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Camouflage Breaking A Review of Contemporary Techniques. Amy Whicker CSCE 867 – Final Project. What is camouflage? The process of masking the foreground to appear as though it is background. Camouflage related work can be divided into two areas: Camouflage assessment and design

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camouflage breaking a review of contemporary techniques

Camouflage BreakingA Review of Contemporary Techniques

Amy Whicker

CSCE 867 – Final Project

slide2
What is camouflage?

The process of masking the foreground to appear as though it is background.

Camouflage related work can be divided into two areas:

  • Camouflage assessment and design
  • Camouflage breaking
    • Little has been researched in this area
slide3
Why is camouflage breaking important?
  • Military tactics
  • Background subtraction
  • Helps in the understanding of extraction of non-camouflaged objects
  • Helps in developing algorithm to locates object in the foreground
slide4
Camouflage Breaking Methods
  • Multiple Camouflage Breaking by Co-occurrence and Canny

Method developed by:P. Nagabhushan and Nagappa U. Bhajantri

  • Convexity-based Camouflage Breaking

Method developed by:Ariel Tankus and Yehezkel Yeshurun

co occurrence and canny method
Co-occurrence and Canny Method

Part 1: Determine if there is a camouflaged object in the image.

  • Create a gray level co-occurrence probability matrix.
  • Assess the co-occurrence matrix’s texture parameters.

Part 2: Achieve effective visualization of camouflage objects.

  • Repeatedly apply the Canny edge detection operator
slide6

Calculating the co-occurrence matrix

Example from P. Nagabhushan and Nagappa U. Bhajantri.Multiple Camouflage Breaking by Co-occurrence and Canny.

results of the co occurrence and canny method
Results of the Co-occurrence and Canny Method

Images from P. Nagabhushan and Nagappa U. Bhajantri.Multiple Camouflage Breaking by Co-occurrence and Canny.

convexity based method
Convexity-based Method
  • This method uses an operator (Darg) to create an output image whose intensity level is a reflection of the convexity of the original image.
  • The Darg operator is defined by the sum of Yarg, rotated 0°, 90°, 180°, and 270°.
  • Yarg is the y-derivative of the polar coordinates of the gradient argument of the original image. Yarg detects the zero-crossing of the gradient argument.
convexity based method1
Convexity-based Method

Images from Ariel Tankus and Yehezkel Yeshurun. A model for visual camouflage breaking.

why convexity
Why Convexity?

Thayer’s principle of counter shading

Images from Ariel Tankus and Yehezkel Yeshurun. Convexity-based Camouflage Breaking.

  • A cylinder of constant albedo under top lighting. (b) A counter shaded cylinder under ambient lighting. (c) Thayer’s principle: the combined effect of counter-shading albedo and top lighting breaks up the shadow effect (or convex intensity function).
convexity based method2
Convexity-based Method
  • Though edge based methods have their advantages, this method overcomes some of the flaws of an edge-based approach such as,
      • Sensitivity to illumination
      • Scale
      • Strong effect of the surroundings
      • Cluttered or textured images
how does the convexity based method handle changes in illumination scale or orientation
How does the Convexity-based method handle changes in Illumination, Scale or Orientation?

Images from Ariel Tankus and Yehezkel Yeshurun. Convexity-based visual Camouflage Breaking.

convexity based method3
Convexity-based Method

Invariance to derivable strongly monotonically increasing transformation of the gray-level function.

Images from Ariel Tankus and Yehezkel Yeshurun. Detection of regions of interest and camouflage breaking by direct convexity estimation.

convexity based method4
Convexity-based Method

Images from Ariel Tankus and Yehezkel Yeshurun. A Model for Visual Camouflage Breaking.

convexity based method5
Convexity-based Method

Images from Ariel Tankus and Yehezkel Yeshurun. A Model for Visual Camouflage Breaking.

slide16

Convexity-based Method

Images from Ariel Tankus and Yehezkel Yeshurun. Detection of regions of interest and camouflage breaking by direct convexity estimation.

slide17

Convexity-based Method

Images from Ariel Tankus and Yehezkel Yeshurun. Detection of regions of interest and camouflage breaking by direct convexity estimation.

slide18

Convexity-based Method

Images from Ariel Tankus and Yehezkel Yeshurun. Detection of regions of interest and camouflage breaking by direct convexity estimation.

conclusion
Conclusion
  • Co-occurrence and Canny Method
    • Advantages
      • Simple
      • Creates a good outline of the object
    • Disadvantage
      • Does not extract the object
      • Must have the known background
      • Only tested on synthetic images and may not be effective in real application
conclusion1
Conclusion
  • Convexity-based Method
    • Advantages:
      • Robust algorithm
      • Precise in finding foreground objects
    • Disadvantage:
      • Does not extract the object
      • Threshold must be determined, which can change the results
references
References

[1] P. Nagabhushan and Nagappa U. Bhajantri.Multiple Camouflage Breaking by Co-occurrence and Canny, University of Mysore, Manasa Ganotri, 2004.

[2] Ariel Tankus, Yehezel Yeshurun, and N. Intrator. Face Detection by Direct Convexity Estimation, Pattern Recognition Letters 18(9) (1997), 913-922.

[3] Ariel Tankus and Yehezkel Yeshurun. Detection of regions of interest and camouflage breaking by direct convexity estimation, IEEE International Workshop on Visual Surveillance, pages 42-48, Bombay, India, January 1998. In conjunction with ICCV 1998.

[4] Ariel Tankus and Yehezkel Yeshurun. A model for visual camouflage breaking, 1st IEEE International Workshop on Biologically Motivated Computer Vision (BMCV), pages 139-149, Seoul, Korea, May 2000.

[5] Ariel Tankus and Yehezkel Yeshurun. Convexity-based camouflage breaking, International Conference on Pattern Recognition (ICPR), pages 454-457, Barcelona, Spain, September 2000.

[6] Ariel Tankus and Yehezkel Yeshurun. Convexity Based Visual Camouflage Breaking, Computer Vision and Image understanding 82, (2001) 208-237.