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REU Report I

REU Report I. Alla Petrakova UCF. Material covered. MATLAB Derivatives, Filters, Thresholding , Equalization, etc. Correlation, Convolution Edge Detection ( Sobel , Laplacian of Gaussian, Canny) Harris Corner Detector SIFT Adaboost , face detection SVM Optical Flow Bag of Features.

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REU Report I

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  1. REU Report I Alla PetrakovaUCF

  2. Material covered • MATLAB • Derivatives, Filters, Thresholding, Equalization, etc. • Correlation, Convolution • Edge Detection (Sobel, Laplacian of Gaussian, Canny) • Harris Corner Detector • SIFT • Adaboost, face detection • SVM • Optical Flow • Bag of Features

  3. Edge detection - Sobel

  4. Gaussian

  5. Edge detection - Sobel

  6. Laplacian of Gaussian

  7. CANNY

  8. Harris Corner Detector

  9. Harris corner detector

  10. SIFT – dense sampling

  11. SIFT – descriptors included

  12. Optical Flowcomparing outputs – Ce Liu

  13. Optical Flow Window size = 70 Window size = 40

  14. SVM & Bag Of words • SVM • One of the biggest challenges • Tried with sift, dense sift, scaled data • Stubbornly stuck on 53% accuracy • Bag Of Words • 47% to 53% accuracy • Possible solution: • “A Practical Guide to Support Vector Classification Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin “ • http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

  15. Research projects • “Trajectory Clustering: A Motion Pattern approach” by Mahdi M. Kalayeh • Pattern recognition • Analysing effects of applying various similarity measures • Probabilistic Predictive Modelling • “Clustering in High Dimensional Data” by Gonzalo Vaca-Castano • “Cell Tracking and Lineage Construction” with Sarfaraz Hussein

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