Found this YouTube channel through Day. Vihart posts about fun things like spirals, knot theory, and fractals. Check it out! More »
The world is complicated. More »
Meh, I’m definitely not the target audience for this. I regularly hook up my Blackbook to my TV, connect a wireless keyboard and mouse, and I’m good to go. Hulu, Netflix, YouTube, etc. are all at my fingertips. The intriguing part, though, is that some TVs are going to be built with Google TV. So essentially these TVs become internet devices without having to attach a computer. That’s a pretty neat idea, especially for the non-tech-savvy. The Google TV box, on the other hand, seems like it’s just going to be another cuboid to silently drain energy. Why buy another box when you could hook up a computer to your tv?
School’s out! So why am I writing about the Hough transform again? Well, in my previous post, I just kind of took the Hough transform of a few gifs and didn’t really think twice about it. Now that my paper is written, I can actually explain just what’s going on.
The standard Hough transform finds lines within an image. It does this by taking a binary edge image (more on this in a future post), and transforming the points into “Hough-space”. Each point of the edge image is turned into a sinusoid in Hough-space. Points that tend to form a line will create a “knot” in Hough-space, while points that don’t line up have no such knot.
The two figures above (from Wikipedia) depict how a point in real-space (that is, a point within the image) is transformed into a sinusoid in Hough-space. Multiple co-linear points will create a knot in Hough-space seen in the second figure.
The brightness of a pixel in Hough-space denotes the relative “strength” of a knot. Thus, in the YouTube video above, one can see the bright point migrate from the right to the left as the bar spins in the GIF.
So how would you use the Hough transform? Well, one can use the Standard Hough Transform (which finds lines) and then a threshold on Hough-space to determine whether or not a line exists. The Hough transform itself has been generalized to find other shapes like circles or ellipses. There’s even a version where you can create your own shape that you would like to find. This generality is why the Hough transform is used quite often in computer vision (as I’m told).
As promised, a review of Andy McKee’s Joyland. In it, Andy has branched out from his normal solo guitar to include other instrumentation like the strings in the titular song. Always calming, ever relaxing, this album stays true to the feel of previous releases like Art of Motion and Games of Gnomeria. There are three stand out songs I’d like to mention, the first “Everybody Wants to Rule the World”, I already mentioned in a previous post. The second, “For Now”, is such an awesome way to end the album. There isn’t singing in any of Andy’s songs, but the melody really just sings. The final mention, Hunter’s Moon, is one of those songs, much like the song Drifting, that needs to be seen, as well as heard. And thus we end this blog post with the YouTube video of “Hunter’s Moon”.