Kylie Gorman WEEK 1-2 REVIEW

CONVERTING AN IMAGE FROM RGB TO HSV AND DISPLAY CHANNELS

Original HSV Version

Hue Saturation Value

EDGE DETECTION

Sobel and Roberts Sobel X Sobel Y Roberts X Roberts Y

Canny Edge Detector 1. Smooth image with Gaussian filter 2. Compute derivative of filtered image 3. Find magnitude and orientation of gradient 4. Apply “Non- maximum Suppression” 5. Apply “Hysteresis Threshold”

HARRIS CORNER DETECTOR

Harris Corner Steps Compute x and y derivatives of image Compute products of derivatives at every pixel: Ix2 Ixy Iy2 Compute the sums of the products of the derivatives at each pixel Place each pixel into a matrix H Compute R = Det(H) – k(Trace(H))^2 Threshold on value of R

My Own Implementation

Harris Corner Function in MATLAB

SCALE INVARIANT FEATURE TRANSFORM (SIFT)

SIFT Algorithm: Finding Keypoints Use Difference-of-Gaussian Function Good approximation of Laplacian of Gaussian, but faster to compute Construct Scale Space Key Point Localization Use Scale Space to Find Extrema Throw Out Poorly Defined Peaks Orientation Assignment Multiple Orientations Improves Stability of Matching Keypoint Descriptor Computed from Local Image Gradients

SIFT using Vl_feat

Using SIFT to Match Same Image

Different Images

SUPPORT VECTOR MACHINES (SVM)

Linear SVM

Multi-Class SVM

OPTICAL FLOW

Optical Flow with Lucas-Kanade The Optical Flow Equation f x u + f y v = -f t has 2 unknown variables 3x3 window gives 9 equations with 2 unknown variables Put equations into matrix to get Au = f t To solve, multiply by the transpose of A: A T Au = A T f t u = (A T A) -1 A T f t Least Square Fit Solve for u and v

Lucas-Kanade with Images

Lucas-Kanade with Video Original Clip:

Lucas-Kanade with Vector Results

With Roberts Derivative Resized Image to ½ Original Resized Image to ¼ Original

With Sobel Derivative Resized Image to ½ Original Resized Image to ¼ Original

ADA BOOST

ADA Boost Expert is a pattern and a threshold Convolve an image with pattern and plot value on a number line Search for threshold

Face Detection

BAG OF WORDS/ FEATURES

Bag of Words/ Features Step One: Feature Extraction Extract Regions (SIFT, Harris) Compute Descriptors (SIFT) Step Two: Quantization Find Clusters and Frequencies (K-means) Step Three: Classification Compute Distance Matrix Classification (SVM)

PROJECT POSSIBILITIES

Final Project Project: Color-Attributes-Related Image Retrieval Graduate Student: Yang Zhang Goal: Enable people to retrieve an image according to an object with attributes or attributes alone. The project will focus on color as the starting attribute. Program: MATLAB

Steps 1. Validating Model: Download other code and compare it to our own code. 2. Coding: Add more features to the system the improve its performance. 3. Collecting Dataset: There are not any existing color image datasets on the Internet. Use automatic image collecting tool to create our own color object dataset. 4. Possible Bonus: Implement novel ideas about general attribute image retrieval system. Determine if it is effective or not.