Marr s framework for vision
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Marr’s framework for vision. 2-1/2D sketch. Primal sketch. Object Recognition. Early processing. 3D estimation. Image. Primal sketch. Local edges Corners T-junctions Blobs Groups of features. Derivatives as edge finders. Edges are sharp changes in image intensity.

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Marr’s framework for vision

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Marr s framework for vision

Marr’s framework for vision

2-1/2D

sketch

Primal

sketch

Object

Recognition

Early

processing

3D

estimation

Image


Primal sketch

Primal sketch

  • Local edges

  • Corners

  • T-junctions

  • Blobs

  • Groups of features


Derivatives as edge finders

Derivatives as edge finders

  • Edges are sharp changes in image intensity.

  • 1st derivative of the image intensity peaks at an edge

  • 2nd derivative is zero at edges

  • Edges are at zero-crossings of the second derivative


2 d second derivative operator the laplacian

2-D second derivative operator: the laplacian


Multi scale filtering

Multi-scale filtering

  • Find zero-crossings at multiple scales

    • Filter with Laplacian of Gaussian filters that have different sizes

  • Edges = zero-crossing sat all scales

    • Find spatial coincidence of zero-crossings across scales


Mirage

Mirage

  • Filter with three Laplacian of Gaussians (different sizes)

  • Seperately sum negative and positive parts

  • Mark zero (Z) regions, positive response regions (R+) and negative response regions (R-)

  • Rules

    • Z region = luminance plateau

    • R region with only one Z on a side = edge

    • R region with Z on both sides = bar


Ways to improve edge detection

Ways to improve edge detection

  • Take advantage of the spatial structure of edges

    • Edges are oriented

      • Use directional derivatives

        • Simple cells as directional derivatives

      • Compute local oriented contrast “energy”


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