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
OpenCV has functionality built in for template matching. The template matching implementation is basically matching a subimage against a main image by sliding it across the entire image using one of the matching methods, in my implementations I used the normalized square difference matching method. Below is a summarized excerpt for template matching within Quadrant […]
Below are examples of the haar-like feature sets that are used in training. It displays what features are used based on the “mode” option used as well as the string representation that will be used in each of the AdaBoostCARTHaarClassifier.txt files created for each stage on the classifier. haar_x2 haar_y2 haar_x3 haar_y3 haar_x2_y2 haar_x4 haar_y4 […]
Training Options Used – -npos 1134 -nneg 625 -nstages 20 -mode ALL The number of positive images used to train this classifier was 1134, the number of negative images used was 625 and specified that the number of stages the trainer should try to complete is 20. Used the mode ALL switch, which not only […]
Training Options Used – -npos 1898 -nneg 625 -nstages 20 -mode ALL The number of positive images used to train this classifier was 1898, the number of negative images used was 625 and specified that the number of stages the trainer should try to complete is 20. Used the mode ALL switch, which not only […]
In running through tests against all the haarcascades I create, I noticed that the number of Misses in Quadrant III seemed surprisingly high, even when the cavity was a very distinct one. Here is an example of the original image that was used to train the cascades. Here is an output file from the […]
Thought I would share this video I found, I think it gives alot of insight into the importance of solar research as well as it has some great info and visuals for solar weather and the benefits of the new observation satellite SDO. Enjoy!