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A Hardware/Software Co-Design
Architecture Implementation of Canny
Edge Detection using FPGA and MATLAB
Mallavarapu.Ramu1
; T.V.S.Adinarayana2
M.Tech (Embedded Systems & VLSI)1
Associate Professor2
, Dept of ECE1, 2
, Prakasam engineering college, kandukuru1, 2
Affiliated to JNTU, Kakinada, Andhra Pradesh, INDIA
ramu.mallavarapu@yahoo.com1
; aditvs@yahoo.com2
ABSTRACT Canny edge detection
plays an important role in digital image
processing and computer vision
applications. This algorithm is very
complex due to heavy stages like
smoothing, gradient calculation,
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thinning, thresholding, and hysteresis
and these steps contain multiplications,
divisions and arc tan Processing high
quality images in programmable
processors such as digital signal
processors are slow, multiple
processing elements r required. We
present the hardware/software co-
design architecture of canny edge
detection using FPGA and Matlab. The
simplified equations of smoothing,
gradient calculation, thinning,
thresholding for hardware
implementation of canny edge
algorithm in custom architecture, to
efficiently use the hardware resources
and improve the average rate of
successful message delivery, and some
hardware features like pipelining must
be employed here the images are
converted to Matlab vector by using
NTSC standard method or by using
Matlab software and the vector is
processed on FPGA to get the canny
edge Matlab vector. Targeted for
spatran3e xilinx14.7, simulated in
Modelsim And the mat lab vector is
converted into image in mat lab
software.
General Terms
Hardware/software co-design; Image
processing; FPGA; Matlab; NTSC; Xilinx
14.7; Modelsim; canny edge detection
INTRODUCTION
Image edges are useful for image
segmentation, pattern recognition, and
image categorization because these
edges represent the fundamental
features of image that gives important
information such as direction and
shape. The Canny edge detection is an
optimized algorithm for precise edge
detection especially when signal-to-
noise ratio is high. Direct
implementation of the canny edge
detection results in slow
implementation when implemented in a
single core processor such as digital
signal processors.
Recently, several methods to improve
canny edge detection have been
proposed. Wang and Jin proposed an
improved template algorithm that
improves the amplitude calculation
accuracy. It also has a non-maxima
suppression process, where the factor
ratio of four quadrants of linear
interpolation is improved to achieve
better detection results. He and Yuan
proposed a new self-adaptive threshold
canny algorithm and Zhao et al. build a
new model for real-time pavement edge
detection. All abovementioned works
are either fully in software or hardware.
The issue of software implementation is
its low speed compared to the hardware
however hardware implementation is
less flexible compared to the software
implementation. This paper proposes
hardware/software co-design
architecture of canny algorithm. The
operations of each step are analyzed to
find the time consuming operations.
These operations are implemented in
hardware and simple operations remain
in software which is suitable for
resource efficient hardware/software
co-design. The hardware/software co-
design can speed up processing time.
The rest of paper is organized as
follows; Section II discusses a brief
about canny algorithm and its
complexity. Section III describes how
canny algorithm is modified to suit
hardware implementation. Section IV is
about hard-ware/software co-design
architecture which explains profiling of
the canny algorithm, memory access,
performance computation and resource
usage. Finally, conclusion is in Section V.
Canny Edge Detection
The canny edge detector is and edge
detection operator that uses multi-
stage algorithm to detect a wide range
of edges in images. Canny edge
detection aim was to discover the
optimal edge detection algorithm. In
this situation, an "optimal" edge
detector has mainly three
characteristics.
The first characteristic is the SNR which
states that important edges should not
to be missed, and non-edge points
should not be detected to have a high
signal-to-noise ratio as much as
possible.
The second characteristic indicates that
the location of the detected edge should
be as close as the position of the real
edge.
The third characteristic indicates that
minimizes the response to a single edge
ensure that edge have single response.
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The detection process of canny
algorithm consists of four steps as
shown in figure 1.Each step is explained
separately in the following subsections.
Step 1
The Canny edge detector is susceptible to
noise present in raw unprocessed image
data, it uses a filter based on a Gaussian
(bell curve), where the raw image
is convolved with a Gaussian filter. The
result is slightly blurred version of the
original which is not affected by a single
noisy pixel to any significant degree. The
larger the width of the Gaussian
mask, the lower is the detector's
sensitivity to noise. The localization
error in the detected edges also
increases
Slightly as the Gaussian width is
increased. The Gaussian mask used in
my implementation is shown below.
G is blurred image vector, I is original
image vector, and 3*3 matrix is Gaussian
masks. (The asterisk denotes
a convolution operation.)
Figure 1 canny algorithm steps
Step 2(a)
After smoothing the image and eliminating
the noise, the next step is to find the edge
strength by taking the gradient of the image.
The Sobel operator performs a 2-D spatial
gradient measurement on an image. Then,
the approximate absolute gradient
magnitude (edge strength) at each point can
be found. The Sobel operator uses a pair of
3x3 convolution masks, one estimating the
gradient in the x-direction (columns) and
the other estimating the gradient in the y-
direction (rows). They are shown below:
-1 0 +1
-2 0 +2
-1 0 +1
TABLE 1
GX & GY
The magnitude, or edge strength, of the
gradient is then approximated using the
formula:
|G| = |Gx| + |Gy|
Step 2(b)
The direction of the edge is computed using
the gradient in the x and y directions.
