2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)最新文献_第3页

A robust framework for mathematical formula detection 数学公式检测的鲁棒框架

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585197

M. Tran, Tri Pham, Tien Nguyen, Tien Do, T. Ngo

{"title":"A robust framework for mathematical formula detection","authors":"M. Tran, Tri Pham, Tien Nguyen, Tien Do, T. Ngo","doi":"10.1109/MAPR53640.2021.9585197","DOIUrl":"https://doi.org/10.1109/MAPR53640.2021.9585197","url":null,"abstract":"Mathematical formulas identification is a crucial step in the pipeline of many tasks such as mathematical information retrieval, storing digital science documents, etc. For basic mathematical formulas recognition, all these tasks need to detect the bounding boxes of mathematical expression as a prerequisite step. Currently, deep learning-based object detection methods work well for mathematical formula detection (MFD). These methods are divided into two categories: anchor self-study and anchor not self-study. The anchor self-study method is efficient with large quantity labels but not so well with small quantities, whereas the second type of method works better with small quantities. Therefore, we proposed an algorithm that keeps the good prediction of each type and then merges both into final results. To demonstrate the hypothesis, we select two typical object detection methods: YOLOv5 and Faster RCNN as the representation of two kind approaches to building an MFD framework. Our experiment results on ICDAR2021-MFD1 achieved the F1 score of the whole system is 89.3 while the single detector just reached 74.2, 88.9 (Faster RCNN and YOLOv5 respectively) that proving the effectiveness of the proposal.","PeriodicalId":233540,"journal":{"name":"2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126534853","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

Boundary delineation of reflux esophagitis lesions from endoscopic images using color and texture 内镜图像中反流性食管炎病变边界的颜色和纹理划分

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585290

Danh H. Vu, Long-Thuy Nguyen, Van-Tuan Nguyen, Thanh-Hai Tran, V. Dao, Hai Vu

{"title":"Boundary delineation of reflux esophagitis lesions from endoscopic images using color and texture","authors":"Danh H. Vu, Long-Thuy Nguyen, Van-Tuan Nguyen, Thanh-Hai Tran, V. Dao, Hai Vu","doi":"10.1109/MAPR53640.2021.9585290","DOIUrl":"https://doi.org/10.1109/MAPR53640.2021.9585290","url":null,"abstract":"Automatic assessment of medical images and endoscopic images in particular is an attractive research topic recent years. To achieve this goal, many tasks must be conducted for example lesions detection, segmentation and classification. In order to design suitable models for such tasks, it would be preferable to know at first: i) which characteristics that differentiate a lesion from a normal region; ii) how large is the boundary of these two regions that still allows to distinguish them. This paper presents an in-depth study of the role of color and texture features for delineation of boundary between a lesion region and a background region. To this end, from the groundtruth contour of a manually segmented lesion, we first expand two margins in two directions. We name inner margin in the lesion region and outer margin in the background region. We then extract color dependent features in different color spaces (HSV, RGB, Lab) and texture features (LBP, HOG, GLCM) on these two margins. Finally we deploy the Support Vector Machine (SVM) technique to classify two classes (lesion and non-lesion). Extensive experiments conducted on a dataset of endoscopic images answer to our aforementioned questions and give some suggestions for designing suitable models of lesion detection in the future.","PeriodicalId":233540,"journal":{"name":"2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124995559","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

Unweighted Bipartite Matching For Robust Vehicle Counting 鲁棒车辆计数的非加权二部匹配

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585273

Khanh Ho, H. Le, K. Nguyen, Thua Nguyen, Tien Do, T. Ngo, Thanh-Son Nguyen

{"title":"Unweighted Bipartite Matching For Robust Vehicle Counting","authors":"Khanh Ho, H. Le, K. Nguyen, Thua Nguyen, Tien Do, T. Ngo, Thanh-Son Nguyen","doi":"10.1109/MAPR53640.2021.9585273","DOIUrl":"https://doi.org/10.1109/MAPR53640.2021.9585273","url":null,"abstract":"Intelligent Transportation System (ITS) plays an essential role in smart cities. Through ITS, local authorities could handle enormous traffic flows with minimal effort and solve traffic-related problems such as traffic congestion or traffic regulation violating behaviours. In this work, we designed a system that has the ability to count vehicles moving in specific directions on the road. Such automated systems also have to deal with the diverse weather and instabilities in captured media, making current tracking algorithms become prone to errors. This problem is even more challenging in Vietnam and other developing countries, where traffic on the road is much more complex with the presence of small vehicles such as bicycles and motorbikes, thus tracking algorithms would be more likely to fail. Our proposed method for Track Joining was built on top of deepSORT, incorporating Taylor Expansion and Unweighted Bipartite Maximum Matching to predict missing movements or identify duplicated vehicle tracks, then attempt to merge them. In HCMC AI City Challenge 20201, our whole system outperforms other approaches by achieving the lowest overall RMSE score: an average of 1.39 fails per video segment on a benchmark dataset.","PeriodicalId":233540,"journal":{"name":"2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131827252","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

Exploring Zero-shot Cross-lingual Aspect-based Sentiment Analysis using Pre-trained Multilingual Language Models 使用预训练的多语言模型探索零概率跨语言基于方面的情感分析

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585242

Khoa Thi-Kim Phan, D. Hao, D. Thin, N. Nguyen

引用次数: 8

Siamese Attention and Point Adaptive Network for Visual Tracking 视觉跟踪的连体注意和点自适应网络

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585250

T. Dinh, Long Tran Quoc, Kien Thai Trung

引用次数: 0

Visual-guided audio source separation: an empirical study 视觉引导音频源分离的实证研究

2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) Pub Date : 2021-10-01 DOI: 10.1109/MAPR53640.2021.9585244

Thanh Thi Hien Duong, Huu Manh Nguyen, Hai Nghiem Thi, Thi-Lan Le, Phi-Le Nguyen, Q. Nguyen

引用次数: 3