Color object segmentation with eigen-based fuzzy C-means

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

doi:10.1109/ISCAS.2000.857354

Color object segmentation with eigen-based fuzzy C-means

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

Institute of Computer and Communication Engineering

Research output: Contribution to journal › Conference article › peer-review

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 language	English
Pages (from-to)	V-25-V-28
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	5
DOIs	https://doi.org/10.1109/ISCAS.2000.857354
Publication status	Published - 2000
Event	Proceedings of the IEEE 2000 International Symposium on Circuits and Systems, ISCAS 2000 - Geneva, Switz, Switzerland Duration: 2000 May 28 → 2000 May 31

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

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10.1109/ISCAS.2000.857354

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@article{85e20b904e2d438f9ab89eeb24172c4b,

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.",

author = "Yang, {Jar Ferr} and Hao, {Shu Sheng} and Chung, {Pau Choo} and Huang, {Chich Ling}",

year = "2000",

doi = "10.1109/ISCAS.2000.857354",

language = "English",

volume = "5",

pages = "V--25--V--28",

journal = "Proceedings - IEEE International Symposium on Circuits and Systems",

issn = "0271-4310",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings of the IEEE 2000 International Symposium on Circuits and Systems, ISCAS 2000 ; Conference date: 28-05-2000 Through 31-05-2000",

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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

Y1 - 2000

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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U2 - 10.1109/ISCAS.2000.857354

DO - 10.1109/ISCAS.2000.857354

M3 - Conference article

AN - SCOPUS:18144444375

SN - 0271-4310

VL - 5

SP - V-25-V-28

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

T2 - Proceedings of the IEEE 2000 International Symposium on Circuits and Systems, ISCAS 2000

Y2 - 28 May 2000 through 31 May 2000

ER -

Color object segmentation with eigen-based fuzzy C-means

Abstract

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