18-10-2016, 11:33 AM
FPGA Based Design and Implementation of Image Edge Detection Using Xilinx System
Generator
1459590043-basepaper.pdf (Size: 458.81 KB / Downloads: 16)
Abstract:
Edge detection serves as a pre-processing step for many
image processing algorithms such as image
enhancement, image segmentation, tracking and
image/video coding. The edge detection is one of the key
stages in image processing and object recognition. Edge
detection is a basic operation in image processing, it
refers to the process identifying and locating sharp
discontinuities in an image, the discontinuities are
abrupt changes in pixel intensity which characterize
boundaries of objects in a scene. The proposed system
we use canny algorithm, in canny edge detection can
produce good detection of the edge with the thin and
smooth it’s very useful for further study of
segmentation
Introduction
The edges of image are considered to be most
important image attributes that provide valuable
information for human image perception. The edge
detection is a terminology in image processing, particularly
in the areas of feature extraction, to refer to algorithms
which aim at identifying points in a digital image at which
the image brightness changes sharply .The data of edge
detection is very large, so the speed of image processing is
a difficult problem.
The main objective of image processing is to improve
the quality of the images for human interpretation or the
perception of the machines independent of the images for
human interpretation or the perception of the machines
independently. This paper focuses in the processing pixel
to pixel of an image and in the modification of pixel
neighborhoods and of course the transformation can be
applied to the whole image or only a partial region. The
need to process the image in real time, leading to the
implementation level hardware, which offers parallelism,
Thus significantly reduces the processing time, which was
why decided to use Xilinx System Generator, a tool with
graphical interface under the Mat lab, Simulink, based
blocks which makes it very easy to handle with respect to
other software for hardware description. In addition to
offering all the tools for easy graphical simulation level.
This article presents architecture of image processing
application generator, which is an extension of Simulink
and consists of a bookstore called “Blocks Xilinx”, which
are mapped architectures, entities, signs, ports and
attributes, which script file to produce synthesis in
FPGAs, HDL simulation and development tools. The tool
retains the hierarchy of Simulink when it is converted into
VHDL.
II. Existing system
In edge detection, the Sobel operator is used
commonly. The Sobel operator is a classic first order edge
detection operator, computing an approximation of the
gradient of the image intensity function. At each point in
the image, the result of the Sobel operator is the
corresponding norm of this gradient vector. The Sobel
operator only considers the two orientations which are
0°and 90°convolution kernels. The operator uses the two kernels which are convolved with the original image to
calculate approximations of the gradient. The two
convolution kernels are designed to respond maximally to
edges running vertically and horizontally relative to the
pixel grid, one kernel for each of the two perpendicular
orientations.
The kernels can be applied separately to the input
image, to produce separate measurements of the gradient
component in each orientation (call these Gx and
Gy).These can then be combined together to find the
absolute magnitude of the gradient at each point. The
gradient magnitude is given by:
│G│=√GX
2+GY
2
Typically, an approximate magnitude is computed using:
│G│=│GX│+│GY│
This is much faster to compute. The Sobel operator has the
advantage of simplicity in calculation. But the accuracy is
relatively low because it only used two convolution kernels
to detect the edge of image