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2015-08-18 06:10:15 -0500 asked a question CvDTree fitting algorithm.


In the documentation for the CvDTree for __max_categories__ section it is written that

In case of regression and categorical variable the optimal split can be found efficiently without employing clustering, thus the parameter is not used in these cases.

I was wondering how is this achieved algorithmically. I tried to find the algorithm in the paper that is cited there but found none.

2015-08-11 11:35:05 -0500 asked a question find_split_cat_reg() implementation.

hello, I have a question about this function.

It is supposed to find the best split of categorial variable. I am not getting how does it work. Especially, the part here with double pointer.

Can somebody explain me this, please?

2015-08-07 03:22:42 -0500 received badge  Supporter (source)
2015-08-07 03:17:26 -0500 commented answer Multiblock Local Binary Pattern in object detect module confusion.

Thank you for your response.

Really sorry but my question is more about the theory behind the scenes. I am interested in how OpenCV creates a binary tree out of Local Binary Patterns. In case of the Haar features it's obvious as they are continious and the decision boundary can be easily found. But Local Binary Patterns are not continious, it's just simple categorial values. And in the paper they use a tree with 256 branches (all the possible values of Local Binary Patterns). My question is how OpenCV manages to create binary tree in case of Local Binary Patterns.

Once again really sorry: I think I made my question a little bit vague and you misunderstood it.

2015-08-07 02:41:53 -0500 received badge  Enthusiast
2015-08-06 08:15:46 -0500 asked a question Multiblock Local Binary Pattern in object detect module confusion.


Recently I was following the original paper about the gentle adaboost training.

I was a little bit confused by the fact that in the paper they use multi-branch tree with 256 values, but the implementation of traincascade and cascade xml files have a two branch tree. Is it a problem or a special trick. I didn't find any information in documentation. I would be really glad if someone briefly explains this thing or point me out the part in documentation where this thing is described.

I am trying to implement the training stage myself and I am interested in how it's made under the hood of OpenCV. Because the the way that the authors of the paper fit the tree and the way it is done in the OpenCV is clearly different and I didn't find any documentation on that.

Thank you.

2015-07-08 05:15:48 -0500 asked a question Partition() function underlying algorithm.


I was wondering is somebody can tell me what algorithm does the OpenCV use in the partition()function.

Any help will be appreciated.

2015-07-04 22:01:30 -0500 received badge  Student (source)
2015-06-28 12:54:21 -0500 asked a question OpenCv detectMultiScale implementation.

Does OpenCv scales the image itself when doing the object detection using lbp or haar features or it scales the features itself?

Thank you.

2015-06-28 07:08:21 -0500 commented question OpenCV detectMultiScale() neighbours algorithm

@berak thank you. I already did the most part of it. The only thing that is written in the paper is that they treat the joint regions and make them into one average. Do you know where I can find the best algorithm in this case to find joint regions?

2015-06-28 06:23:22 -0500 asked a question OpenCV detectMultiScale() neighbours algorithm


I was wondering where I can read about how the neighbours algorithm works in OpenCV. The minNeighbors parameter controls the work of the algorithm.

I just want to implement the same algorithm for my face detection implementation.

Thank you.