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Particle picking
Carlos Óscar S. SorzanoVahid Abrishami
Instruct Image Processing Center
Particle picking
• The problem• Preprocessing• Automatic picking
– 3D Model-based picking– 2D Model-based picking– Feature-based picking
• Screening• Consensus picking
The problem
The problem
The problem
Preprocessing
• Downsampling• Fourier filtering• Wavelet filtering• Quantization
Automatic picking: 3D model based
• Correlation peaks:• Cross-correlation• Fourier-correlation• Local-correlation• Normalized-correlation
• Threshold criteria:
Automatic picking: 2D model based
• Correlation peaks:• Cross-correlation• Fourier-correlation• Local-correlation• Normalized-correlation
• Threshold criteria:
Automatic picking: Feature based
91D vector
Automatic picking: Feature based
• Classifier:• SVM• Naive Bayesian• Neural network• LDA
• Cascaded classifiers:• AdaBoost
Manual supervision
Automatic Screening
20D vector
Screening: Mahalanobis distance
Automatic Screening
Automatic Screening
Consensus picking
Conclusions
• Picking families:– 2D/3D Model based: “correlation”+threshold
criterion– Feature based: nD features+classifier
• A posteriori screening:– nD features+distance rank
• Consensus picking• State-of-the-art: 85% precision, 70% recall