Groupwise Non-rigid Image Alignment With Graph-based Initialisation
| dc.contributor.author | Aal-Yhia, Ahmad | en_US |
| dc.contributor.author | Malcolm, Paul | en_US |
| dc.contributor.author | Akanyeti, Otar | en_US |
| dc.contributor.author | Zwiggelaar, Reyer | en_US |
| dc.contributor.author | Tiddeman, Bernard | en_US |
| dc.contributor.editor | {Tam, Gary K. L. and Vidal, Franck | en_US |
| dc.date.accessioned | 2018-09-19T15:15:01Z | |
| dc.date.available | 2018-09-19T15:15:01Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Groupwise image alignment automatically provides non-rigid registration across a set of images. It has found applications in facial image analysis and medical image analysis by automatically generating statistical models of shape and appearance. The main approaches used previously include iterative and graph-based approaches. In iterative approaches, the registration of each image is iteratively updated to minimise an error measure across the set. Various metrics and optimisation strategies have been proposed to achieve this. Graph-based methods perform registration of each pair of images in the set, to form a weighted graph of the ''distance'' between all the images, and then finds the optimal paths between the most central image and every other image. In this paper, we use a graph-based approach to perform initialisation, which is then refined with an iterative approach. Pairwise registration is performed using demons registration, then shortest paths identified in the resulting graph are used to provide an initial warp for each image by concatenating warps along the path. The warps are refined using an iterative Levenberg-Marquardt minimisation to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimisation approach is efficient, has very few free parameters to tune and we show how to tune the few remaining parameters. We compare the combined approach to both the iterative and graph-based approaches used independently. Results demonstrate that the combined method improves the alignment of various datasets, including two face datasets and a difficult medical dataset of prostate MRI images. | en_US |
| dc.description.sectionheaders | Vision and Learning | |
| dc.description.seriesinformation | Computer Graphics and Visual Computing (CGVC) | |
| dc.identifier.doi | 10.2312/cgvc.20181203 | |
| dc.identifier.isbn | 978-3-03868-071-0 | |
| dc.identifier.pages | 15-21 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/cgvc20181203 | |
| dc.identifier.uri | https://doi.org/10.2312/cgvc.20181203 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | Computing methodologies | |
| dc.subject | Computer vision | |
| dc.subject | Matching | |
| dc.subject | Image processing | |
| dc.title | Groupwise Non-rigid Image Alignment With Graph-based Initialisation | en_US |
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