Wavelet-based circular hough transform and its application in embryo development analysis

Marcelo Cicconet, Davi Geiger, Kris Gunsalus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Detecting object shapes from images remains a challenging problem in computer vision, especially in cases where some a priori knowledge of the shape of the objects of interest exists (such as circle-like shapes) and/or multiple object shapes overlap. This problem is important in the field of biology, particularly in the area of early-embryo development, where the dynamics is given by a set of cells (nearly-circular shapes) that overlap and eventually divide. We propose an approach to this problem that relies mainly on a variation of the circular Hough Transform where votes are weighted by wavelet kernels, and a fine-tuning stage based on dynamic programming. The wavelet-based circular Hough transform can be seen as a geometric-driven pulling mechanism in a set of convolved images, thus having important connections with well-stablished machine learning methods such as convolution networks.

Original languageEnglish (US)
Title of host publicationVISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
Pages669-674
Number of pages6
StatePublished - 2013
Event8th International Conference on Computer Vision Theory and Applications, VISAPP 2013 - Barcelona, Spain
Duration: Feb 21 2013Feb 24 2013

Publication series

NameVISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications
Volume1

Other

Other8th International Conference on Computer Vision Theory and Applications, VISAPP 2013
CountrySpain
CityBarcelona
Period2/21/132/24/13

Keywords

  • Biological cells
  • Circular hough transform
  • Machine vision

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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