International Conference on Images, Signals, and Computing最新文献_第2页

Highly reliable on-board computer software design and verification for space radiation 空间辐射高可靠性星载计算机软件设计与验证

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692006

Ping Wang, Xuemei Zhu, Gang Fang, J. Ma, guangcan mao

引用次数: 0

Methods of entity resolution in dataspaces 数据空间中实体解析的方法

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692046

Yuelin Jia, Wei Lu, Chang Su

引用次数: 0

A high-resolution image dehazing GAN model in icing meteorological environment 结冰气象环境下高分辨率图像去雾GAN模型

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691796

Xinling Yang, Wenjun Zhou, Chenglin Zuo, Yifan Wang, Bo Peng

引用次数: 0

Low-complexity moving object detection algorithm in dynamic background 动态背景下低复杂度运动目标检测算法

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692459

Y. Zheng, Yujun Li, Laibo Zheng, Q. Chen

引用次数: 0

3D point cloud target detection based on pseudo segmentation for autonomous driving 基于伪分割的自动驾驶三维点云目标检测

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2691803

Zixuan Zeng, Xi Luo, Jun Liu, Jules Karangwa

引用次数: 0

Computer vision based crystallization monitoring in automated laboratories 基于计算机视觉的自动化实验室结晶监测

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692822

Simon‐Johannes Burgdorf, T. Roddelkopf, A. Cooper, K. Thurow

{"title":"Computer vision based crystallization monitoring in automated laboratories","authors":"Simon‐Johannes Burgdorf, T. Roddelkopf, A. Cooper, K. Thurow","doi":"10.1117/12.2692822","DOIUrl":"https://doi.org/10.1117/12.2692822","url":null,"abstract":"Research into new functional materials has been ongoing on for many years. The successes are based on a classic trialand-error method. In the years that followed, various methods such as computer-aided calculations and high-throughput screening were added. Since the beginning of the 21st century, immense progress has been made in the field of artificial intelligence, which has since found its way into a wide variety of specialist disciplines and everyday life. With the advent of artificial intelligence in the research of new materials, there is hope for new results and savings in time and money. The approach presented here serves to monitor crystallization processes. Crystallization processes are used to evaporate new compositions of substances dissolved in a solvent. Evaporation produces crystals, which are then used for further investigations into the material properties. However, the crystallization process is very time-consuming and highly dependent on the solution and the environmental parameters. As a result, the timing of the process is difficult to predict and very lengthy. Therefore, this paper presents a method combines two areas, computer vision and artificial intelligence, and thus offers the possibility to monitor a crystallization process. The significant points, the start and end point, are detected, and the course of the crystallization process over time is also recorded. For this purpose, a pre-trained ResNet34 network is used, which has been trained on the characteristics of crystals through transfer learning, and a visual analyzer unit for in-situ sample acquisition. With this precise measurement setup, crystallization processes can be monitored and subsequently automated. This can save time and money and accelerate research into new materials.","PeriodicalId":361127,"journal":{"name":"International Conference on Images, Signals, and Computing","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124797696","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

Learning rate range test for the vision transformer 视觉变压器的学习速率范围测试

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692013

Rinka Kiriyama, A. Sashima, I. Shimizu

引用次数: 0

Improved k-means-based FAKM clustering method for scientific and technical literature 基于k-均值的改进科技文献FAKM聚类方法

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692027

Baosheng Yin, Meishu Zhao

{"title":"Improved k-means-based FAKM clustering method for scientific and technical literature","authors":"Baosheng Yin, Meishu Zhao","doi":"10.1117/12.2692027","DOIUrl":"https://doi.org/10.1117/12.2692027","url":null,"abstract":"Research on rapid clustering technology based on bibliographic information of scientific and technical literature aims to efficiently realize the correlation analysis of scientific and technical literature, laying the foundation for discovering hot spots and trends in the research field, conducting interdisciplinary and cross-border research, and accurately recommending scientific and technical literature. Focusing on the analysis of clustering algorithms, we proposed an improved k-meansbased Firefly Algorithm k-means (FAKM) clustering method, which effectively solved the problem of randomly selecting the initial center points of class cluster when using k-means algorithm for clustering in the clustering stage, which leads to local optimum, low accuracy and large gap between the division of class clusters and the real situation of clustering results. The use of FAKM clustering algorithm resulted in better clustering performance, high accuracy, and fewer iterations. The experimental results showed that the method achieved a silhouette coefficient of 0.54 and adjust mutual information of 0.69 on the same scientific and technical literature data set, which proved the good performance of the method.","PeriodicalId":361127,"journal":{"name":"International Conference on Images, Signals, and Computing","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133548007","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

