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

2. Particle picking The problem Preprocessing Automatic picking – 3D Model-based picking – 2D Model-based picking – Feature-based picking Screening Consensus picking

3. The problem

6. Preprocessing Downsampling Fourier filtering Wavelet filtering Quantization

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

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

9. Automatic picking: Feature based 91D vector

10. Automatic picking: Feature based Classifier: SVM Naive Bayesian Neural network LDA Cascaded classifiers: AdaBoost

11. Manual supervision

12. Automatic Screening 20D vector

13. Screening: Mahalanobis distance

14. Automatic Screening

16. Consensus picking

17. 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