Adaptive filtering based on LAB transform for FCM color image segmentation

Ning Li; Shucheng Xu; Zhongliang Deng

doi:10.1117/12.2282075

21 July 2017 Adaptive filtering based on LAB transform for FCM color image segmentation

Ning Li, Shucheng Xu, Zhongliang Deng

Proceedings Volume 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017); 1042025 (2017) https://doi.org/10.1117/12.2282075
Event: Ninth International Conference on Digital Image Processing (ICDIP 2017), 2017, Hong Kong, China

Abstract

In this paper, we propose an adaptive mean filter FCM algorithm based on LAB transform to improve the anti-noise ability of the traditional FCM in image segmentation, which lack of using spatial information. The algorithm firstly counts the peak value of the whole image pixel, and then weights the transformed color component according to the statistical result, and gives the different color space different information amount. At the same time, the algorithm combines the variance of the image to filter the image, and adjusts the filter degree adaptively to use the spatial information most effectively. The experimental results show that our algorithm is better than the traditional algorithm in segmentation effect, and has great improvement in anti-noise ability.

Citation Download Citation

Ning Li, Shucheng Xu, and Zhongliang Deng "Adaptive filtering based on LAB transform for FCM color image segmentation", Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 1042025 (21 July 2017); https://doi.org/10.1117/12.2282075

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available