1. Edge Detection Exploring Edge Detection Techniques By Asa Kusuma
Computer Systems Research

2. Basic Edge Detection Calculates difference between adjacent pixels
Averages Difference in the X and Y direction

3. Is This Part of the Image an Edge?

4. No, it's only Noise

5. Analysis of Basic Edge Detection
Advantages
Fast
Easy to code
Disadvantages
Only works well with simple images
Confused by noise
Narrow view

6. Tackling the Problem of Noise
What is Noise?
Random changes in pixel value
Usually associated with texture
Like sandpaper
Detecting Noise
Looking at the entire image
Searching for line patterns
Blurring

7. Blurring Technique Blur the image
Look for differences across larger pixel gaps

8. Recursive Blurring Technique
Blur image
Find biggest pixel difference gaps
Recursively go through and find adjacent gaps

9. Problems and Issues Speed Language Choice Blurring takes a lot of time
Finding lines takes time
Language Choice
Currently using Python
Easy to code different algorithims
Slow
C/C++?
Faster

10. Project Direction Analysis of running biomechanics Pronation
Image from behind runner
Angle of ankle mass
Compare to angle of leg
Heel Strike
Image from the side of runner
Measure bend in knees after impact