Feature Recognition and Classification

(Suraj Shrestha)068/BCT/539(Sanjeev Paudel)068/BCT/537

Feature

• A feature usually refers to a region of a part with some interesting geometric or topological properties.

Feature Recognition

Feature Detector

Comparator

Library

Recognitioni/psamples

Template matching and

Cross co-relation

Simple Template Matching

Templates

Target

Reds are matched pixelsBlue are unmatched ones.

Net score= Reds - Blues

Cross Co-relation

Measure of similarity between two signals

At (i,j) cross co-relation is given by:

Where :B and T are the pixel brightness values for the image(template) and target respectively.

The Denominator is for Normalization.

Example

Another one

Parametric Description

Successful Feature recognition applications:• Face Recognition • Fingerprint Recognition

They uses feature specific measurement parameters KA Parametric Description Method

Uses different transformation parameters

Classification

• Imposed Criteria(the expert system)

• Supervised Classification(KNN)

• Unsupervised Classification(cluster analysis)

Decision points

•Histogram parameter value overlap

•Need for decision threshold with acceptable error percentage

Multidimensional classification

• Histograms and Probability Distribution Functions are plotted as function of single parameter.

• If plotted as function of different parameters classification would be easier.

Learning

ConstraintsRegularity

Explanation BasedOne Shot

Pattern Recognition Work Of TheoreticianMimicking Biology

Learning

Learning Systems

• Supervised Learning

• Unsupervised Learning

K Nearest Neighbor

• Non parametric method• Contrary to histogram or LDA method it saves

actual n dimensional coordinates for each of the identified feature

• Larger storage is required• Processing Power Requirement increases

Class A and B are previously identified features

So it is supervised classification

Special Case:When k=1, each training vector defines a region in space, defining a Voronoi partition of the space

Clustering

• Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters)

Hierarchical Clustering

K-means Clustering

K-means separates data into Voronoi-cells, which assumes equal-sized clusters

Next it is necessary to consider how to apply these class boundaries as a set of rules for the identification of subsequent features.

Expert System

•Rules are supplied by human expert.

•Order of execution of rules determined by system software

• Simple classification systems like this are sometimes called decision trees or production rules, consisting of an ordered set of IF…THEN relationships(rules)

• Our previous example was Binary Decision Tree.

• Most real expert systems have far more rules than this one and the order in which they are to be applied is not necessarily obvious

•It is feed forward structure.

•This approach does not test all possible paths from observations to conclusions.

•Heuristics to control the order in which possible paths are tested are very important

Some Expert Systems

• Rice-Crop Doctor• AGREX• CaDet• DXplain

THANK YOU