Introduction to Morphological Operators 18-May-151

Introduction to Introduction to Morphological Morphological

OperatorsOperators

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About this lecture

• In this lecture we introduce INFORMALLY the most important operations based on morphology, just to give you the intuitive feeling.

• In next lectures we will introduce more formalism and more examples.

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EROSIONEROSION

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What We Do Today. 2nd Part

• Introduction to Morphological Operators

– Used generally on binary images, e.g., background subtraction results!

– Used on gray value images, if viewed as a stack to binary images.

• Good for, e.g.,– Noise removal in background

– Removal of holes in foreground / background

• Check: www.cee.hw.ac.uk/hipr

A first Example: Erosion• Erosion is an important morphological

operation

• Applied Structuring Element:

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Example for Erosion

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

0Output Image

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Example for Erosion

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

0 0Output Image

111

Example for Erosion

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

0 0 0Output Image

111

Example for Erosion

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

0 0 0 0Output Image

111

Example for Erosion

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

0 0 0 0 1Output Image

111

Example for Erosion

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

0 0 0 0 1 0Output Image

111

Example for Erosion

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

0 0 0 0 1 0 0Output Image

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Example for Erosion

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

0 0 0 0 1 0 0 0Output Image

111

Introduction

• Structuring Element• Erosion• Dilation• Opening• Closing• Outlook: Hit-and-miss Operation, Thinning,

Thickening

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Structuring Element (Kernel)• Structuring Elements can have varying sizes• Usually, element values are 0,1 and none(!)• Structural Elements have an origin• For thinning, other values are possible• Empty spots in the Structuring Elements are don’t

care’s!

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Box

Disc

Examples of stucturing elements

Dilation & Erosion

• Basic operations

• Are dual to each other:– ErosionErosion shrinks foreground, enlarges

Background

– Dilation enlarges foreground, shrinks background

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Erosion

• Erosion is the set of all points in the image, where the structuring element “fits into”.

• Consider each foreground pixel in the input image– If the structuring element fits in, write a “1” at the

origin of the structuring element!

• Simple application of pattern matching• Input:

– Binary Image (Gray value)– Structuring Element, containing only 1s!

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Another example of erosion

• White = 0, black = 1, dual property, image as a result of erosion gets darker

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Erosion on Gray Value Images

• View gray value images as a stack of binary images!

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Erosion on Gray Value Images

• Images get darker!

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Counting Coins

• Counting coins is difficult because they touch each other!

• Solution: Binarization and Erosion separates them!

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DILATIONDILATION

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Example: Dilation• Dilation is an important morphological

operation

• Applied Structuring Element:

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Dilation

• Dilation is the set of all points in the image, where the structuring element “touches” the foreground.

• Consider each pixel in the input image– If the structuring element touches the foreground

image, write a “1” at the origin of the structuring element!

• Input:– Binary Image

– Structuring Element, containing only 1s!!

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Example for Dilation

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

1Output Image

111

Example for Dilation

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

1 0Output Image

111

Example for Dilation

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

1 0 1Output Image

111

Example for Dilation

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

1 0 1 1Output Image

111

Example for Dilation

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

1 0 1 1 1Output Image

111

Example for Dilation

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

1 0 1 1 1 1Output Image

111

Example for Dilation

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

1 0 1 1 1 1 1Output Image

111

Example for Dilation

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1 0 0 0 1 1 1 0 1 1Input image

Structuring Element

1 0 1 1 1 1 1 1Output Image

111

Another Dilation Example

• Image get lighter, more uniform intensity

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Dilation on Gray Value Images

• View gray value images as a stack of binary images!

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Dilation on Gray Value Images

• More uniform intensity

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Edge Detection

• Edge Detection

1. Dilate input image

2. Subtract input image from dilated image

3. Edges remain!

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Opening & Closing

• Important operations

• Derived from the fundamental operations – Dilatation– Erosion

• Usually applied to binary images, but gray value images are also possible

• Opening and closing are dual operations

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OPENINGOPENING

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Opening

• Similar to Erosion– Spot and noise removal– Less destructive

• Erosion next dilation• the same structuring element for both operations.• Input:

– Binary Image– Structuring Element, containing only 1s!

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Opening

• Take the structuring element (SE) and slide it around inside each foreground region. – All pixels which can be covered by the SE with the SE

being entirely within the foreground region will be preserved.

