feature-extraction

Does anyone know the difference between FeatureDetection and DescriptorExtraction in OpenCV 2.3? I understand that the latter is required for matching using DescriptorMatcher. If that's the case, what is FeatureDetection used for? Thank you. Feature detection In computer vision and image processing the concept of feature detection refers to methods that aim at computing abstractions of image information and making local decisions at every image point whether there is an image feature of a given type at that point or not. The resulting features will be subsets of the image domain, often in the

I am trying to extract features using OpenCV's HoG API, however I can't seem to find the API that allow me to do that. What I am trying to do is to extract features using HoG from all my dataset (a set number of positive and negative images), then train my own SVM. I peeked into HoG.cpp under OpenCV, and it didn't help. All the codes are buried within complexities and the need to cater for different hardwares (e.g. Intel's IPP) My question is: Is there any API from OpenCV that I can use to extract all those features / descriptors to be fed into a SVM ? If there's how can I use it to train my

I have already seen that OpenCV provides a classifier based on LBP histograms : But I want to have access to the LBP histogram itself. For instance: histogram = calculate_LBP_Histogram( image ) Is there any function that performs this in OpenCV? You can get the C++ code for computing LBP using OpenCV's Mat data structure here: http://www.bytefish.de/blog/local_binary_patterns You should be able to find the Python version as well on the same site. The code is written by Philipp Wagner, who I believe contributed the face recognition code you mentioned to OpenCV, so it should be the same thing.

I want to encode 3 categorical features out of 10 features in my datasets. I use preprocessing from sklearn.preprocessing to do so as the following: from sklearn import preprocessing cat_features = ['color', 'director_name', 'actor_2_name'] enc = preprocessing.OneHotEncoder(categorical_features=cat_features) enc.fit(dataset.values) However, I couldn't proceed as I am getting this error: array = np.array(array, dtype=dtype, order=order, copy=copy) ValueError: could not convert string to float: PG I am surprised why it is complaining about the string as it is supposed to convert it!! Am I

SURF is patented, as is SIFT. ORB and BRIEF are not patented, but their features are not scale-invariant, seriously limiting their usefulness in complex scenarios. Are there any feature extractors that can extract scale-invariant features as fast as SURF and are not so strictly patented as SURF and SIFT? Rui Marques Although you already choose BRISK, you might find FREAK interesting. Author claims to have better results than BRISK and ORB. I should also add that ORB is scale-invariant but has some problems in that area. So I would still recommend it for someone to try it. The FREAK source code