fourteen

The main goal of this activity is to separate a colored region from a separately colored background.  To do this, each pixel coordinate can be normalized by the equations shown below and thus become Normalized Chromaticity Coordinates (NCC)

Shown below are the corresponding colors of the r and g values.

Figure 1. Normalized chromaticity space where the x-axis is r and y-axis is g.

Before any color segmentation can occur, a region of interest (ROI) is obtained from the selected image.  The image below is from the My Pictures folder of my computer.  Thank you again, Windows. 

Figure 2. (top) The original image and (bottom) the corresponding region of interest (ROI).

When the NCC and Gaussian histograms of the ROI are taken and compared with Figure 1, we can see that the location corresponds to the red and orange colors.

Figure 3. (left) Non-parametric: 2D histogram of NCC of ROI and (right) Parametric: Gaussian distribution fit of NCC values for the ROI.

From these histograms, masks were created to filter only the pixels of the color we’re interested in. 

Figure 4. Binarized Masks: (left) Non-parametric and (right) Parametric.

These masks were applied to the original image, hence only the colored ROI are seen.

Figure 5. Color Segmentation: (left) Non-parametric and (right) Parametric.

Well, there.  That was fun.  In conclusion, I think that the Parametric method is better than the Non-Parametric Method.

Grade: 9/10

REFERENCES:

1. A13 – Color Image Segmentation, Applied Physics 186 Manual