– All foreground pixels which can not be reached by the structuring element without lapping over the edge of the foreground object will be eroded away!

• Opening is idempotent: Repeated application has no further effects!

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Opening

• Structuring element: 3x3 square

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Opening Example

• Opening with a 11 pixel diameter disc

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Opening Example

• 3x9 and 9x3 Structuring Element

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3*9

9*3

Opening on Gray Value Images

• 5x5 square structuring element

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Use Opening for Separating Blobs

• Use large structuring element that fits into the big blobs

• Structuring Element: 11 pixel disc

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CLOSINGCLOSING

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Closing

• Similar to Dilation– Removal of holes– Tends to enlarge regions, shrink background

• Closing is defined as a Dilatation, followed by an Erosion using the same structuring element for both operations.

• Dilation next erosion!• Input:

– Binary Image– Structuring Element, containing only 1s!

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Closing

• Take the structuring element (SE) and slide it around outside each foreground region. – All background pixels which can be covered by the SE

with the SE being entirely within the background region will be preserved.

– All background pixels which can not be reached by the structuring element without lapping over the edge of the foreground object will be turned into a foreground.

• Opening is idempotent: Repeated application has no further effects!

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Closing

• Structuring element: 3x3 square

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Closing Example

• Closing operation with a 22 pixel disc

• Closes small holes in the foreground

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Closing Example 1

1. Threshold

2. Closing with disc of size 20

Apr 18, 2023 51 Thresholded closed

Closing Example 2

• Good for further processing: E.g. Skeleton operation looks better for closed image!

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skeleton of Thresholded

skeleton of Thresholded and next closed

Closing Gray Value Images

• 5x5 square structuring element

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Opening & Closing

• Opening is the dual of closing

• i.e. opening the foreground pixels with a particular structuring element

• is equivalent to closing the background pixels with the same element.

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HIT and HIT and MISSMISS

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Hit-and-miss Transform

• Used to look for particular patterns of foreground and background pixels

• Very simple object recognition• All other morphological operations can be

derived from it!!• Input:

– Binary Image– Structuring Element, containing 0s and 1s!!

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Hit-and-miss Transform

• Example for a Hit-and-miss Structuring Element• Contains 0s, 1s and don’t care’s.• Usually a “1” at the origin!

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Hit-and-miss Transform

• Similar to Pattern Matching:

• If foreground and background pixels in the structuring element exactly match foreground and background pixels in the image, then the pixel underneath the origin of the structuring element is set to the foreground color.

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Corner Detection with Hit-and-miss Transform

• Structuring Elements representing four corners

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Corner Detection with Hit-and-miss Transform

• Apply each Structuring Element

• Use OR operation to combine the four results

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Basic Basic THINNINGTHINNING

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Thinning

1. Used to remove selected foreground pixels from binary images

2. After edge detection, lines are often thicker than one pixel.

3. Thinning can be used to thin those line to one pixel width.

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Definition of Thinning

• Let K be a kernel and I be an image

with 0-1=0!!• If foreground and background fit the structuring

element exactly, then the pixel at the origin of the SE is set to 0

• Note that the value of the SE at the origin is 1 or don’t care!

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KIIKI ,HitAndMiss,thin

Example Thinning

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We use two Hit-and-miss Transforms

Basic Basic THICKENINGTHICKENING

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Thickening

• Used to grow selected regions of foreground pixels

• E.g. applications like approximation of convex hull

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Definition Thickening

• Let K be a kernel and I be an image

with 1+1=1• If foreground and background match exactly the

SE, then set the pixel at its origin to 1!• Note that the value of the SE at the origin is 0 or

don’t care!

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KIIKI ,HitAndMiss,thicken

Example Thickening

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PROBLEMSPROBLEMS

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Problems

1. Consider the images on slide 17! Why are the images getting darker under erosion? Explain!

2. Consider the images on slide 31! Why are the intensities becoming more uniform? Explain!

3. Compare Dilatation and Erosion! How are they related? Verify your answers with Matlab!

4. Apply Erosion and Dilatation for noise removal!

5. Consider slide 38: Remove the artefacts remaining with the horizontal lines.

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Problems

6. Derive dilatation and erosion from the Hit-and-miss transformation

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Sources Used

Volker Krüger & Rune Andersen

University of Aalborg, Esbjerg

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