2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)最新文献_第2页

Research on Image Liquid Level Measurement Technology Based on Hough Transform 基于霍夫变换的图像液位测量技术研究

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520745

Yanqing Fu, Yongqing Peng, P. Liu, Weikui Wang

引用次数: 0

Driver’s Illegal Driving Behavior Detection with SSD Approach 基于SSD方法的驾驶员非法驾驶行为检测

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520735

Tao Yang, Jin Yang, Jicheng Meng

引用次数: 0

Text Detection in Tibetan Ancient Books: A Benchmark 藏文古籍文本检测:一个标杆

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520727

Xiangxiang Zhi, Dingguo Gao, Qijun Zhao, Shuiwang Li, Ci Qu

{"title":"Text Detection in Tibetan Ancient Books: A Benchmark","authors":"Xiangxiang Zhi, Dingguo Gao, Qijun Zhao, Shuiwang Li, Ci Qu","doi":"10.1109/PRML52754.2021.9520727","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520727","url":null,"abstract":"The digitization of Tibetan ancient books is of great significance to the preservation of Tibetan culture. This problem involves two tasks: Tibetan text detection and Tibetan text recognition. The former is undoubtedly crucial to automatic Tibetan text recognition. However, there are few works on Tibetan text detection, and lack of training data has always been a problem, especially for deep learning methods which require massive training data. In this paper, we introduce the TxTAB dataset for evaluating text detection methods in Tibetan ancient books. The dataset is established based upon 202 treasured handwritten ancient Tibetan text images and is densely annotated with a multi-point annotation method without limiting the number of points. This is a challenging dataset with good diversity. It contains blurred images, gray and color images, the text of different sizes, the text of different handwriting styles, etc. An extensive experimental evaluation of 3 state-of-the-art text detection algorithms on TxTAB is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements particularly for detecting Tibetan text in images of low quality.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125741088","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}

引用次数: 0

Transformer Based Multimodal Speech Emotion Recognition with Improved Neural Networks 基于变压器的改进神经网络多模态语音情感识别

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520692

Rutherford Agbeshi Patamia, Wu Jin, Kingsley Nketia Acheampong, K. Sarpong, Edwin Kwadwo Tenagyei

引用次数: 5

Generation and Transformation Invariant Learning for Tomato Disease Classification 番茄病害分类的生成与转换不变量学习

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520693

Getinet Yilma, Kumie Gedamu, Maregu Assefa, Ariyo Oluwasanmi, Zhiguang Qin

引用次数: 1

DeepComp: A Deep Comparator for Improving Facial Age-Group Estimation DeepComp:一种改进面部年龄组估计的深度比较器

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520698

Ebenezer Nii Ayi Hammond, Shijie Zhou, Hongrong Cheng, Qihe Liu

引用次数: 0

Cardiac Arrhythmia Recognition Using Transfer Learning with a Pre-trained DenseNet 使用迁移学习和预训练DenseNet识别心律失常

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520710

Hadaate Ullah, Yuxiang Bu, T. Pan, M. Gao, Sajjatul Islam, Yuan Lin, Dakun Lai

{"title":"Cardiac Arrhythmia Recognition Using Transfer Learning with a Pre-trained DenseNet","authors":"Hadaate Ullah, Yuxiang Bu, T. Pan, M. Gao, Sajjatul Islam, Yuan Lin, Dakun Lai","doi":"10.1109/PRML52754.2021.9520710","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520710","url":null,"abstract":"Recent findings demonstrated that deep neural networks carry out features extraction itself to identify the electrocardiography (ECG) pattern or cardiac arrhythmias from the ECG signals directly and provided good results compared to cardiologists in some cases. But, to face the challenge of huge volume of data to train such networks, transfer learning is a prospective mechanism where network is trained on a large dataset and learned experiences are transferred to a small volume target dataset. Therefore, we firstly extracted 78,999 ECG beats from MIT-BIH arrhythmia dataset and transformed into 2D RGB images and used as the inputs of the DenseNet. The DenseNet is initialized with the trained weights on ImageNet and fine-tuned with the extracted beat images. Optimization of the pre-trained DenseNet is performed with the aids of on-the-fly augmentation, weighted random sampler, and Adam optimizer. The performance of the pre-trained model is assessed by hold-out evaluation and stratified 5-fold cross-validation techniques along with early stopping feature. The achieved accuracy of identifying normal and four arrhythmias are of 98.90% and 100% for the hold-out and stratified 5-fold respectively. The effectiveness of the pre-trained model with the stratified 5-fold by transfer learning approach is surpassed compared to the state-of-art-the approaches and models, and also explicit the maximum generalization of imbalanced classes.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127352697","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}

引用次数: 4

Research on the Methods of Speech Synthesis Technology 语音合成技术方法研究

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520718

Jinyao Hu, A. Hamdulla

引用次数: 0

A Mixing and Separation Method of Signals + Color Images Based on Two-Dimensional CCA 一种基于二维CCA的信号+彩色图像混合分离方法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520716

C. Kexin, Fan Liya, Yang Jing

引用次数: 0

Automatic Assessment of Facial Paralysis Based on Facial Landmarks 基于面部标志的面瘫自动评估

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520746

Yuxi Liu, Zhimin Xu, L. Ding, Jie Jia, Xiaomei Wu

{"title":"Automatic Assessment of Facial Paralysis Based on Facial Landmarks","authors":"Yuxi Liu, Zhimin Xu, L. Ding, Jie Jia, Xiaomei Wu","doi":"10.1109/PRML52754.2021.9520746","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520746","url":null,"abstract":"Unilateral peripheral facial paralysis is the most common case of facial paralysis. It affects only one side of the face, which will cause facial asymmetry. Clinically, unilateral peripheral facial paralysis is often classified by clinicians according to evaluation scales, based on patients’ condition of facial symmetry. A prevalent scale is House-Brackmann grading system (HBGS). However, assessment results from scales are often with great subjectivity, and will bring high interobserver and intraobserver variability. Therefore, this manuscript proposed an objective method to provide assessment results by using facial videos and applying machine learning models. This grading method is based on HBGS, but it is automatically implemented with high objectivity. Images with facial expressions will be extracted from the videos to be analyzed by a machine learning model. Facial landmarks will be acquired from the images by using a 68-points model provided by dlib. Then index and coordinate information of the landmarks will be used to calculate the values of features pre-designed to train the model and predict the result of new patients. Due to the difficulty of collecting facial paralysis samples, the data size is limited. Random Forest (RF) and support vector machine (SVM) were compared as the classifiers. This method was applied on a data set of 33 subjects. The highest overall accuracy rate reached 88.9%, confirming the effectiveness of this method.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122489309","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}

引用次数: 4