I am just being adventurous and taking my first baby step toward computer vision. I tried to implement the Hough Transformation on my own but I just don\'t get the whole pic
Here's another perspective (one used in the pilot episode of the T.V show Numbers): Imagine a fountain-like lawn sprinkler was somewhere on a lawn earlier, casting out water droplets around itself. Now the sprinkler is gone, but the drops remain. Imagine turning each drop into its own sprinkler, itself casting out droplets around itself - in all directions because the drop doesn't know what direction it came from. This will scatter a lot of water thinly around on the ground, except there will be a spot where a whole lot of water hits from all drops at once. That spot is where the original sprinkler was.
The application to (e.g) line detection is similar. Each point in the image is one of the original droplets; when it acts as a sprinkler it sends its own droplets marking all of the lines that could be passing through that point. Places where a whole lot of secondary droplets land represent the parameters of a line that passes through a whole lot of image points - VOILA! Line detected!