presentation reu in computer vision 2014 amari lewis crcv university of central florida
TRANSCRIPT
PRESENTATION REU IN COMPUTER VISION
2014AMARI LEWIS
CRCV
UNIVERSITY OF CENTRAL FLORIDA
IMPLEMENTING DIFFERENT WAYS TO IMPROVE PICTURES…
OriginalThe top image combines
the different channels and uses convolution
F *h= Σ Σ f(k,l)h(-k,-l)
F= imageH=kernel
COMBINE CHANNELS
GAUSSIAN
Type of smoothing, a weighted average of the surrounding pixels
using this formula:
The sigma value determines the amount of
‘blurr’ the image will display.
Gaussian smoothing
Original
‘LAPLACIAN’
Finds the 2nd Derivative of Gaussian
HISTOGRAM – USED TO REPRESENT EACH COLOR IN THE IMAGE
OBSERVE BELOW
EDGE DETECTION-
Roberts
Roberts: finds edges using the Roberts approximation to the derivative. It returns edges at those points where the gradient of I is maximum.
Canny
Uses two thresholds to determine between weak and strong edges
Canny
Roberts
EDGE DETECTION WITH THRESHOLD
Sobel X: [1 0 -1, 2 0 -2, 1 0 -1]Y: [1 2 1, 0 0 0, -1 -2 -1]Calculates: √(d/x)²+(d/dy)²
PYRAMIDS
ADABOOST – FACE DETECTIONBoosting defines a classifier using an additive
modelF(x) = ∂1f1(x) +∂2f2(x)+∂3f3(x)….
F:strong classifierX- feature vectorsSigma= weight
f – weak classifiers
TRIAL 2
SVM • SVM (Support Vector Machine) classifier is able to test trained data to analyze and divide results. (object ore non—object)
• This is an example of linear classification
• Linearsvm calculates : f(x) = w^Tx+b
• where w is the normal line or weight vector and b is the bias
RESIZING MULTIPLE IMAGES THROUGH FOR LOOPS..
LUCAS KANADE (LEAST OF SQUARES)
• Optical flow equation-
• Considers a 3x3 window
Lucas Kanade
OPTICAL FLOW
LUCAS KANADE
WITH PYRAMIDS
CLUSTERING, BAG OF FEATURES
THE PROJECT I’M INTERESTED IN WORKING ON
• THE APPLICATIONS OF LIGHT FIELDS IN COMPUTER VISION
AIDEAN SHARGHI
THANK YOU !!
• I APPRECIATE THE OPPORTUNITY ONCE AGAIN AND I AM LEARNING A LOT FROM THIS EXPERIENCE
THANKS,
OLIVER NINA
DR. LOBO
DR. SHAH