Geometric Computer Vision: Difference between revisions
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;Laplacian of Gaussian | ;Laplacian of Gaussian | ||
* Edges are zero crossings of the Laplacian of Gaussian convolved with the signal. | * Edges are zero crossings of the Laplacian of Gaussian convolved with the signal. | ||
Effect of <math>\sigma</math> Gaussian kernel size: | |||
* Large sigma detects large scale edges. | |||
* Small sigma detects fine features. | |||
;Scale Space | |||
* With larger sigma, the first derivative peaks (i.e. zero crossings) can move. | |||
* Close-by peaks can also merge as the scale increases. | |||
* An edge will never split. | |||
===Subtraction=== | |||
* Create a smoothed image by convolving with a Gaussian | |||
* Subtract the smoothed image from the original image. | |||
===Finding lines in an image=== | |||
Option 1: Search for line everywhere. | |||
Option 2: Use Hough transform voting. | |||
===Hough Transform=== | |||
Duality between lines in image space and points in Hough space. | |||
Equation for a line in <math>d = x \cos \theta + y \sin \theta</math>. | |||
<pre> | |||
for all pixels (x,y) on an edge: | |||
for all d, theta: | |||
if d = x*cos(theta) + y*sin(theta): | |||
H(d, theta) += 1 | |||
</pre> |