**Pyramid Thresholds**

- Changing Pyramid Levels from 4 to 6 makes the regions bigger ( threshold2 = 0)
- Significant differences with each step observed at lower end of threshold2.

**DFT**

- Use pictures of simple geometry to better tell the characteristics of output. Real life images seem to just give a gray noisy output.
- http://www.cs.ioc.ee/~khoros2/linear/dft-pulse-example/front-page.html
- My understanding of DFT is it breaks down an image into a series of simpler images superimposed together, similar to the way Fourier Thereom describes. A periodic function is made up of a series of sine waves of different amplitudes in frequency multiples of the original, of different phases. I got this idea after reading from the website above and a quick read of DCT Basis Functions section from Digital Video Decompression.

**Distance Transform**

- Good introduction: http://homepages.inf.ed.ac.uk/rbf/HIPR2/distance.htm
- The contours gets 'rounder' as going C -> L1 -> L2.
- Grid size 3, 5, Exact only makes significant differences in L2 from my tests using lena.jpg and stuffs.jpg
- The objects of which their edges are detected varies with the edge-threshold. It makes sense as more details are revealed of a certain object when the threshold gets close to the intensity level of the object (in grayscale).

**HoughLines**

- Added HoughCircles to the sample program. The radius is often wrong with small circles (pic1.png). The centers are correct. It is consistent with a comment from the OpenCV book regarding the radius value should not be used.
- HoughCircles() has built-in Canny. Running a second Canny on a Canny Edge Detected image would add an dotted edge inside that first one. Resulting in a double-circle. And somehow this would get more accurate radius value.
- Still unable to get all the circles detected from pic1.png test image.
- >>> See later post on Randomized Hough Transforms for ellipse detection. <<<

**Laplace Edge Detector**

- Added a switch to disable the edge detector, to compare the effects of different blurring methods.
- Output from Median filtering is like drawing with a thick paint brush.
- Added Bilateral Filtering to the rotation - significantly slows down the movie.
- Median filtering is slower than Gaussian, but a bit faster than Bilateral.
- Bilateral filtering shows thick edges like Median, and preserve more edges than the latter.
- Getting more bang for the buck with Gaussian.
- Higher Sigma -> Larger Window -> Slower and Blurrier images except Bilateral.
- Made the Laplace aperture (odd and < 31) a function of Sigma. Reasonable output obtained within 7. Fewer and thinner edges with smaller aperture.

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