Minimum Description Length Based 2D Shape Description
Abstract
The problem of 2-D shape description, particularly with contour partitioning, grouping, and classification in terms of straight and curved, based on the minimum description length (MDL) criterion and shape-fitting techniques, is discussed. The MDL criterion is used to detect outliers in connection with shape fitting. Using the MDL criterion, it is possible to derive for a given data set and a class of models a description which best explains the data. A new algorithm for fitting 2-D points to an ellipse is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Li. "Minimum Description Length Based 2D Shape Description." IEEE/CVF International Conference on Computer Vision, 1993. doi:10.1109/ICCV.1993.378170Markdown
[Li. "Minimum Description Length Based 2D Shape Description." IEEE/CVF International Conference on Computer Vision, 1993.](https://mlanthology.org/iccv/1993/li1993iccv-minimum/) doi:10.1109/ICCV.1993.378170BibTeX
@inproceedings{li1993iccv-minimum,
title = {{Minimum Description Length Based 2D Shape Description}},
author = {Li, Mengxiang},
booktitle = {IEEE/CVF International Conference on Computer Vision},
year = {1993},
pages = {512-517},
doi = {10.1109/ICCV.1993.378170},
url = {https://mlanthology.org/iccv/1993/li1993iccv-minimum/}
}