method as its special case.Then one constructs the discrete probability distribution by using the separation between segmentation threshold and the average gray-scale values of the object and background of the image so as to compute the information energy of the probability distribution.The impact of the regularized parameter selection on the optimal segmentation threshold of the regularized minimum error threshold method is investigated.To verify the effectiveness of the proposed regularized minimum error threshold method,one selects typical grey-scale images and performs segmentation tests.The segmentation results obtained by the regularized minimum error threshold method are compared with those obtained with the traditional minimum error threshold method.The segmentation results and their analysis show that the regularized minimum error threshold method is feasible and produces more satisfactory segmentation results than the minimum error threshold method.It does not exert much impact on object acquisition in case of the addition of a certain noise to an image.Therefore,the method can meet the requirements for extracting a real object in the noisy environment.

A novel image segmentation approach for wood plate surface defect classification through convex optimization

Detection of wood plate surface defects using image processing is a complicated problem in the forest industry as the image of the wood surface contains different kinds of defects. In order to obtain complete defect images, we used convex optimization(CO) with different weights as a pretreatment method for smoothing and the Otsu segmentation method to obtain the target defect area images. Structural similarity(SSIM) results between original image and defect image were calculated to evaluate the performance of segmentation with different convex optimization weights. The geometric and intensity features of defects were extracted before constructing a classification and regression tree(CART) classifier. The average accuracy of the classifier is 94.1% with four types of defects on Xylosma congestum wood plate surface: pinhole, crack,live knot and dead knot. Experimental results showed that CO can save the edge of target defects maximally, SSIM can select the appropriate weight for CO, and the CART classifier appears to have the advantages of good adaptability and high classification accuracy.

Optimization of Quantizer’s Segment Threshold Using Spline Approximations for Optimal Compressor Function

In this paper, the optimization of quantizer’s segment threshold is done. The quantizer is designed on the basis of approximative spline functions. Coefficients on which we form approximative spline functions are calculated by minimization mean square error (MSE). For coefficients determined in this way, spline functions by which optimal compressor function is approximated are obtained. For the quantizer designed on the basis of approximative spline functions, segment threshold is numerically determined depending on maximal value of the signal to quantization noise ratio (SQNR). Thus, quantizer with optimized segment threshold is achieved. It is shown that by quantizer model designed in this way and proposed in this paper, the SQNR that is very close to SQNR of nonlinear optimal companding quantizer is achieved.

Automated Extraction for Water Bodies Using New Water Index from Landsat 8 OLI Images

The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.

Multi-verse Optimizer with Rosenbrock and Diffusion Mechanisms for Multilevel Threshold Image Segmentation from COVID-19 Chest X-Ray Images

Coronavirus Disease 2019(COVID-19)is the most severe epidemic that is prevalent all over the world.How quickly and accurately identifying COVID-19 is of great significance to controlling the spread speed of the epidemic.Moreover,it is essential to accurately and rapidly identify COVID-19 lesions by analyzing Chest X-ray images.As we all know,image segmentation is a critical stage in image processing and analysis.To achieve better image segmentation results,this paper proposes to improve the multi-verse optimizer algorithm using the Rosenbrock method and diffusion mechanism named RDMVO.Then utilizes RDMVO to calculate the maximum Kapur’s entropy for multilevel threshold image segmentation.This image segmentation scheme is called RDMVO-MIS.We ran two sets of experiments to test the performance of RDMVO and RDMVO-MIS.First,RDMVO was compared with other excellent peers on IEEE CEC2017 to test the performance of RDMVO on benchmark functions.Second,the image segmentation experiment was carried out using RDMVO-MIS,and some meta-heuristic algorithms were selected as comparisons.The test image dataset includes Berkeley images and COVID-19 Chest X-ray images.The experimental results verify that RDMVO is highly competitive in benchmark functions and image segmentation experiments compared with other meta-heuristic algorithms.

