Improving Detection of Deformable Objects in Volumetric Data
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.77
Abstract
In this paper, we investigate class level object detection of deformable objects. To this end, we aim for cell detection in volumetric images of dense plant tissue (Arabidopsis Thaliana), obtained from a confocal laser scanning microscope. In 3D volumetric data, the detection model does not have to deal with scale, occlusion and viewpoint dependent changes of the appearance, however, our application needs high recall and precision. We implement Felsenszwalb's Deformable Part Model for volumetric data. Corresponding locations for part training are obtained via elastic registration. We identify limitations of its star shaped deformation model and show that a pairwise connected detection model can outperform the DPM in this setting.
Session
Poster Session
Files
Extended Abstract (PDF, 1 page, 984K)Paper (PDF, 12 pages, 2.2M)
Bibtex File
Citation
Dominic Mai, and Olaf Ronneberger. Improving Detection of Deformable Objects in Volumetric Data. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.77
title = {Improving Detection of Deformable Objects in Volumetric Data},
author = {Mai, Dominic and Ronneberger, Olaf},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.77 }
}