This document discusses various methods for feature recognition and classification, including template matching, cross-correlation, parametric description, supervised classification using K-nearest neighbors (KNN), and unsupervised classification using cluster analysis. It also discusses learning systems, hierarchical and K-means clustering, expert systems, and some examples of expert systems like Rice-Crop Doctor and AGREX.

1. Feature Recognition and
Classification
(Suraj Shrestha)068/BCT/539
(Sanjeev Paudel)068/BCT/537

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

3. Feature Recognition

4. Feature
Detector
Comparator
Library
Recognition
i/p
samples

5. Template matching
and
Cross co-relation

6. Simple Template Matching
Templates
Target
Reds are matched pixels
Blue are unmatched ones.
Net score= Reds - Blues

7. Cross Co-relation
Measure of similarity between two signals

10. 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.

11. Example

12. Another one

13. Parametric Description
Successful Feature recognition applications:
• Face Recognition
• Fingerprint Recognition
They uses feature specific measurement parameters KA
Parametric Description Method

15. Uses different
transformation parameters

16. Classification
• Imposed Criteria(the expert system)
• Supervised Classification(KNN)
• Unsupervised Classification(cluster analysis)

17. Decision points

18. •Histogram parameter value overlap
•Need for decision threshold with acceptable error percentage

20. 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.

21. Learning

22. Constraints
Regularity
Explanation BasedOne Shot
Pattern Recognition Work Of TheoreticianMimicking Biology
Learning

23. Learning Systems
• Supervised Learning
• Unsupervised Learning

24. 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

25. Class A and B are previously
identified features
So it is supervised classification

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

28. 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)

29. Hierarchical Clustering

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

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

33. Expert System
•Rules are supplied
by human expert.
•Order of execution
of rules determined
by system software

34. • 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

35. •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

36. Some Expert Systems
• Rice-Crop Doctor
• AGREX
• CaDet
• DXplain

37. THANK YOU