International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
AN FPGA ARCHITECTURE BASED ON LINEAR and
MORPHOLOGICAL IMAGE FILTERING
*BALAKRISHNA RUPAVATH1
M.KEDARESWARA RAO2
1PG Student, Dept. of ECE, Avanthi Institute of Engineering And Technology, Vishakapatnam,
AP, India
2Associate Professor, Dept. of ECE, Avanthi Institute of Engineering And Technology,
Vishakapatnam, AP, India
Abstract: A 3D computer model was evident in the Mask for image segmentation. Morphing is a Technique used
to transfer from one image to another. The idea is to make it appear as if one item is physically changing into the
other. This paper proposes with the application Of morphing involves working mask worn by the implementation
platform which typically consists
routing can be programmed aliens in Stargate. This provided a very good
illusion. We have to pre-and-post processing the image for better extraction of required image from the acquired
image. FPGA can implement these observed results. The image will be transferred from computer to FPGA board
using JTAG cable. the FPGA is for communication model. Morphological image filtering using a FPGA Nexys II ,
Xilinx, Spartan 3E, with educational purposes is presented.
Keywords: FPGA architecture, image filtering, segmentation.
processing
1. Introduction
methods.
There
exist
several
Ever since the introduction of television, real-
approaches for image segmentation methods for
time monitoring has been a growing market.
image processing. The after sheds transformation is
Adding a video recorder opened up a new world
studied in this thesis as a particular method of a
for the security industry. Video surveillance soon
region-based approach to the segmentation of an
made its way into the court rooms and became
image. The complete transformation incorporates a
convicting evidence. Today, video surveillance
pre-processing and post-processing stage that deals
systems are omnipresent and part of everyday life
with embedded problems such as edge ambiguity
and can be found in department stores , banks , bus
and the output of a large number of regions.
terminals, etc. They are not only used for crime
Multiscale Morphological Gradient (MMG) and
prevention purposes but also play their role in more
Region Adjacency Graph (RAG) are two methods
social and industry related applications, e.g. traffic
that are pre-processing and post-processing stages,
monitoring, processing monitoring, and customer
respectively.
statistics. With continuously increasing fields of
criteria to merge adjacent homogeneous regions.
application and integration into our lives.
RAG
incorporates
dissimilarity
In this paper, the proposed system has been
applied to a set of co-aligned images, which
Image segmentation is one of the most
include a pair of intensity and range images. It is
important categories of image processing. The
expected that the hidden edges within the intensity
purpose of image segmentation is to divide an
image can be detected by observing range data or
original image into homogeneous regions. It can be
vice versa. Also it is expected that the contribution
applied as a pre-processing stage for other image
of the range image in region merging can
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
compensate for the dominance of shadows within
2. MORPHOLOGICAL IMAGE
the intensity image regardless of the original
intensity of the object.
PROCESSING
Morphology is a theory and technique for the
analysis and processing of geometrical structures ,
based on set theory and random functions. Morphology
is most commonly applied to digital images, but it can
be employed as well on graphs, meshes, solids, and
many other spatial structures. Morphology was
originally developed for binary images , and was later
extended to grayscale functions and images. The
purpose of morphological processing is primarily to
Figure 1 Elements of Image analysis
remove imperfections added during segmentation. The
Image processing and analysis is an important
basic operations are erosion and dilation .Using the
area in the field of robotics. This is particularly true
basic operations we can perform opening and closing
for the operation of autonomous vehicles. The
.More advanced morphological operation can then be
operation of an autonomous vehicle is based on
implemented using combinations of all of these.
first acquiring data that describe its environment.
Indeed, the motion planning and control of a fully
autonomous
vehicle
requires
an
Binary Morphology:
Hardware
architectures
for
binary
intelligent
morphological image is nothing but erosion and
controller to be able to make decisions to allow the
dilation. the objective is to minimize the number of
autonomous vehicle to maneuver in an unknown
operations, memory requirement, and memory
field based on these data. These data sets include
accesses per pixel. Therefore, a fast stall-free low
range data, 2D images, and position measurements.
complexity architecture is proposed that takes
This data is used to identify and avoid obstacles
advantage of the morphological duality principle
and to map the surrounding terrain.
