A13 - Color Image Segmentation

Color image segmentation has many applications in medical imaging, biomechanics, human and face recognition, remote sensing, and calculating percent cover in marine science. In this example, we want to segment the yellow of Haneka's dress. In my analysis, I used the Normalized Chromaticity Coordinates (NCC) so that the color is independent of the brightness information. The color segmentation itself can be done in two ways.

The first is the parametric method where the image is segmented using the joint probabilities of a pixel being in the region of interest using a Gaussian distribution and the mean and standard deviation of the pixel values in a cropped sample of the color of interest. The result is shown below.



The second is the non-parametric method where we get the 2D histogram of a cropped portion of the region of interest. We can see that the histogram is correct because it indicates a region in between the green and the red which corresponds to yellow.



I used this as a look up table for assigning values for the different pixels in the image. The results of histogram backprojection are shown below.



We can observe that the parametric method is more accurate in defining the region of interest based on the color than histogram backprojection. Histogram backprojection on the other hand is faster and more lenient because even reflections and other parts of an image light up. Histogram backprojection would still need a threshold to be accurate. On the other hand, the parametric method can be fairly stand alone.

For this activity, I give myself a grade of 15 for developing my own code for histogram backprojection far earlier than the start of this class.