Hugh transform and using an edge detector
do not exclude one another. For example,
an edge detector can be used to reduce
the number of pixels that go into Hough.
On the other hand, Hough can be used to
bridge gaps where the edge image is
interrupted or fuzzy.
Regards,
Joachim
Maybe, these links will be useful:
https://www.researchgate.net/profile/Vladimir_Lukin2/topics
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6869302&url=http%3A%2F%2Fieeexplore.ieee.org%2Fiel7%2F6862972%2F6869176%2F06869302.pdf%3Farnumber%3D6869302
https://www.researchgate.net/publication/224407930_Edge_detection_and_filtering_of_images_corrupted_by_nonstationary_noise_using_robust_statistics
Conference Paper Edge detection and filtering of images corrupted by nonstati...
As implicit by M. George comment, the Hough transform is very good for LINE (or other parametric forms like circles, ellipses, known templates, etc.) detection, not general edges which will depend on the objects contours and will in general be locally curved.
One example of a good application of the Hough transform would be to find the vanishing points in a building image based on the lines detected by the building edges, windows, etc.
Hugh transform and using an edge detector
do not exclude one another. For example,
an edge detector can be used to reduce
the number of pixels that go into Hough.
On the other hand, Hough can be used to
bridge gaps where the edge image is
interrupted or fuzzy.
Regards,
Joachim
Canny edge detection method seems to be a relatively optimal method. You can take a look at the following link:
http://www.indiana.edu/~dll/B657/B657_lec_hough.pdf
Good luck
Hello Priti.
prewitt, Canny and sobel edge detection method is better than hough transform. Canny's output are much better than sobel & prewitt, but performance degraded.
Here you can find the matlab implementation of canny edge detector.
Regards
Ehtesham
http://www.mathworks.com/matlabcentral/fileexchange/45459-canny-edge-detection-in-2-d-and-3-d?s_tid=answers_rc2
Hi Priti,
Canny edge detection method is better than other edge detection. But, in recent years diffusion based edge detection is very popular. So, better you go with Anisotropic diffusion based edge detection (example: Perona - Malik diffusion method).
Note: Perona- Malik method can be utilizing in image noise removal also.
Best wishes
Muthukumaran
create your own one. you can collect the line point, in a buffer. then, apply the linear regression.
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