Binary Image Analysis: Part 2 Readings: Chapter 3: 3.5-3.7

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Binary Image Analysis: Part 2 Readings: Chapter 3: 3.5- 3.7 • mathematical morphology • region properties • region adjacency 1

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Binary Image Analysis: Part 2 Readings: Chapter 3: 3.5-3.7. mathematical morphology region properties region adjacency. Mathematical Morphology. Binary mathematical morphology consists of two basic operations dilation and erosion and several composite relations closing and opening - PowerPoint PPT Presentation

Transcript of Binary Image Analysis: Part 2 Readings: Chapter 3: 3.5-3.7

Page 1: Binary Image Analysis: Part 2 Readings: Chapter 3: 3.5-3.7

Binary Image Analysis: Part 2Readings: Chapter 3: 3.5-3.7

• mathematical morphology

• region properties

• region adjacency

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Page 2: Binary Image Analysis: Part 2 Readings: Chapter 3: 3.5-3.7

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Mathematical Morphology

Binary mathematical morphology consists of twobasic operations

dilation and erosion

and several composite relations

closing and opening conditional dilation . . .

Page 3: Binary Image Analysis: Part 2 Readings: Chapter 3: 3.5-3.7

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Dilation

Dilation expands the connected sets of 1s of a binary image.

It can be used for

1. growing features

2. filling holes and gaps

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Erosion

Erosion shrinks the connected sets of 1s of a binary image.

It can be used for

1. shrinking features

2. Removing bridges, branches and small protrusions

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Structuring ElementsA structuring element is a shape mask used inthe basic morphological operations.

They can be any shape and size that isdigitally representable, and each has an origin.

The origin is usually the center if the structuringelement is symmetric.

boxhexagon disk

something

box(length,width) disk(diameter)

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Dilation with Structuring Elements

The arguments to dilation and erosion are

1. a binary image B2. a structuring element S

dilate(B,S) takes binary image B, places the originof structuring element S over each 1-pixel, and ORsthe structuring element S into the output image atthe corresponding position.

0 0 0 00 1 1 00 0 0 0

11 1

0 1 1 00 1 1 10 0 0 0

originBS

dilate

B S

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Erosion with Structuring Elements

erode(B,S) takes a binary image B, places the origin of structuring element S over every pixel position, andORs a binary 1 into that position of the output image only ifevery position of S (with a 1) covers a 1 in B.

0 0 1 1 00 0 1 1 00 0 1 1 01 1 1 1 1

111

0 0 0 0 00 0 1 1 00 0 1 1 00 0 0 0 0

B S

origin

erode

B S

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Example to Try

0 0 1 0 0 1 0 0 0 0 1 1 1 1 1 0 1 1 1 1 1 1 0 01 1 1 1 1 1 1 10 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0

1 1 11 1 11 1 1

erode

dilate with same structuring element

SB

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 00 0 0 1 1 0 0 00 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 00 0 1 1 1 1 0 00 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0

This kind of erosion plus dilation iscalled an opening.

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Opening and Closing

• Closing is the compound operation of dilation followed by erosion (with the same structuring element)

• Opening is the compound operation of erosion followed by dilation (with the same structuring element)

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Use of Opening

Original Opening Corners

What kind of structuring element was used in the opening?

How did we get the corners?

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Gear Tooth Inspection

originalbinary image

detecteddefects

How didthey do it?

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Some Details (see Ch 3)1. Original image 2. Find centers of holes by erosion with a circular ring element

3. Dilate by a 4. OR the hexagons hexagon mask into the original

5. Use disc the size of the body, open 6. AND result of 5 to remove teeth. with 1 to get Dilate. Subtract. just the teeth.

7. Dilate 6 with a 7. Show defects small element in red for customer that leaves the defects as holes

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Region Properties

Properties of the regions can be used to recognize objects.

• geometric properties (Ch 3)

• gray-tone properties

• color properties

• texture properties

• shape properties (a few in Ch 3)

• motion properties

• relationship properties (1 in Ch 3)

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Geometric and Shape Properties

• area• centroid• perimeter • perimeter length• circularity• elongation• mean and standard deviation of radial distance• bounding box• extremal axis length from bounding box• second order moments (row, column, mixed)• lengths and orientations of axes of best-fit ellipse

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Region Adjacency Graph

A region adjacency graph (RAG) is a graph in whicheach node represents a region of the image and an edgeconnects two nodes if the regions are adjacent.

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1 2

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What’s all this for?

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Once regions are identified and their propertiesand relationships computed, we can start usingthem for higher-level tasks such as:

• inspection• object recognition• image matching• classification in general