Trial Runs

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 November 28th, 2011

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 […]

Continue reading about Trial ::: haarcascade21 :::

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 :::

In an attempt to analyze some data as well as performance of my classifier and implementation on solar cavity detection I decided it would be beneficial to see the data in terms of individual quadrant performance. Below is the confusion matrix for the outcomes of the Quadrant IV, based on 100 q4 (quadrant 4) positive […]

Continue reading about Template Matching – Confusion Matrix Analysis – Quadrant IV

In an attempt to analyze some data as well as performance of my classifier and implementation on solar cavity detection I decided it would be beneficial to see the data in terms of individual quadrant performance. Below is the confusion matrix for the outcomes of the Quadrant III, based on 100 q3 (quadrant 3) positive […]

Continue reading about Template Matching – Confusion Matrix Analysis – Quadrant III

In an attempt to analyze some data as well as performance of my classifier and implementation on solar cavity detection I decided it would be beneficial to see the data in terms of individual quadrant performance. Below is the confusion matrix for the outcomes of the Quadrant II, based on 100 q2 (quadrant 2) positive […]

Continue reading about Template Matching – Confusion Matrix Analysis – Quadrant II

In an attempt to analyze some data as well as performance of my classifier and implementation on solar cavity detection I decided it would be beneficial to see the data in terms of individual quadrant performance. Below is the confusion matrix for the outcomes of the Quadrant I, based on 100 q1 (quadrant 1) positive […]

Continue reading about Template Matching – Confusion Matrix Analysis – Quadrant I