Trial ::: haarcascade21 :::

Through this process I have been trying to increase my sample sets, so I am currently up to about 3600 positive images resulting from rotating one positive image and tracking the ROI (region of interest) across all subsequent images, as well as increasing the negative set. Below are the commands and parameters used to create this specific classifier as well as the outcomes and stats from the performance application.

CreateSamples

G:\CreateSamples\Debug>CreateSamples.exe -info "G:\Thesis Research\Test Sets\Positi
ves_With_Rotations\Markup_List.txt" -vec "G:\Thesis Research\Test Sets\Positive
s_With_Rotations\Markup_List_Zangle2_3000Samples.vec" -num 3000 -maxzangle 1.1


Training

OpenCV2.2\bin>opencv_haartraining.exe -data "C:\Thesis Research Runs\HaarClas
sifier\haarcascade_21" -vec "C:\Thesis Research Runs\Test Sets\Positives_With_Rot
ations\Markup_List_Zangle2_3000Samples.vec" -bg "C:\Thesis Research Runs\Test Se
ts\Negative Solar Cavities\extended_negatives_3342.txt" -npos 3600 -nneg 3342 -n
stages 20


Performance

C:\OpenCV2.2\bin>opencv_performance.exe -data "C:\Thesis Research Runs\HaarClass
ifier\haarcascade_21\haarcascade_21.xml" -info "C:\Thesis Research Runs\Test Sets\
Positives_With_Rotations\Markup_List.txt" -maxSizeDiff 2


So the below chart shows the distribution of HITS, MISSES and FALSE ALARMS. For this training set I used the default -maxfalsealarm setting to have the trainer finish quickly. The results reflect this by showing such a high false alarm rate. Which is fine right now, since I feel we are on a better path with trying to get the HIT count up. So then we can go about it the same way and let the trainer run for longer to reduce the false alarms as well as do some filtering implementations to alleviate ones that we know are not “true” cavities.

The below hit rate was achieved when running the new classifier against the initial training set.
Hit rate ≈ 95.5%

haarcascade_21_sizeDiff_2

Tags: testing outcomes, trials