Unformatted text preview: ics S. Venkannah Mechanical and Production Engineering Department Statistical pattern recognition
• Object recognition is based on assigning classes to objects, and the device that does
these assignments is called the classifier. The number of classes is usually known
beforehand, and typically can be derived from the problem specification.
• The classifier does not decide about the class from the object itself rather, sensed
object properties called patterns are used.
• For statistical pattern recognition, quantitative description of objects is characteristic,
elementary numerical descriptions features are used. The set of all possible patterns
forms the pattern space or feature space. The classes form clusters in the feature
space, which can be separated by discrimination hypersurfaces.
• A statistical classifier is a device with n inputs and 1 output. Each input is used to
enter the information about one of n features measured from an object to be
classified. An R class classifier generates one of R symbols ωr, the class identifiers.
• Classification parameters are determined from a training set of examples during
classifier learning. Two common learning strategies are probability density
estimation and direct loss minimization.
• Some classification methods do not need training sets for learning. Cluster analysis
methods divide the set of processed patterns into subsets (clusters) based on the
mutual similarity of subset elements.
Neural nets
• Most neural approaches are based on combinations of elementary processors
(neurons), each of which takes a number of inputs and generates a single output.
Associated with each input is a weight, and the output is a function of the weighted
sum of inputs. Pattern recognition is one of many application areas of neural
networks.
• Feed forward networks are common in pattern recognition problems. Their training
uses a training set of examples and is often based on the back propagation algorithm.
• Self organizing networks do not require a training set to cluster the processed
patterns.
• Hopfield neural networks do not have designated inputs and outputs, but rather the
current configuration represents the state. The Hopfield net acts as an associative
memory where the exemplars are stored.
Syntactic pattern recognition
• For syntactic pattern recognition, qualitative description of objects is characteristic.
The elementary properties of the syntactically described objects are called primitives.
Relational structures are used to describe relations between the object primitives.
• The set of all primitives is called the alphabet. The set of all words in t eh alphabet
that can describe objects from one class is named the description language. A
grammar represents a set of rules that must be followed when words of the specific
language are constructed from the alphabet.
• Grammar construction usually requires significant human interaction. In simple
cases, an automated process of grammar construction from examples called grammar
inference can be applied.
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Faculty of Engineering Robotics Technology MECH 4041 B. Eng (Hons.) Mechatronics S. Venkannah
• Mechanical and Production Engineering Department The recognition decision of whether or not the word can be generated by a particular
grammar is made during syntactic analysis. Recognition as graph matching
• Matching of a model and an object graph description can be used fro recognition. An
exact match of graphs is called graph isomorphism. Determination of graph
isomorphism is computationally expensive.
• In the real world, the object graph usually does not match the model graph exactly.
Graph isomorphism cannot assess the level of mismatch. To identify objects
represented by similar graphs, graph similarity can be determined.
Optimization techniques in recognition
• Optimization problems seek minimization or maximization of an objective function.
Design of the objective function is a key factor in the performance of optimization
algorithms.
• Most conventional approaches to optimization use calculus based hill climbing
methods. For these, the search can easily end in a local maximum, and the global
maximum can be missed.
• Genetic algorithms use natural evolution mechanisms of the survival of t he fittest to
search for the maximum of an objective function. Potential solutions are represented
as strings. Genetic algorithms search from a population of potential solutions, not a
single solution. The sequence of reproduction, crossover, and mutation generates a
new population of strings from the previous population. The fittest string represents
the final solution.
• Simulated annealing combine two basic optimization principles, divide and conquer
and iterative improvement (hill climbing). This combination avoids getting stuck in
local optima.
Fuzzy systems.
• Fuzzy systems are capable of representing diverse, non exact, uncertain, and
inaccurate knowledge or information. They use qualifiers that are very close to the
human way of expressing knowledge.
• Fuzzy reasoning is performed in the context of a fuzzy system model that consists of
control, solution, and working data variables; fuzzy sets; hedges; fuzzy rules; and a
control mechanism.
• Fuzzy sets represent properties of fuzzy spaces. Membership functions represent the
fuzziness of the description and assess the degree of certainty about the membership
of an element in the particular fuzzy set. Shape of fuzzy membership functions can
be modified using fuzzy set hedges. A hedge and its fuzzy set constitute a single
semantic entity called a linguistic variable.
• Fuzzy ifthen rule represent fuzzy associative memory in which knowledge is stored.
• In fuzzy reasoning, information carried in individual fuzzy sets is combined to make a
decision. The functional relationship determining the degree of membership in
related fuzzy regions is called the method of composition and results in definition of a
fuzzy solution space. To arrive at the decision, defuzzification is performed.
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Faculty of Engineering Robotics Technology MECH 4041 B. Eng (Hons.) Mechatronics S. Venkannah Mechanical and Production Engineering Department Processes of composition and defuzzification form the basis of fuzzy reasoning.
Problem 1
(Class)
Area
Height Width No. of
No. of (cx, cy)
Character
Holes Strokes center
‘A’
Med
Hi
¾
1
3
½, 2/3
‘B’
Med
Hi
¾
2
1
1/3, ½
‘8’
Med
Hi
2/3
2
0
½, ½
‘0’
Med
Hi
2/3
1
0
½, ½
‘1’
Lo
Hi
¼
0
1
½, ½
‘W’
Hi
Hi
1
0
4
½, 2/3
‘X’
Hi
Hi
¾
0
2
½, ½
‘*’
Med
Lo
½
0
0
½, ½
‘‘
Lo
Lo
2/3
0
1
½, ½
‘/’
Lo
Hi
2/3
0
1
½, ½
Note: Med Medium, Lo Low, Hi High, Lar Large Best
axis
90
90
90
90
90
90
?
?
0
60 Least
inertia
Med
Lar
Med
Lar
Lo
Lar
Lar
Lar
Lo
Lo Draw a decision tree that implements the above classification procedure.
Reference :
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6. http://www.dai.ed.ac.uk/CVonline
http://css.engineering.uiowa.edu/~dip/LECTURE
Image Processing, Analysis, and Machine Vision by Milan Sonka et al
Robotics: Control, Sensing, Vision, and Intelligence by K. S. Fu et al
Computer Vision by Shapiro and Stockman
Digital Image Processing by R. C. Gonzalez & R. E. Woods 29
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 Spring '14
 Mechatronics, Graph Theory, Faculty of Engineering, Production Engineering Department, Robotics Technology MECH

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