..on color image segmentation

From a given image, our goal is to pick out the region of interest (ROI). To do this, we first have to get rid of the effect of brightness on the image (shadowing), and just consider the chromaticity. We represent the color space not by the RGB but by normalized chromaticity coordinates (NCC). Per pixel, we solve for the corresponding r,g,b values given by:




Since r+g+b=1, the chromaticity can be represented using only 2 values (r & g). Below is the normalized chromaticity space (x-axis is r and y-axis is g).











We now proceed with picking out the ROI. There are two methods: the Parametric and the Non Parametric.

Parametric

In this method, we first crop a subregion in the ROI. From this patch, we take the mean and standard deviation of r and g. We then compute for the distribution probability (we assume a Gaussian) using:





We do the same for p(g). The joint probability is just the product p(r)p(g). Below are sample the results:








Original Image


















Parametric
ROI: Blue Tiles

















Parametric
ROI: Yellow Tiles










Non Parametric (Histogram Backprojection)

In this method, we take the 2D r-g histogram of the crop subregion of the ROI. We then backproject the value to the pixel location of the original image. Below are sample the results:









Original Image
















Parametric
ROI: Blue Tiles



















Parametric
ROI: Yellow Tiles









Comparing between the two method, we could observe that the Parametric method has a better result.






Original Image














Parametric













Non Parametric









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