Seminar report for the fulfillment of B.Tech final year. Digital image processing subject seminar report.

1. 1
Real Time Edge Detection Using Canny Algorithm
Author: Shashank kapoor*
, Siddharth Sharma
Faculty of Engineering, Dayalbagh Educational Institute, Dayalbagh, Agra
_____________________________________________________________________________
Abstract: The purpose of detecting sharp changes in image brightness is to capture
important events and changes in properties of the world. In the ideal case, the result
of applying an edge detector to an image may lead to a set of connected curves that
indicate the boundaries of objects, the boundaries of surface markings as well as
curves that correspond to discontinuities in surface orientation. Thus, applying an
edge detection algorithm to an image may 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. If
the edge detection step is successful, the subsequent task of interpreting the
information contents in the original image may therefore be substantially
simplified. However, it is not always possible to obtain such ideal edges from real
life images of moderate complexity.
Introduction
Edge detection includes a variety of
mathematical methods that aim at
identifying points in a digital image at
which the image brightness changes
sharply or, more formally, has
discontinuities. The points at which
image brightness changes sharply are
typically organized into a set of
curved line segments termed edges.
The same problem of finding
discontinuities in one-dimensional
signals is known as step detection and
the problem of finding signal
discontinuities over time is known
as change detection.
*for correspondence
Shashank Kapoor
B.Tech IVth
Yr ,154169
Shashankkapoor1994@gmail.com
Edge detection is a fundamental tool
in image processing, machine vision
and computer vision, particularly in
the areas of feature detection
and feature extraction.
It can be shown that under rather
general assumptions for an image
formation model, discontinuities in
image brightness are likely to
correspond to:
• Discontinuities in depth,
• Discontinuities in surface
orientation,
• Changes in material properties and
• Variations in scene illumination.
Edges extracted from non-trivial
images are often hampered
by fragmentation, meaning that the
edge curves are not connected,
missing edge segments as well

2. 2
as false edges not corresponding to
interesting phenomena in the image –
thus complicating the subsequent task
of interpreting the image data.
Edge detection is one of the
fundamental steps in image
processing, image analysis, image
pattern recognition, and computer
vision techniques.
Edge Properties
The edges extracted from a two-
dimensional image of a three-
dimensional scene can be classified as
either viewpoint dependent or
viewpoint independent. A viewpoint
independent edge typically reflects
inherent properties of the three-
dimensional objects, such as surface
markings and surface shape.
A viewpoint dependent edge may
change as the viewpoint changes, and
typically reflects the geometry of the
scene, such as objects occluding one
another.
A typical edge might for instance be
the border between a block of red
color and a block of yellow. In
contrast a line (as can be extracted by
a ridge detector) can be a small
number of pixels of a different color
on an otherwise unchanging
background. For a line, there may
therefore usually be one edge on each
side of the line.
Approach
There are many methods for edge
detection, but most of them can be
grouped into two categories,
• search-based
• zero-crossing based.
The search-based methods detect
edges by first computing a measure of
edge strength, usually a first-order
derivative expression such as the
gradient magnitude, and then
searching for local directional
maxima of the gradient magnitude
using a computed estimate of the local
orientation of the edge, usually the
gradient direction.
The zero-crossing based methods
search for zero crossings in a second-
order derivative expression computed
from the image in order to find edges,
usually the zero-crossings of
the Laplacian or the zero-crossings of
a non-linear differential expression.
As a pre-processing step to edge
detection, a smoothing stage,
typically Gaussian smoothing, is
almost always applied.
The edge detection methods that have
been published mainly differ in the
types of smoothing filters that are
applied and the way the measures of
edge strength are computed. As many
edge detection methods rely on the
computation of image gradients, they
also differ in the types of filters used
for computing gradient estimates in
the x- and y-directions.
Canny Algorithm
Canny edge detection is a multi-
stage algorithm to detect a wide range
of edges in images to extract useful
structural information from different
vision objects and dramatically reduce
the amount of data to be processed.
Canny has found that the
requirements for the application of
edge detection on diverse vision

