A Fully Statistical Framework for Shape Detection in Image Primitives
Abstract
We present a fully statistical framework for detecting pre-determined shape classes in 2D clouds of primitives (points, edges, and arcs), which are in turn extracted from images. An important goal is to provide a likelihood, and thus a confidence, of finding a shape class in a given data. This requires a model-based approach. We use a composite Poisson process: 1D Poisson process for primitives belonging to shapes and a 2D Poisson process for primitives belonging to clutter. An additive Gaussian model is assumed for noise in shape primitives. Combining these with a past stochastic model on shapes of continuous 2D contours, and optimization over unknown pose and scale, we develop a generalized likelihood ratio test for shape detection. We demonstrate the efficiency of this method and its robustness to clutter using both simulated and real data.
Cite
Text
Su et al. "A Fully Statistical Framework for Shape Detection in Image Primitives." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010. doi:10.1109/CVPRW.2010.5543730Markdown
[Su et al. "A Fully Statistical Framework for Shape Detection in Image Primitives." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2010.](https://mlanthology.org/cvprw/2010/su2010cvprw-fully/) doi:10.1109/CVPRW.2010.5543730BibTeX
@inproceedings{su2010cvprw-fully,
title = {{A Fully Statistical Framework for Shape Detection in Image Primitives}},
author = {Su, Jingyong and Zhu, Zhiqiang and Srivastava, Anuj and Huffer, Fred W.},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2010},
pages = {17-24},
doi = {10.1109/CVPRW.2010.5543730},
url = {https://mlanthology.org/cvprw/2010/su2010cvprw-fully/}
}