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Corner Detection & Color Segmentation

Corner Detection & Color Segmentation. CSE350/450-011 9 Sep 03. Administration. Clarifications to Homework 1 Questions?. Class Objectives. Linear Algebra Review Review how corners can be extracted from computer images

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Corner Detection & Color Segmentation

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

  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 AlgorithmTrucco & Verri, 1998 • Compute the image gradients • Define a neighborhood size as an area of interest around each pixel 3x3 neighborhood

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

  10. Corner Detection Algorithm (cont’d) • For each matrix C(i,j), determine the 2 eigenvalues λ(i.j)=[λ1, λ2]. • Construct Λ-image where Λ(i,j)=min(λ(i.j)). • Threshold Λ-image. Anything greater than threshold is a corner. ISSUE: The corners obtained will be a function of the threshold !

  11. Corner Detection Sample Results Threshold=25,000 Threshold=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. “Greyscale” Y= 0.30*R+0.59*G+0.11*B YCbCr (YUV/YIQ) Color Space • 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 Red Green Blue

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

  18. Simple RGB Color Segmentation Red Green Blue Segmented Color Image & &

  19. Color Tracking Demo

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