We introduce a novel method of splitting up color spaces into different components and then performing edge detection
on individual color planes. The two general approaches taken for this are monochromatic and vector based. Also a new
color space will be introduced in this paper, which is an improved version of the PCA algorithm. By analyzing the
results of these algorithms we are able to determine which color space and edge detector is best suited for each
algorithm. We test these methods using a number of well known edge detectors and color spaces. All the algorithms are
tested on 17 different color images (12 natural, 5 synthetic). To analyze the results we use Pratt's Figure of Merit and
Bovik's SSIM measures.