Mosaic line-scan camera based on FPGA

Because single line-scan camera loses light in the edge of the sensor when the field of view is large, a mosaic cam- era based on field programmable gate array （FPGA） is presented by putting multiple cameras arranged in a straight line to share the field of view and reduce the view angle of every camera. For detecting doping micro particles with the designed mosaic line-scan camera, a detection algorithm of the target＇s location in FPGA is proposed. Finally, the practicability and stability of the system were validated experimentally. The results of the experiment show that the camera can get images clearly with less light loss and can accurately distinguish the target and the background.

Knowledge based recognition of harbor target

A fast knowledge based recognition method of the harbor target in large gray remote-sensing image is presented. First, the distributed features and the inherent feature are analyzed according to the knowledge of harbor targets; then, two methods for extracting the candidate region of harbor are devised in accordance with different sizes of the harbors; after that, thresholds are used to segment the land and the sea with strategies of the segmentation error control; finally, harbor recognition is implemented according to its inherent character （semi-closed region of seawater）.

A Novel Approach for Brain Tumor Detection Using MRI Images

Processing magnetic resonance images are very complex and constantly studied by the researchers to give doctors better ability to diagnose the patients. In order to detect automatically suspicious regions or tumors, we present a new approach inspired by threshold segmentation and based on morphological operations in this paper. The advantages of our approach come from the complementarities between these two approaches. The morphological operations extract roughly the tumor region and eventually can affect healthy while the threshold segmentation method gives a clear picture of the structure of the different brain and therefore these two approaches improve significantly the threshold segmentation and detection and extraction of the tumor zone based on morphological operations.

A Novel Vision Localization Method of Automated Micro-Polishing Robot

Based on photogrammetry technology,a novel localization method of micro-polishing robot,which is restricted within certain working space,is presented in this paper.On the basis of pinhole camera model,a new mathematical model of vision localization of automated polishing robot is established.The vision localization is based on the distance-constraints of feature points.The method to solve the mathematical model is discussed.According to the characteristics of gray image,an adaptive method of automatic threshold selection based on connected components is presented.The center coordinate of the feature image point is resolved by bilinear interpolation gray square weighted algorithm.Finally,the mathematical model of testing system is verified by global localization test.The experimental results show that the vision localization system in working space has high precision.

TFM imaging of aeroengine casing ring forgings with curved surfaces using acoustic field threshold segmentation and vector coherence factor

The aeroengine casing ring forgings have complex cross-section shapes,when the conventional ultrasonic or phased array is applied to detect such curved surfaces,the inspection images always have low resolution and even artifacts due to the distortion of the wave beam.In this article,taking a type of aeroengine casing ring forging as an example,the Total Focusing Method(TFM)algorithms for curved surfaces are investigated.First,the Acoustic Field Threshold Segmentation(AFTS)algorithm is proposed to reduce background noise and data calculation.Furthermore,the Vector Coherence Factor(VCF)is adopted to improve the lateral resolution of the TFM imaging.Finally,a series of 0.8 mm diameter Side-Drilled Holes(SDHs)are machined below convex and concave surfaces of the specimen.The quantitative comparison of the detection images using the conventional TFM,AFTS-TFM,VCF-TFM,and AFTS-VCF-TFM is implemented in terms of data volume,imaging Signal-to-Noise Ratio(SNR),and defect echo width.The results show that compared with conventional TFM,the data volume of AFTS-VCF-TFM algorithm for convex and concave is decreased by 32.39%and 73.40%,respectively.Moreover,the average SNR of the AFTS-VCF-TFM is gained up to 40.0 dB,while the average 6 dB-drop echo width of defects is reduced to 0.74 mm.

Automatic grading of apples based on multi-features and weighted K-means clustering algorithm

In this paper,a fast and effective method based on multiple image features and a weighted K-means clustering algorithm is proposed to achieve the automatic grading of apples.The method provides a novel way of using four images(top,bottom and two sides)and average gray values for each apple to distinguish between the apple defects,stem and calyx.Furthermore,weighted features(MCSAD(maximum cross-sectional average diameter),circularity,PRA(proportion of red area)and defect regions)were carefully selected according to the requirements of the national apple grading standard,which improves the practicality of the proposed method.Finally,qualitative and quantitative evaluation results demonstrate that the total accuracy of the proposed multi-feature grading method is greater than 96%,which provides encouragement for the additional research and implementation of multifeature automatic grading for the fruit industry.

