4 ms·
Can someone shed some further light on the hough transform image used in the article [0]. I can't seem to make sense of why the hough transform of the canny edg
by Omnipresent 10y ago
Can someone shed some further light on the hough transform image used in the article [0]. I can't seem to make sense of why the hough transform of the canny edged image looks like that. Are they using an adaptive hough transform?
[0] https://blogs.dropbox.com/tech/2016/08/fast-and-accurate-document-detection-for-scanning/#jp-carousel-2864 https://blogs.dropbox.com/tech/2016/08/fast-and-accurate-doc...
- yxiongdropbox 10y agoVery good question. As stated in the blog post (one line above that figure), we actually used a polar parametrization r=x·sinθ+y·cosθ than the slope-intercept version y=mx+b. If we were to use y=mx+b, then the hough transform image would look like many straight lines intercepting at a few points, which makes most intuitive sense. The issue with this form is it gets ill-formed when the line becomes near vertical (m goes to infinity). The polar parametrization r=x·sinθ+y·cosθ solves this problem, and in the hough space, the axes will be r and θ. A point in image space maps to a sinusoid in hough space, which is why the transformed image looks like that.
- Omnipresent 10y agoThanks for the additional input. This is really fascinating. Now I remember having seen this in the HoughTransform function in OpenCV [0] but could never make sense to how it relates to the real world [0]: http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/hough_lines/hough_lines.html http://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/ho...