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**An introduction Presented by: Ani Starrenburg**

Camouflage Detection An introduction Presented by: Ani Starrenburg

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**General Camouflaging Strategies**

Cryptic Camouflage Little Button Quail Traditional US Army Camouflage Pattern

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**General Camouflaging Strategies**

Mimicry Dronefly Rose Greenbow, Confederate Spy

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**General Camouflaging Strategies**

Disruption Sumatran Tiger Dazzle Camouflage

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**General Camouflaging Strategies**

Countershading Impala Non-Countershaded Warship

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**General Camouflaging Strategies**

Translucence/Transparency Seawasp Invisibility Cloak

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**Detecting Camouflaged Objects:**

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**Camouflage Detection Methods**

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

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Edge Detectors: Gaussian Gradient Laplacian Laplacian With Gaussian

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**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

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**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

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**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.

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**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

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**Motion Breaks Camouflage**

Region of common velocity is perceived As a unit and stands out against the static background

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**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.

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**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

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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

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**An aside: Research on Active Camouflage**

Animals that can escape edge detection Animals that can camouflage motion

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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.

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**Is there a core visual system?**

F L G E A R T

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Bibliography Motion Illusions and Active Camouflage, Lewis Dartnell ,http://www.ucl.ac.uk/~ucbplrd/motion/motion_middle.html Canny Edge Detection Tutorial, Bill Green, Honeybee, Ground-dwelling birds, Sumatran tiger, Biomimicry, Countershading, Translucence, Canny Edge Detection, Optical Camouflage, Multi-Channel Gradient Model,

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MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.

MASKS © 2004 Invitation to 3D vision Lecture 3 Image Primitives andCorrespondence.

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