Hats off to people like him who are helping to make computers understand humans. Facial recognition and speech recognition are two gigantic hurdles, followed by understanding language. I am amazed that we've come so far.
***** Yes, that is true. Alone, the counting rule applies to the data, with no compressions possible accept for one to one mapping with a few small values.
+D347Hza for 8 bits there exists 255 possible LBP patterns, by counting how many of each LBP pattern we see in an image we can predict what it might be.
I'm currently studying the suject for a school project, in fact there's 58 uniform patterns (with only 2 or less transitions in the bit sequence) and 256-59 = 198 non uniform patterns ("00010000" is uniform "011100001" isn't) but in the experiment you observe that 90% of obtained patterns are uniforms rather that 22.6% so to encode the image, you create a 59 dimension histogram and in the first 58 dimensions you count the number of occurence of each individual uniform patterns whereas in the 59th you count the number of every non uniform patterns. Then you do this histograms for regions of the image (containing multiple 3x3 cells) and you concatenate (put together but not merge) every histograms and you get the full picture ^^ To predict you make the weighted average of the previous positives histograms and you compare with the new one and there u go.
This guy looks exactly like my friend. It's not an "oh yeah, he does look a bit similar" type thing, it's genuinely as if they could be twins separated at birth. It's pretty spooky to be honest.
The example of 3D computation of LBP is interesting, using adjacent frames. But I ask myself if we can use this method in coloured image RGB? Especially to recognize coloured texture for get the variation between colour. Or maybe this kind of approach is different.
Why not say....remove the 8 edges of the cube which are less time/space relevant in terms of information in relation to the central pixel? Much more simple...
thank you for your explanation, is that possible that we create lbp algorithm (code) for face recognition from zero (without any libraries or modules)? because it will be easier if I can learn from the source code
Please make a video about self driving cars. There are a lot of conflicting opinions about whether or not we should allow computers to operate a motorized vehicle on public roads, and there has been some news of cars being hacked. As a student of Computer Science, it seems to me that if done correctly it should be safe, all the same I'd like to know the opinions of others as well.
So what your getting at here is that doing it in the last way mentioned increases the speed that it calculates the data but there has to be at least one advantage in calculating it at 2^26 is there not? Also does each pixel undergo this calculation or is it just every 9 pixels? I may just not grasp the full concept, Still not as tech savvy as I could be.
Ok for getting into the thinking of image processing but that technique is nowhere robust enough to handle the real world. The short answer is neural nets.
+Ethan uhm.. it reduces the search space from 2^26 to 3*2^8, the number of blocks encoded was not the point there. (just for for those curious, 3*2^8 is 768, 2^26 is 67.1 billion, quite the reduction)
+Ethan it does go down to 8, because it only considers dimensions 2 at a time. Each pair has 2^8 possible transitions, and 3 dimensions can be paired in 3 ways (XY, Xt, Yt), so this orthogonal method has 3*2^8 possible values. Don't be tricked by the image, the method is not just taking away the edges
Even though we've achieved amazing progress on object recognition in computing, we're not even coming close to a viable solution about it. I'm truly convinced that mathematics is NOT the answer here, as is not a good answer for every other NP problem. But, the video is great to show the very reason why - at any point, the algorithm is "aware" of only one single dot of reality. And a pixel is meaningless in the real world.
+Peter Walker I don't know :) If I knew, I'd probably have several Nobel prizes on my chest. Maybe some magic branch of mathematics' paradigms and models we haven't yet dreamed of. What I'm saying that the current mathematical and computing models of "real world awareness" is totally useless. Well, not totally, but far, far away from anything close to real-time.
