1. Corner Detection & Color Segmentation CSE350/450-011 9 Sep 03

2. Administration Clarifications to Homework 1 Questions?

3. Class Objectives Linear Algebra Review Review how corners can be extracted from computer images Review how color is represented and can be segmented in a computer image

4. Supporting References “A Tutorial on Linear Algebra” by Professor C. T. Abdallah, University of New Mexico Edge & Corner Detection: Introductory Techniques for 3-D Computer Vision, Trucco & Verri, 1998 CVOnline “Color Image Processing” Lecture Notes Poynton's Color FAQ

5. Edge Detection Review INPUT IMAGE 1) Noise Smoothing EDGE IMAGE 2) Edge Enhancement Horizontal [-1 0 1] Vertical [-1 0 1] T “GRADIENT” IMAGE 3)Threshold

6. Linear Algebra Review 

7. Corner Detection Motivation Corners correspond to point in the both the world and image spaces Tracking multiple point across consecutive images allows us to estimate the relative rotation and translation of the camera –Hartley’s 8-point algorithm Since the camera moves with our robot, we can infer robot motion “simply” by tracking eight or more corners

8. Corner Detection Algorithm Trucco & Verri, 1998 1.Compute the image gradients 2.Define a neighborhood size as an area of interest around each pixel 3x3 neighborhood

9. 3.For each image pixel (i,j), construct the following matrix from it and its neighborhood values e.g. Corner Detection Algorithm (cont’d)

10. 3.For each matrix C (i,j), determine the 2 eigenvalues λ (i.j) = [λ 1, λ 2 ]. 4.Construct Λ-image where Λ(i,j)=min(λ (i.j) ). 5.Threshold Λ-image. Anything greater than threshold is a corner. Corner Detection Algorithm (cont’d) ISSUE: The corners obtained will be a function of the threshold !

11. Corner Detection Sample Results Threshold=25,000Threshold=10,000 Threshold=5,000

12. Color Segmentation Motivation Computationally inexpensive (relative to other features) “Contrived” colors are easy to track Combines with other features for robust tracking

13. What is Color? Color is the perception of light in the visible region of the spectrum Wavelengths between 400nm - 700nm Imagers –Retina (humans) –CCD/CMOS (cameras)

14. RGB Color Space Motivated by human visual system –3 color receptor cells (rods) in the retina with different spectral response curves Used in color monitors and most video cameras

15. YCbCr (YUV/YIQ) Color Space “Greyscale” Y= 0.30*R+0.59*G+0.11*B Separates luma (“brightness”) from the chroma (“color”) channels: Y = 0.30*R+0.59*G+0.11*B, Cb = B-Y, Cr=R-Y YUV/YIQ are similar variants based upon NTSC/PAL television signals

16. Defining Colors in an RGB Image RedGreenBlue

17. How do we represent a “single” color? Sample set for orange hat

18. Simple RGB Color Segmentation && RedGreenBlue Segmented Color Image

19. Color Tracking Demo