1. Automated Solar Cavity Detection
Image Processing & Pattern Recognition
Athena Johnson

2. Outline Introduction Background Problem Statement Proposed Solution
Experiments
Conclusions
Future Work

3. Introduction

4. background Solar Dynamics Observatory (SDO)
Extreme Ultraviolet Variability Experiment (EVE)
Helioseismic and Magnetic Imager (HMI)
Atmospheric Imaging Assembly (AIA)
1.5 Terabytes (TB) of data per day
-- I suggest completely remove any discussion about CME.
-- Instead, focus on large volume of solar images using numbers and facts, and thus the need for automated detection of solar cavities.
-- My understanding is there is not much previous research on solar cavities. But you do need to explain all you know about previous research on solar cavity detection. Ultimately, the audience wants to clearly know: what have been done? What is your approach?

5. Atmospheric Imaging Assembly (AIA)
Images the Corona of the Sun
Study of solar storms
How they are created?
How they propagate upward?
How they emerge from the Sun?
How magnetic fields heat the corona?
-- I suggest completely remove any discussion about CME.
-- Instead, focus on large volume of solar images using numbers and facts, and thus the need for automated detection of solar cavities.
-- My understanding is there is not much previous research on solar cavities. But you do need to explain all you know about previous research on solar cavity detection. Ultimately, the audience wants to clearly know: what have been done? What is your approach?

6. SOLAR CAVITIES
Currently an increase in implementations focused on Solar Cavities
Off limb structures
Darker elliptical structure,
encompassed by lighter regions
Hypothesized to be precursors to
solar events
Aid in establishing a predictive
solar weather system
-- I suggest completely remove any discussion about CME.
-- Instead, focus on large volume of solar images using numbers and facts, and thus the need for automated detection of solar cavities.
-- My understanding is there is not much previous research on solar cavities. But you do need to explain all you know about previous research on solar cavity detection. Ultimately, the audience wants to clearly know: what have been done? What is your approach?

7. SOLAR CAVITIES Labrosse, Dalla and Marshall (2010)
Radial intensity profiles
Support Vector Machine (SVM)
Region growing
Calculation of metrics
Running difference on subsequent images
-- I suggest completely remove any discussion about CME.
-- Instead, focus on large volume of solar images using numbers and facts, and thus the need for automated detection of solar cavities.
-- My understanding is there is not much previous research on solar cavities. But you do need to explain all you know about previous research on solar cavity detection. Ultimately, the audience wants to clearly know: what have been done? What is your approach?

8. SOLAR CAVITIES Durak and Nasraoui (2010)
Exraction of principal contours
Calculations on contours
Adaboost
-- I suggest completely remove any discussion about CME.
-- Instead, focus on large volume of solar images using numbers and facts, and thus the need for automated detection of solar cavities.
-- My understanding is there is not much previous research on solar cavities. But you do need to explain all you know about previous research on solar cavity detection. Ultimately, the audience wants to clearly know: what have been done? What is your approach?

9. Detections based on metrics Weak events missed Multiple detections
Problem statement
Computation times
Detections based on metrics
Weak events missed
Multiple detections
Multiple events missed
Low hit rates
-- show a few different types of solar cavities to help with your points.

10. Haar Classifier
Method that Paul Viola and Michael Jones published in 2001
Four key concepts
Haar-like features
Integral Image
Adaboosting
Cascade of Classifiers

11. Haar-Like Features Aids in satisfying real time requirements
Rectangular regions
Reduces Computation
Good.

12. Integral images
Rapid computation of Haar-like features

13. Integral images 8+6+2+5+6+3 = 30 50-17-5+2 = 30 Original Image

14. adaboosting Aids in increasing the accuracy and speed
Begins with uniform weights over training examples
Obtain a weak classifier
Update weights
Weak Classifier h1(x)
Like integral image, start with statement on the reason why Adaboosting is used, then explain how it works.

15. adaboosting
Weak Classifier h2(x)
Weak Classifier h3(x)

16. adaboosting
Weak classifiers combined to form the strong classifier

17. Cascade of classifiers
Increases the speed of detections
All Haar-like features from all stages combined into a final Classifier Model
Cascade of boosted classifiers with Haar-like features
Again, why a cascade of classifiers is used?

18. Cascade of classifiers
A series of classifiers are applied to every subwindow of image
A positive result from the first classifier, triggers evaluation from the second classifier and so on

19. Initial solution
-- Talk about the problems with the first model first, then the second model.
-- focus on the differences when you explain the model.

20. Results Manually selected Training Image Sets
This slide is completely out of place.
If you want to show the result of the first model, show and explain the model first.
Manually selected Training Image Sets
Positive Samples = 100
Negative Samples = 400
≈ 79.6% Correct detection rate was achieved

21. Results Missed detections in specific quadrants
Detections on the Sun’s disk
Overlapping detections

22. Proposed Solution
-- Talk about the problems with the first model first, then the second model.
-- focus on the differences when you explain the model.

23. Minimized training sets
10 Positive Images
10 Negative Images
Do not just use “experiment.” Use more specific title that is in consistent with the model.

24. Mark regions of interest and rotate
Deriving images from selected images
Rotation applied to both training sets
Use more specific title that is in consistent with the model.

25. Transform regions of interest
Transformations on cavities
Use more specific title that is in consistent with the model.

26. Preprocessing Edge Detection Hough Lines Calculate the radius
Use more specific title that is in consistent with the model.

27. Results Derived Training Image Sets
Initial image in sets = 10
Positive Samples = 3600
Negative Samples = 3600
≈ 96% Correct detection rate was achieved
I understand this 96% is the result of performance testing result. Please check out how this rate is calculated. Average of 10 runs? 20 runs? From 10-fold cross validation?

28. Final image with detections
For each slide, you want to tell the audience something. If possible, use more specific slide title.

29. Conclusion Less manual work Short training times
< 22 hours
Wider range of detections
Weak and strong cavities
Fast run times
< 1 second per image
Higher hit rates
Let the facts talk. When you say “short training time” How long exactly?

30. Future work Technique Improvement Reduction of False Positives
Contour Detections
Template Matching
Customized Haar-like features

31. Future work Find optimal number of training sets Extract Metrics
User Interface

32. QUESTIONS?