Color object segmentation with eigen-based fuzzy C-means

Jar Ferr Yang, Shu Sheng Hao, Pau Choo Chung, Chich Ling Huang

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

In this paper, we propose an eigen-based fuzzy C-means (FCM) method for color object segmentation. After sampling a few color samples, we can form the sampled covariance matrix and its related eigenvectors of the desired color space. Then, we transform the original color space into signal and noise planes of the desired color. Followed the transformation, the proposed eigen-based FCM algorithm is finally applied to the signal and noise subspaces individually. After few iterated classification processes, the desired color objects can be easily identified without using any threshold procedure. Inspecting the segmented results, the desired color objects without any pre- and post-processes can be extracted easily and robustly.

Original languageEnglish
Pages (from-to)V-25-V-28
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume5
Publication statusPublished - 2000 Jan 1
EventProceedings of the IEEE 2000 Internaitonal Symposium on Circuits and Systems - Geneva, Switz
Duration: 2000 May 292000 May 31

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Color
Covariance matrix
Eigenvalues and eigenfunctions
Sampling

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

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title = "Color object segmentation with eigen-based fuzzy C-means",
abstract = "In this paper, we propose an eigen-based fuzzy C-means (FCM) method for color object segmentation. After sampling a few color samples, we can form the sampled covariance matrix and its related eigenvectors of the desired color space. Then, we transform the original color space into signal and noise planes of the desired color. Followed the transformation, the proposed eigen-based FCM algorithm is finally applied to the signal and noise subspaces individually. After few iterated classification processes, the desired color objects can be easily identified without using any threshold procedure. Inspecting the segmented results, the desired color objects without any pre- and post-processes can be extracted easily and robustly.",
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Color object segmentation with eigen-based fuzzy C-means. / Yang, Jar Ferr; Hao, Shu Sheng; Chung, Pau Choo; Huang, Chich Ling.

In: Proceedings - IEEE International Symposium on Circuits and Systems, Vol. 5, 01.01.2000, p. V-25-V-28.

Research output: Contribution to journalConference article

TY - JOUR

T1 - Color object segmentation with eigen-based fuzzy C-means

AU - Yang, Jar Ferr

AU - Hao, Shu Sheng

AU - Chung, Pau Choo

AU - Huang, Chich Ling

PY - 2000/1/1

Y1 - 2000/1/1

N2 - In this paper, we propose an eigen-based fuzzy C-means (FCM) method for color object segmentation. After sampling a few color samples, we can form the sampled covariance matrix and its related eigenvectors of the desired color space. Then, we transform the original color space into signal and noise planes of the desired color. Followed the transformation, the proposed eigen-based FCM algorithm is finally applied to the signal and noise subspaces individually. After few iterated classification processes, the desired color objects can be easily identified without using any threshold procedure. Inspecting the segmented results, the desired color objects without any pre- and post-processes can be extracted easily and robustly.

AB - In this paper, we propose an eigen-based fuzzy C-means (FCM) method for color object segmentation. After sampling a few color samples, we can form the sampled covariance matrix and its related eigenvectors of the desired color space. Then, we transform the original color space into signal and noise planes of the desired color. Followed the transformation, the proposed eigen-based FCM algorithm is finally applied to the signal and noise subspaces individually. After few iterated classification processes, the desired color objects can be easily identified without using any threshold procedure. Inspecting the segmented results, the desired color objects without any pre- and post-processes can be extracted easily and robustly.

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