During the previous activities, we have been separating the regions of interest using histogram manipulation. In this activity, ROI separation is obtained using color. Grayscale value is not enough since it only provides a two-dimension space to select unique features from the scene. Using color, it provides three representations to provide unique features of a scene. However, colored objects will have variations in brightness or in shading that is RGB is not enough to represent color distinguish ROI. In this activity, the Normalized Chromaticity Coordinates (NCC) is being introduced to separate color and brightness information.
With this kind of representation, we can now present 3D to 2D coordinate system only.
There are two ways of obtaining the ROI using NCC. One is the Parametric probability distribution where this method calculate the probability that a certain color belongs to the ROI while in the Non-parametric probability distribution, it is mainly concerned on the histogram of the ROI and using the idea back in Activity 4, pixel per pixel values are backprojected from the rg histogram to the image. The first technique is purely computational while the latter involves ‘manual’ pixel per pixel replacement.
Above is sample ROI segmentation using parametric estimation. Note that the flag image doesn’t have a shading variation in its color so it is expected that this image will be segmented accurately. A yellow patch is obtained for sample segmentation. Even if non-parametric estimation is used, there will be no change in the quality of segmentation produced.
Displayed above are ROI segmentation results using parametric and non-parametric estimates. Beside the original image is the patch that is used to select the ROI. Parametric estimate is the one that is displayed to the left and for the non-parametric is the one displayed to the right. It can be observed that parametric estimates have a poor segmentation quality as compared to the non-parametric. This is because only a probability of the color information is obtained. From the formula, the method computes for the mean and the standard deviation of a color from the image and it is compared to the probability that it has the same statistics as that of the ROI. The non-parametric estimate then provide better segmentation results because it follows a look-up table process and is one-to-one pixel per pixel replacement.
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Sorry mam, I failed to rate my work. I give myself 10 points for finishing and understanding this activity.
Your self-grade please?
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