2021 IEEE International Conference on Image Processing Challenges (ICIPC)最新文献

筛选
英文 中文
Special Session Organizers 特别会议组织者
2021 IEEE International Conference on Image Processing Challenges (ICIPC) Pub Date : 2021-09-19 DOI: 10.1109/icipc53495.2021.9620191
{"title":"Special Session Organizers","authors":"","doi":"10.1109/icipc53495.2021.9620191","DOIUrl":"https://doi.org/10.1109/icipc53495.2021.9620191","url":null,"abstract":"","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124440620","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
ICIPC 2021 Cover Page ICIPC 2021封面
2021 IEEE International Conference on Image Processing Challenges (ICIPC) Pub Date : 2021-09-19 DOI: 10.1109/icipc53495.2021.9620188
{"title":"ICIPC 2021 Cover Page","authors":"","doi":"10.1109/icipc53495.2021.9620188","DOIUrl":"https://doi.org/10.1109/icipc53495.2021.9620188","url":null,"abstract":"","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126439671","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
Localizing Features with Masking for Satellite and Debris Classification 卫星和碎片分类的掩蔽特征定位
2021 IEEE International Conference on Image Processing Challenges (ICIPC) Pub Date : 2021-09-19 DOI: 10.1109/ICIPC53495.2021.9620178
Shubham Chaudhary, Parima Jain, V. Jakhetiya, Sharath Chandra Guntuku, B. Subudhi
{"title":"Localizing Features with Masking for Satellite and Debris Classification","authors":"Shubham Chaudhary, Parima Jain, V. Jakhetiya, Sharath Chandra Guntuku, B. Subudhi","doi":"10.1109/ICIPC53495.2021.9620178","DOIUrl":"https://doi.org/10.1109/ICIPC53495.2021.9620178","url":null,"abstract":"In this work, we propose a localization and masking-based satellite and debris classification technique. SPAce-craft Recognition leveraging Knowledge of space environment (SPARK) dataset consists of 120K images where both RGB and corresponding Depth images are available. However, the depth images are noisy and inaccurate and significantly affect the classification task performance. To address this issue, we first create mask images of the RGB images which are used as input to the Convolutional Neural Network (CNN) for efficient classification of different satellites and debris. The depth images are first de-noised and hole filled using a simple morphological opening operation. Then masked images are calculated using both RGB and processed depth images. This masking operation provides two advantages: 1. it removes noise and fills the holes in the depth images and 2. it highlights satellites and debris while suppressing other information which does not contribute towards the classification task. We use the pre-trained EfficientNet B4 architecture and fine-tuned it with an edition of Global average pooling (GAP) and three dense layers. Our results show that the inclusion of the masking operation significantly improves the overall classification performance, achieving 97.76% accuracy on the validation data.","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121557702","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
RGB-D Based Multimodal Convolutional Neural Networks for Spacecraft Recognition 基于RGB-D的多模态卷积神经网络航天器识别
2021 IEEE International Conference on Image Processing Challenges (ICIPC) Pub Date : 2021-09-19 DOI: 10.1109/ICIPC53495.2021.9620192
Nouar Aldahoul, H. A. Karim, Mhd Adel Momo
{"title":"RGB-D Based Multimodal Convolutional Neural Networks for Spacecraft Recognition","authors":"Nouar Aldahoul, H. A. Karim, Mhd Adel Momo","doi":"10.1109/ICIPC53495.2021.9620192","DOIUrl":"https://doi.org/10.1109/ICIPC53495.2021.9620192","url":null,"abstract":"Spacecraft recognition is a significant component of space situational awareness (SSA), especially for applications such as active debris removal, on-orbit servicing, and satellite formation. The complexity of recognition in actual space imagery is caused by a large diversity in sensing conditions, including background noise, low signal-to-noise ratio, different orbital scenarios, and high contrast. This paper addresses the previous problem and proposes multimodal convolutional neural networks (CNNs) for spacecraft detection and classification. The proposed solution includes two models: 1) a pre-trained ResNet50 CNN connected to a support vector machine (SVM) classifier for classification of RGB images. 2) an end-to-end CNN for classification of depth images. The experiments conducted on a novel SPARK dataset was generated under a realistic space simulation environment and has 150k of RGB images and 150k of depth images with 11 categories. The results show high performance of the proposed solution in terms of accuracy (89 %), F1 score (87 %), and Perf metric (1.8).","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127387922","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: 3
Spacecraft Recognition Leveraging Knowledge of Space Environment: Simulator, Dataset, Competition Design and Analysis 利用空间环境知识的航天器识别:模拟器、数据集、竞赛设计与分析
2021 IEEE International Conference on Image Processing Challenges (ICIPC) Pub Date : 2021-09-19 DOI: 10.1109/ICIPC53495.2021.9620184
M. A. Musallam, Vincent Gaudillière, Enjie Ghorbel, Kassem Al Ismaeil, M. Perez, Michel Poucet, Djamila Aouada
{"title":"Spacecraft Recognition Leveraging Knowledge of Space Environment: Simulator, Dataset, Competition Design and Analysis","authors":"M. A. Musallam, Vincent Gaudillière, Enjie Ghorbel, Kassem Al Ismaeil, M. Perez, Michel Poucet, Djamila Aouada","doi":"10.1109/ICIPC53495.2021.9620184","DOIUrl":"https://doi.org/10.1109/ICIPC53495.2021.9620184","url":null,"abstract":"SPARK represents the first edition of the SPAcecraft Recognition leveraging Knowledge of space environment competition organized by the Interdisciplinary Centre for Security, Reliability and Trust (SnT) in conjunction with the 2021 IEEE International Conference in Image Processing (ICIP 2021). By providing a unique synthetic dataset composed of 150k annotated multi-modal images, SPARK aims at encouraging researchers to develop innovative solutions for space target recognition and detection. This paper introduces the proposed dataset and provides a global analysis of the results obtained for the 17 submissions.","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123137243","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: 10
Spark Challenge: Multimodal Classifier for Space Target Recognition 星火挑战:空间目标识别的多模态分类器
2021 IEEE International Conference on Image Processing Challenges (ICIPC) Pub Date : 2021-09-19 DOI: 10.1109/ICIPC53495.2021.9620183
I. Lahouli, M. Jarraya, G. Aversano
{"title":"Spark Challenge: Multimodal Classifier for Space Target Recognition","authors":"I. Lahouli, M. Jarraya, G. Aversano","doi":"10.1109/ICIPC53495.2021.9620183","DOIUrl":"https://doi.org/10.1109/ICIPC53495.2021.9620183","url":null,"abstract":"In this paper, we propose a multi-modal framework to tackle the SPARK Challenge by classifying satellites using RGB and depth images. Our framework is mainly based on Auto-Encoders (AE)s to embed the two modalities in a common latent space in order to exploit redundant and complementary information between the two types of data.","PeriodicalId":246438,"journal":{"name":"2021 IEEE International Conference on Image Processing Challenges (ICIPC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121977617","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信