Performance

Athena on January 12th, 2012

In a meeting with my advisers, it was said by one of their colleagues that the AIA 211 images might be more detailed in the features to detect. Since originally I decided to use AIA 193, because visually it was easier for me to see and identify the solar cavities, I decided to try the […]

Continue reading about Running Classifier on Composite Validation Set

Now that a decent Hit Rate has been achieved, the focus turns to reducing the False Alarms. One step to reducing the false alarms is to eliminate overlapping regions of interest, so that the false alarms picked up in about the same vicinity will count as 1 or 2 false alarms as opposed to 6 […]

Continue reading about Trial ::: haarcascade21 ::: [Reduction of overlapping ROIs]

Athena on December 15th, 2011

Now that a decent Hit Rate has been achieved, the focus turns to reducing the False Alarms. One step to reducing the false alarms is to black out the suns disk, so that the false alarms picked up in the suns disk will eliminated from the count. Original False Alarm count was 95058, after some […]

Continue reading about Trial ::: haarcascade21 ::: [Black Out the Suns disk]

Athena on December 9th, 2011

In testing the classifiers produced, I ran it initially against the training set that was used to create the classifier, as seen in the previous post. So now we are going to start running tests on new image sets, to see how it truly performs on images it has never “seen” before. The below hit […]

Continue reading about Running Classifier on Validation Set

Athena on October 3rd, 2011

So now that the limitation of the performance application has been identified what does that mean for the previous haar classifiers we have tested and analyzed this far? Well to put it bluntly they were wrong. The results did not truly reflect the classifiers accuracy. So I took the haarcascade15, since this classifier had the […]

Continue reading about Performance ::: haarcascade15 :::

Athena on October 2nd, 2011

When creating a classifier to use for detection of solar cavities, the steps taken are essentially creating the samples to train with, train, and then test the performance. In testing the performance, I began looking through the output, and noticed some oddity. For some of the images the performance output would classify a MISS, so […]

Continue reading about Performance Testing Accuracy