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2016-05-29 11:51:23 -0600	commented answer	HOG detectmultiscale weight scale explanation That is indeed confusing. Thank you for the explanation! I wished that OpenCV docs were a bit more user friendly, it expects you to know every detail of the algorithm.
2016-05-29 06:52:43 -0600	commented answer	HOG detectmultiscale weight scale explanation Thank you very much for the explanation! The actual name of the parameter is finalThreshold. I'm using the Java interface so maybe there is a difference in the C++ version.
2016-05-28 10:11:32 -0600	received badge	● Scholar (source)
2016-05-28 10:10:50 -0600	commented answer	HOG detectmultiscale weight scale explanation So If I get this right a higher probability is further away from the SVM hyperplane, which makes it more confident. The detectmultiscale function has a threshold and maxThreshold parameter, my understanding of those parameters is that they are the minimal and maximum distance of the separating SVM hyperplane. So if I set the minimal threshold at 1, it should only return probabilities that have a distance to the hyperplane of at least 1, right?
2016-05-28 10:00:08 -0600	received badge	● Supporter (source)
2016-05-28 07:47:59 -0600	received badge	● Student (source)
2016-05-27 12:56:42 -0600	asked a question	HOG detectmultiscale weight scale explanation Hello all, I'm currently working with the HOG detector in OpenCV and it returns fairly good results. I only have one question, the `detectmultiscale` function sets the weights for each detection ROI. But what is actually the scale of good to bad? Maybe this is a really dumb question but I can't find it. I thought close to 1.0 is good but I'm getting weights of 2.0. So what is the actual scale because I thought it is from 0.0 to 1.0