License plate recognition using machine learning 使用机器学习识别车牌

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.3002521

Md. Tawsifur Rahman, Ahmed Nur Merag, Ali Muhtasim, Md. Wahidur Rahman Araf, Md Humaion Kabir Mehedi, Annajiat Alim Rasel

{"title":"License plate recognition using machine learning","authors":"Md. Tawsifur Rahman, Ahmed Nur Merag, Ali Muhtasim, Md. Wahidur Rahman Araf, Md Humaion Kabir Mehedi, Annajiat Alim Rasel","doi":"10.1117/12.3002521","DOIUrl":"https://doi.org/10.1117/12.3002521","url":null,"abstract":"Car owners altering license plates using different typefaces and designs violate the law that strictly forbids such behaviour. Traffic police officers claim that changing the license plates makes it impossible to read the registration numbers due to an increase in fatal street collisions and car thefts. They worry that it may be difficult to track down vehicles used in hit-and-run incidents. It is challenging to impose further limitations on any algorithm used to identify and recognise license plates in a developing nation like Bangladesh. This work has the primary objective of designing a reliable detection and recognition system for transitional, standard car license plates, which are frequently seen in developing countries. Increase the effectiveness of reading license plates drawn or printed in various styles and typefaces employing cuttingedge technology, including machine learning (ML) models. For this study, You Only Look Once (YOLOv3) is used to utilising the most recent version of the object detection method. The raw image is pre-processed to increase its quality and then divided into appropriate-sized grid cells to determine where the license plate should be placed. After that, the data is post-processed, and the accuracy of the proposed model is evaluted using industry-recognised standards. A sizeable image dataset was used to be tested using this proposed methodology. The presented system is expected to be essential for vehicle monitoring, parking fee collection, lowering traffic accidents, and identifying unregistered vehicles. The results demonstrate that the suggested method achieves 97.1% mAP, 95.3% precision and 96.8% in plate detection","PeriodicalId":361127,"journal":{"name":"International Conference on Images, Signals, and Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129128687","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

Global temporal pyramid for human abnormal action recognition 人类异常行为识别的全局时间金字塔

International Conference on Images, Signals, and Computing Pub Date : 2023-08-21 DOI: 10.1117/12.2692166

Shengnan Chen, Yuanyao Lu, Pengju Zhang, Yixian Fu

{"title":"Global temporal pyramid for human abnormal action recognition","authors":"Shengnan Chen, Yuanyao Lu, Pengju Zhang, Yixian Fu","doi":"10.1117/12.2692166","DOIUrl":"https://doi.org/10.1117/12.2692166","url":null,"abstract":"With the development of monitoring technology and the improvement of people's security awareness, intelligent human abnormal action recognition technology in the field of action recognition is increasingly high. In most cases, abnormal human action may have little difference in appearance compared with normal behavior, so the control of visual rhythm information becomes an important factor affecting action recognition, but people often focus on the appearance information of the action and ignore the rhythm information. In this paper, we introduce the temporal pyramid module to process the visual tempos information, meanwhile, the traditional LSTM local history information transfer method is very easy to lose the context information, which is not conducive to the grasp of global information and thus will greatly affect the processing effect of the temporal pyramid. This paper introduces a non-local neural network module to enhance the network's ability to grasp global information and the model's long-range modeling capability, which is used to supplement the temporal pyramid module. Finally, this paper uses the mainstream anomaly dataset UCF-Crime to test the network performance, and the improved network model recognition accuracy AUC reaches 0.82, which is better than other stateof-the-art methods.","PeriodicalId":361127,"journal":{"name":"International Conference on Images, Signals, and Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130519724","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