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

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

A Camera-Aware Three-Stage Method for Fully Unsupervised Person Re-identification 一种摄像机感知的完全无监督人员再识别三阶段方法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520689

Guyu Fang, Hongtao Lu

引用次数: 1

Research on Pre-training of Tibetan Natural Language Processing 藏文自然语言处理的预训练研究

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520714

Zhensong Li, Jie Zhu, Hong Cao

引用次数: 1

A VideoSAR Moving Target Detection Method Based on GMM 基于GMM的视频sar运动目标检测方法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520711

Meng Yan, L. Li, Haochuan Chen

引用次数: 0

An Improved Bounding Box Regression Loss Function Based on CIOU Loss for Multi-scale Object Detection 基于CIOU损失的改进边界盒回归损失函数用于多尺度目标检测

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520717

Shuangjiang Du, Baofu Zhang, Pin Zhang, Peng Xiang

{"title":"An Improved Bounding Box Regression Loss Function Based on CIOU Loss for Multi-scale Object Detection","authors":"Shuangjiang Du, Baofu Zhang, Pin Zhang, Peng Xiang","doi":"10.1109/PRML52754.2021.9520717","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520717","url":null,"abstract":"The regression loss function is a key factor in the training and optimization process of object detection. The current mainstream regression loss functions are Ln norm loss, IOU loss and CIOU loss. This paper proposes the Scale-Sensitive IOU(SIOU) loss, a new loss function different from the above all, which could solve the issues that the current loss functions cannot distinguish the two bounding boxes in some special cases when the target area scales in one image vary greatly during training process, thereby leading to the improper regression loss calculation and the slowing down of the optimization. An area scale regulating factor Y is added on the basis of CIOU loss to adjust the loss values of the bounding boxes, which could distinguish all the boxes quantitatively in theory thus gets a faster converging speed and better optimization. Through analysis and simulation comparison among the several loss functions, the superiority of SIOU loss is verified. Furthermore, by incorporating SIOU loss into YOLO v4, Faster R-CNN and SSD on the two mainstream aerial remote sensing datasets, i.e., DIOR and NWPU VHR-10, the detection precisions improve by 10.2% than IOU loss and 2.8% than CIOU loss respectively.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"12 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":"124215119","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: 12

INFIN: An Efficient Algorithm for Fast Mining Frequent Itemsets 一种快速挖掘频繁项集的有效算法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520736

Shaopeng Wang, Yufei Wang, Chunkai Feng, ChaoYu Niu

{"title":"INFIN: An Efficient Algorithm for Fast Mining Frequent Itemsets","authors":"Shaopeng Wang, Yufei Wang, Chunkai Feng, ChaoYu Niu","doi":"10.1109/PRML52754.2021.9520736","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520736","url":null,"abstract":"The negFIN is the current state-of-the art algorithm for frequent itemsets mining. It employs a novel BMC (bitmap code) encoding model for nodes in a prefix tree based on the bitmap representation of sets. The encoding of each node is a binary number of which bit number is the number of frequent items, and is stored in the form of decimal integer number. The key operations of negFIN are all performed based on the bitwise operation of the encoding. The main problem of BMC is that the maximal bit number of the data type which is used to store the decimal integer number in current general compiling systems is 64, so if the number of frequent items exceeds 64, the encoding cannot work effectively. In this work, we propose B-BMC (block bitmap code) encoding model, a more efficient encoding model. The B-BMC is a dividing of BMC based on the block size in essential. For facilitating the work of B-BMC, the B-BMC tree and TNC(terminal node code) table are devised as an alternative to the BMC tree of negFIN. Based on these two structures, we present an efficient algorithm called INFIN (improved negFIN) to mining frequent itemsets. Our experiments illustrate that the B-BMC can overcome the drawback of BMC, and the INFIN is the most efficient one in time and space when the block size takes value 64 on condition that the number of frequent items exceeds 64.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"22 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":"131938185","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: 1

Anti-Corner Reflector Array Method Based on Pauli Polarization Decomposition and BP Neural Network 基于泡利极化分解和BP神经网络的反角反射器阵列方法

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520744

Liang Ziyao, Yu Yong, Zhang Bin

{"title":"Anti-Corner Reflector Array Method Based on Pauli Polarization Decomposition and BP Neural Network","authors":"Liang Ziyao, Yu Yong, Zhang Bin","doi":"10.1109/PRML52754.2021.9520744","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520744","url":null,"abstract":"The radar echoes of the corner reflector array and the ship target are very similar, and the existing algorithms are difficult to identify them effectively in time, frequency and spatial domain. Aiming at the problem that the terminal guidance radar of anti-ship missile can’t detect and track the real target effectively under the deception jamming of corner reflector array, this paper designs a countermeasure method based on Pauli polarization decomposition and BP neural network. Firstly, the Pauli polarization decomposition of the full polarization scattering matrix of the target measured in the fixed angle window is used to obtain four normalized coefficients and form the eigenvector, and the differences between the ship target and the corner reflector are analyzed. Then, the BP neural network model is trained and optimized as the training sample. The simulation and test results show that the feature vectors can distinguish the two kinds of targets, and the trained network can identify the ship and the corner reflector Array effectively, and the overall success rate is close to 97%.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"202 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":"134480157","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: 1

Tree-Based Models Using Random Grid Search Optimization for Disease Classification Based on Environmental Factors: A Case Study on Asthma Hospitalizations 基于环境因素的随机网格搜索优化疾病分类树模型:以哮喘住院病例为例

2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML) Pub Date : 2021-07-16 DOI: 10.1109/PRML52754.2021.9520720

P. Nanthakumaran, L. Liyanage

{"title":"Tree-Based Models Using Random Grid Search Optimization for Disease Classification Based on Environmental Factors: A Case Study on Asthma Hospitalizations","authors":"P. Nanthakumaran, L. Liyanage","doi":"10.1109/PRML52754.2021.9520720","DOIUrl":"https://doi.org/10.1109/PRML52754.2021.9520720","url":null,"abstract":"An understanding on the exposure to environmental factors aggravating global disease burden can aid mitigating it. Generally, a class of generalized linear models and generalized additive models are used in predicting disease burden whereas, tree-based models are underused. The objective of this paper is to evaluate the performance of different tree-based models namely decision tree, random forest, gradient boosted tree and stochastic gradient boosted trees in predicting asthma attack based on short-term exposure to environmental factors and to examine the environmental factors triggering asthma attack. A sample of patients during 2013 - 2015 from different parts of Victoria was considered. The study area for the considered study period had reasonably good air quality and relatively humid environment. The tree-based models were tuned using random grid search optimization with bootstrapping to address over-fitting. The models considered performed well in predicting asthma attacks in terms of area under the receiver operating curve (ROC AUC) (>0.82). All the gradient boosted trees (accuracy = 76%; recall = 63%; F2-score = 64%) showed better overall prediction whereas decision tree (accuracy = 71%; recall = 75%; F2-score = 71%) outperformed other models in identifying the positive cases. Tree-based models revealed that O3 exposure consistently influence Asthma. Further, decision tree revealed O3 exposure < 13 ppb or with high O3 exposure >= 13 ppb, and with [SO2 exposure < 0.5 ppb and maximum wind speed > 5.4. km/hr.] influenced Asthma. In addition, relative humidity and exposure to CO were also detected in other tree-based models as relevant predictors triggering asthma attacks.","PeriodicalId":429603,"journal":{"name":"2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML)","volume":"1 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":"131194625","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