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IMAGE EDGE DETECTION BASED ON FPGA Presented By: M.Ashok

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Page 1: PRJCT SEMINAR

IMAGE EDGE DETECTION BASED ON FPGA

Presented By:M.Ashok

Page 2: PRJCT SEMINAR

CONTENTS• Objective• Existing and proposed system• Introduction to edge detection• Sobel operator and need for FPGA• Project overview• Module-1,Module-2 and Module-3• Algorithm• Results• Applications• References

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ObjectiveThis project presents a kind of parallel processing

construction of edge detection algorithm.The edge of the grey image can be located

efficiently. Image analysis is to distinguish and count

objects, and recognize the shape of objects from an image.

A solution to implement complex algorithms at faster frame rates is to build custom hardware.

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Existing systemEXISTING SYSTEM• In the existing conventional segmentation

techniques, intensity values used for thresholding are not detected properly and result in segmentation errors.

• This processing can be performed on an FPGA rather than a microprocessor or DSP.

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PROPOSED SYSTEM

PROPOSED SYSTEM:Sobel Edge detection is implemented in

FPGA due to its programming flexibility.

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Edges and edge detection

EDGES:• Edges are those places

in an image that correspond to object boundaries.

• Edges are pixels where image brightness changes abruptly.

 

Brightness vs. Spatial Coordinates

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EDGE DETECTION?

• An edge is an abrupt change in brightness as we move from one pixel to its neighbor in an image.

• Edge information for a particular pixel is obtained by exploring the brightness of pixels in the neighborhood of that pixel.

• If all the pixels in neighborhood are of same brightness levels means no edge, if there is change in brightness there is more chance of an edge.

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Comparing Edge Operators

-1 0 1

-1 0 1

-1 0 1

1 1 1

0 0 0

-1 -1 1

Gradient:

Roberts (2 x 2):

Prewitts (3 x 3):

0 1

-1 0

1 0

0 -1

Good LocalizationNoise SensitivePoor Detection

Poor LocalizationLess Noise SensitiveGood Detection

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Gradient Methods – Sobel Operator

• The 3X3 convolution mask smoothes the image by some amount , hence it is less susceptible to noise.

• Convolution Mask

Gx = Gy=

• The differences are calculated at the center pixel of the mask. • The gradient is calculated by using he sobel operator for each pixel in

the picture.• If the absolute value exceeds a threshold, the pixel belongs to an edge.

-1 0 1

-2 0 2

-1 0 1

1 2 1

0 0 0

-1 -2 -1

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WHY SOBEL

• Because of its weighted coefficient ,it is immune to noise

• The accuracy rates are high when compared to other gradient methods

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Hardware Module

Power Supply 3.3V

Power Supply 3.3V

SRAMSRAM

FPGASPARTAN 3

XC3S200

FPGASPARTAN 3

XC3S200UARTUART

Clock50MHZClock

50MHZ

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Existing System DSP processors

Disadvantages Inflexible architecture Fixed data width Limited number of MAC units Time-shared MAC unit Serial processing limits data

throughput Multiple DSPs required to meet

bandwidth needs

Proposed System FPGA

Advantageso Parallel design methodology

o Reconfigurable

o Implement designs at gate level

o MAC capability

o Low power Dissipation

o Small in size

Why FPGA?

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Need for FPGA

Low power DissipationSmall in sizeAccuracyFloating point Supports parallelismEasily reconfigurableCan implement larger logic functions.

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FPGA – Spartan-3 XC3S200 220K system gates, 4320 equiv. logic cells 480 total CLB (configurable logic block) 30K distributed RAM bits 216K block RAM bits 12 dedicated multipliers 4 DCM (digital clock multiplier) 173 user I/O, 76 differential I/O pairs

CONFIGURABLE LOGIC BLOCK Main logic resource for implementing

synchronous and combinatorial circuits Comprised of four slices Two logic function generators, two storage

elements, wide-function multiplexers, carry logic, and arithmetic gates

left-hand pair also supports: storing data using Distributed RAM and shifting data with 16-bit registers.

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Control system For FPGAFPGAs are programmable digital logic chips. We can

program them to do almost any digital function.We can compile the "logic function" on your

computer, using a software provided by the FPGA vendor that creates a binary file that can be downloaded into the FPGA.

We can connect a cable from the computer to the FPGA, and the binary file can be downloaded to the FPGA.

That's it! our FPGA behaves according to our "logic function".

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Project Overview

Module 1: Conversion of the input image into text file format using Matlab.

Module 2: Developing custom logic & Softcore processor design.

