Radial Basis Functions and Application in Edge Detection

1. Radial Basis Functions and Application in Edge Detection Chris Cacciatore Tian Jiang Kerenne Paul

2. Abstract • This project focuses on the use of Radial Basis Functions in Edge Detection in both one-dimensional and two-dimensional images. • Use a 2-D iterative RBF edge detection method. • Vary the point distribution and shape parameter. • Quantify the effects of the accuracy of the edge detection on 2-D images. • Study a variety of Radial Basis Functions and their accuracy in Edge Detection.

3. Radial Basis Functions • Multi-Quadric RBF: • Inverse Multi-Quadric RBF: • Gaussian RBF: ()

4. Project with Maple Leaf Initial image The most accurate image epsilon = 0.1

5. Epsilon Variable epsilon = 0.01 epsilon = 0.05 epsilon = 0 epsilon = 1 epsilon = 2 epsilon = 0.1

6. Total Image

7. Edge Detection with another image Initial image

8. Epsilon Variable epsilon = 0 epsilon = 0.05 epsilon = 0.1 epsilon = 0.3 epsilon = 1 epsilon = 0.2

9. Epsilon Variable Epsilon=0.2 Epsilon=0.3 more accurate

10. What to do: • Get familiar with MATLAB and use it to help analyze the code • Find other factors in the code rather than epsilon to make the image look different • Research further into how the code used works with Radial Basis Function (Multi-Quadric RBF) • Investigate the other two RBFs and their practicality in edge detection