# An introduction Presented by: Ani Starrenburg

## Presentation on theme: "An introduction Presented by: Ani Starrenburg"— Presentation transcript:

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

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

General Camouflaging Strategies
Mimicry Dronefly Rose Greenbow, Confederate Spy

General Camouflaging Strategies
Disruption Sumatran Tiger Dazzle Camouflage

General Camouflaging Strategies

General Camouflaging Strategies
Translucence/Transparency Seawasp Invisibility Cloak

Detecting Camouflaged Objects:

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

Edge Detectors: Gaussian Gradient Laplacian Laplacian With Gaussian

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

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

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.

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

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

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.

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

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

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

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.

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

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,