Module 3: Configuration into FPGA using EDK.

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Module 1

Inputimage

Mat labGUI

1-Dpixels

Textfiles

2-Dpixels

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How to read an image

a =imread('cameraman.tif');imshow(a);pixval on;

a =imread('flowers.tif');imshow(a);pixval on;

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Convert Image into One dimensional

a = imread('cameraman.tif');

[r c]=size(a);

Len=r*c;

b=reshape(a,[1 Len]);

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• GUI is a user interface built with graphical objects

• MATLAB implements GUIs as figure windows

• MATLAB generates an M-file that contains code to

handle the initialization and launching of the GUI

• This M-file provides a framework for the

implementation of the callbacks

Graphical User Interface

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Module 2 -Development tool

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Embedded DevelopmentTool Flow Overview

Data2MEM

Bitstream

Compiler/Linker

(Simulator)

C Code

Debugger

Standard Embedded SWDevelopment Flow

CPU code in on-chip memory

?CPU code in off-chip memory

Download to Board & FPGA

Object Code

Standard FPGA HWDevelopment Flow

Synthesizer

Place & Route

Simulator

VHDL/Verilog

?

Download to FPGA

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XPS FunctionsProject management

◦ MHS or MSS file◦ XMP file

Software application management

SoftwareDesign

XPS

HW/SWSimulation

HW/SWDebug

Hardware Design

• Platform management– Tool flow settings– Software platform

settings– Debug and

simulation

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LOGIC BEHIND THE CODEK1 K2 K3

K4 K5 K6

K7 K8 K9

R1 R2 R3

R4 R5 R6

R7 R8 R9

The coefficients of the kernel are multiplied with the pixel values of the image taking the center pixel as R5The resultant values are added FOR EXAMPLE:

Y=(R1*K1)+(R2*K2)+(R3*K3)+………+(R9*K9)If the pixel output value is greater than the threshlod ,the pixel is detected as the edge

KERNEL PICTURES

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ALGORITHM

• Start• Read the pixel values and Kernel values • Perform the calculations(multiplicaton followed by addition)• Compare the output with threshhold • If

output>thresholdedge pixel is considered

• End if

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FLOW CHART

Start

Read the pixel and kernal values

Perform the calculation

1

if If output>threshold

Edge pixel detected

stop1

1

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Configuring the FPGA

• Download the bit stream– Input file → download. bit– This downloads the download. bit file onto the target

board using the Xilinx impact tool – XPS uses the etc/download.cmd file for downloading the

bit stream.

EDK Intro 27

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DOWNLOADING THE BITSTREAM

• For downloading the bit stream launch XMD and type “dow executable. elf”.

• This will be successful only when .elf file is generated.

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Running and detecting the edge

• After a selecting the textfile from VB, type “run” in the XMD.

• The text file is nothing but the input image which is converted using MATLAB .

• The image edges will then be detected once the run command is entered.

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Launching XMD

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Input Image for Edge Detection

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Edge Detected Output

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Tools usedSoftwareXilinx ISE 8.1iXilinx Platform StudioMatlabVisual basic

HardwareFPGA Spartan3

Language used : VHDL & C

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Applications

• Enhancement of noisy images – satellite images, x-rays, medical images like cat scans

• Text detection• Mapping of roads• Video surveillance, etc.

September 13, 2005 EE - 6358 Computer Vision 35

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CONCLUSION

• the edge of the Image can be effectively detected using Sobel operator apart from the various Gradient operators.

• The result of the complex image can be obtained at higher rates by using FPGA instead of DSP Processors.

• The Sobel operator adding the orientation of the convolution kernels can locate accurately the edge, and not be sensitive to noise.

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REFRERENCES[1] P. Athanas and A. Abbott. Real-time image processing on acustom

computing platform. In IEEE Computer, Feb. 1995.[2]2010 Ninth International Symposium on Distributed Computing and

Applications to Business, Engineering and Science.[3] Chanda, B. and Dutta, D. Majumdar. (2001). Digital Image Processing

and Analysis,Prentice-Hall of India.[4] Gonzalez, Rafael C. and Woods, Richard E. (2002). Digital Image

Processing, Pearson inc.[5] D. T.Saegusa, T.Maruyama, Y.Yamaguchi, “How fast is an FPGA in

image processing?”, IEICE Technical Report,Vol.108. No.48, 2008, pp.83–88

[6] Yangli ,Yangbing. “Study of FPGA based Parallel Processing of Sobel Operator” AI Modern Electronics Technique 2005.J.

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THANK YOU