Introduction
NEU Surface Defect Database
In-718 Charpy Fracture Surface Dataset
Analysis Pipeline
Image Processing
Feature Extraction
Dimensionality Reduction
t-distributed Stochastic Neighbor Embedding
Classification
Results and Discussion
Classification Accuracy
Conclusion
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Developing a transfer learning pipeline that utilizes the fully-connected layer of a pre-trained convolutional neural network (the VGG16 CNN trained on the ImageNet database) as the image representation.
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Applying unsupervised learning (t-distributed Stochastic Neighbor Embedding) to discover visually distinct clusters of images within two microstructural data sets.
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Classifying micrographs using minimally supervised clustering approaches (k-means).
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Demonstrating that this approach successfully classifies images both in a dataset with visually distinctive classes (NEU surface defects) and in a dataset that humans have difficulty classifying (In-718 Charpy fracture surfaces).
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Showing that the unsupervised, transfer learning method gives results comparable to fully supervised, custom built approaches on the NEU dataset.
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Python code for this analysis pipeline can be found at arkitahara.github.io