Deep Neural Network Based Cardio Vascular Disease Prediction Using Binarized Butterfly Optimization

In this digital era,Cardio Vascular Disease(CVD)has become the lead-ing cause of death which has led to the mortality of 17.9 million lives each year.Earlier Diagnosis of the people who are at higher risk of CVDs helps them to receive proper treatment and helps prevent deaths.It becomes inevitable to pro-pose a solution to predict the CVD with high accuracy.A system for predicting Cardio Vascular Disease using Deep Neural Network with Binarized Butterfly Optimization Algorithm(DNN–BBoA)is proposed.The BBoA is incorporated to select the best features.The optimal features are fed to the deep neural network classifier and it improves prediction accuracy and reduces the time complexity.The usage of a deep neural network further helps to improve the prediction accu-racy with minimal complexity.The proposed system is tested with two datasets namely the Heart disease dataset from UCI repository and CVD dataset from Kag-gle Repository.The proposed work is compared with different machine learning classifiers such as Support Vector Machine,Random Forest,and Decision Tree Classifier.The accuracy of the proposed DNN–BBoA is 99.35%for the heart dis-ease data set from UCI repository yielding an accuracy of 80.98%for Kaggle repository for cardiovascular disease dataset.

Line Patterns Segmentation in Blurred Images Using Contrast Enhancement and Local Entropy Thresholding

Finger vein extraction and recognition hold significance in various applications due to the unique and reliable nature of finger vein patterns. While recently finger vein recognition has gained popularity, there are still challenges associated with extracting and processing finger vein patterns related to image quality, positioning and alignment, skin conditions, security concerns and processing techniques applied. In this paper, a method for robust segmentation of line patterns in strongly blurred images is presented and evaluated in vessel network extraction from infrared images of human fingers. In a four-step process: local normalization of brightness, image enhancement, segmentation and cleaning were involved. A novel image enhancement method was used to re-establish the line patterns from the brightness sum of the independent close-form solutions of the adopted optimization criterion derived in small windows. In the proposed method, the computational resources were reduced significantly compared to the solution derived when the whole image was processed. In the enhanced image, where the concave structures have been sufficiently emphasized, accurate detection of line patterns was obtained by local entropy thresholding. Typical segmentation errors appearing in the binary image were removed using morphological dilation with a line structuring element and morphological filtering with a majority filter to eliminate isolated blobs. The proposed method performs accurate detection of the vessel network in human finger infrared images, as the experimental results show, applied both in real and artificial images and can readily be applied in many image enhancement and segmentation applications.

FPGA-based acceleration for binary neural networks in edge computing

As a core component in intelligent edge computing,deep neural networks(DNNs)will increasingly play a critically important role in addressing the intelligence-related issues in the industry domain,like smart factories and autonomous driving.Due to the requirement for a large amount of storage space and computing resources,DNNs are unfavorable for resource-constrained edge computing devices,especially for mobile terminals with scarce energy supply.Binarization of DNN has become a promising technology to achieve a high performance with low resource consumption in edge computing.Field-programmable gate array(FPGA)-based acceleration can further improve the computation efficiency to several times higher compared with the central processing unit(CPU)and graphics processing unit(GPU).This paper gives a brief overview of binary neural networks(BNNs)and the corresponding hardware accelerator designs on edge computing environments,and analyzes some significant studies in detail.The performances of some methods are evaluated through the experiment results,and the latest binarization technologies and hardware acceleration methods are tracked.We first give the background of designing BNNs and present the typical types of BNNs.The FPGA implementation technologies of BNNs are then reviewed.Detailed comparison with experimental evaluation on typical BNNs and their FPGA implementation is further conducted.Finally,certain interesting directions are also illustrated as future work.

