UCF REU: Weeks 1 & 2

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UCF REU: Weeks 1 & 2

description

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 ). - PowerPoint PPT Presentation

Transcript of UCF REU: Weeks 1 & 2

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UCF REU:Weeks 1 & 2

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GradientCode

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Gradient

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Direction of the Gradient:Calculating theta

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Picking a threshold for the gradient map

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Subtract the gradient from the original image display

it

Detailing oncamera

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Edge Detection

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Sobel approximation to the derivative

Filters

Lapacian of Gaussian (LoG)

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Canny Edge DetectorGaussian filter to smooth image1st derivative kernel to detect edgesNon-maximal suppression

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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.

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Harris Corner Detector

Shifting the window in any direction should yield a large change in appearance

-aka what happens at a corner!

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Code for Harris Corner

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Optical Flow (Lucas Kanade with

pyramids)

Shows the vectors of motion.

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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:

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• 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

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Scale Invariant Feature Transform (SIFT)

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Code (for one image)

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Code to collect all imagesHave to do these 2 pieces of code for every 15 categories

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Input imagesBasic matching using SIFT points

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Result

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Code

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Adaboost

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SVM (Support Vector Machine)

Pick classifier where the distance between the support vectors and the linear classifier is maximized.

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Bag Of Words

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Project Preference 1Project: Multimodal data analysis for the

detection of Attention Deficit Hyperactive Disorder

Mentor: Soumyabrata DeyBegan his reading most recent paper on the

projectHave taken discrete systems which has graph

theory which will help because: looking at the brain as a series of nodes and edges

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Project Preference 2Project: Deep TrackerMentor: Afshin Dehghan I know pythonLooked at papers on deep learning

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Project Preference 3Project: Action Recognition in Temporally

Untrimmed videosMentor: Amir R. ZamirHave a solid grasp of frame workVery interested in topic