haar-classifier

I am trying to create a Haar classifier to recognise objects however I can't seem to figure out what the results table that is produced at each stage stands for. E.g. 1 ===== TRAINING 1-stage ===== <BEGIN POS count : consumed 700 : 700 NEG count : acceptanceRatio 2500 : 0.452161 Precalculation time: 9 +----+---------+---------+ | N | HR | FA | +----+---------+---------+ | 1| 1| 1| +----+---------+---------+ | 2| 1| 1| +----+---------+---------+ | 3| 1| 1| +----+---------+---------+ | 4| 1| 1| +----+---------+---------+ | 5| 1| 0.7432| +----+---------+---------+ | 6| 1| 0.6312| +----+---------+

This is the first time I use openCV library. I want to use it to detect the eyes. I have used the FdActivity code available in this tutorial: http://romanhosek.cz/android-eye-detection-updated-for-opencv-2-4-6/ The tutorial uses OpenCV 2.4.6, but I have downloaded version 3.1 in my project. Due to the version differences I have changed the lines that uses putText, rectangle, and circle to be imported from imgproc instead of Core. This is all what I've changed. I have added haarcascade_lefteye_2splits.xml and lbpcascade_frontalface.xml to the raw folder under res folder. When running the app I

I'm currently using Haar classifiers, to detect objects. On my way, I didn't understand what is the minNeighbors parameter, what is it representing? Actually I don't understand what are the neighbors of the detection candidate rectangle. Please can anybody define the neighboring idea? Haar cascade classifier works with a sliding window approach. If you look at the cascade files you can see a size parameter which usually a pretty small value like 20 20. This is the smallest window that cascade can detect. So by applying a sliding window approach, you slide a window through out the picture than