@inproceedings {577,
	title = {Color image segmentation using connected regions},
	booktitle = {16th Canadian Conference on Electrical and Computer Engineering},
	year = {2003},
	abstract = {<p>A color image segmentation algorithm based on region growing is presented. Each region is characterized using two parameters: within region color contrast and between region color contrast. The first parameter is the distance between the two most distant pixels in terms of color. The second parameter is the distance between the candidate pixel and its nearest neighbor in the region. The color similarity measure used is the vector angle, which is invariant to shading. Highlight invariance is accomplished by using a highlight removal transformation, which removes the average pixel intensity from each RGB coordinate. The first calculation is very computationally intensive. To reduce this computational burden, the algorithm keeps track of which pixels already in the region are furthest spatially from the pixel being considered. The assumption would be that the pixels the furthest away would be the ones most different from the pixel being considered. We will present results on artificial and real images to illustrate the effectiveness of the method.</p>
},
	keywords = {color image segmentation, color similarity measure, connected region, highlight invariance, highlight removal transformation, image colour analysis, image segmentation, pixel intensity, real image, realistic images, RGB coordinate, shading invariance, vector angle},
	doi = {http://dx.doi.org/10.1109/CCECE.2003.1226114},
	author = {S Wesolkowski and P Fieguth}
}
@inproceedings {569,
	title = {Color image segmentation using a region growing method},
	booktitle = {2001 Advanced Imaging Conference},
	year = {2001},
	address = {Rochester, NY},
	abstract = {<p>A new region growing color image segmentation algorithm is presented in this paper. This algorithm is invariant to highlights and shading. This is accomplished in two steps. First, the average pixel intensity is removed from each RGB coordinate. This transformation mitigates the effects of highlights. Next, region seeds are obtained using the Mixture of Principal Components algorithm. Each region is characterized using two parameters. The first is the distance between the region prototype and the candidate pixel. The second is the distance between the candidate pixel and its nearest neighbor in the region. The inner vector product or vector angle is used as the similarity measure which makes both of these measures shading invariant. Results on a real image illustrate the effectiveness of the method.</p>
},
	keywords = {color image segmentation, highlight invariance, region growing, shading invariance},
	author = {S Wesolkowski and P Fieguth}
}