Vehicle Plate Number Localization Using Memetic Algorithms and Convolutional Neural Networks

This paper introduces the third enhanced version of a genetic algorithm-based technique to allow fast and accurate detection of vehicle plate numbers(VPLN)in challenging image datasets.Since binarization of the input image is the most important and difficult step in the detection of VPLN,a hybrid technique is introduced that fuses the outputs of three fast techniques into a pool of connected components objects(CCO)and hence enriches the solution space with more solution candidates.Due to the combination of the outputs of the three binarization techniques,many CCOs are produced into the output pool from which one or more sequences are to be selected as candidate solutions.The pool is filtered and submitted to a new memetic algorithm to select the best fit sequence of CCOs based on an objective distance between the tested sequence and the defined geometrical relationship matrix that represents the layout of the VPLN symbols inside the concerned plate prototype.Using any of the previous versions will give moderate results but with very low speed.Hence,a new local search is added as a memetic operator to increase the fitness of the best chromosomes based on the linear arrangement of the license plate symbols.The memetic operator speeds up the convergence to the best solution and hence compensates for the overhead of the used hybrid binarization techniques and allows for real-time detection especially after using GPUs in implementing most of the used techniques.Also,a deep convolutional network is used to detect false positives to prevent fake detection of non-plate text or similar patterns.Various image samples with a wide range of scale,orientation,and illumination conditions have been experimented with to verify the effect of the new improvements.Encouraging results with 97.55%detection precision have been reported using the recent challenging public Chinese City Parking Dataset(CCPD)outperforming the author of the dataset by 3.05%and the state-of-the-art technique by 1.45%.

An Analysis of Gender Binarism in McCullers’The Heart Is a Lonely Hunter

This paper provides an analysis of gender binarism in Carson McCullers’novel,The Heart Is a Lonely Hunter,situated within the socio-cultural milieu of Southern America.It examines the depiction of persisting challenges posed by binary gender paradigms and the portrayal of potential emancipation within the narrative.The analysis focuses on two central characters,interpreting them as contrasting cases.One character represents the paradox inherent in the rebellious endeavors,highlighting how these actions,influenced by Phallocentrism and a broader framework of hierarchical structures,might inadvertently reinforce gender binarism.The other character exemplifies a triumphant departure from the binary gender paradigm through striving to attain a state of equilibrium marked by the harmonious coexistence of gender differences.Through this analysis,the paper reveals the author’s dual perspectives in her exploration of gender binarism using these two distinct protagonists.At last,it employs the traditional Chinese philosophical concept of“harmony in diversity”in conjunction with feminist and gender theories to elucidate the encouraged path toward emancipation from gender binarism within McCullers’narrative.

An improved binarization algorithm of wood image defect segmentation based on non-uniform background

In this study,an image binarization optimization algorithm,based on local threshold algorithms,is proposed because global and traditional local threshold segmentation algorithms cannot effectively address the problems of nonuniform backgrounds of wood defect images.The proposed algorithm calculates the threshold by the mean,standard deviation and the extreme value of the window.The results indicate that this modified algorithm enhances the image segmentation for wood defect images on a complex background,which is much superior to the global threshold algorithm and the Bernsen algorithm,and slightly better than the Niblack algorithm and Sauvola algorithm.Compared with similar models,the algorithm proposed in this paper has higher segmentation accuracy,as high as 92.6%for wood defect images with a complex background.

An enhanced image binarization method incorporating with Monte-Carlo simulation

We proposed an enhanced image binarization method.The proposed solution incorporates Monte-Carlo simulation into the local thresholding method to address the essential issues with respect to complex background,spatially-changed illumination,and uncertainties of block size in traditional method.The proposed method first partitions the image into square blocks that reflect local characteristics of the image.After image partitioning,each block is binarized using Otsu’s thresholding method.To minimize the influence of the block size and the boundary effect,we incorporate Monte-Carlo simulation into the binarization algorithm.Iterative calculation with varying block sizes during Monte-Carlo simulation generates a probability map,which illustrates the probability of each pixel classified as foreground.By setting a probability threshold,and separating foreground and background of the source image,the final binary image can be obtained.The described method has been tested by benchmark tests.Results demonstrate that the proposed method performs well in dealing with the complex background and illumination condition.

Method of Edge Extraction Based on Wavelet Transform for the Image of Mechanical Engineering Drawings

A method, by which the broken edge of mechanical engineering drawings being binarised can be eliminated and the whole edge of mechanical engineering drawing can be got, is given. To all points of a connected area on the image of the modular maximum value of wavelet transform at scale 2 2, the averaging grey value method is used to their grey values, then the edge of the dim place is continuous after the maximum variance threshold method is used. All these methods are fast, they can be used for all linear graphics having nothing to do with the grey value, its application scope is wide.

