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Picture Comparison; now with shapes!

Picture Comparison; now with shapes!. Slightly weak during MS1, only colour comparison Several comparisons will be done Turn picture into greyscale to do shape comparison Use colour picture to do a colour histogram, and averaging/mixing. Histogram. Picture. Greyscale. Greyscale shapes.

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Picture Comparison; now with shapes!

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  1. Picture Comparison; now with shapes! Slightly weak during MS1, only colour comparison Several comparisons will be done Turn picture into greyscale to do shape comparison Use colour picture to do a colour histogram, and averaging/mixing Histogram Picture Greyscale

  2. Greyscale shapes Use greyscale picture and take contour From the contour you can describe the shape The first way is through approximation with central moments Centeral Moments are position invariant Grayscale Contour

  3. The Hero Ming-Kuei Hu Central Moments describes the polygon through probability. Hu-Moments are based on central moments Hu-Moments are rotation and skewing invariant. Using Hu-moments to describe the polygon means it doesn't matter how it's rotated, skewed, scaled or its position. There is 7 Hu-moments and when you use them you get a single number for each of them, making them useful for searching.

  4. The Hero Ming-Kuei Hu Central Moments describes the polygon through probability. Hu-Moments are based on central moments Hu-Moments are rotation and skewing invariant. Using Hu-moments to describe the polygon means it doesn't matter how it's rotated, skewed, scaled or its position. There is 7 Hu-moments and when you use them you get a single number for each of them, making them useful for searching.

  5. Fourier Descriptors Second method to describe polygons is through Fourier descriptors Describes the polygon with approximation using waves. Each new wave makes the approximation more exact. By using lower number of waves the approximation get rough, which at times is useful.

  6. Colour matching Use a histogram of colours in picture to see if they have similar set of colours Mix together colours to bigger group to get rough placement of colours.(still dependant on rotation then) Use a fully mixed picture(one colour) and histogram, for searching.

  7. Searching Use a rough search that is low cost Use more expensive search when they are accepted by low cost search. Pre-process pictures and tag them for the low cost search.

  8. OpenCV Contours Fourier Centeral Moments(which can easily be used for Hu moments) Histograms Picture Handling Pretty much everything I'd imagine needing

  9. Papers! Visual pattern recognition by moment invariants by Hu Ming-Kuei Shape-based image retrieval using generic Fourier descriptor by Dengsheng Zhang & Guojun Lu A comparative study of Fourier descriptors and Hu's seven moment invariants for image recognition by Qing Chen Robust and Efficient Fourier–Mellin Transform Approximations for Gray-Level Image Reconstruction and Complete Invariant Description by Stéphane Derrodea & Faouzi Ghorbel

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