Testing Outcomes
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
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 […]
Training Options Used – -npos 448 -nneg 587 -nonsym -mode ALL The number of positive images used to train this classifier was 448, the number of negative images used was 587 and did not specify the number of stages the trainer should try to complete, so by default it tries to achieve 14 stages. The […]
Training Options Used – -npos 448 -nneg 587 -nstages 20 The number of positive images used to train this classifier was 44, the number of negative images used was 587and specified that the number of stages the trainer should try to complete is 20. With this cascade the trainer was able to reach its set […]
Training Options Used – -npos 89 -nneg 479 -nstages 20 The number of positive images used to train this classifier was 89, the number of negative images used was 479 and specified that the number of stages the trainer should try to complete is 20. During the training phase of this cascade it finished in […]
Training Options Used – -npos 1134 -nneg 625 -nstages 20 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. With this cascade the trainer was able to reach its […]
Training Options Used – -npos 89 -nneg 479 -nonsym -mode ALL The number of positive images used to train this classifier was 89, the number of negative images used was 479 and did not specify the number of stages the trainer should try to complete, so by default it tries to achieve 14 stages. The […]
During testing and attempting to find the perfect (well…most nearly perfect) classifier, below are the trial runs and their statistical summaries. The Trial numbers are clickable links to a more verbose detailed section of how the run was constructed and graphs of the data from the outputs. Trial Number Hits Misses Total False Alarms Hits […]