REU Week 1

REU Week 1Presented by Christina Peterson

Edge DetectionSobel

◦Convolve image with derivative masks: x:

y:

◦Calculate gradient magnitude◦Apply threshold

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Edge DetectionMarr Hildreth

◦Apply Laplacian of Gaussian to an image

◦Find zero crossings {+,-}, {+, 0, -}, {-, +}, {-, 0, +}

◦Mark edges Apply threshold to slope of zero-crossings

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Edge DetectionCanny

◦Convolve image with first derivative of gaussian

◦Find magnitude of gradient and orientation

◦Apply non-max suppression For each pixel, check if it is a local max

by comparing it to neighbor pixels along normal direction to an edge

◦Apply hysteria thresholding

Canny Example

Original Image Canny Output

Harris Corner DetectorImplemented Harris Corner Detector

◦1. x and y derivatives Ix=conv2(double(I), maskx, ‘same’) Iy=conv2(double(I), masky, ‘same’)

◦2. products of derivatives Ix2=Ix.*Ix Iy2=Iy.*Iy Ixy=Ix.*Iy

◦3. sums of products of derivatives Sx2=gauss_filter(Ix2, sigma, kernel_size) Sy2=gauss_filter(Iy2, sigma, kernel,size) Sxy=gauss_filter(Ixy, sigma, kernel_size)

Harris Corner Detector◦4. Define matrix H(x,y):

For j=1:columns, For i=1:rows, H{ i, j } = [Sx2(i, j) Sxy(i, j); Sxy(i, j) Sy2(i, j)

◦5. Response Detector For j=1:columns,

For i=1:rows, R( i, j ) = det(H{ i, j } )– k*(trace(H{ i, j }))^2

◦6. Apply threshold to R Edge: R < -10000 Corner: R > 10000

Harris Corner Detector

SiftPurpose

◦To identify features of an image regardless of scale and rotation

Scale Space◦Resize image to half size (octave)◦Blur image by adjusting sigma ◦4 octaves and 5 blur levels are

recommended

SiftSift Features

◦Divide image into 4 x 4 windows◦Divide each window into 4 x 4

subwindows• Calculate magnitude and gradient for

each subwindow◦Generate a histogram of 8 bins for each 4 x 4 window• Each bin represents a gradient orientation

• 4 x 4 x 8 = 128 dimensions

Sift using Vl_feat

Sift using Vl_feat

Match candidates by finding patches that have the most similar SIFT descriptor

Optical FlowLucas Kanade Optical Flow

Does not work for areas of large motion◦Resolved by Pyramids

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Optical Flow

Bag of FeaturesImplemented a Bag of Word

classificationDivided image into framesConcatenated sift descriptors for

each frameKmeans2 to cluster featuresImage represented as histogramUsed histograms as training data

for SVM

Bag of FeaturesResults for 8 frames and 20

clusters:◦9.5% accuracy on test data

Conclusions:◦Increase frames and clusters to

improve accuracy

Research Topics

1. Survey on Multiple Human Tracking by Detection Methods • Afshin Dehghan

2. Data Driven Attributes for Action Detection• Rui Hou

3. Subspace Clustering via Graph Regularized Sparse Coding• Nasim Souly