{"title":"Design and Matlab Simulation of Persian License Plate Recognition Using Neural Network and Image Filtering for Intelligent Transportation Systems","authors":"O. Akbarzadeh, M. Khosravi, Linss T. Alex","doi":"10.52810/tpris.2021.100098","DOIUrl":"https://doi.org/10.52810/tpris.2021.100098","url":null,"abstract":"Vehicles play a vital role in modern-day intelligent transportation systems (ITS). Plate characters provide a standard means of identification for any vehicle. To serve this purpose, an automatic license plate recognition system is studied. In this paper, we intend to create an optimized algorithm for implementing the scheme. Firstly, we undertake several challenging stages. The first step is introduced as the determination of plate location. Then, in the second phase, we apply an initial improvement to decline the likely noises using the Gaussian function to provide an appropriate filter for this target. Next, the rest of the project is organized as follows, finding the edge of images, enhancing modified pictures, and selecting the exact place of the plate. Afterward, tilting and plate rotation improvement and plate characters' extraction are considered two essential steps in this regard. Eventually, the final step of this project consists of several stages, such as employing a neural network to extract the plate characters automatically.","PeriodicalId":437588,"journal":{"name":"ASP Transactions on Pattern Recognition and Intelligent Systems","volume":"35 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133685698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Finger Vein Image Enhancement Based on Guided Tri-Gaussian Filters","authors":"Liping Zhang, Xinran Wang, Xiaoli Dong, Linjun Sun, Weiwei Cai, X. Ning","doi":"10.52810/TPRIS.2021.100012","DOIUrl":"https://doi.org/10.52810/TPRIS.2021.100012","url":null,"abstract":"In the process of image acquisition, the contrast between veins and non-veins in finger vein images is not high due to the influence of the fuzzy light source, skin scattering and finger movement. To solve this problem, a finger vein image enhancement method is proposed (GTGFs), which enhances finger vein patterns by setting guided image as input image firstly. On this basis, the tri-Gaussian model is based on disinhibitory properties of the concentric receptive field used to locally enhancing the image. The parameters of the tri-Gaussian model are determined based on the finger vein width information. The experiment results show that the proposed enhancement method can significantly enhance the finger vein patterns and improve the recognition effect of the methods based on vein pattern segmentation.","PeriodicalId":437588,"journal":{"name":"ASP Transactions on Pattern Recognition and Intelligent Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127270009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Polynomial Fitting Algorithm Based on Neural Network","authors":"Yuerong Tong, Lina Yu, Sheng Li, Jingyi Liu, Hong-ying Qin, Weijun Li","doi":"10.52810/TPRIS.2021.100019","DOIUrl":"https://doi.org/10.52810/TPRIS.2021.100019","url":null,"abstract":"As a method of function approximation, polynomial fitting has always been the main research hotspot in mathematical modeling. In many disciplines such as computer, physics, biology, neural networks have been widely used, and most of the applications have been transformed into fitting problems using neural networks. One of the main reasons that neural networks can be widely used is that it has a certain sense of universal approximation. In order to fit the polynomial, this paper constructs a three-layer feedforward neural network, uses Taylor series as the activation function, and determines the number of hidden layer neurons according to the order of the polynomial and the dimensions of the input variables. For explicit polynomial fitting, this paper uses non-linear functions as the objective function, and compares the fitting effects under different orders of polynomials. For the fitting of implicit polynomial curves, the current popular polynomial fitting algorithms are compared and analyzed. Experiments have proved that the algorithm used in this paper is suitable for both explicit polynomial fitting and implicit polynomial fitting. The algorithm is relatively simple, practical, easy to calculate, and can efficiently achieve the fitting goal. At the same time, the computational complexity is relatively low, which has certain application value.","PeriodicalId":437588,"journal":{"name":"ASP Transactions on Pattern Recognition and Intelligent Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130329038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Weiwei Cai, Zhanguo Wei, Runmin Liu, Yuan Zhuang, Yan Wang, X. Ning
{"title":"Remote Sensing Image Recognition Based on Multi-attention Residual Fusion Networks","authors":"Weiwei Cai, Zhanguo Wei, Runmin Liu, Yuan Zhuang, Yan Wang, X. Ning","doi":"10.52810/TPRIS.2021.100005","DOIUrl":"https://doi.org/10.52810/TPRIS.2021.100005","url":null,"abstract":"Since each sample in a hyperspectral remote sensing image is made up of high-dimensional features and contains a wealth of remote sensing features, feature selection and mining become more difficult. To address this issue, a multi-attention residual integrated network (MARB-Net) algorithm is proposed, which reduces redundant features while increasing feature fusion and, as a result, improves hyperspectral image recognition. First, assign multiple weights to each feature using multiple attention mechanism models; then, deep mine and integrate the features using the residual network; and finally, perform contextual semantic integration on the deep fusion features using the Bi-LSTM network. The recognition task should be completed by the Softmax classifier. The experimental results on three multi-class public data sets show that the MARB-Net algorithm proposed in this paper is effective.","PeriodicalId":437588,"journal":{"name":"ASP Transactions on Pattern Recognition and Intelligent Systems","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123349040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}