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systems are relatively similar. Thus,
an edge detection solution to address
these requirements can be
implemented in a wide range of
situations. The general criteria for
edge detection include:
1. Detection of edge with low
error rate, which means that the
detection should accurately
catch as many edges shown in
the image as possible
2. The edge point detected from
the operator should accurately
localize on the center of the
edge.
3. A given edge in the image
should only be marked once,
and where possible, image
noise should not create false
edges.
To satisfy these requirements Canny
used the calculus of variations – a
technique which finds
the function which optimizes a
given functional. The optimal
function in Canny's detector is
described by the sum of
four exponential terms, but it can be
approximated by the first derivative of
a Gaussian.
Among the edge detection methods
developed so far, Canny edge
detection algorithm is one of the most
strictly defined methods that provides
good and reliable detection. Owing to
its optimality to meet with the three
criteria for edge detection and the
simplicity of process for
implementation, it became one of the
most popular algorithms for edge
detection.
Canny edge detection algorithm
The process can be broken down into
5 different steps:
1. Apply Gaussian filter to smooth
the image in order to remove
the noise
2. Find the intensity gradients of
the image
3. Apply non-maximum
suppression to get rid of
spurious response to edge
detection
4. Apply double threshold to
determine potential edges
5. Track edge by hysteresis:
Finalize the detection of edges
by suppressing all the other
edges that are weak and not
connected to strong edges.
Gaussian filter
Since all edge detection results are
easily affected by image noise, it is
essential to filter out the noise to
prevent false detection caused by
noise. To smooth the image, a
Gaussian filter is applied to convolve
with the image. This step will slightly
smooth the image to reduce the
effects of obvious noise on the edge
detector. The equation for a Gaussian
filter kernel of size (2k+1)×(2k+1) is
given by:
Here is an example of a 5×5 Gaussian
filter, used to create the adjacent
image, with = 1.4. (The asterisk
denotes a convolution operation.)

4. 4
It is important to understand that the
selection of the size of the Gaussian
kernel will affect the performance of
the detector. The larger the size is, the
lower the detector’s sensitivity to
noise. Additionally, the localization
error to detect the edge will slightly
increase with the increase of the
Gaussian filter kernel size. A 5×5 is a
good size for most cases, but this will
also vary depending on specific
situations.
Finding the intensity gradient of the
image
An edge in an image may point in a
variety of directions, so the Canny
algorithm uses four filters to detect
horizontal, vertical and diagonal
edges in the blurred image. The edge
detection operator (such
as Roberts, Prewitt, or Sobel) returns
a value for the first derivative in the
horizontal direction (Gx) and the
vertical direction (Gy). From this the
edge gradient and direction can be
determined :
where G can be computed using
the hypot function and atan2 is the
arctangent function with two
arguments. The edge direction angle
is rounded to one of four angles
representing vertical, horizontal and
the two diagonals (0°, 45°, 90° and
135°). An edge direction falling in
each color region will be set to a
specific angle values, for instance θ in
[0°, 22.5°] or [157.5°, 180°] maps to
0°.
Non-maximum suppression
Non-maximum suppression is applied
to find "the largest" edge. After
applying gradient calculation, the
edge extracted from the gradient value
is still quite blurred. There should
only be one accurate response to the
edge. Thus non-maximum
suppression can help to suppress all
the gradient values (by setting them to
0) except the local maxima, which
indicate locations with the sharpest
change of intensity value. The
algorithm for each pixel in the
gradient image is:
1. Compare the edge strength of
the current pixel with the edge
strength of the pixel in the
positive and negative gradient
directions.
2. If the edge strength of the
current pixel is the largest
compared to the other pixels in
the mask with the same
direction (i.e., the pixel that is
pointing in the y-direction, it
will be compared to the pixel
above and below it in the
vertical axis), the value will be
preserved. Otherwise, the value
will be suppressed.
Double threshold
After application of non-maximum
suppression, remaining edge pixels
provide a more accurate
representation of real edges in an

