The document discusses edge detection in images, explaining that edges correspond to object boundaries and are characterized by abrupt changes in brightness. It details various edge detection methods and operators, including Roberts, Sobel, Prewitt, Kirsch, Robinson, and Laplacian operators, highlighting their advantages, disadvantages, and performance. The content also mentions the use of MATLAB for implementing these methods and provides a quick note on MATLAB's image processing toolbox for edge detection.

1. Edge Detection
Hao Huy Tran
Computer Graphics and Image Processing
CIS 581 – Fall 2002
Professor: Dr. Longin Jan Latecki

2. Edge Detection
• What are edges in an image?
• Edge Detection
• Edge Detection Methods
• Edge Operators
• Matlab Program
• Performance

3. What are edges in an image?
 Edges are those places
in an image that
correspond to object
boundaries.
 Edges are pixels where
image brightness
changes abruptly.
Brightness vs. Spatial Coordinates

4. More About Edges
 An edge is a property attached to an
individual pixel and is calculated from the
image function behavior in a neighborhood
of the pixel.
 It is a vector variable (magnitude of the
gradient, direction of an edge) .
 More information about edges can be found
in Dr. Latecki’s Lecture on Filter.

5. Image To Edge Map

6. Edge Detection
 Edge information in an image is found by looking
at the relationship a pixel has with its
neighborhoods.
 If a pixel’s gray-level value is similar to those
around it, there is probably not an edge at that
point.
 If a pixel’s has neighbors with widely varying gray
levels, it may present an edge point.

7. Edge Detection Methods
 Many are implemented with convolution
mask and based on discrete approximations
to differential operators.
 Differential operations measure the rate of
change in the image brightness function.
 Some operators return orientation
information. Other only return information
about the existence of an edge at each point.

8. Roberts Operator
 Mark edge point only
 No information about edge orientation
 Work best with binary images
 Primary disadvantage:
 High sensitivity to noise
 Few pixels are used to approximate the gradient

9. Roberts Operator (Cont.)
 First form of Roberts Operator
 Second form of Roberts Operator
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10. Sobel Operator
 Looks for edges in both horizontal and vertical
directions, then combine the information into a single
metric.
 The masks are as follows:
Edge Magnitude = Edge Direction =
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11. Prewitt Operator
 Similar to the Sobel, with different mask
coefficients:
Edge Magnitude = Edge Direction =
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12. Kirsch Compass Masks
 Taking a single mask and rotating it to 8
major compass orientations: N, NW, W, SW,
S, SE, E, and NE.
 The edge magnitude = The maximum value
found by the convolution of each mask with
the image.
 The edge direction is defined by the mask
that produces the maximum magnitude.

13. Kirsch Compass Masks (Cont.)
 The Kirsch masks are defined as follows:
 EX: If NE produces the maximum value, then the edge
direction is Northeast
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3
3
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NW

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SW

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5
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0
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SE


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
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5
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NE

14. Robinson Compass Masks
 Similar to the Kirsch masks, with mask
coefficients of 0, 1, and 2:









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

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N
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0
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2
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0
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2
1
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W
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
1
2
1
0
0
0
1
2
1
S
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


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2
1
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E
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SW
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1
2
1
0
0
0
1
2
1
SE




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

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


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1
0
1
0
1
0
1
2
NE

15. Laplacian Operators
 Edge magnitude is approximated in digital
images by a convolution sum.
 The sign of the result (+ or -) from two
adjacent pixels provide edge orientation and
tells us which side of edge brighter

16. Laplacian Operators (Cont.)
 Masks for 4 and 8 neighborhoods
 Mask with stressed significance of the
central pixel or its neighborhood














0
1
0
1
4
1
0
1
0

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






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






1
1
1
1
8
1
1
1
1



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




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


1
2
1
2
4
2
1
2
1














2
1
2
1
4
1
2
1
2

17. Edge Map In Matlab Program
 Implement all methods in this presentation
 Set up edge detection mask(s)
 Use convolution method (filter2 function)
 Calculate edge magnitude
 Show the result of edge map
 No calculation of edge direction

18. Performance
 Sobel and Prewitt methods are very
effectively providing good edge maps.
 Kirsch and Robinson methods require more
time for calculation and their results are not
better than the ones produced by Sobel and
Prewitt methods.
 Roberts and Laplacian methods are not very
good as expected.

19. A Quick Note
 Matlab’s image processing toolbox provides
edge function to find edges in an image.
 Edge function supports six different edge-
finding methods: Sobel, Prewitt, Roberts,
Laplacian of Gaussian, Zero-cross, and
Canny.
 Edge is a powerful edge-detection method