However, an error will be generated when
sum X is equal to zero. So in the code there
has to be a restriction set whenever this
takes place. Whenever the gradient in the x
direction is equal to zero, the edge direction
has to be equal to 90 degrees or 0 degrees,
depending on what the value of the gradient
in the y-direction is equal to. If GY has a
value of zero, the edge direction will equal 0
degrees. Otherwise the edge
direction will equal 90 degrees. The formula
for finding the edge direction is just
Theta = inv tan (Gy / Gx)
Step 2(c)
Once the edge direction is known, the next
step is to relate the edge direction to a
direction that can be traced in an image. So
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+1 +2 +1
0 0 0
-1 -2 -1

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if the pixels of a 3x3 image are aligned as
follows:
x x x
x a x
x x x
Then, it can be seen by looking at pixel "a",
there are only four possible directions
when describing the surrounding pixels - 0
degrees (in the horizontal direction), 45
degrees (along the positive diagonal), 90
degrees (in the vertical direction), or 135
degrees (along the negative diagonal). So
now the edge orientation has to be
resolved into one of these four directions
depending on which direction it is closest
to (e.g. if the orientation angle is found to
be 3 degrees, make it zero degrees).
Therefore, any edge direction falling within
the range (0 to 22.5 & 157.5 to 180
degrees) is set to 0 degrees. Any edge
direction falling in the range (22.5 to 67.5
degrees) is set to 45 degrees. Any edge
direction falling in the range (67.5 to
112.5 degrees) is set to 90 degrees. And
finally, any edge direction falling within the
range (112.5 to 157.5 degrees) is set to
135 degrees.
Step 3
After the edge directions are known, non-
maximum suppression now has to be
applied. Non-maximum suppression is used
to trace along the edge in the edge direction
and suppress any pixel value (sets it equal
to 0) that is not considered to be an edge.
This will give a thin line in the output image.
As an example, suppose there is a 10 pixel
vertical edge
With different values from left to right
respectively: 70, 80, 80, 100, 100, 100, 80,
80, 70 and all are bigger than TH (high
threshold). To have an edge with the width
of one pixel, the pixel with maximum value
should be chosen and the rest will be
eliminated. Here is where the direction of
gradient is needed. For 90◦ edge direction,
the value of pixels in the same row and
neighboring columns are compared. If the
value of current pixel is higher than both of
the left and the right neighboring pixels, it
will be considered as an edge since it is
bigger than the TH.
Step 4
Finally, hysteresis is used as a means of
eliminating streaking. Streaking is the
breaking up of an edge contour caused by
the operator output fluctuating above and
below the threshold. If a single threshold,
T1 is applied to an image, and an edge has
an average strength equal to T1, then due to
noise, there will be instances where the
edge dips below the threshold. Equally it
will also extend above the threshold making
an edge look like a dashed line. To avoid
this, hysteresis uses 2 thresholds, a high and
a low. Any pixel in the image that has a
value greater than T1 is presumed to be an
edge pixel, and is marked as such
immediately. Then, any pixels that are
connected to this edge pixel and that have a
value greater than T2 are also selected as
edge pixels. If you think of following an
edge, you need a gradient of T2 to start but
you don't stop till you hit a gradient below
T1.
3. MODIFIED CANNY EDGE
DETECTION
This section explains how every step of
canny algorithm
is simplified to suit hardware
implementation.
FIGURE 2 HW implementation of canny
edge detection
Smoothing Using Gaussian
Filter
To smooth an image, it is convolved with
Gaussian mask.
Fig. 3 shows a graphical concept of the
convolution of an
Image with Gaussian mask
Where, H(x, y) is filtered image, G(k, l)
represents Gaussian kernel, I(k, l)
represents original image and 2N +1is
kernel size. In this work, the size of
Gaussian mask has been chosen equal to 3 ×
3 and σ has been set to 16 to remove weak
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edges and just strong and important edges
are maintained. The following Gaussian
mask (G) is obtained using Matlab.
I1 I2 I3
I4 I5 I6
I7 I8 I9
Original image 3×3 Gaussian
mask
H(X, Y) =I1G1+I2G2+I3G3+I4G4+I5G5
+I6G6+I7G7+I8G8+I9G9.
By using the following equations we can
easily get the smoothed
H1=
By having 15 adders, this pixel can be
smoothed in two
Cycles. The comparison between two pixels
at same location of smoothed images using
estimated and non-estimated Gaussian
mask for 50 different images has been
calculated. Among all 50 images, the
maximum difference between two
corresponding pixels is four unit of pixel
intensity.
