基于深度神经网络的立体腹腔镜图像自动三维点集重建

International Conference on Pervasive and Embedded Computing and Communication Systems Pub Date : 2016-07-25 DOI:10.5220/0006008001160121

B. Antal

{"title":"基于深度神经网络的立体腹腔镜图像自动三维点集重建","authors":"B. Antal","doi":"10.5220/0006008001160121","DOIUrl":null,"url":null,"abstract":"In this paper, an automatic approach to predict 3D coordinates from stereo laparoscopic images is presented. The approach maps a vector of pixel intensities to 3D coordinates through training a six layer deep neural network. The architectural aspects of the approach is presented and in detail and the method is evaluated on a publicly available dataset with promising results.","PeriodicalId":298357,"journal":{"name":"International Conference on Pervasive and Embedded Computing and Communication Systems","volume":"39 2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks\",\"authors\":\"B. Antal\",\"doi\":\"10.5220/0006008001160121\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an automatic approach to predict 3D coordinates from stereo laparoscopic images is presented. The approach maps a vector of pixel intensities to 3D coordinates through training a six layer deep neural network. The architectural aspects of the approach is presented and in detail and the method is evaluated on a publicly available dataset with promising results.\",\"PeriodicalId\":298357,\"journal\":{\"name\":\"International Conference on Pervasive and Embedded Computing and Communication Systems\",\"volume\":\"39 2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Conference on Pervasive and Embedded Computing and Communication Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5220/0006008001160121\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Pervasive and Embedded Computing and Communication Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5220/0006008001160121","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 4

摘要

提出了一种从立体腹腔镜图像中自动预测三维坐标的方法。该方法通过训练六层深度神经网络将像素强度向量映射到3D坐标。详细介绍了该方法的架构方面，并在公开可用的数据集上对该方法进行了评估，结果很有希望。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Automatic 3D Point Set Reconstruction from Stereo Laparoscopic Images using Deep Neural Networks

In this paper, an automatic approach to predict 3D coordinates from stereo laparoscopic images is presented. The approach maps a vector of pixel intensities to 3D coordinates through training a six layer deep neural network. The architectural aspects of the approach is presented and in detail and the method is evaluated on a publicly available dataset with promising results.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

International Conference on Pervasive and Embedded Computing and Communication Systems

自引率

0.00%

发文量