{"title":"Robust Implementation of Coplanarity-Based Method for Camera Pose Estimation","authors":"Ye. V. Goshin","doi":"10.3103/S1060992X24700541","DOIUrl":null,"url":null,"abstract":"<p>In this paper, we consider a method for estimating camera motion parameters from images acquired from this camera, which is based on the use of vector coplanarity estimation. It has been previously shown that the proposed approach can be effectively applied to three-dimensional scenes invariant to their depth. However, due to the criterion used, it is difficult to utilize the RANSAC method to ensure the robustness of the developed method. In this paper, an approach based on the minimum covariance determinant estimation method is proposed. The proposed approach allows us to select the most consistent observations and make an estimation based on these observations. An experimental study of the proposed approach on synthetic data has been carried out. It is shown that the proposed algorithm can provide a significant increase in the reliability of motion parameters determination even in conditions of a small number of corresponding points</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"33 2 supplement","pages":"S261 - S269"},"PeriodicalIF":1.0000,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optical Memory and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.3103/S1060992X24700541","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we consider a method for estimating camera motion parameters from images acquired from this camera, which is based on the use of vector coplanarity estimation. It has been previously shown that the proposed approach can be effectively applied to three-dimensional scenes invariant to their depth. However, due to the criterion used, it is difficult to utilize the RANSAC method to ensure the robustness of the developed method. In this paper, an approach based on the minimum covariance determinant estimation method is proposed. The proposed approach allows us to select the most consistent observations and make an estimation based on these observations. An experimental study of the proposed approach on synthetic data has been carried out. It is shown that the proposed algorithm can provide a significant increase in the reliability of motion parameters determination even in conditions of a small number of corresponding points
期刊介绍：
The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
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