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A survey of Light Source Detection Methods. Nathan Funk University of Alberta Nov. 2003. What is Light Source Detection?. Problem of Computer Vision Typically given a single image of a scene Where is the light coming from? Goal:

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A survey of Light Source Detection Methods

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A survey of light source detection methods l.jpg

A survey ofLight Source Detection Methods

Nathan Funk

University of Alberta

Nov. 2003


What is light source detection l.jpg

What is Light Source Detection?

  • Problem of Computer Vision

  • Typically given a single image of a scene

  • Where is the light coming from?

  • Goal:

    • Recoverdirections, intensities, and types(directional, point, area…)of light sources.


Example 1 l.jpg

Example 1


Example 2 l.jpg

Example 2


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Motivations, Applications

  • Scene reconstruction

    • Find shape of objects

    • Shape from shading

  • Augmented reality

    • Place an artificial objectin a real scene

    • Wrong lighting is obvious to us

Real

Artificial

[Zhang “Illumination Determination…”, 2000]


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Common Assumptions

  • Directional light sources

  • Lambertian surface

  • Smooth surfaces

  • Other:

    • Analysis of specific object

    • Known number of sources

    • Orthographic projection


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Pentland (1982)

  • Statistical approach

  • Analyse intensity changesin X and Y directions

  • Only single source

  • Similar methods:

    • Lee & Rosenfeld (1985) – targeted for sphere

    • Brooks & Horn (1985) – attempt to recover shape

Y

X


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Weinshall (1990)

  • Analyse intensities alongoccluding boundaries

  • Look for extreme points of intensity profile

  • Single source

  • Yang & Yuille (1991) use similar approach

    • Extended to detect multiple sources

[Nillius “Automatic Estimation…”, 2001]


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Zhang & Yang (2000)

  • Uses sphere model

  • Find cut-off curves High precision estimation of direction

  • Each cut-off curves identifies the direction of a light source

  • Detects multiple sources


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Wang & Samaras (2002)

  • Similar to Zhang & Yang

  • Known geometry

  • Map arbitrary surface to sphere

  • Then apply same techniques as Zhang


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Li, Lin, Lu, and Shum (2003)

  • “Multiple-cue Illumination Estimation”

  • Uses shading, shadows, and specular reflections

  • First technique to deal with textured objects


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Feature Comparison


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Challenges

  • Processing real images is difficult!

    • Arbitrary unknown objects

    • Textured objects

    • Other types of light sources(not just directional ones)

    • Reflected light


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