Polarimetric synthetic aperture radar change detection for specific land cover types

This paper presents a supervised polarimetric synthetic aperture radar(PolSAR)change detection method applied to specific land cover types.For each pixel of a PolSAR image,its target scattering vector can be modeled as having a complex multivariate normal distribution.Based on this assumption,the joint distribution of two corresponding vectors in a pair of PolSAR images is derived.Then,a generalized likelihood ratio test statistic for the equality of two likelihood functions of such joint distribution is considered and a maximum likelihood distance measure for specific land cover types is presented.Subsequently,the Kittler and Illingworth minimum error threshold segmenta-tion method is applied to extract the specific changed areas.Experiments on two repeat-pass Radarsat-2 fully polarimetric images of Suzhou,China,demonstrate that the proposed change detection method gives a good performance in determining the specific changed areas in PolSAR images,especially the areas that have changed to water.

Quantification of urban greenery using hemisphere-view panoramas with a green cover index

Urban greenery has positive impacts on the well-being of residents and provides vital ecosystem services.A quantitative evaluation of full-view green coverage at the human scale can guide green space planning and management.We developed a still camera to collect hemisphere-view panoramas(HVPs)to obtain in situ heterogeneous scenes and established a panoramic green cover index(PGCI)model to measure human-scale green coverage.A case study was conducted in Xicheng District,Beijing,to analyze the quantitative relationships of PGCI with the normalized difference vegetation index(NDVI)and land surface temperature(LST)in different land use scenarios.The results show that the HVP is a useful quantization tool:(1)the method adaptively distinguishes the green cover characteristics of the four functional areas,and the PGCI values are ranked as follows:recreational area(29.6)>residential area(19.0)>traffic area(15.9)>commercial area(12.5);(2)PGCI strongly explains NDVI and LST,and for each unit(1%)increase in PGCI,NDVI tends to increase by 0.007,and(3)LST tends to decrease by 0.21 degrees Celsius.This research provides government managers and urban planners with tools to evaluate green coverage in complex urban environments and assistance in optimizing human-scale greenery and microclimate.

An IoT-based agriculture maintenance using pervasive computing with machine learning technique

Purpose-In cultivation,early harvest offers farmers an opportunity to increase production while decreasing the chances of lower crop production rates,ensuring that the economy remains balanced.The significant reason is to predict the disease in plants and distinguish the type of syndrome with the help of segmentation and random forest optimization classification.In this investigation,the accurate prior phase of crop imagery has been collected from different datasets like cropscience,yesmodes and nelsonwisc.In the current study,the real-time earlier state of crop images has been gathered from numerous data sources similar to crop_science,yes_modes,nelson_wisc dataset.Design/methodology/approach-In this research work,random forest machine learning-based persuasive plants healthcare computing is provided.If proper ecological care is not applied to early harvesting,it can cause diseases in plants,decrease the cropping rate and less production.Until now different methods have been developed for crop analysis at an earlier stage,but it is necessary to implement methods to advanced techniques.So,the detection of plant diseases with the help of threshold segmentation and random forest classification has been involved in this investigation.This implemented design is verified on Python 3.7.8 software for simulation analysis.Findings-In this work,different methods are developed for crops at an earlier stage,but more methods are needed to implement methods with prior stage crop harvesting.Because of this,a disease-finding system has been implemented.The methodologies like“Threshold segmentation”and RFO classifier lends 97.8% identification precision with 99.3%real optimistic rate,and 59.823 peak signal-to-noise(PSNR),0.99894 structure similarity index(SSIM),0.00812 machine squared error(MSE)values are attained.Originality/value-The implemented machine learning design is outperformance methodology,and they are proving good application detection rate.