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UCF REU: Weeks 1 & 2

UCF REU: Weeks 1 & 2. Gradient Code. Gradient. D irection of the Gradient: Calculating theta. Picking a threshold for the gradient map. Subtract the gradient from the original image display it . Detailing on camera. Edge Detection. Filters. Lapacian of Gaussian ( LoG ).

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UCF REU: Weeks 1 & 2

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  1. UCF REU:Weeks 1 & 2

  2. GradientCode

  3. Gradient

  4. Direction of the Gradient:Calculating theta

  5. Picking a threshold for the gradient map

  6. Subtract the gradient from the original image display it Detailing on camera

  7. Edge Detection

  8. Filters Lapacian of Gaussian (LoG) Sobel approximation to the derivative

  9. Canny Edge Detector • Gaussian filter to smooth image • 1st derivative kernel to detect edges • Non-maximal suppression

  10. Median filter • B = medfilt2(im) does a median filtering of the image im in two dimensions. • Each output pixel contains the median value in the 3 x 3 neighborhood around the corresponding pixel in the input image. It pads the image with zeros on the edges, so the median values for points on the edge of the image may appear darker. • Median filtering is most often used to reduce "salt and pepper" noise. • A median filter is more effective than convolution when the goal is to simultaneously reduce noise and preserve edges.

  11. Harris Corner Detector Shifting the window in any direction should yield a large change in appearance -aka what happens at a corner!

  12. Code for Harris Corner

  13. Optical Flow (Lucas Kanade with pyramids) Shows the vectors of motion.

  14. Optical Flow (Liu) • Found YouTube clip of Olympic diver: https://www.youtube.com/watch?v=djou2oLUeuo • Clipped the video into a 3 second clip of just the action of diving • Converted the clipped video into a sequence of jpg images • Example:

  15. Made a loop in demo flow to run through the sequence of diving images • Took the optical flow images and converted them into an animated gif: • Olympic Diver: http://makeagif.com/I27cPy

  16. Scale Invariant Feature Transform (SIFT)

  17. Code (for one image)

  18. Code to collect all images Have to do these 2 pieces of code for every 15 categories

  19. Input images Basic matching using SIFT points

  20. Result

  21. Code

  22. Adaboost

  23. SVM (Support Vector Machine) Pick classifier where the distance between the support vectors and the linear classifier is maximized.

  24. Bag Of Words

  25. Project Preference 1 • Project: Multimodal data analysis for the detection of Attention Deficit Hyperactive Disorder • Mentor: SoumyabrataDey • Began his reading most recent paper on the project • Have taken discrete systems which has graph theory which will help because: • looking at the brain as a series of nodes and edges

  26. Project Preference 2 • Project: Deep Tracker • Mentor: Afshin Dehghan • I know python • Looked at papers on deep learning

  27. Project Preference 3 • Project: Action Recognition in Temporally Untrimmed videos • Mentor: Amir R. Zamir • Have a solid grasp of frame work • Very interested in topic

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