EDGE
DETECTION
ARCHANA IYER
AADHAR AUTHENTICATION
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Edges are boundaries between different
textures.
Image Texture gives us information about
the spatial arrangement of color or
intensities in an image or selected region of
an image.
Edge detection is a set of mathematical
methods which aim at identifying points in
a digital image at which the image
brightness changes sharply or in other
words, has discontinuities
Why use edge detection?
 The
result of applying an edge detector to an
image may lead to a set of connected curves that
indicate the boundaries of objects
 It will significantly reduce the amount of data to be
processed and may therefore filter out information
that may be regarded as less relevant, while
preserving the important structural properties of an
image
DIFFERENT EDGE DETECTION
TECHNIQUES
 Robert’s
Edge Detection
 Prewitt Edge Detection
 Sobel Edge Detection
 Canny’s Edge Detection
 Laplacian Edge Detection
SOBEL OPERATOR
Find approximate absolute gradient magnitude for
each pixel
PREWITT
Sobel and Prewitt
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Advantage:
Easy to implement because mask is small
You get information about magnitude and
direction
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Disadvantage:
Result is 3 pixels wide which requires thinning
Sensitive to noise
Produces thick edges and sometimes misses edges
Not useful for large images
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CANNY’S EDGE DETECTION
 Canny’s
method is preferred since it
produces single pixel thick, continuous
edges.
 It is not affected much by noise.
 It is robust and adapts to changes in
noise.
 It has a better signal to noise ratio and it
uses probability for finding error rate.
SMOOTHING
 Filter
out any noise in the original image
before trying to locate and detect any
edges
 Done using a Gaussian filter with standard
convolution
 Preferable use a smaller mask
FINDING GRADIENT & EDGE DIRECTION
 Done
using the Sobel filter
 Problem
is that output image is a few
pixels thick
NON MAXIMUM SUPPRESSION
 Converts
the blurred image to sharp
images
 Done by preserving all the local maxima
and deleting everything else
 You compare pixels with the ones on top
& bottom & then retain the maximum
pixel
 Gives a thin line
HYSTERESIS
 It
eliminates streaking
 Makes use of 2 threshold values
 You classify edges as weak and strong
based on the 2 threshold values
 Strong edges are definitely edges
 Weak edges may or may not be
THANK YOU!