I'm trying to make a fire detection using Machine learning. My features are mean RGB, variance RGB, and Hu moments. So what I'm doing right now is I first segment an image based on this paper
According to the paper I use the rules
r > g && g > b
r > 190 && g > 100 && b < 140
here is the result of my color segmentation for the negative and positive images
The pictures on the right are now in
vector<Mat> processedImage
After that I get the hu moments of each picture by converting it into gray scale and blurring it.
cvtColor(processedImage[x], gray_image, CV_BGR2GRAY);
blur(gray_image, gray_image, Size(3, 3));
Canny(gray_image, canny_output, thresh, thresh * 2, 3);
findContours(canny_output, contours, hierarchy, CV_RETR_TREE,CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
cv::Moments mom = cv::moments(contours[0]);
cv::HuMoments(mom, hu); // now in hu are your 7 Hu-Moments
Now I am stuck I'm not sure if my images are okay to obtain useful hu moments because the negative images are so scattered. Am I on the right track with regards to Hu moments extraction? Will I do the same on testing where I do color segmentation before extracting hu moments?