Image Edge Detection based on FPGA
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Joined: Feb 2011
28-02-2011, 02:15 PM
Image Edge Detection based on FPGA.ppt (Size: 380.5 KB / Downloads: 328)
Image Edge Detection based on FPGA
Sobel operator edge detection.
Accurately locate the image edges.
Implementing parallel construction of sobel edge detection enhancement algorithm.
The algorithm is designed with a FPGA chip and it can process 1024×1024×8 Gray Scale Image successfully.
VHDL code for Sobel edge enhancement algorithm.
Implementing in the Xilinx Spartan3 FPGA by ISE9.2i.
Sobel edge detection :
Quickly get the result of one pixel in only one clock periods.
Find the gradient vector of the image.
It only consider two orientation convolution kernels.
Sobel edge detection:
3*3 pixel generation
Generate 3*3 pixels in the image.
The image data input according to the clock signal,
so P1, P2,•••,P9 is the 3 × 3 image data template.
Compared the two orientations of convolutions kernels.
To get maximum gradient values of the image.
Controls the 3*3 pixels generation and sobel enhancement.
It gives the current edge pixel locations.
Applying different Threshold values to outputs of edge control and sobel enhancement.
By threshold values get the final results of the edges of the image.
Joined: Apr 2012
14-06-2012, 05:33 PM
Image Edge Detection Based on FPGA
Field Programmable Gate Array (FPGA) is an
effective device to realize real-time parallel processing of vast
amounts of video data because of the fine-grain reconfigurable
The edges of image are considered to be most important
image attributes that provide valuable information for human
image perception [1-3]. 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 [4-6].
SOBEL EDGE DETECTION ENHANCEMENT
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.
FPGA HARDWARE IMPLEMENTATION
This design uses 3×3 convolution kernels, processing
1024×1024×8 Gray Scale Image. The architecture is shown
in Fig.2.The system is divided into four modules: 3×3 pixel
generation module, Sobel enhancement operator module
edges control module and binary segmentation [10,11].
The design was implemented in the XILINX Spartan3
XC3S200 FPGA by ISE9.2. The device utilisation summary
is given in Table. I. Small resource is taken up, so there is
possibility of implementing some more parallel processes
with this architecture on the same FPGA.
The Sobel operator adding the orientation of the
convolution kernels can locate accurately the edge, thin the
boundary lines, and not be sensitive to noise. The FPGA
implementation of it meets the real-time requirements. This
architecture based on FPGA is much better than processing
images on software platform using high level programming
languages like C or C++ .
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