An Efficient Bar Code Image Recognition Algorithm for Sorting System

In the sorting system of the production line,the object movement,fixed angle of view,light intensity and other reasons lead to obscure blurred images.It results in bar code recognition rate being low and real time being poor.Aiming at the above problems,a progressive bar code compressed recognition algorithm is proposed.First,assuming that the source image is not tilted,use the direct recognition method to quickly identify the compressed source image.Failure indicates that the compression ratio is improper or the image is skewed.Then,the source image is enhanced to identify the source image directly.Finally,the inclination of the compressed image is detected by the barcode region recognition method and the source image is corrected to locate the barcode information in the barcode region recognition image.The results of multitype image experiments show that the proposed method is improved by 5+times computational efficiency compared with the former methods,and can recognize fuzzy images better.

Effect of Direct Statistical Contrast Enhancement Technique on Document Image Binarization

Background:Contrast enhancement plays an important role in the image processing field.Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image.Objective:This paper proposed a novel method based on statistical data from the local mean and local standard deviation.Method:The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories:background,foreground,and problematic(contrast&luminosity)region.Experimental results from both visual and objective aspects show that the proposed method can normalize the contrast variation problem effectively compared to Histogram Equalization(HE),Difference of Gaussian(DoG),and Butterworth Homomorphic Filtering(BHF).Seven(7)types of binarization methods were tested on the corrected image and produced a positive and impressive result.Result:Finally,a comparison in terms of Signal Noise Ratio(SNR),Misclassification Error(ME),F-measure,Peak Signal Noise Ratio(PSNR),Misclassification Penalty Metric(MPM),and Accuracy was calculated.Each binarization method shows an incremented result after applying it onto the corrected image compared to the original image.The SNR result of our proposed image is 9.350 higher than the three(3)other methods.The average increment after five(5)types of evaluation are:(Otsu=41.64%,Local Adaptive=7.05%,Niblack=30.28%,Bernsen=25%,Bradley=3.54%,Nick=1.59%,Gradient-Based=14.6%).Conclusion:The results presented in this paper effectively solve the contrast problem and finally produce better quality images.

Contour representation based on wedgelet

Aiming at the shortcomings of the existing wedgelet compression arithmetics, a novel contour-representing algorithm based on wedgelets is presented in this paper. Firstly the input image is binarized and the most optimized wedgelets are found by means of quadtree framework. Then the contours are reconstructed by applying the wedgelets, the data volume is compressed, and the shortcomings of the contour representation based on normal wavelet are ameliorated, the better effect for the visualization is obtained, too.

Novel Adaptive Binarization Method for Degraded Document Images

Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast,bleedthrough,and nonuniform illumination effects.Unlike the existing baseline thresholding techniques that use fixed thresholds and windows,the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better binarization.To enhance a low-contrast image,we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and,simultaneously,increasing pixel contrast at edges or near edges,which results in an enhanced image.For the enhanced image,we propose a new method for deriving adaptive local thresholds for dynamic windows.The dynamic window is derived by exploiting the advantage of Otsu thresholding.To assess the performance of the proposed method,we have used standard databases,namely,document image binarization contest(DIBCO),for experimentation.The comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.

A Target-Orientated Marker Image Binarization Method for Orthopaedic Surgical Navigation System

Camera calibration is the key technique in a C-arm based orthopaedic surgical navigation system. The extraction of marker location information is a necessary step in the calibration process. Ideal marker images should possess uniform background and contain marker shadow only, but in fact marker images always possess nonuniform background and are contaminated by noise and unwanted anatomic information, making the extraction very difficult. A target-orientated marker shadow extraction method was proposed. With this method a proper threshold for marker image binarization can be determined.

Fingerprint Recognition on Various Authentication Sensors

Fingerprint is a very popular and an ancient biometric technology to uniquely identify a person. In this paper, a fingerprint matcher is proposed which uses the global and local adaptive binarization and global minutia features. The fingerprint data is collected using three different authentication devices based on optical sensors. The experimental results are compared with the National Institute of Standards and Technology （NIST） Bozorth algorithm and various authentication fingerprint sensors. The accuracy of the proposed algorithm has been improved significantly compared with that of the NIST Bozorth algorithm.

