cascaded classifier for automatic crater detection henry z. lo advisor: wei ding domain scientist:...
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![Page 1: Cascaded Classifier for Automatic Crater Detection Henry Z. Lo Advisor: Wei Ding Domain Scientist: Tomasz Stepinski Knowledge Discovery Lab University](https://reader035.vdocuments.site/reader035/viewer/2022062619/55178a705503463e368b5471/html5/thumbnails/1.jpg)
Cascaded Classifier for Automatic Crater Detection
Henry Z. Lo
Advisor: Wei DingDomain Scientist: Tomasz Stepinski
Knowledge Discovery LabUniversity of Massachusetts Boston
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Overview
• Introduction:o Cascading classifier.o Experimental road map.
• Experiments:o Tests on feature sets.o Tests on positive example training set content.o Tests on negative example training set size.o Tests on negative example training set content.
• Discussion:o Implications of results.o Unresolved issues.o Future directions.
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Cascading Classifier
• Architecture:o Layers of Adaboost classifiers.o Each layer trained on the FP of previous layer. o Input must be accepted by all, sequentially, to be
considered a crater.o Rejection can happen at any stage.
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Cascading Classifier
• Features:o Exclusively uses Haar-like features.o Can be calculated in constant time.o Contrast based.o Scanned over entire subwindow.
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Cascading Classifier
• Implementation:o Used OpenCV implementation.o Free and open source. o Many variables:
Number of layers. "Minimum hit rate" - false positive rate. "Max false alarm" - false negative rate. 3 feature sets.
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Experimental Road Map
• Tweak for performance:o OpenCV parameters.o Features.o Training set.
• The following OpenCV parameters improve performance:o Minimum hit rate.o Max false alarm.o Number of layers.
• Still need to tweak features and training sets for:o Training time.o Generalizability.
• L
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Experimental Road Map
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• Each of these factors will be tested individually for effect on precision, recall, and F1.
• We avoid studying interaction effects for simplicity.
• In the future, we will investigate how to combine different
features and test sets for optimal result.
Experimental Road Map
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• We use tile 3-24 for both training and testing.• This tile was chosen for its relatively smooth surface.• Future studies will test on other tiles as well.
Experimental Road Map
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Feature Set Variation
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Feature Set Variation
• OpenCV offers 3 different feature sets: o CORE: 1a, 1b, 2a, 2c.o BASIC: CORE + 2b, 2d, 3ao ALL: all features
• Since ALL is a superset of CORE and BASIC, it should
perform best.
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Feature Set Variation
• In recall, CORE and BASIC outperformed ALL.
• In precision and F1, the exact opposite was true.
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Haar Features
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Haar Features
• Inclusion of tilted features beneficial to performance. • More features than those given may provide further benefit.
• It is not obvious how to create Haar features in OpenCV.
• Postponing creation of specialized Haar features.
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Ground Truth Windows
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Ground Truth Windows
• Positive examples contained tightly cropped craters. • No crater rims or surrounding area.
• Experimented with including area around craters. • • Range: 1x crater radius - 2x crater radius, in steps of .1.
1.0 1.2 1.4 1.6 1.8 2.0
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Ground Truth Windows
• As the subwindow increased, precision and F1 increased.
• However, recall suffered.
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Negative Example Set Size
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Negative Example Set Size
• All classifiers tested were trained on 300 negative examples. • By providing the classifier with more negative examples, we
give it more information. • Performance should increase with more negative examples.
• Tested classifiers trained on 300, 400, 500, 600, and 700
negative examples.
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Negative Example Set Size
• F1 and precision increase with more negative examples.
• Recall decreases.
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Negative Example Manipulation
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Negative Example Manipulation
• The idea is to put some false positives back into the training set.
• This will teach the classifier using its own mistakes.
• However, selecting the false positives is rather difficult, as
we will see later.
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Result Implications
• Window scaling has the most noticeable effect on F1, recall, and precision.
• Next most important is the feature set used.
• The number of negative training examples is the least
important; however, this may be due to the small range of values being tested.
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Future Directions
• Once optimal features and training sets are found, we can
manipulate OpenCV variables. • Recall that the classifier may be improved by the following:
o More layers in the classifier.
o Setting the minimum hit rate (recall).•
o Setting the max false alarm rate (precision).• • Time complexity of classifier training requires further study.
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Future Directions
• Further exploration of cascaded classification algorithm:
o Testing classifier on other tiles. • Exploration of other object detection algorithms.
o Neural networks.
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Questions?