Kylie Gorman WEEK 1-2 REVIEW. CONVERTING AN IMAGE FROM RGB TO HSV AND DISPLAY CHANNELS.

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 filter2. Compute derivative of filtered image3. Find magnitude and orientation of gradient4. 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 fxu + fyv = -ft has 2 unknown variables• 3x3 window gives 9 equations with 2 unknown variables

• Put equations into matrix to get Au = ft

• To solve, multiply by the transpose of A: • ATAu = ATft

• u = (ATA)-1AT ft

• Least Square Fit• Solve for u and v

Lucas-Kanade with Images

Lucas-Kanade with Video

Original Clip: http://www.youtube.com/watch?v=y6r8i_008SU

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.