Automated Solar Cavity DETCTION

Image Processing & Pattern RecognitionAutomated Solar Cavity Detection1Athena JohnsonOutlineIntroductionBackgroundProblem StatementProposed SolutionExperimentsConclusionsFuture Work2Introduction

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backgroundSolar 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

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-- 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?

Atmospheric Imaging Assembly (AIA)Images the Corona of the SunStudy of solar stormsHow they are created?How they propagate upward?How they emerge from the Sun?How magnetic fields heat the corona?

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-- 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?

SOLAR CAVITIESCurrently an increase in implementations focused on Solar CavitiesOff limb structures Darker elliptical structure, encompassed by lighter regionsHypothesized to be precursors to solar eventsAid in establishing a predictive solar weather system

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-- 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?

SOLAR CAVITIESLabrosse, Dalla and Marshall (2010)Radial intensity profilesSupport Vector Machine (SVM)Region growingCalculation of metricsRunning difference on subsequent images

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-- 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?

SOLAR CAVITIESDurak and Nasraoui (2010)Exraction of principal contoursCalculations on contoursAdaboost

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-- 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?

Problem statementComputation timesDetections based on metricsWeak events missedMultiple detectionsMultiple events missedLow hit rates9

-- show a few different types of solar cavities to help with your points.Haar ClassifierMethod that Paul Viola and Michael Jones published in 2001

Four key conceptsHaar-like featuresIntegral ImageAdaboostingCascade of Classifiers

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10Haar-Like FeaturesAids in satisfying real time requirementsRectangular regionsReduces Computation11

Good. Integral imagesRapid computation of Haar-like features

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Integral imagesOriginal Image 8+6+2+5+6+3 = 30Integral Image 50-17-5+2 = 3013

adaboostingAids in increasing the accuracy and speedBegins with uniform weights over training examplesObtain a weak classifierUpdate weights

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Weak Classifier h1(x)Like integral image, start with statement on the reason why Adaboosting is used, then explain how it works.adaboosting15

Weak Classifier h2(x)Weak Classifier h3(x)adaboostingWeak classifiers combined to form the strong classifier

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Cascade of classifiersIncreases the speed of detectionsAll Haar-like features from all stages combined into a final Classifier ModelCascade of boosted classifiers with Haar-like features17

Again, why a cascade of classifiers is used?Cascade of classifiersA series of classifiers are applied to every subwindow of imageA positive result from the first classifier, triggers evaluation from the second classifier and so on18

Initial solution19

-- Talk about the problems with the first model first, then the second model. -- focus on the differences when you explain the model.ResultsManually selected Training Image SetsPositive Samples = 100Negative Samples = 400 79.6% Correct detection rate was achieved

20This slide is completely out of place. If you want to show the result of the first model, show and explain the model first.ResultsMissed detections in specific quadrantsDetections on the Suns diskOverlapping detections

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Proposed Solution22-- Talk about the problems with the first model first, then the second model. -- focus on the differences when you explain the model.Minimized training sets10 Positive Images10 Negative Images23

Do not just use experiment. Use more specific title that is in consistent with the model. Mark regions of interest and rotateDeriving images from selected imagesRotation applied to both training sets

24Use more specific title that is in consistent with the model.

Transform regions of interestTransformations on cavities

25Use more specific title that is in consistent with the model.

PreprocessingEdge DetectionHough LinesCalculate the radius26

Use more specific title that is in consistent with the model.

ResultsDerived Training Image SetsInitial image in sets = 10Positive Samples = 3600Negative Samples = 3600 96% Correct detection rate was achieved

27I 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?Final image with detections28

For each slide, you want to tell the audience something. If possible, use more specific slide title.

ConclusionLess manual workShort training times< 22 hours Wider range of detectionsWeak and strong cavitiesFast run times< 1 second per imageHigher hit rates

29Let the facts talk. When you say short training time How long exactly?29Future workTechnique ImprovementReduction of False PositivesContour DetectionsTemplate MatchingCustomized Haar-like features

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Future workFind optimal number of training setsExtract MetricsUser Interface

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32QUESTIONS?