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Sayısal Görüntü İşleme Teknikleri

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Sayısal Görüntü İşleme Teknikleri

Doç. Dr. Mehmet Serdar Güzel

Slides are mainly adapted from the following course page:

http://www.comp.dit.ie/bmacnamee and

https://www.harrisgeospatial.com/docs/DetectEdges.html

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Lecturer

 Instructor: Assoc. Prof Dr. Mehmet S Güzel

 Office hours: Tuesday, 1:30-2:30pm

 Open door policy – don’t hesitate to stop by!

 Watch the course website

 Assignments, lab tutorials, lecture notes

slide

2

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Image Enhancement

(Spatial Filtering 2

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Sharpening Spatial Filters

Sharpening spatial filters seek out to highlight satisfactory detail

 Eliminate blurring from images

 Highlight edges

Sharpening filters are based on spatial differentiation

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1 st Derivative

The formula for the 1 st derivative of a function is as follows:

It’s just the difference between subsequent values and measures the rate of change of the function. In image processing basically the differences between subsequent pixels

) (

) 1

( y f y

y f

f   

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Sobel Mask

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What are edges

 We can also say that sudden changes of discontinuities in an image are called as edges. Significant transitions in an image are called as edges.

 Types of edges

 Generally edges are of three types:

 Horizontal edges

 Vertical Edges

 Diagonal Edges

 Why detect edges

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 Linear filters or smoothing filters:

 Prewitt Operator

 Sobel Operator

 Robinson Compass Masks

 Krisch Compass Masks

 Laplacian Operator.

 Above mentioned all the filters are

 Prewitt operator is used for detecting edges horizontally and vertically.

 Sobel Operator:is very similar to Prewitt operator. It is

also a derivate mask and is used for edge detection. It

also calculates edges in both horizontal and vertical

direction.

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1 st Derivative based examples

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1 st Derivative based examples

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2 nd Derivative

The formula for the 2 nd derivative of a function is given as

Considers the values both before and after the current value

) ( 2

) 1 (

) 1

2 (

2

x f

x f

x x f

f     

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2 nd Derivative

Laplacian Filtering

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Comprasion between 1 nd

Derivative && 2 nd Derivative

Referanslar

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