Image processing - task no.3

author: Petr Sladek (sladep1) , Milan Kratochvil (kratom4) at CTU Prague, FEE

The Basics:
  • A matrix calculator (matrix of collineation)~spoctiA, HTML: src/amat.html | mfile: src/amat.m
  • Derivation of inverse collineation (how to get inversed A for collineation, is it inv(A) okay?): mfile: HTML: src/amat.m
  • Collineation [x1 x2]=collin(A,[u1 u2]), u1,2 x1,x2 are matricies m x n HTML: dat/collin.html | mfile: src/collin.m
  • eval - utility - get some transformed pixels from T matrix (image transf. matrix) mfile: src/eval.m
Diffrerent types of the image collineation methods (demos): Core task - interpolated collineation Comparison of the different passpoints locations:
  • HTML: dat/demo.html | mfile: src/demo.m
  • the most important is:
    • very close passpoints results in high inaccuracy
      Experience: do not put passpoints very close, spread them over the whole image.
    • passpoints in one straight line results in the SVD malfunction (high numeric error)
      Experience: do not put passpoints into one line (avoid any linearities)
    • more than 4 passpoints spread over the whole image is good practice
Image fixing (skyscraper):