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

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Computers, Materials & Continua 2019年第3期58卷 665-677页

作者： Wentao Ma Jiaohua Qin Xuyu Xiang Yun Tan Yuanjing Luo Neal NXiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410114China Department of Mathematics and Computer Science Northeastern State UniversityOklahoma74464USA.

As the first barrier to protect cyberspace,the CAPTCHA has made significant contributions to maintaining Internet security and preventing malicious *** researching the CAPTCHA,we can find its vulnerability and improve the security of ***,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 *** are many kinds of noise and distortion in the CAPTCHA images of this *** propose an adaptive median filtering algorithm based on divide and conquer in this ***,the filtering window data quickly sorted by the data correlation,which can greatly improve the filtering ***,the size of the filtering window is adaptively adjusted according to the noise *** demonstrated in the experimental results,the proposed scheme can achieve superior performance compared with the conventional median *** algorithm can not only effectively detect the noise and remove it,but also has a good effect in preservation ***,this algorithm can be one of the most strong tools for various CAPTCHA image recognition and related applications.

关键词： image preprocessing machine learning CAPTCHA recognition adaptive median filtering algorithm.

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

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Computers, Materials & Continua 2023年第3期74卷 5967-5982页

作者： Abishek Mahesh Deeptimaan Banerjee Ahona Saha Manas Ranjan Prusty A.Balasundaram School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India Centre for Cyber-Physical Systems Vellore Institute of TechnologyChennai600127India

A brain tumor is the uncharacteristic progression of tissues in the *** are very deadly,and if it is not diagnosed at an early stage,it might shorten the affected patient’s life ***,their classification and detection play a critical role in *** Brain tumor detection is done by biopsy which is quite *** is usually not preferred at an early stage of the *** detection involvesMagneticResonance Imaging(MRI),which is essential for evaluating the *** paper aims to identify and detect brain tumors based on their location in the *** 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 *** site of tumors in the brain is one feature that determines its effect on the functioning of an ***,this CNN architecture classifies brain tumors into four categories:No tumor,Pituitary tumor,Meningioma tumor,andGlioma *** 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 *** model will undoubtedly reduce the complications in detection and aid radiologists without taking invasive steps.

关键词： Brain tumor image preprocessing contour extraction disease classification transfer learning

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

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Chinese Journal of Biomedical Engineering(English Edition) 2022年第4期31卷 147-159页

作者： XU Meng-bi Southeast University of Science and Technology Mianyang Sichuan 621010China

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 *** this paper,a PIV image background interference removal algorithm based on improved neighborhood Otsu processing is *** 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 *** 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.

关键词： particle image velocimetry(PIV) image preprocessing Otsu threshold method moving average threshold

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

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Journal of Signal and Information Processing 2016年第1期7卷 49-59页

作者： Mohamed A. El-Sayed M. Hassaballah Mohammed A. Abdel-Latif Department of Mathematics Faculty of Science Fayoum University Fayoum Egypt CS Department College of Computers and Information Technology Taif University Taif KSA Department of Mathematics Faculty of Science South Valley University Qena Egypt

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.

关键词： image preprocessing Gabor Filter SVM Authentication Identification Verification Retinal Features Feature Extraction Query image

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

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Journal of Cyber Security 2021年第1期3卷 45-54页

作者： Yunzuo Zhang Yi Li Wei Guo Lei Huo Jiayu Zhang Kaina Guo School of Information Science and Technology Shijiazhuang Tiedao UniversityShijiazhuang050043China

In the field of educational examination,automatic marking technology plays an essential role in improving the efficiency of marking and liberating the labor *** present,the implementation of the policy of expanding erolments has caused a serious decline in the teacher-student ratio in colleges and *** 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 *** 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 *** this paper,filtering and de-noise processing and binary processing are used as preprocessing methods for handwriting *** 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 *** extracted feature values are used to train the neural network to realize the recognition of handwritten English letters and numerical *** 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.

关键词： image preprocessing BP neural network handwriting recognition marking system

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

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Journal of Computer and Communications 2021年第9期9卷 85-97页

作者： Bongkyo Moon Jiwon Choi Juehyun Lee Minyoung Lee Department of Computer Science and Engineering Dongguk University Seoul South Korea Graduate School of Information Security KAIST Daejeon South Korea Electronics and Telecommunications Research Institute (ERTI) Daejeon South Korea

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.

关键词： Object Localization image preprocessing Sliding Window Vanishing Line Autonomous Vehicle

Ship Detection in Remote Sensing image based on Faster R-CNN with Dilated Convolution

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Ship Detection in Remote Sensing Image based on Faster R-CNN...

Chip Surface Character Recognition Based on Improved LeNet-5 Convolutional Neural Network

第三十九届中国控制会议

作者： Shuaihao Wei Huimin Chen Xiaojin Zhu Hesheng Zhang School of Mechatronic Engineering and Automation Shanghai University

Ship detection based on remote sensing image can provide effective assistance for maritime reconnaissance, port and fisheries management. This paper mainly presents an improved Faster R-CNN algorithm for ship detection with dilated convolution. Firstly, the image of remote sensing ship is preprocessed. This paper uses dark channel prior method to dehaze image and enhance dehazed image quality by grey world algorithm. Second, The Faster R-CNN is improved in ship *** dilated convolutional is introduced into Faster R-CNN to increases its feature extraction capability. Finally, ship detection software platform is built. The remote ship image preprocessing and ship detection network is integrated into an application software. The ship detection network can be trained end to end. Experimental results on the HRSC2016 datasets illustrated that the proposed presents a good performance for ship detection task.

关键词： Dilated convolution Faster R-CNN image preprocessing Ship detection

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Chip Surface Character Recognition Based on Improved LeNet-5...

Detection Method of Wet Shotcrete images in Complex Tunnel Construction Environment

第三十九届中国控制会议

作者： Yiyun Zhu Hesheng Zhang Long Zhang Xiaojin Zhu School of Mechatronic Engineering and Automation Shanghai University

It is very important to detect the correctness of character information on the surface of IC chip in the process of packaging and testing. In this paper, a character segmentation method based on rectangle contour is proposed and the LeNet-5 network model is optimized. The character segmentation algorithm based on contour can effectively segment the characters in both horizontal and vertical directions. The improved convolutional neural network model is verified on the surface character data set of IC chip and has achieved good results.

关键词： image preprocessing Optical Character Recognition Convolutional Neural Network

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Detection Method of Wet Shotcrete Images in Complex Tunnel C...