Image back-light compensation with fuzzy C-means learning algorithm and fuzzy inferencing

In this paper, a two-stage processing technique utilizing the fuzzy c-means learning mechanism and the fuzzy logic rule inference is proposed to compensate the back-light images. The advantages of this approach are: (1) the subject region can be compensated independently, (2) the brightness of subje...

Full description

Saved in:
Bibliographic Details
Published in2003 7th International Symposium on Signal Processing and Its Applications Vol. 1; pp. 433 - 436 vol.1
Main Authors Daw-Tung Lin, Chia-Ching Huang
Format Conference Proceeding
LanguageEnglish
Published IEEE 2003
Subjects
Brightness
Cameras
Clustering algorithms
Color
Equations
Flowcharts
Fuzzy logic
Histograms
Image segmentation
Inference algorithms
Online AccessGet full text
ISBN0780379462
9780780379466
DOI10.1109/ISSPA.2003.1224733

Cover

More Information
Summary:In this paper, a two-stage processing technique utilizing the fuzzy c-means learning mechanism and the fuzzy logic rule inference is proposed to compensate the back-light images. The advantages of this approach are: (1) the subject region can be compensated independently, (2) the brightness of subject region can be enhanced without interference of the background, (3) the contrast of subject region can be enhanced adequately, (4) the original background image can be reserved. We have implemented the proposed compensation method and tested on 30 pictures. The performance is better than that of global histogram equalization and global brightness adjustment.
ISBN:0780379462
9780780379466
DOI:10.1109/ISSPA.2003.1224733