and structuring element decomposition. The main
The elements of an image analysis system are
advantage of this architecture is that for the
shown in Figure 1. Image analysis usually starts
common class of flat and rectangular structuring
with a pre-processing stage, which includes
elements, complexity and number of memory
operations such as noise reduction. For the actual
accesses per pixel is independent of both image
recognition stage, segmentation should be done
and structuring element size.
before it to extract out only the part that has useful
information. Image segmentation is a primary and
3. LABELING ALGORITHM BASED ON
critical component of image analysis. The quality
CONTOUR TRACING
of the final results of an image analysis could
The algorithm has lower memory requirements
depend on the segmentation step. On the other
compared to other labeling techniques and can
hand, segmentation is one of the most difficult
guarantee labeling of a predefined number of
tasks in image processing, especially automatic
clusters independent of their shape. In addition,
image segmentation.
features especially important in this particular
application are extracted during the contour tracing
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with little increase in hardware complexity. The
Hit-and-Miss
implementation is verified on an FPGA in an
When eroding, the ―0‖s in the structuring element
embedded system environment with an image
act like ―don’t care‖ conditions—they don’t really
resolution of 320×240 at a frame rate of 25 fps.
require that the image be on or off at that point, only
that the remaining ―1 pixels fit inside the object. In
other words, it finds places that ―look like this‖ (for
the 1s), but has no way to say ―but doesn’t look like
this‖ (for the 0s). We can combine erosion and dilation
to produce an operator that acts like this: the ―hit and
miss‖ operator. The operator takes two elements: one
that must ―hit‖ and one that must ―miss‖. The
operator is defined as follows. If the structuring
Figure 2 Morphological Dilation of a Binary Image
elements J (hit) and K (miss) are applied to the image
A: A ⊗ (J,K) = (A _ J) ∩ (Ac _ K)
In other words, the structuring element J must fit
inside the object and the element K must fit outside the
Region Filling
object at that position.
Adding the intelligence to detect a black inner point
This gives us a form of binary template
of sphere, we can use region filling to fill up the sphere
matching. For example, the following structuring
to be completely white.
elements give an ―upper right‖ corner detector:
000
110
010
011
001
000
Figure 3 Region Filling
JK
Pruning
The J element finds the points with connected left
Pruning methods are an essential complement to
the procedures that tend to leave parasitic components
that need to be cleaned up‖ by post processing. For
example, the automated recognition of hand printed
characters.
and lower neighbors, and the H element finds the points
without upper, upper right, and right neighbors. In some
cases, we will simply use a single structuring element
B, with the assumption that
J = B and
Thickening
K = Bc.
The structuring elements have the same form as in
thinning but with all 1’s and 0’s interchanged,
i.e., A ⊗ B = A ⊗ (B, Bc)
= (AB) ∩ (Ac _ Bc)
However, a separate algorithm for thickening is seldom
Notice that this doesn’t, however, allow for a third
used in practice. The usual procedure is to thin the
case: don’t care‖ pixels ones that could be either inside
background instead.
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or outside the shape. Some authors will for this reason
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write the two operators as a single one using 1s for the
The creation of the verification platform is
for the don’t care
optional and is based on the hardware platform. The
positions. In the previous example, this would be
MHS file is taken as an input by the Simgen tool to
written as
create simulation files for a specific simulator. Three
x00
types of simulation models can be generated by the
110
Simgen tool: behavioral, structural and timing models.
x1x
Some other useful tools available in EDK are Platform
This form is perhaps more useful for visualizing
Studio which provides the GUI for creating the MHS
what the structuring element is designed to find.
and MSS files. Create / Import IP Wizard which allows
Remember, though, that you still have to apply two
the creation of the designer's own peripheral and import
different operators, one for hit (the 1s) and one for miss
them into EDK projects. Platform Generator customizes
(the 0s), when implementing hit-and-miss.
and generates the processor system in the form of
hits, 0s for the misses, and x‖s
hardware netlists.
