Elliptical object detection by a modified ransac with sampling constraint from boundary Curves' clustering

Yingdi Xie*, Jun Ohya

*この研究の対応する著者

研究成果査読

5 被引用数 (Scopus)

抄録

This paper proposes a method for detecting ellipses from an image despite (1) multiple colors within the ellipses, (2) partially occluded ellipses' boundaries, (3) noisy, locally deformed boundaries of ellipses, (4) presence of multiple objects other than the ellipses in the image, and (5) combinations of (1) through (4). After boundary curves are obtained by edge detection, by utilizing the first-order difference curves of the edge orientation of each pixel in the boundary curves, a segment-reconnect method obtains boundary clusters. Then, a modified RANSAC detects ellipses by choosing five pixels randomly from the boundary clusters, where overlapped ellipses are merged. Experimental results using synthesized images and real images demonstrate the effectiveness of the proposed method together with comparison with the Randomized Hough Transform, a wellknown conventional method.

本文言語English
ページ(範囲)611-623
ページ数13
ジャーナルIEICE Transactions on Information and Systems
E93-D
3
DOI
出版ステータスPublished - 2010

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

フィンガープリント

「Elliptical object detection by a modified ransac with sampling constraint from boundary Curves' clustering」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル