feature recognition and classification
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Feature Recognition and Classification
(Suraj Shrestha)068/BCT/539(Sanjeev Paudel)068/BCT/537
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Feature
• A feature usually refers to a region of a part with some interesting geometric or topological properties.
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Feature Recognition
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Feature Detector
Comparator
Library
Recognitioni/psamples
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Template matching and
Cross co-relation
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Simple Template Matching
Templates
Target
Reds are matched pixelsBlue are unmatched ones.
Net score= Reds - Blues
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Cross Co-relation
Measure of similarity between two signals
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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.
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Example
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Another one
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Parametric Description
Successful Feature recognition applications:• Face Recognition • Fingerprint Recognition
They uses feature specific measurement parameters KA Parametric Description Method
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Uses different transformation parameters
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Classification
• Imposed Criteria(the expert system)
• Supervised Classification(KNN)
• Unsupervised Classification(cluster analysis)
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Decision points
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•Histogram parameter value overlap
•Need for decision threshold with acceptable error percentage
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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.
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Learning
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ConstraintsRegularity
Explanation BasedOne Shot
Pattern Recognition Work Of TheoreticianMimicking Biology
Learning
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Learning Systems
• Supervised Learning
• Unsupervised Learning
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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
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Class A and B are previously identified features
So it is supervised classification
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Special Case:When k=1, each training vector defines a region in space, defining a Voronoi partition of the space
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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)
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Hierarchical Clustering
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K-means Clustering
K-means separates data into Voronoi-cells, which assumes equal-sized clusters
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Next it is necessary to consider how to apply these class boundaries as a set of rules for the identification of subsequent features.
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Expert System
•Rules are supplied by human expert.
•Order of execution of rules determined by system software
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• 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
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•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
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Some Expert Systems
• Rice-Crop Doctor• AGREX• CaDet• DXplain
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THANK YOU