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