| Paper: | IMDSP-L5.5 | ||
| Session: | Image Segmentation | ||
| Time: | Wednesday, May 17, 15:20 - 15:40 | ||
| Presentation: | Lecture | ||
| Topic: | Image and Multidimensional Signal Processing: Image Segmentation | ||
| Title: | Statistical region-based active contours with exponential family observations | ||
| Authors: | François Lecellier, Stéphanie Jehan-Besson, Jalal Fadili, GREYC UMR 6072 CNRS, France; Gilles Aubert, Laboratoire J.A Dieudonne, UMR 6621 CNRS, France; Marinette Revenu, GREYC UMR 6072 CNRS, France | ||
| Abstract: | In this paper, we focus on statistical region-based active contour models where image features (e.g. intensity) are random variables whose distribution belongs to some parametric family (e.g. exponential) rather than confining ourselves to the special Gaussian case. Using shape derivation tools, our effort focuses on constructing a general expression for the derivative of the energy (with respect to a domain) and derive the corresponding evolution speed. A general result is stated within the framework of multi-parameter exponential family. More particularly, when using Maximum Likelihood estimators, the evolution speed has a closed-form expression that depends simply on the probability density function, while complicating additive terms appear when using other estimators, e.g. moments method. Experimental results on both synthesized and real images demonstrate the applicability of our approach. | ||
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