Camouflage Breaking A Review of Contemporary Techniques

1. Camouflage BreakingA Review of Contemporary Techniques Amy Whicker CSCE 867 – Final Project

2. 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

3. 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

4. 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

5. 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

6. Calculating the co-occurrence matrix Example from P. Nagabhushan and Nagappa U. Bhajantri.Multiple Camouflage Breaking by Co-occurrence and Canny.

7. Results of the Co-occurrence and Canny Method Images from P. Nagabhushan and Nagappa U. Bhajantri.Multiple Camouflage Breaking by Co-occurrence and Canny.

8. 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.

9. Convexity-based Method Images from Ariel Tankus and Yehezkel Yeshurun. A model for visual camouflage breaking.

10. 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).

11. 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

12. 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.

13. 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.

14. Convexity-based Method Images from Ariel Tankus and Yehezkel Yeshurun. A Model for Visual Camouflage Breaking.

15. Convexity-based Method Images from Ariel Tankus and Yehezkel Yeshurun. A Model for Visual Camouflage Breaking.

16. Convexity-based Method Images from Ariel Tankus and Yehezkel Yeshurun. Detection of regions of interest and camouflage breaking by direct convexity estimation.

17. Convexity-based Method Images from Ariel Tankus and Yehezkel Yeshurun. Detection of regions of interest and camouflage breaking by direct convexity estimation.

18. Convexity-based Method Images from Ariel Tankus and Yehezkel Yeshurun. Detection of regions of interest and camouflage breaking by direct convexity estimation.

19. 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

20. 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

21. 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.