Bhushan M. Deore presented on edge detection techniques at the Department of Electronics & Telecommunication at PLITMS Buldana on October 2, 2013. The presentation covered various edge detection methods including first order derivative methods (Roberts, Sobel, Prewitt), second order derivative methods (Laplacian, LoG, DoG), and optimal edge detection using Canny edge detection. Code examples were provided to demonstrate edge detection on video streams and applications in areas like video surveillance, traffic management, and remote sensing were discussed.

1. Presentation by
Bhushan M. Deore
Guided by
Prof. P. B. Shelke sir
Department Of
Electronics & Telecommunication
PLITMS , Buldana
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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2. CONTENTS





Introduction
Types of Edges
Steps in Edge Detection
Importance of Edge detection
Methods of Edge Detection
 First Order Derivative Methods
 Second Order Derivative Methods
 Optimal Edge Detectors
 Canny Edge Detection
 Coding and Result
 Application
 Conclusion and Reference
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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3. INTRODUCTION
 Edge
 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
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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4. TYPES OF EDGES
 Variation of Intensity /
Gray Level
 Step Edge
 Ramp Edge
 Line Edge
 Roof Edge
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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5. Steps in Edge Detection
 Filtering – 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
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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6. METHODS OF EDGE DETECTION
 First Order Derivative / Gradient Methods
 Roberts Operator
 Sobel Operator
 Prewitt Operator
 Second Order Derivative
 Laplacian
 Laplacian of Gaussian
 Difference of Gaussian
 Optimal Edge Detection
 Canny Edge Detection
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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7. First Derivative
 The 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
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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8. Gradient Methods – Roberts Operator
 Simplest operator
 The mask for Robert operator is obtain using following equation
 Gx=(Z9-Z5) & Gy=(Z8-Z6)
Ideal 3x3 convolution mask
Masks used by Robert operator
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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9. Roberts Operator - Example
 Detection of image at
low noise
 Thick borderline
 Localization is not good
 Weak response to
genuine edge
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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10. Gradient Methods – Sobel Operator




Detection of horizontal & vertical edges
Convolution Mask
Masks 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
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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11. Sobel Operator - Example
 Compare 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 edges
Outputs of Sobel (top) and Roberts operator
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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12. Gradient Methods – Prewitt Operator
 It is similar to the Sobel operator but uses slightly different
masks
 Convolution Mask
 Gx = (Z3+Z6+Z9)-(Z1+Z4+Z7) & Gy = (Z1+Z3+Z9)-(Z7+Z8+Z9)
-1 0
1
 Gx = -1 0
1
-1 0
1
1
1
1
Gy = 0 0 0
-1 -1 -1
 Detecting vertical and horizontal edge of image
 Presence of noise false edge detected
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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13. Second Order Derivative Methods
 Zero crossing of the second derivative of a function
indicates the presence of a maxima edge get detected
Fist derivative
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
Second derivative
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14. 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
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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15. Second Order Derivative Methods - Laplacian
of Gaussian
 Also called Mexican Hat Operator
 Steps
 Noise filtering & smooth the image
 Image enhance
 Edge detection
image
Oct 2, 2013
Gaussian
Filter
Laplacian
of
Operator
Dept. of E&TC, PLITMS, Buldana.
edge
map
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16. Second Order Derivative Methods Difference of Gaussian - DoG
 Similar to Log
 Less computation time
in DoG
 It is a band pass filter
 Subtraction of blurred
image for edge detection
 Increases visibility of
edge
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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17. Optimal Edge Detector-Canny Edge Detector
 Step 1
 Noise is filtered out – usually a Gaussian filter is used
 Step 2
 Finding the edge strength
 Step 3
 Find the edge direction
 Step 4
 Tracing the edge as per direction
 Step 5
 Non-maximum suppression
 Step 6
 Use hysteresis thresholding to eliminate streaking
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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18. Canny Edge Detector
 Compare the results of Sobel and Canny
Canny
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
Sobel
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19. 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];
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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20.  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
 end
Result
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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21. Applications
 Detecting shape and size
 Video surveillance
 Traffic management
 Canny Edge Detector for Remote Sensing
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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22. Conclusion
 Roberts 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.
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23. 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, 1988
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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24. THANK YOU
Oct 2, 2013
Dept. of E&TC, PLITMS, Buldana.
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