Report #4( edge Detection )
These ideas can be realized by using the gradient idea. One can obtain the amplitude with this formula,
dx= |-2 0 2|
|-1 0 1| / 8
| 1 2 1|
dy= | 0 0 0|
|-1 -2 -1| / 8
Now with these tools in place I would like to show my results after varying the amplitudes and directions on the lax image. Also I would like to apply edge detection to these images after becoming influenced with the Gaussian Smoothing filter and the Low Pass filter.
|Original Lax||Gradient Intensity||Equalized Gradient|
These are images of LAX and the gradient of the LAX. I equalized the gradient since it is very dark. This may be due to the lack of edges present in the image. After equalizing the image the edges became more noticeable.
As the threshold level increases the edges with the most definition, like those among the buildings, remain. The edges with less intensity diminish proportionaly. I shall use the amplitude threshold of ??? to work on the directional aspect of this assignment since it seems to represent the edges the best.
Thresholding Amplitude & Direction
|Amplitude\Direction||100 +/-10 degrees||10 +/-10 degrees||45 +/-10 degrees|
With an angle of 100 degrees we get those edges very clearly in that direction. The same holds true for those edges with a 10 degree slant. Taking the directional threshold of 45 degrees really does not show any edges as well as it should not.
Influence of Gaussian Smoothing
|Sigma\Misc||Before edging||After edging||Applying amplitude=150|
|Sigma = 1.0|
|Sigma = 2.7|
Influence of Low Pass Filtering
|Range to Impulse Response\||Before edging||After edging||Threshold = 10|
|Close to Impulse Response|