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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