Word Count : 523

---

The Hough transform is a numerical technique used for edge linking by converting local edge points in the image space into global geometric structures in a parameter space. In an edge-detected image, edges usually appear as broken or noisy pixels, and linking them into meaningful straight lines or curves is difficult by direct spatial analysis. The Hough transform solves this by using the mathematical description of a line and allowing many edge points to vote for possible lines that could pass through them. A straight line in the image plane can be represented in slope–intercept form as y = mx + c, but this form fails for vertical lines where m becomes infinite. Therefore, the Hough transform uses the normal form x cos θ + y sin θ = ρ, ______ ___ _____ ____ ________ __________ __________ _____ __________ ___ ______ ___.
______ ___ _________ _______ ____ _______ __________ ________ __________ ________ _____ _____.
__________ __________ _____ _______ __________ __________ ________ _____ ________ ___ _____.
________ ____ ________ ___ _________ ________ _______.
_________ ___ ____ _____ ___ ____ _________ ______ ______ ________.
____ ___ _________ _____ _______ _____ ____ _____ _____ _____ __________ _____.
_________ ________ ________ ________ ________ _______ _____.
____ _____ ___ _________ ______ _________.
_______ ______ _______ _________ ____ _____ ____ ________ __________.
_____ _______ _______ _______ ____ _____ _____ ______ ____ ______ ___.
_____ _____ ______ __________ _______ ______ _______ ______ _____.
___ __________ __________ ________ _____ _________ ________ ____ _____.
__________ __________ _______ ________ ____ ___ ____ _______ ___ ___ ______ __________.
_____ ___ __________ _______ ________ _____ _______ ____ ____ ________.
_____ __________ ____ ___ _________.
________ ______ ____ ____ ________ _____ __________ _________ ___ ______ _______.
________ _______ _________ _________ _________.
_______ ______ ___ ________ __________ __________ ____ _________ __________ _____ ______.
_______ ____ _______ ____ ______ __________ _________.
____ ________ __________ ________ ____ _________ ___ _______ _______ ______ ______ __________.
_____ ___ _________ _____ ___ _____ ___.
_____ ________ _____ ________ ______ ____ _______ ___ ______.
____ ____ _______ ________ _____ _________ _______ ___.
______ ______ ___ ______ ___ ____ ________ ___ ________.
_________ ________ ______ ________ ________ _______ _________ ______ ________ _____ _______.
_____ _______ ____ _________ _________ __________ ___ _________.
___ ___ _______ ___ _________ _____ ___ ______.
_________ _________ ______ _____ _________.
__________ _______ _________ _____ _________ _____ ________ ______ _______ _____ ______ _________.
_______ ____ __________ ____ ___ _____ ______ _______ ______ ____ __________.
_________ _______ ______ __________ ____ __________ _______ _______ ______ _________.
_______ ______ __________ _____ _________ _________ _______ _____ _____.
____ _____ __________ ___ __________ __________ ______ ___ _____.
________ ____ __________ ___ ___.
__________ _______ _______ ___ __________.
___ ______ _________ ________ __________ ______ ___ ______ ________.
_____ ______ __________ ________ ______ ______ ____ _________ ________ __________ _____.
__________ _______ ___ _______ ________ ________ ___ ______ ________.
________ _____ ________ _____ ___ ______ ___ _______ _______ _____.
_________ _______ __________ ________ _________ ________ _________ ___.
________ ____ ________ ____ __________.
___ ___ ______ ____ _______ ________ ______ _____ __________ _____ _______.
___ _______ __________ ______ ______ ____ _____ _________.
______ _______ ______ ___ ______ ________ __________ __________.
Get Full Answer on WhatsApp