Haar Detection Ultra Rapid Object Detection
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Haar Detection Description

Viola and Jones used 160000 rectangular features, so the cascade of classifiers is large. In my version I rather manually devised only 115 rectangular features that closly mimic eigenfaces PCA basis. Also I used artificial neural networks (ANNs) as a classifiers in a cascade. So my tasks were to develop the code library and investigate if the very small subset of features will result in robust object detection with non-linear clasifiers. For the object I used face detection problem, thanks there are great face databases as CBCL or CMU to solve the problem.To start with the executable just click enum button and select capture device, then click Init AI to load cascade of ANN classifiers and push start button to initiate capture and face detection. You can alter capture rate with slider control. At the bottom status static the detection fps will be shown.One precaution, compared to my eigenfaces face detection lib, there you can get every individual rectangle and histogram equalize it before further processing, you can not do the same trick with integral image. Only adaptive histogram equalization will work for entire image before computing integral version of it. So the presented classifiers might not be as good to bad lightning conditions as eigenfaces code. So make sure you've got your face lit enogth from the front.The Haar Detection application was developed to be a small test tool which provides ultra rapid object detection.

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