1. An introduction Presented by: Ani Starrenburg
Camouflage Detection
An introduction
Presented by: Ani Starrenburg

2. General Camouflaging Strategies
Cryptic Camouflage
Little Button Quail
Traditional US Army Camouflage Pattern

3. General Camouflaging Strategies
Mimicry
Dronefly
Rose Greenbow,
Confederate Spy

4. General Camouflaging Strategies
Disruption
Sumatran Tiger
Dazzle Camouflage

5. General Camouflaging Strategies
Countershading
Impala
Non-Countershaded Warship

6. General Camouflaging Strategies
Translucence/Transparency
Seawasp
Invisibility Cloak

7. Detecting Camouflaged Objects:

8. Camouflage Detection Methods
Standard Object Detection Methods
Edge Detection Models
Contrast Energy Detection Model
Motion Detection
Correlation Models
Gradient Models
Energy Models

9. Edge Detectors:
Gaussian
Gradient
Laplacian
Laplacian
With Gaussian

10. Canny Detector Optimal Edge Detector Multiple Stage Algorithm
Perform Gaussian smoothing
Find edge strengths
|G| = |Gx| + |Gy|
Detection of edge direction
theta = invtan(Gy/Gx)
Relate edge direction to
a direction that can be traced
in an image
Apply non-maximum
suppression
Use hysteresis to eliminate streaking

11. LaPlacian or LoG Smooth with a Gaussian mask
Calculate the second derivatives
Search for zero crossings Or
Convolve the image with the
Laplacian of the Gaussian

12. Contrast Energy (CE) Model
Uses the output signal from similarly-oriented
odd o[x] and even e[x] filters.
Energy function is defined as:
E2(x) = e2(x) + o2(x)
Always positive
Shows high output when o(x), e(x) or both are high.

13. Camouflage Detection Methods to be Discussed
Convexity-Based Detection – exploits the principle of countershading to detect camouflaged objects
Texture Detection – intensive texture analysis distinguishes camouflaged object from background. Also, uses Canny detector to bring up edges

14. Motion Breaks Camouflage
Region of common velocity is perceived
As a unit and stands out against the static
background

15. Reichardt Correlation Model
Computes motion as the ratio of the partial derivatives of the input image brightness with respect to space and time.
Two spatially-separate detectors.
Output of one of the detectors is delayed.
The two outputs are multiplied to determine if there is a correlation.

16. Multichannel Gradient Model
Uses multiple channels of higher derivatives
The more derivatives used lowers the chance of that all will be zero at the same time
Uses a least sqaures approximation of the derivatives

17. Motion Energy Model
Uses two sets of oriented detectors(leftwards and rightwards), each composed of an odd and an even filter.
Energy is calculated by summing the squares of the two similarly-oriented filters.
Calculate opponent energy (difference of leftward and rightward results)
Normalize by dividing by static energy to give velocity estimates

18. An aside: Research on Active Camouflage
Animals that can escape edge detection
Animals that can camouflage motion

19. To Do List:
Apply edge detectors and contrast energy detectors to camouflaged and illusory images and view results.
Research visual models developed from observing animal behavior and development.
Research studies in psychology for further understanding of vision process.

20. Is there a core visual system?F L G E A R T

21. Bibliography
Motion Illusions and Active Camouflage, Lewis Dartnell ,
Canny Edge Detection Tutorial, Bill Green,
Honeybee,
Ground-dwelling birds,
Sumatran tiger,
Biomimicry,
Countershading,
Translucence,
Canny Edge Detection,
Optical Camouflage,
Multi-Channel Gradient Model,