**Wavelet-Based Circular Hough Transform and its Application in Embryo Development Analysis**

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.

BibTeX:
```
@misc{Cicconet2013,
author = {M. Cicconet and D. Geiger and K. Gunsalus},
title = {Wavelet-Based Circular Hough Transform
and its Application in Embryo Developmental Analysis},
howpublished = {8th International Conference
on Computer Vision Theory and Applications},
year = {2013},
note = {Barcelona, Spain}
}
```