4.2 Image Segmentation
This technique is used to identify the object of interest in image.
Or its purpose is to identify the object and discard remaining
pixels of image. After image acquisition each image is preprocessed
to clean the noise and to get more details form it. The
RGB image was separate into three channels and we enhances the
intensity or value channel as mention above, after enhancement
we combine the three channels and converted back to RGB image.
The enhanced RGB image was normalised by splitting the
channels and dividing each pixel value by the sum of pixel’s value
over all channels gives you the normalised channel, and merging
them back gives you the normalised RGB image. Further, the
normalised image was converted to HSV for the segmentation
purposes. From the HSV planes, the HSV values of the each pixel
are normalized be in range of (0 and 1).
In the current case it has been experimentally observed that color
of logs possesses a hue value that is in between 0.05 and 0.15
ranges. So for segmentation of the logs, all the pixels within this
range are kept and the rest of the pixels are discarded thereby
leaving the frame mainly with the logs on a binary image (see
Figure 6(e)). More recently good level of detail concerning color
differences in the HSV color space has been detailed in [9].
Once the object of interest has been segmented and displayed in
the other mask, the canny edges of original image are
superimposed against the segmented one to create a geometric
projection of logs on segmented image (see Figure 6(f)). The
edges will act as boundary between individual objects (logs), once
the boundary (edges) has been added to the segmented image, the
boundary pixels values are merged with the background of
segmented image, leaving only the individual logs separated from
each other. (see Figure (6(g))