Blob Analysis

8/3/2019 Blob Analysis

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BLOBsLecture on the image part (5)

Automatic Perception 9

Thomas Moeslund

Computer Vision and Media Technology lab.

Aalborg University

[email protected]


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Fundamental Steps in Computer Vision

Knowledge baseProblem

domain Image

acquisition

Preprocessing

SegmentationRepresentation

and description

Recognition

and

InterpretationResult

Point 1: 22,33

Point 2: 24, 39

..

Actor sitting

So far:

Preprocessing Segmentation

Today

BLOBs

Segmentation

Representation

Feature extraction

Representation


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Agenda

BLOBs

Finding the pixels for each object

Extracting features

Calculate features

For example size and shape

Feature vector = [2,1,,3]


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BLOBwhat is it?

Blob versus BLOB

BLOB = Binary Large Object

Also know as a Particle

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What can BLOBs be used for?

Isolate the different objects (BLOBs)

Represented as a list of pixels

Representing each object (BLOB) by a setof features (=characteristics)

Calculated from the list of pixels

For example the position and size of an object


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Why calculate features for a BLOB? When more objects are present after segmentation,

how will you figure out which is the one ofinterest? (very relevant for your projects!)

Solution: Make a model of the object

you are looking for

Calculate its characteristics (features)

Calculate the characteristics (features) of each BLOB inthe image

Compare the features of each BLOB with the featuresof the model

The best match defines the object


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Isolating a BLOB

What we want:

For each object in the image, a list with its pixels

How do we get that? Connected component analysis

(Region growing)

Define: connectivity

Who are my neighbors? 4-connected

8-connected


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Connected component analysis

Binary image (0,1)

Seed point: where do we start?

Grassfire concept

Delete (burn) the pixels we visit

Visit all CONNECTED (4 or 8) neighbors

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Recursive algorithm

(show: blob, TH, analyze (set: outlines))


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Extracting features


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Agenda: Extracting features

What is the purpose?

What kind of features can be extracted and

how?

Feature matching

Relating Features and Pattern Recognition


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Why feature extracting?

Generate some entities (numbers) that can be used in patternrecognition For example size and shape

From image operations to mathematical operations Input: a list of pixel positions

Output: Feature vector First step: remove too small, too big, and border BLOBs

(show: AuPbSn40, TH, analyze (set: outlines, size, border))



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Features


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Features

Area (number of pixels)

Used to remove noise (small/big objects)

Number of holes in the object

Holes area

Total area = area + holes area

Perimeter = length of contour

Bounding box Upper left corner

Height and width of bounding box

One BLOB


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Features

Center of mass (xm,ym)

Compactness

Minimum for a disc

Minimum for a rectangle

Circularity Longest distance (Ferets diameter)

Orientation of the object

Orientation of Ferets diameter

objecti

im xx

objecti

im yy

areaPerimeter

2

Area

Perimeter2

HeightWidth

Area


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Features

Shapes

Bounding box ratio: Height / Width

Says something about the elongation How well does the object fit a rectangle

Can be derived from: Area and Perimeter

How well does the object fit an ellipse

Can be derived from: Area and Perimeter Many other features: see the notes for today and

be creative!

Degree of elongation


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Feature Matching


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Feature Matching

But what if more BLOB have the samenumber of TRUEs?

Solution: Measure the DISTANCE from eachBLOB to the model and pick the BLOB withthe shortest distance, i.e. the BLOB mostsimilar to the model

A much better method as it is based on statistics!

How do we define a distance?


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Feature Matching

Solution: Measure the DISTANCE from the

BLOB to each of the models and pick the

model with the shortest distance, i.e. themodel most similar to the BLOB

How do we define a distance?


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Feature Matching

Distance in feature-space

Feature 1: Area

Feature 2: Circularity

2 dimensional feature space

BLOB:

Model types:

Feature 1

Feature 2MATCH!


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Feature Matching: Distance

features

j

fjifjfiffif BMBMBMiD#

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2,2

2

1,1 )()()()(

Feature 1

Feature 2

Euclidean distance


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Feature Matching

Problem:

Area is measured in 1000s and circularity [0,1]

That means that the area will dominate thedistance measure completely!

Solutions:

Use ratios: So W/H is a better feature than W or H

Normalize all feature to the same interval, e.g., [0,1] Let some of the features be TRUE/FALSE and

others Euclidean distances


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Feature Matching

features

j

fjifjjfiffif BMWBMWBMWiD#

1

2

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2

2,22

2

1,11)()()()(

What if we trust some features more thanothers?

For example, with small objects the perimetermight be uncertain

Solution: Weigthing

features

j

fjifjfiffif BMBMBMiD#

1

2

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2

2,2

2

1,1)()()()(


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What to remember

BLOB = Binary Large OBject Find a list of pixels for each object

Connectivity: 4 versus 8 connected

Connected component analysis Features (=characteristics) of an object

Many exist

Many more can be derived

Feature matching

TRUE/FALSE Euclidean distance

Normalize

Learn the features of the model


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Exercises Questions to the lecture?

What was good about the lecture and what could have beenbetter?

Discuss the PE-questions

In what order will the pixels be marked if a

4-connected kernel is used? 8-connected kernel is used?

Calculate the perimeter of this object using

different distance measures

Euclidean distance, city-block (manhatten) distance, chessboard distance

Find out what they are by searching the internet

Play around in ImageJ with the BLOB analysis (Analyze)

perhaps you can combine it with morphology

Find the approximate centers of all dots in the image: dots-lines


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Exercises

If youre using images in you project: Which features could you imagine using?

Imagine you are using the Area of a BLOB as a feature in

your project. Find a way to normalize the feature matching,so the result always is in the interval: [0,1] (where 0 meansa very poor match and 1 means a perfect match)

What features can be used to classify the different letters inthe image: alphabeth?

Draw the signature for a box What is the problem with this signature compared with thesignature for the circle?

How can this problem be handled?

How is region growing different from connectedcomponent analysis?

Documents

Blob Analysis