Summary: Projections

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  • 01 Introduction

    This is a preview. There are 3 more flashcards available for chapter 01
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  • Why visual search engine?

    -   Growing data requires more efficient solution

    -   Manual indexing is costly and time-consuming

    -   New technologies are needed to automate processes and to unlock possibilities of big data

    -   Metadata standards are needed
  • 1 Image Filtering

    This is a preview. There are 4 more flashcards available for chapter 1
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  • What is an edge (derivative)?

    An edge is a place of rapid change in the image intensity function
  • What is the computational complexity advantage for a separable filter of size k x k, in terms of number of operations per output pixel?

    For a k x k Gaussian filter, 2D convolution requires k^2 operations per pixel

    But using the separable filters, we reduce this to 2k operations per pixels (3 from top to bottom and 3 from left to right)
  • What is the difference between cross-correlation and convolution?

    Flip the filter (a, b, c...) > (i, h, g)
  • Why do you use the Gaussian kernel?

    To find edges. 

    The image is made smooth so the edges are better to be seen.
  • How to filter noise?

    1.   Let's replace each pixel with an average of all the values in its neighbourhood.

    2.   Apply a gaussian filter.

    Correlation filtering ( G = H (X) F)
    Convolution (G = H * F)

  • What is f(x, y)?

    It gives the intensity at position (x, y)
  • What is a color image?

    Three functions pasted together 

    f(x,y) = [r(x,y) g(x,y) b(x,y)
  • What is (vector) quantization?

    -   The process of clustering features
    -   Building the visual vocabulary

  • Name three different types of noise

    Salt and pepper noise (white and black pixels)

    impulse noise (white pixels)

    Gaussian noise (variations in intensity drawn from a Gaussian normal distribution

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