Computing Magnitude and
Orientation of Gradient
To compute the gradient of an image, Sobel
masks shown
In Fig. 4 are used to compute the gradient of
the image in
x and y directions. Sobel also has masks for
45 and 135
Degrees but by using 0 and 90 degrees all
edges can be
Detected . To compute the gradient of the
central pixel
(I5), the convolution of the image with
Sobel masks should be computed:
GX=-I1-(I4<<1)-I7+I3+(I6<<1)+I9
GY=-I1-(I2<<1)-I3+I7+(I8<<1)+I9
For an 8-bit image, Gx and Gy are 11-bit to
consider
The worst case scenarios. The sign bit of
these two registers should be checked in
order to take the absolute value. The
magnitude may be more than maximum
value of 255. Any value greater than this is
replaced by 255.
If the pixels of a 3x3 image are aligned as
follows:
x x x
x a x
x x x
Val= ((GY/GX) <<3)
Direction 0 When (-3<val≤3);
45 When (3<val≤19);
90 When (Val ≤-19 and Val
>19);
135 When (-19<Val≤-3);
The biggest value for the direction is 135
that needs an
8-bit register. However, directions 45, 90
and 135 can be
Replaced by 1, 2 and 3 respectively. In this
case only a 2-
Bit register is needed. Using the modified
arctan,
Thinning and Thresholding
These two steps involve the comparisons
among central
Pixel, corresponding neighbors, TH and TL.
These steps can be implemented using
comparators and conditional blocks made
of multiplexers.
4. HARDWARE/SOFTWARE
CO-DESIGN
ARCHITECTURE
A. Profiling of Canny Algorithm
This project is on Field Programmable Gate
Array (FPGA) is a general-purpose device
filled with digital logic building blocks. The
most primitive FPGA building block is called
either a Logic Cell (LC) by Xilinx this
building block consists of a look-up table
(LUT) for logical functions and a flip-flop for
storage. In addition to the LC/LE block,
FPGAs also contain memory, clock
management, input/output (I/O), and
multiplication blocks. For the purposes of
this study, LC/LE consumption is used in
determining system cost. The test image is
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G1 G2 G3
G4 G5 G6
G7 G8 G9

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stored in memory of the FPGA. 24-bit
(three8-bit) sample input image. All
execution times are in cycle unit at 50MHz
frequency.
HARDWARE SOFTWARE CO-
DESIGN ARCHETECTURE OF
FINAL DESIGN
Architecture of HW/SW co-design canny
edge detection
Fig 4: Top module of canny edge
detection
SIMULATION RESULTS
Fig 5: wave form of canny edge
detection
Original image Output
image
Total
logic
elements
Total
combinational
functions
Dedicated
logic
registers
Total
memory
bits
Total
pins
16685 15865 2465 64488 71
Design module
resources
APPLICATION OF EDGE
DETECTION
Enhancement of noisy images like satellite
images, x-ray images and medical images
can be made and only the important needful
information about the image is taken out. It
is also very useful in text detection,
mapping of roads. Canny edge detector is
used in remote sensing images extensively.
The reason to go with canny edge detector
is that remote sensed images are inherently
noisy and other edge detectors are very
sensitive to noise.
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CONCLUSION
In the hardware/software co-design
architecture of canny edge detection using
FPGA and Matlab. The simplified equations
for smoothing, gradient calculation,
thinning, thresholding in hardware
implementation for canny edge algorithm in
custom architecture, to efficiently use the
hardware resources and improve the
average rate of successful message delivery,
and some hardware features like pipelining
employed here . The total logic elements for
final co-design increased about four times
of software design but execution time
become faster. Output image at least is 24
times smaller than original image.
REFERENCES
[1] X. Wang and J. Q. Jin, “An edge detection
algorithm based on improved Canny
operator,” in Seventh International
Conference on Intelligent Systems Design
and Applications (ISDA 2007), October 2007,
pp. 623–628.
[2]
http://en.wikipedia.org/wiki/Canny_edge_
detector
[3] J. Canny, “A computational approach to
edge detection,” IEEE Transactions on
Pattern Analysis and Machine Intelligence
[4] H. Zhao, G. Qin, and X. Wang,
“Improvement of Canny algorithm based on
pavement edge detection,” in 3rd
International Congress on Image and Signal
Processing.
[5]www.ijceronline.com/papers/Vol2_issue
7/AD02701770181.pdf
[6]. Parvinder Singh Sandhu, Mamata Juneja
and Ekta Walia “Comparative Analysis of
Edge Detectin Techniques for extracting
Refined Boundaries” 2009 International
Conference on Machine Learning and
Computing ,IPCSIT vol 3, 2011.
[7]. Wenhao He and Kui Yuan “An Improved
Canny Edge Detector and its Realization on
FPGA” IEEE Proceedings of the 7th World
Congress on Intelligent Control and
Automation, Chongqing, China, June 25 - 27,
2008, pp. 6561-6564.
[8]. Osman Z.E.M; Hussin ;Ali, N.B.Z
“Optimization of Processor Architecture for
Image Edge Detection Filter” IEEE
transaction on Computer Modelling and
Simulation, 2010, pp 648-652.
[9]. Alasdair Mc Andrew. “Introduction to
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[10]. Gao Jie and Liu Ning “An improved
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algorithm”, IEEE International Conference
on Computer Science and Electronics
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