There are two options available for debugging the
Morphological Smoothing
Since opening suppresses bright details smaller
application
created
using EDK namely:
Xilinx
than the specified SE, and closing suppresses dark
Microprocessor Debug (XMD) for debugging the
details, they are often used in combination as
application software using a Microprocessor Debug
morphological filters for image smoothing and noise
Module (MDM) in the embedded processor system, and
removal
Software Debugger that invokes the software debugger
Top‐hat and bottom‐hat transformations
corresponding to the compiler being used for the
Combining image subtraction with openings and
closings
results
in
top-hat
and
bottom
processor. C.
hat
transformations. The top-hat transformation of a grayscale image f is defines as f minus its opening:
That ( f ) = f − ( f b)
Similarly, the bottom-hat transformation of a grayscale image f is defines as the closing of minus f: B( f)
= f •b − f
One principal application of these transforms is in
removing objects from an image by using an SE in the
opening and closing that does not fit the objects to be
removed. The difference then yields an image with only
the removed objects.
The top-hat is used for light objects on a dark
background and the bottom-hat – for dark objects on a
light background
Figure 4 Simulation Results
4. RESULTS
The software application can be written in a "C or
C++" then the complete embedded processor system for
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International Journal of Engineering Trends and Technology (IJETT) - Volume4 Issue6- June 2013
user application will be completed, else debug &
download the bit file into FPGA. Then FPGA behaves
like processor implemented on it in a Xilinx Field
Programmable Gate Array (FPGA) device.
[3]
Bruce A. Draper, J. Ross Beveridge, A.P. Willem Böhm,
Charles Ross, Monica Chaw the, ―Accelerated Image Processing
on FPGAs‖, IEEE Transactions on Image Processing, Vol. 12,
No. 12. Pp. 1543-1551, 2003.
[4]
A. Castillo, J. Vázquez, J. Ortegón y C. Rodriguez,
―Prácticas de laboratorio Para estudiantes de ingeniería con
FPGA‖, IEEE Latin America Transactions, Vol. 6, No.2, pp. 130-
5. Conclusions
In this paper, we
have proposed to make it
136, 2008.
[5]
K. T. Gribbon, D. G. Bailey and C. T. Johnston, ―Design
appear as if one item is physically changing into the
Patterns for Image Processing Algorithm Development on
other. The purpose of morphological processing is
FPGAs‖, TENCON 2005,pp. 1-6, November 21-24, 2005.
primarily to remove imperfections added during
[6]
Bob L. Sturm and Jerry D. Gibson, ―Signals and Systems
Using MATLAB: An Integrated Suite of Applications for
segmentation. The basic operations are erosion and
dilation .Using the basic operations we can perform
opening and closing .More advanced morphological
operation
can
then
be
implemented
using
combinations of all of these. The proposed method is
inherently parallel, since computations for each pixel
of each sequence frame can be done concurrently with
no need for communications. This can help in
Exploring and Teaching Media Signal Processing‖, 35th
ASEE/IEEE Frontiers in Education Conference, pp. 21-25,
October 19 – 22, Indianapolis, Indiana,USA,2005.
[7]
Javier Vicente, Begoña García, Ibon Ruiz, Amaia Méndez,
Oscar Lage,―EasySP: Nueva Aplicación Para la Enseñanza de
Procesado de Señal‖, IEEE-RITA, Vol. 2, No. 1, 2007.
[8]
David Báez-López, David Báez-Villegas, René Alcántara,
Juan José Romero, Tomás Escalante, ―A package for filter design
based on MATLAB‖, Computer Applications in Engineering
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lowering
execution
times
for
high-resolution
sequences. Moreover, the approach is suitable to be
adopted in a layered framework, where, operating at
region-level, it can improve detection results allowing
to more efficiently tackle the camouflage problem and
to
distinguish
morphological
Image
by
the
morphological operator.
Acknowledgements
The
authors
would
like
to thank the
anonymous reviewers for their comments which were
very helpful in improving the quality and presentation
of this paper.
References:
[1]
C.T. Johnston, K.T.Gribbon, D.G.Bailey, ―Implementing
Image Processing Algorithms on FPGAs‖, Eleventh Electronics
New Zealand Conference, Palmerston North, New Zealand, 2004.
[2]
D.G. Bariamis, D.K. Iakovidis, D.E. Maroulis, S. A.
Karkanis, ―An FPGA-based Architecture for Real Time Image
Feature Extraction‖, Proceedings of the 17th International
Conference on Pattern Recognition, August 23-26, Cambridge,
UK, 2004.
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