5. 5
image. However, some edge pixels
remain that are caused by noise and
color variation. In order to account for
these spurious responses, it is
essential to filter out edge pixels with
a weak gradient value and preserve
edge pixels with a high gradient
value. This is accomplished by
selecting high and low threshold
values. If an edge pixel’s gradient
value is higher than the high threshold
value, it is marked as a strong edge
pixel. If an edge pixel’s gradient value
is smaller than the high threshold
value and larger than the low
threshold value, it is marked as a
weak edge pixel. If an edge pixel's
value is smaller than the low
threshold value, it will be suppressed.
The two threshold values are
empirically determined and their
definition will depend on the content
of a given input image.
Edge tracking by hysteresis
So far, the strong edge pixels should
certainly be involved in the final edge
image, as they are extracted from the
true edges in the image. However,
there will be some debate on the weak
edge pixels, as these pixels can either
be extracted from the true edge, or the
noise/color variations. To achieve an
accurate result, the weak edges caused
by the latter reasons should be
removed. Usually a weak edge pixel
caused from true edges will be
connected to a strong edge pixel while
noise responses are unconnected. To
track the edge connection, blob
analysisis applied by looking at a
weak edge pixel and its 8-connected
neighborhood pixels. As long as there
is one strong edge pixel that is
involved in the blob, that weak edge
point can be identified as one that
should be preserved.

6. 6
Canny Edge detection Code
# OpenCV program to perform Edge detection in real time
# import libraries of python OpenCV
# where its functionality resides
import cv2
# np is an alias pointing to numpy library
import numpy as np
# capture frames from a camera
cap = cv2.VideoCapture(0)
# loop runs if capturing has been initialized
while(1):
# reads frames from a camera
ret, frame = cap.read()
# converting BGR to HSV
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of red color in HSV
lower_red = np.array([30,150,50])
upper_red = np.array([255,255,180])
# create a red HSV colour boundary and
# threshold HSV image
mask = cv2.inRange(hsv, lower_red, upper_red)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame,frame, mask= mask)
# Display an original image
cv2.imshow('Original',frame)
# finds edges in the input image image and
# marks them in the output map edges
edges = cv2.Canny(frame,100,200)
# Display edges in a frame
cv2.imshow('Edges',edges)
# Wait for Esc key to stop
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
# Close the window
cap.release()
# De-allocate any associated memory usage
cv2.destroyAllWindows()

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Original image /live image:
Output image/live image:

8. 8
Conclusion
The Canny algorithm is adaptable to
various environments. Its parameters
allow it to be tailored to recognition
of edges of differing characteristics
depending on the particular
requirements of a given
implementation. In Canny's original
paper, the derivation of the optimal
filter led to a Finite Impulse
Response filter, which can be slow to
compute in the spatial domain if the
amount of smoothing required is
important (the filter will have a large
spatial support in that case). For this
reason, it is often suggested to use
Rachid Deriche's infinite impulse
response form of Canny's filter
(the Canny–Deriche detector), which
is recursive, and which can be
computed in a short, fixed amount of
time for any desired amount of
smoothing. The second form is
suitable for real time implementations
in FPGAs or DSPs, or very fast
embedded PCs. In this context,
however, the regular recursive
implementation of the Canny operator
does not give a good approximation
of rotational symmetry and therefore
gives a bias towards horizontal and
vertical edges.
References
1. https://www.cscjournals.org/
library/manuscriptinfo.php?
mc=IJIP-15
2. https://link.springer.com/art
icle/10.1007/s00500-005-
0511-y
3. https://www.researchgate.ne
t/profile/Eko_Supriyanto5/p
ublication/260555151_Ultras
ound_images_edge_detectio
n_using_anisotropic_diffusio
n_in_canny_edge_detector_f
ramework/links/550213ae0cf
2d60c0e62995e/Ultrasound-
images-edge-detection-
using-anisotropic-diffusion-
in-canny-edge-detector-
framework.pdf
4.https://sites.google.com/site/s
etiawanhadi2/1CannyEdgeD
etectionTutorial.pdf
5. https://www.researchgate.ne
t/profile/Eko_Supriyanto5/p
ublication/260555151_Ultras
ound_images_edge_detectio
n_using_anisotropic_diffusio
n_in_canny_edge_detector_f
ramework/links/550213ae0cf
2d60c0e62995e/Ultrasound-
images-edge-detection-
using-anisotropic-diffusion-
in-canny-edge-detector-
framework.pdf

9. 9
6. https://ieeexplore.ieee.org/ab
stract/document/4767851
7. https://ieeexplore.ieee.org/ab
stract/document/1471712