EDGE DETECTION OF MOVING OBJECT

Presentation by Guided by Bhushan M. Deore Prof. P. B. Shelke sir

Department OfElectronics & Telecommunication

PLITMS , Buldana

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CONTENTS IntroductionTypes of EdgesSteps in Edge Detection Importance of Edge detectionMethods of Edge Detection

First Order Derivative MethodsSecond Order Derivative MethodsOptimal Edge Detectors

Canny Edge DetectionCoding and ResultApplicationConclusion and Reference

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INTRODUCTIONEdge

Edge detection of moving object

Use of Edge Detection – Extracting information about the image. E.g. location of objects present in the image, their shape, size, image sharpening and enhancement

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TYPES OF EDGESVariation of Intensity

/ Gray LevelStep EdgeRamp EdgeLine EdgeRoof Edge

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Steps in Edge DetectionFiltering – Filter image to improve performance

of the Edge Detector wrt noise

Enhancement – Emphasize pixels having significant change in local intensity

Detection – Identify edges – thresholding

Localization – Locate the edge accurately, estimate edge orientation

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METHODS OF EDGE DETECTIONFirst Order Derivative / Gradient

MethodsRoberts OperatorSobel OperatorPrewitt Operator

Second Order DerivativeLaplacianLaplacian of GaussianDifference of Gaussian

Optimal Edge DetectionCanny Edge Detection

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First DerivativeThe gradient

method detects the edges by looking for the maximum and minimum in the first derivative of the image

1-D SIGNAL

1st Derivative of 1-D signal

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Gradient Methods – Roberts OperatorSimplest operatorThe mask for Robert operator is obtain using

following equationGx=(Z9-Z5) & Gy=(Z8-Z6)

Masks used by Robert operator

Ideal 3x3 convolution mask

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Roberts Operator - Example

Detection of image at low noise

Thick borderline Localization is not good Weak response to

genuine edge

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Gradient Methods – Sobel OperatorDetection of horizontal & vertical edgesConvolution MaskMasks of size 3×3matrics is obtain by following eqn

Gx = (Z3 + 2Z6 +7Z9)-( Z1+2 Z4+ Z7) & Gy= (Z1+2Z2+ Z3)-( Z7+ 2Z8+ Z9)

Smoothing the image by some amount , less susceptible to noise. But it produces thicker edges. So edge localization is poor

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Sobel Operator - ExampleCompare the output of

the Sobel Operator with that of the Roberts Operator:The spurious edges

are still present but they are relatively less intense compared to genuine lines

Roberts operator has missed a few edges

Sobel operator detects thicker edgesOct 2, 2013 Dept. of E&TC, PLITMS, Buldana. 11

Outputs of Sobel (top) and Roberts operator

Gradient Methods – Prewitt OperatorIt is similar to the Sobel operator but uses slightly

different masksConvolution MaskGx = (Z3+Z6+Z9)-(Z1+Z4+Z7) & Gy = (Z1+Z3+Z9)-

(Z7+Z8+Z9)

Gx = Gy =

Detecting vertical and horizontal edge of imagePresence of noise false edge detected

-1 0 1

-1 0 1

-1 0 1

1 1 1

0 0 0

-1 -1 -1

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Second Order Derivative Methods

Zero crossing of the second derivative of a function indicates the presence of a maxima edge get detected

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Fist derivative

Second derivative

Second Order Derivative Methods - Laplacian

Pierre Simon de Laplace

Defined as

The Laplacian method searches for zero crossings in the second derivative of the image to find edges.

Isotropic function very susceptible to noise, filtering required, use Laplacian of Gaussian

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Second Order Derivative Methods - Laplacian of Gaussian

Also called Mexican Hat OperatorSteps

Noise filtering & smooth the imageImage enhanceEdge detection

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Laplacianof

Operatorimage

edgemap

GaussianFilter

Second Order Derivative Methods - Difference of Gaussian - DoG

Similar to LogLess computation

time in DoGIt is a band pass

filterSubtraction of

blurred image for edge detection

Increases visibility of edge

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Optimal Edge Detector-Canny Edge DetectorStep 1

Noise is filtered out – usually a Gaussian filter is usedStep 2Finding the edge strengthStep 3

Find the edge directionStep 4

Tracing the edge as per directionStep 5

Non-maximum suppressionStep 6

Use hysteresis thresholding to eliminate streaking

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Canny Edge DetectorCompare the results of Sobel and

Canny

Canny SobelOct 2, 2013 Dept. of E&TC, PLITMS, Buldana. 18

Coding and Result Edge detection of video by using Matlab code hVideoSrc = signalblks.MultimediaFileReader('vipmen.avi',

'ImageColorSpace', 'Intensity', 'VideoOutputDataType', 'Inherit'); hEdge = video.EdgeDetector( 'Method', 'Prewitt', 'ThresholdSource',

'Property', 'Threshold', 15, 'EdgeThinning', true); hAB = video.AlphaBlender('Operation', 'Highlight selected pixels'); WindowSize = [190 150]; hVideoOrig = video.VideoPlayer('WindowCaption', 'Original'); hVideoOrig.WindowPosition = [10 hVideoOrig.WindowPosition(2) WindowSize]; hVideoEdges = video.VideoPlayer('WindowCaption', 'Edges'); hVideoEdges.WindowPosition = [210 hVideoOrig.WindowPosition(2) WindowSize]; hVideoOverlay = video.VideoPlayer('WindowCaption', 'Overlay'); hVideoOverlay.WindowPosition = [410 hVideoOrig.WindowPosition(2) WindowSize];

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while ~isDone(hVideoSrc) frame = step(hVideoSrc); % Read input video edges = step(hEdge, frame); % Edge detection composite = uint8(step(hAB, frame, edges)); % AlphaBlender  step(hVideoOrig, frame); % Display original step(hVideoEdges, edges); % Display edges step(hVideoOverlay, composite); % Display edges

overlayed endResult

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Applications

Detecting shape and size

Video surveillance

Traffic management

Canny Edge Detector for Remote

Sensing

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ConclusionRoberts operator is simple & very sensitive to

noise.Sobel & Preewitt operator is similar.In first order derivative localization is not

good.Laplacian, are very sensitive to noise.LoG & DoG is similar.Localization is good in second order

derivative.Canny is good in noise suppression &

localization.Oct 2, 2013 Dept. of E&TC, PLITMS, Buldana. 22

References www.google.co.in/.com www.mathworks.com Image processing using MATLAB: Rafeal C.Gonzalez, Richard

E.Woods J. Canny, “A computational approach to edge detection,” IEEE

Trans. Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698,

Nov. 1986 R. C. Gonzalez and R. E. Woods, Digital Image Processing. Upper

Saddle River, NJ: Prentice-Hall, 2001, pp. 572-585 Harris and M. Stephens, “A Combined Corner and Edge Detector,”

Proceedings of The Fourth Alvey Vision Conference, Manchester, pp. 147-151,

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THANK YOU

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