嵌入式視覺Pattern Recognition for

Embedded Vision

•Template matching•Statistical / Structural Pattern Recognition •Neural networks

Embedded Vision System

Image acquisitionImage ProcessingFeature ExtractionDecision Making(Pattern Recognition)

Pattern Recognition model

1. Template matching

2. Statistical Pattern Recognition:

based on underlying statistical model of patterns and pattern classes.

3. Structural (or syntactic) Pattern Recognition :

pattern classes represented by means of formal structures as grammars, automata, strings, etc.

4. Neural networks:

classifier is represented as a network of cells modeling neurons of the human brain (connectionist approach).

Pattern Representation

• A pattern is represented by a set of d features, or attributes, viewed as a d-dimensional feature vector.

1 2( , , , )Tdx x xx

PreprocessingPreprocessingFeature

MeasurementFeature

MeasurementClassificationClassification

testpattern

Classification Mode

PreprocessingPreprocessing

FeatureExtraction/Selection

FeatureExtraction/Selection

LearningLearningtrainingpattern

Training Mode

Two Process for Pattern Recognition system

Generic concepts for PR

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nx

x

x

2

1Feature vector

- A vector of observations (measurements). - is a point in feature space .

Hidden state

- Cannot be directly measured.

- Patterns with equal hidden state belong to the same class.

Xx

x X

Yy

Task

- To design a classifer (decision rule)

which decides about a hidden state based on an onbservation.YX :q

Pattern

Pattern Representation by Feature Vector for Character

Recognition

X=[x1, x2, … , xn], each xj a real number

Xj may be object measurement

Xj may be count of object parts

Example: object rep. [#holes, Area, moments, ]

Example

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2

1

x

x

height

weight

Task: identity recognition.

The set of hidden state is

The feature space is

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Training examples

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Hy Linear classifier:

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Pattern Recognition system

Image processing

Feature extraction

Classifier

Classassignment

• Image acquisition and image processing.• Feature extraction aims to create discriminative features good for classification.• Classifier.• Learning algorithm sets PR from training examples-- supervised learning

Learning algorithm

Object

Feature extraction

Task: to extract features which are good for classification.

Good features: • Objects from the same class have similar feature values.

• Objects from different classes have different values.

“Good” features “Bad” features

Feature extraction methods

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11φ

2φ

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Feature extraction Feature selection

Problem can be expressed as optimization of parameters of featrure extractor .

Supervised methods: objective function is a criterion of separability (discriminability) of labeled examples, e.g., linear discriminat analysis (LDA).

Unsupervised methods: lower dimesional representation which preserves important characteristics of input data is sought for, e.g., principal component analysis (PCA).

φ(θ)

ClassifierA classifier partitions feature space X into class-labeled regions such that

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1X 3X

2X

1X1X

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The classification consists of determining to which region a feature vector x belongs to. Borders between decision boundaries are called decision regions.

CSE803 Fall 2014 13

Decision-Tree Classifier

Uses subsets of features in seq.

Feature extraction may be interleaved with classification decisions

Can be easy to design and efficient in execution

14

Decision Trees

#holes

moment ofinertia

#strokes #strokes

best axisdirection

#strokes

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2

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90

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Classification using nearest class mean

Compute the Euclidean distance between feature vector X and the mean of each class.

Choose closest class, if close enough (reject otherwise)

Unsupervised learning

Input: training examples {x1,…,x} without information about the hidden state.

Clustering: goal is to find clusters of data sharing similar properties.

Classifier

Learning algorithm

θ

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Classifier

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YΘX :q

Learning algorithm(supervised)

A broad class of unsupervised learning algorithms:

Example of unsupervised learning algorithm

k-Means clustering:

Classifier

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iki

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Goal is to minimize

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Learning algorithm

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