NathanSn's profile - activity

@berak, I have round low contrast objects which are very close to each other. Sometimes they overlap. I need to segment them.

@berak, for segmentation.

Can anyone tell me if OpenCV 3.1.0 has implementation of Active Contours?

In the documentation to OpenCV 3.1.0 I've found

If the responses are scalar, they should be stored as a single row or as a single column. The matrix should have type CV_32F or CV_32S (in the former case the responses are considered as ordered by default; in the latter case - as categorical)

Can anyone explain, please, what is the actual difference between storing responses in CV_32F and CV_32S? Meaning of this

(in the former case the responses are considered as ordered by default; in the latter case - as categorical)

sentence from the documentation.

If I do not use cross check option in BFMatcher then everything works fine for any value of K. Can someone explain what means this cross check option? I thought that for each keypoint it gives exactly one codeword from dictionary.

I am trying to do classification of images with combining SIFT features, Bag of Visual Words and SVM. Now I am on training part. I need to get BoW histograms for each training image to be able to train SVM. For this I am using BOWImgDescriptorExtractor from OpenCV. I am using OpenCV version 3.1.0. The problem is that it computes histogram for some images, but for some of images it gives me this error:

OpenCV Error: Assertion failed (queryIdx == (int)i) in compute, 
file /Users/opencv-3.1.0/modules/features2d/src/bagofwords.cpp, line 200

libc++abi.dylib: terminating with uncaught exception of type 
cv::Exception: /Users/opencv-3.1.0/modules/feature/src/bagofwords.cpp:200: error: (-215) queryIdx == (int)i in function compute

Training images are all of the same size, all have same number of channels. For creating dictionary I use another image set than for training SVM.

Here's part of code:

Ptr<FeatureDetector> detector(cv::xfeatures2d::SIFT::create());
Ptr<DescriptorMatcher> matcher(new BFMatcher(NORM_L2, true));
BOWImgDescriptorExtractor bow_descr(det, matcher);
bow_descr.setVocabulary(dict);
Mat features_svm;
for (int i = 0; i < num_svm_data; ++i) {
    Mat hist;
    std::vector<KeyPoint> keypoints;
    detector->detect(data_svm[i], keypoints);
    bow_descr.compute(data_svm[i], keypoints, hist);
    features_svm.push_back(hist);
}

data_svm is a vector<Mat> type. It is my training set images which I will use in SVM. I have draw keypoints, print hist for images they looks good. I tried changing from SIFT to SURF it works for some images for which SIFT does not, but it also does not work for all. If I train dictionary on all images I also receive the same problem.

When I change size of the BoW dictionary form K = 200 to K = 1000 number of images which raise error decreases.

What the problem can be?