presentation about edge detection

7/27/2019 presentation about edge detection

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BIOIMAGE SEGMENTATION

USING LabVIEW

DONE BY INTERNAL GUIDE

S.PONKULALI(087023) A.UMARANI

T.S.UVASRE(087119) ASP/EIE



CONTENT OF PRESENTATION

Work Objective

Introduction

Block Diagram

Edges of Image Edge Detection

Methods of Edge Detection

Conclusion



WORK OBJECTIVE

To subdivide an image into itscomponent or regions or object.

It should stop when the objects of

interest in an application have beenisolated.



INTRODUCTION

• The purpose of image segmentation is to

partition an image into meaningful regions

with respect to a particular application.• The segmentation is based on

measurements taken from the image and

might be greylevel , colour , texture, depth or motion.



BLOCK DIAGRAM



EDGES IN IMAGE

An edges that correspond to object

boundaries.

Image pixels brightness changes abruptly.

The image function behavior in a

neighborhood of the pixel



EDGE DETECTION

Edge detection is the approach used most

frequently for segmenting images based

on abrupt(local) changes in intensity.

Edge models are classified according totheir intensity profiles



METHODS OF EDGE

DETECTION

First Order Derivative / Gradient Methods

◦ Roberts Operator

◦ Sobel Operator

◦ Prewitt Operator

Second Order Derivative

◦ Laplacian operator

◦ Differential operator



Gray-Level Transition



THE FIRST DERIVATIVE



GRADIENT OPERATORS

The gradient of the image I(x,y) at

location(x,y),is the vector:

The magnitude of the gradient:

The direction of the gradient vector:



CALCULATING THE GRADIENT

For each pixel the

gradient is calculated,

based on a 3x3

neighborhood around this

pixel. z1 z2 z3

z4 z5 z6

z7 z8 z9



ROBERTS OPERATOR

Mark edge point only

Mostly suitable for binary images



PREWITT OPERATOR

Looks for edges in both horizontal andvertical directions, then combine theinformation into a single metric.



SOBEL OPERATOR

Similar to the Prewitt, with different mask coefficients:



LAPLACIAN OPERATOR

Edge magnitude is approximated in digitalimages by a convolution sum.

The sign of the result (+ or -) from two

adjacent pixels provide edge orientationand tells us which side of edge brighter



LAPLACIAN OPERATOR (Cont.)

Masks for 4 and 8 neighborhoods

Mask with stressed significance of the

central pixel or its neighborhood

010

141

010

111

181

111

121

242

121

212

141

212



ORIGINAL IMAGE



RESULTS OBTAINED

ROBERTS EDGE DETECTOR



PREWITT OPERATOR



SOBEL OPERATOR



LAPLACIAN OPERATOR



presentation about edge detection

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