title: Hough transformation vs Contour detection for Rectangle recognition with perspective projection
body: <p>I made rectangle detection work with contour detection and apply polygon with OpenCv to get location of the rectangle before adjusting the perspective projection. And it's working great. But some people in my group suggested Hough transformation instead. I wonder if there is any advantage of using Hough transformation for rectangle detection.</p>
  
  <p><strong>Update:</strong> I tried both of the methods. In my example, both methods worked fine after Canny edge detection. But since Hough transformation produces lines, we have to assume several things such as length of lines and connectability of the lines and should do additional computations such as searching connected lines and find corner points from the connected lines. Personally, I liked contour method better since its concept is simpler. With the method, you just search contours that can be approximated with closed and convex polygons with 4 corners and adjust the polygons for their perspective projections. That's about it. </p>