CE-EEN-B0:Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

A brain tumor is the uncharacteristic progression of tissues in the brain.These are very deadly,and if it is not diagnosed at an early stage,it might shorten the affected patient’s life span.Hence,their classification and detection play a critical role in treatment.Traditional Brain tumor detection is done by biopsy which is quite challenging.It is usually not preferred at an early stage of the disease.The detection involvesMagneticResonance Imaging(MRI),which is essential for evaluating the tumor.This paper aims to identify and detect brain tumors based on their location in the brain.In order to achieve this,the paper proposes a model that uses an extended deep Convolutional Neural Network(CNN)named Contour Extraction based Extended EfficientNet-B0(CE-EEN-B0)which is a feed-forward neural network with the efficient net layers;three convolutional layers and max-pooling layers;and finally,the global average pooling layer.The site of tumors in the brain is one feature that determines its effect on the functioning of an individual.Thus,this CNN architecture classifies brain tumors into four categories:No tumor,Pituitary tumor,Meningioma tumor,andGlioma tumor.This network provides an accuracy of 97.24%,a precision of 96.65%,and an F1 score of 96.86%which is better than already existing pre-trained networks and aims to help health professionals to cross-diagnose an MRI image.This model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.

Efficient Object Localization Scheme Based on Vanishing Line in Road Image for Autonomous Vehicles

This paper proposes an efficient object localization method based on a vanishing line. The proposed method can be much improved in time efficiency since it requires scanning only vanishing line area. It requires the time complexity of O(n) while the existing sliding window method requires the time complexity O(n2) for detecting all objects in the entire image. In addition, the range of detection area can be also remarkably reduced when compared with the sliding window method. As a result, the total range and times for searching in the proposed method can be significantly reduced by considering together the distance and position of the object. The experiment on the proposed method is performed with the virtual road data set known as SYNTHIA, and the competitive results are obtained.

Adaptive Median Filtering Algorithm Based on Divide and Conquer and Its Application in CAPTCHA Recognition

As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious attacks.By researching the CAPTCHA,we can find its vulnerability and improve the security of CAPTCHA.Recently,many studies have shown that improving the image preprocessing effect of the CAPTCHA,which can achieve a better recognition rate by the state-of-theart machine learning algorithms.There are many kinds of noise and distortion in the CAPTCHA images of this experiment.We propose an adaptive median filtering algorithm based on divide and conquer in this paper.Firstly,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering efficiency.Secondly,the size of the filtering window is adaptively adjusted according to the noise density.As demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median filter.The algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation details.Therefore,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications.

Single-Choice Aided Marking System Research Based on Back Propagation Neural Network

In the field of educational examination,automatic marking technology plays an essential role in improving the efficiency of marking and liberating the labor force.At present,the implementation of the policy of expanding erolments has caused a serious decline in the teacher-student ratio in colleges and universities.The traditional marking system based on Optical Mark Reader technology can no longer meet the requirements of liberating the labor force of teachers in small and medium-sized examinations.With the development of image processing and artificial neural network technology,the recognition of handwritten character in the field of pattern recognition has attracted the attention of many researchers.In this paper,filtering and de-noise processing and binary processing are used as preprocessing methods for handwriting recognition.Extract the pixel feature of handwritten characters through digital image processing of handwritten character pictures,and then,get the feature vector from these feature fragments and use it as the description of the character.The extracted feature values are used to train the neural network to realize the recognition of handwritten English letters and numerical characters.Experimental results on Chars74K and MNIST data sets show that the recognition accuracy of some handwritten English letters and numerical characters can reach 90%and 99%,respectively.

Identity Verification of Individuals Based on Retinal Features Using Gabor Filters and SVM

Authentication reliability of individuals is a demanding service and growing in many areas, not only in the military barracks or police services but also in applications of community and civilian, such as financial transactions. In this paper, we propose a human verification method depends on extraction a set of retinal features points. Each set of feature points is representing landmarks in the tree of retinal vessel. Extraction and matching of the pattern based on Gabor filters and SVM are described. The validity of the proposed method is verified with experimental results obtained on three different commonly available databases, namely STARE, DRIVE and VARIA. We note that the proposed retinal verification method gives 92.6%, 100% and 98.2% recognition rates for the previous databases, respectively. Furthermore, for the authentication task, the proposed method gives a moderate accuracy of retinal vessel images from these databases.

An improved adaptive preprocessing method for TDI CCD images

In order to achieve high quality images with time-delayed integration(TDI) charge-coupled device(CCD) imaging system, an improved adaptive preprocessing method is proposed with functions of both denoising and edge enhancement. It is a weighted average filter integrating the average filter and the improved range filter. The weighted factors are deduced in terms of a cost function, which are adjustable to different images. To validate the proposed method, extensive tests are carried out on a developed TDI CCD imaging system. The experimental results confirm that this preprocessing method can fulfill the noise removal and edge sharpening simultaneously, which can play an important role in remote sensing field.

Improved preprocessed Yaroslavsky filter based on shearlet features

An improved preprocessed Yaroslavsky filter（IPYF）is proposed to avoid the nick effects and obtain a better denoising result when the noise variance is unknown.Different from its predecessors,the similarity between two pixels is calculated by shearlet features.The feature vector consists of initial denoised results by the non-subsampled shearlet transform hard thresholding（NSST-HT）and NSST coefficients,which can help allocate the averaging weights more reasonably.With the correct estimated noise variance,the NSST-HT can provide good denoised results as the initial estimation and high-frequency coefficients contribute large weights to preserve textures.In case of the incorrect estimated noise variance,the low-frequency coefficients will mitigate the nick effect in cartoon regions greatly,making the IPYF more robust than the original PYF.Detailed experimental results show that the IPYF is a very competitive method based on a comprehensive consideration involving peak signal to noise ratio（PSNR）,computing time,visual quality and method noise.

Background Interference Removal Algorithm for PIV Preprocessing Based on Improved Local Otsu Thresholding

Due to background light fluctuation,noise interference,voltage fluctuation,and other factors,there will be noise interference of different intensities in the background of the collected image.In this paper,a PIV image background interference removal algorithm based on improved neighborhood Otsu processing is proposed.The algorithm proposed in this paper separates the particle image from the background interference through the adaptive neighborhood improved Otsu threshold segmentation method and uses the common PIV analysis tools PIVLab and para PIV to analyze the flow pattern after the interference is removed.The experimental results demonstrated that the proposed algorithm can obviously improve the quality of PIV results in terms of both PSNR and SSIM in the case of background light interference,and the increase in average performance is nearly 50%compared with traditional preprocessing algorithms,which solves the problem of large flow pattern analysis error caused by poor background light removal effect in the case of irregular grating and other background light interference only using traditional preprocessing.

Speech Dictation System Based on Character Recognition

To solve students’ dictation problems, a speech dictation system basedon character recognition is proposed in this paper. The system applied offlinehandwritten Chinese character recognition technology, denoised the imagethrough Gaussian filter, segmented the text through projection method, and convertedthe image to text through OCR technology. The straight line mark in thepicture was detected by Hough transform technology, and then SKB-FSS algorithmand WST algorithm were used for speech synthesis. Experiments show thatthe system can effectively assist students in dictation.