Binary Fruit Fly Swarm Algorithms for the Set Covering Problem

Currently,the industry is experiencing an exponential increase in dealing with binary-based combinatorial problems.In this sense,metaheuristics have been a common trend in the field in order to design approaches to solve them successfully.Thus,a well-known strategy consists in the use of algorithms based on discrete swarms transformed to perform in binary environments.Following the No Free Lunch theorem,we are interested in testing the performance of the Fruit Fly Algorithm,this is a bio-inspired metaheuristic for deducing global optimization in continuous spaces,based on the foraging behavior of the fruit fly,which usually has much better sensory perception of smell and vision than any other species.On the other hand,the Set Coverage Problem is a well-known NP-hard problem with many practical applications,including production line balancing,utility installation,and crew scheduling in railroad and mass transit companies.In this paper,we propose different binarization methods for the Fruit Fly Algorithm,using Sshaped and V-shaped transfer functions and various discretization methods to make the algorithm work in a binary search space.We are motivated with this approach,because in this way we can deliver to future researchers interested in this area,a way to be able to work with continuous metaheuristics in binary domains.This new approach was tested on benchmark instances of the Set Coverage Problem and the computational results show that the proposed algorithm is robust enough to produce good results with low computational cost.

基于经验似然方法的BINAR(1)过程参数的置信域

考虑将经验似然(EL)方法应用于二维一阶整数值自回归(BINAR(1))过程.先利用该过程条件最小二乘(CLS)估计量的渐近正态性建立经验似然比(ELR)统计量,并寻找其极限分布,以构造参数的置信域,解决参数的假设检验问题;然后通过数值模拟对比由EL方法和正态逼近(NA)法计算的参数置信域的覆盖率.

Basic study on contour tracing method in softwood cell contour extraction

The application of digital image analysis to calculate the parameters of softwood cells and to understand their microstructure is of importance in the area of basic micro-wood research such as wood anatomy. In order to obtain cell parameters, the most important part is to separate the cells from their background in an image, which is accomplished by drawing cell contours. In this study, a contour tracing method based on digital image binarization is introduced to extract the contours of softwood cells. As well, a comparison with other traditional methods is included. The results show that using this contour tracing method, clear and complete contours can be obtained, which solves the problems of traditional methods where sharp comers and turning points in an image could not be addressed.

Application of Image Processing in Automatic Vehicle Guidance

A micro controller unit (MCU) oriented image acquisition and automatic guidance system is proposed. Image rectification and guidance recognition are discussed. The relation between camera setting and sight view is illustrated for smart car. A novel edge detection and trend prediction method is proposed for MCU processing unit for automatic guidance. A hardware-based binary proposal is discussed for guidance information detection with raster data. Keystone distortion correcting algorithm is also designed for MCU application and the performance of track type recognition is improved. Together with rollover preventing calculation, the proposed design is successfully applied to a 1∶10 scale model car.

A method of key positioning based on laser virtual keyboard

The paper puts forward a method of virtual keyboard key positioning based on infrared laser reflection and image processing technology. In this paper, the positioning principle is introduced first. And the experimental system is established based on image acquisition, image transformation, threshold selection and binarization processing and identification of key placement. Tested in the indoor environment, the method can achieve characters input efficiently and accurately. And the key positioning has high accuracy, validity and reliability. So the method which has a high practical value provides a good theoretical basis for design of virtual keyboard application.

Efficient Text Extraction Algorithm Using Color Clustering for Language Translation in Mobile Phone

Many Text Extraction methodologies have been proposed, but none of them are suitable to be part of a real system implemented on a device with low computational resources, either because their accuracy is insufficient, or because their performance is too slow. In this sense, we propose a Text Extraction algorithm for the context of language translation of scene text images with mobile phones, which is fast and accurate at the same time. The algorithm uses very efficient computations to calculate the Principal Color Components of a previously quantized image, and decides which ones are the main foreground-background colors, after which it extracts the text in the image. We have compared our algorithm with other algorithms using commercial OCR, achieving accuracy rates more than 12% higher, and performing two times faster. Also, our methodology is more robust against common degradations, such as uneven illumination, or blurring. Thus, we developed a very attractive system to accurately separate foreground and background from scene text images, working over low computational resources devices.