Particle picking Carlos Óscar S. Sorzano Vahid Abrishami Instruct Image Processing Center.

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Presentation transcript:

Particle picking Carlos Óscar S. Sorzano Vahid 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

Preprocessing Downsampling Fourier filtering Wavelet filtering Quantization

Automatic picking: 3D model based Correlation peaks: Cross-correlation Fourier-correlation Local-correlation Normalized-correlatio n Threshold criteria:

Automatic picking: 2D model based Correlation peaks: Cross-correlation Fourier-correlation Local-correlation Normalized-correlatio n 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

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