Automated Fast Recognition and Location of Arbitrarily Shaped Objects by Image Morphology
Abstract
Morphological operations are used for segmentation, feature generation and location extraction. A recursive adaptive thresholding algorithm transforms a gray-level image into a set of multiple level regions of objects. A distance transformation algorithm then is used to transform a binary image into the minimum distance from each object point to the object's boundary. This algorithm uses a morphological erosion with a large structuring element which may correspond to Euclidean, city-block, or chessboard distance measures. A shape library database with hierarchical features is automatically generated. The features extracted are the shape number and the skeletal local-maximum points radii and coordinates. Object recognition is achieved by comparing the shape number and the hierarchical radii. Object location is detected by a hierarchical morphological bandpass filter.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cite
Text
Shih and Mitchell. "Automated Fast Recognition and Location of Arbitrarily Shaped Objects by Image Morphology." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1988. doi:10.1109/CVPR.1988.196322Markdown
[Shih and Mitchell. "Automated Fast Recognition and Location of Arbitrarily Shaped Objects by Image Morphology." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1988.](https://mlanthology.org/cvpr/1988/shih1988cvpr-automated/) doi:10.1109/CVPR.1988.196322BibTeX
@inproceedings{shih1988cvpr-automated,
title = {{Automated Fast Recognition and Location of Arbitrarily Shaped Objects by Image Morphology}},
author = {Shih, Frank Y. and Mitchell, Owen Robert},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {1988},
pages = {774-779},
doi = {10.1109/CVPR.1988.196322},
url = {https://mlanthology.org/cvpr/1988/shih1988cvpr-automated/}
}