G. Dong, T. I. Baskin, and K. Palaniappan

IEEE Int. Conf. Image Processing, pgs. 1245--1248, 2006

cell detection, cell segmentation, active contours, cell tracking, biomedical

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Abstract

In this paper,a new method for motion flow estimation that considers errors in all the derivative measurements is presented.Basedonthetotalleastsquares(TLS)model,we accurately estimate the motion flow in the general noise case by combining noise model (in form of covariance matrix) with a parametric motion model. The proposed algorithm is tested on two different types of biological motion, a growing plant root and a gastrulating embryo, with sequences obtained microscopically. The local, instantaneous velocity field estimated by the algorithm revealsthebehavioroftheunderlyingcellularelements.

Reference

@inproceedings{GDong:ICIP-2006,
  author = "G. Dong and T. I. Baskin and K. Palaniappan",
  title = "Motion flow estimation from image sequences with applications to biological growth and motility",
  year = 2006,
  booktitle = "IEEE Int. Conf. Image Processing",
  pages = "1245--1248",
  keywords = "cell detection, cell segmentation, active contours, cell tracking, biomedical",
  doi = "10.1109/ICIP.2006.312551"
}

Plain Text

G. Dong, T. I. Baskin, and K. Palaniappan. Motion flow estimation from image sequences with applications to biological growth and motility. IEEE Int. Conf. Image Processing, pages 1245--1248, 2006.

#61: Motion flow estimation from image sequences with applications to biological growth and motility

cell detection, cell segmentation, active contours, cell tracking, biomedical

Abstract

Reference

Plain Text