Computing Diffeomorphic Paths for Large Motion Interpolation.

Dohyung Seo, Ho Jeffrey, Baba C Vemuri
{"title":"Computing Diffeomorphic Paths for Large Motion Interpolation.","authors":"Dohyung Seo,&nbsp;Ho Jeffrey,&nbsp;Baba C Vemuri","doi":"10.1109/CVPR.2013.162","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, we introduce a novel framework for computing a path of diffeomorphisms between a pair of input diffeomorphisms. Direct computation of a geodesic path on the space of diffeomorphisms <i>Diff</i>(Ω) is difficult, and it can be attributed mainly to the infinite dimensionality of <i>Diff</i>(Ω). Our proposed framework, to some degree, bypasses this difficulty using the quotient map of <i>Diff</i>(Ω) to the quotient space <i>Diff</i>(<i>M</i>)/<i>Diff</i>(<i>M</i>) <sub><i>μ</i></sub> obtained by quotienting out the subgroup of volume-preserving diffeomorphisms <i>Diff</i>(<i>M</i>) <sub><i>μ</i></sub> . This quotient space was recently identified as the unit sphere in a Hilbert space in mathematics literature, a space with well-known geometric properties. Our framework leverages this recent result by computing the diffeomorphic path in two stages. First, we project the given diffeomorphism pair onto this sphere and then compute the geodesic path between these projected points. Second, we lift the geodesic on the sphere back to the space of diffeomerphisms, by solving a quadratic programming problem with bilinear constraints using the augmented Lagrangian technique with penalty terms. In this way, we can estimate the path of diffeomorphisms, first, staying in the space of diffeomorphisms, and second, preserving shapes/volumes in the deformed images along the path as much as possible. We have applied our framework to interpolate intermediate frames of frame-sub-sampled video sequences. In the reported experiments, our approach compares favorably with the popular Large Deformation Diffeomorphic Metric Mapping framework (LDDMM).</p>","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2013 ","pages":"1227-1232"},"PeriodicalIF":0.0000,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2013.162","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CVPR.2013.162","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

In this paper, we introduce a novel framework for computing a path of diffeomorphisms between a pair of input diffeomorphisms. Direct computation of a geodesic path on the space of diffeomorphisms Diff(Ω) is difficult, and it can be attributed mainly to the infinite dimensionality of Diff(Ω). Our proposed framework, to some degree, bypasses this difficulty using the quotient map of Diff(Ω) to the quotient space Diff(M)/Diff(M) μ obtained by quotienting out the subgroup of volume-preserving diffeomorphisms Diff(M) μ . This quotient space was recently identified as the unit sphere in a Hilbert space in mathematics literature, a space with well-known geometric properties. Our framework leverages this recent result by computing the diffeomorphic path in two stages. First, we project the given diffeomorphism pair onto this sphere and then compute the geodesic path between these projected points. Second, we lift the geodesic on the sphere back to the space of diffeomerphisms, by solving a quadratic programming problem with bilinear constraints using the augmented Lagrangian technique with penalty terms. In this way, we can estimate the path of diffeomorphisms, first, staying in the space of diffeomorphisms, and second, preserving shapes/volumes in the deformed images along the path as much as possible. We have applied our framework to interpolate intermediate frames of frame-sub-sampled video sequences. In the reported experiments, our approach compares favorably with the popular Large Deformation Diffeomorphic Metric Mapping framework (LDDMM).

计算大运动插值的微分同构路径。
在本文中,我们引入了一种新的框架来计算一对输入微同态之间的微同态路径。在差分同态空间Diff(Ω)上直接计算测地线路径是困难的,这主要归因于Diff的无限维数(Ω)。我们提出的框架在一定程度上绕过了这一困难,使用Diff(Ω)到商空间Diff(M)/Diff(M) μ的商映射,该映射是通过商出保容微分同态的子群Diff(M) μ得到的。这个商空间最近在数学文献中被认定为希尔伯特空间中的单位球,希尔伯特空间具有众所周知的几何性质。我们的框架通过分两个阶段计算微分同构路径来利用这个最新的结果。首先,我们将给定的微分同构对投影到这个球体上,然后计算投影点之间的测地线路径。其次,利用带惩罚项的增广拉格朗日技术求解一个双线性约束的二次规划问题,将球面上的测地线抬升回微分异构空间。这样,我们可以估计出微分同态的路径,首先,停留在微分同态的空间中,其次,尽可能地保留沿路径的形变图像中的形状/体积。我们已经应用我们的框架来插值帧次采样视频序列的中间帧。在报道的实验中,我们的方法与流行的大变形微分同构度量映射框架(LDDMM)相比具有优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
43.50
自引率
0.00%
发文量
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×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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