Color Invariance

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Color Invariance

• Shadow Removal Seminar

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What we passed till now

• Cause to shadows, and what shadows means for us
  (the interpretation of shadows in human brain).
• How to create shadows graphically.
• Some shadow detection techniques

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This lecture Overview

• Intro
• Shadows
• Invariance and color invariance
• Shadow classification
• Shadow segmentation

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Intro - Shadows

• Generation of shadows
• Shadows Types
• Cast shadows
• Self shadows

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Intro - Invariance

• Invariant
• A feature (quantity or property or function) that
  remains unchanged when a particular
  transformation is applied to it.
• What it used for
• Invariance in images
• Matlab demo

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Intro Shadow detection techniques

• Shadow detection techniques classification
• Model based
• Property based

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Shadow identification and classification using
invariant color models

• Elena Salvador, Andrea Cavallaro,
• Touradj Eebrahimi
• 2001

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Overview

• Goal
• Constraints
• Color Invariants
• Algorithm steps
• Results
• Conclusions

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Goal

• Extraction and classification of shadows in color
  images.

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Constraints

• A simple environment assumed where shadows are
  cast on flat or nearly flat non textured surface.
• Objects are uniformly colored.
• Only one light source illuminates the scene.
• Shadows and objects are within the image.
• The light source must be strong.

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Color Invariants

• Photometric color invariants
• Definition
• Models of photometric color invariants
• Normalized rgb
• Hue (H) and saturation (S)
• (C1,C2,C3) and (L1,L2,L3)

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Color Invariants - cont

• C1C2C3 color invariant features defined as

( Color Based Object Recognition Theo Gevers and
Arnold W.M. Smeulders 1999 )
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Algorithm steps

• Shadow candidates identification
• Edge detection
• Finding the outer points of the edge map
• Intensities used as reference
• Morphological processing used to close contours
  of the edge map.

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Algorithm steps - cont

• Shadow classification
• Applying photometric color invariants
• Edge detection
• Classification

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Algorithm steps - summary
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Results
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Conclusions

• This method succeeds in detecting and classifying
  shadows within environmental constraints that are
  less restrictive then other methods.
• Need to define strategy to describe the object
  color discounting the effect of self shadow.

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Cast shadow segmentation using invariant color
features

• Elena Salvador, Andrea Cavallaro and
• Touradj Ebrahimi
• 2004

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Overview

• Goal
• Constraints
• Spectral properties of shadows
• Dichromatic reflection model
• Photometric color invariants
• Algorithm steps
• Results

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Goal

• Detection of cast shadows on video and on still
  images.

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Constraints

• The ambient light assumed to be a proportional to
  direct occluded light.
• Inter-object reflection among different surfaces
  not taken in account.
• Video
• The camera is not moving.

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Dichromatic reflection model

• Radiance of light
• When object obstructing the direct light we have
• Let to be a
  spectral sensitivities of R,G and B sensors of
  color camera.

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Dichromatic reflection model - cont

• The color components of reflected intensity that
  reaching the camera sensors are
• Sensor measurements in direct light
• For a point in shadow the measurements are

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Dichromatic reflection model - cont

• The conclusions are

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Color invariance

• The color invariants are the same as in previous
  article.

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Algorithm steps

• Hypothesis generation
• Dichromatic model
• Accumulation of evidence
• Color invariance test
• Geometric properties test
• Decision

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Hypothesis generation

• Still images
• Find edges with Sobel operator.
• Use reference pixels to find shadow suspected
  areas.
• Video
• Analysis performed only in areas that identified
  by motion detector
• The reference image represents the background of
  the scene.
• To obtain more robustness the analysis performed
  on window

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Hypothesis generation - cont

• Result of the first level
• The candidate shadow points belonging to the
  edge map

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Accumulation of evidence overview

• Color invariance property used to strength or
  cancel the hypothesized shadow areas.
• Checking the existence of shadow line and hidden
  line.

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Accumulation of evidence Still Images

• Color edge detection performed in the invariant
  space.
• Morphological dilation applied on the edge map.
• Isolated pixels removed.

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Accumulation of evidence in video

• Compute invariant feature values by
• Geometric property test
• Position of shadow with respect to the object is
  tested.

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Information integration

• Results of integrating all stages.

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Results
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References

• Shadow identification and classification using
  invariant color models. Elena Salvador, Andrea
  Cavallaro, Touradj Eebrahimi 2001
• Cast shadow segmentation using invariant color
  features. Elena Salvador, Andrea Cavallaro and,
  Touradj Ebrahimi 2004
• http//www.mathworks.com/access/helpdesk/help/tool
  box/images/morph3.html

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The End
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Sobel operator

• Performs a 2-D spatial gradient measurement on an
  image and so emphasizes regions of high spatial
  gradient that correspond to edges.
• Basic Sobel convolution mask

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Pseudo-convolution kernels in general

• We can use a pseudo convolution operator to
  perform these to steps in one step.

G (P12P2P3)-(P72P8P9)(P32P6P9)-(P
12P4P7)
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Morphological dilation of images
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Examples of photometric color invariants

• (L1,L2,L3)

( Color Based Object Recognition Theo Gevers and
Arnold W.M. Smeulders 1999 )