It is popular to use the conventional correlation for pattern recognition. But when using the conventional correlation, the pattern should be the gray-level pattern. In this paper, we discuss how to use discrete quaternion correlation (DQCR) for the application of color pattern recognition. With the algorithm introduced here, we can detect the objects that have the same shape, color, and brightness as the reference pattern. Besides, we can also detect (a) the objects with the same shape, color, but different brightness, (b) the objects with the same shape, brightness, but different color, and (c) the objects just have the same shape as the reference. Our algorithm can classify the objects into 5 classes due to whether their shape, brightness, and color match those of the reference pattern. Besides, with our algorithm, the difference of the brightness and color can also be calculated at the same time.
[1]
H. L. Hime.
“The Elements of Quaternions”
,
1894,
Nature.
[2]
Soo-Chang Pei,et al.
Efficient implementation of quaternion Fourier transform, convolution, and correlation by 2-D complex FFT
,
2001,
IEEE Trans. Signal Process..
[3]
T. Ell,et al.
Quaternion-Fourier transforms for analysis of two-dimensional linear time-invariant partial differential systems
,
1993,
Proceedings of 32nd IEEE Conference on Decision and Control.
[4]
Stephen J. Sangwine,et al.
Hypercomplex auto- and cross-correlation of color images
,
1999,
Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).