system architecture intelligently controlling image processing systems
TRANSCRIPT
System Architecture
Intelligently controlling image processing systems
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Introduction So far
Presented methods of achieving goals Integration of methods?
Controlling execution Incorporating knowledge
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What knowledge? What do algorithms achieve? What is known about the problem
being solved? Relationship between problem and
algorithm?
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Knowledge representation Implied Feature vectors Relational structures Hierarchical structures Rules Frames
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Implied knowledge Knowledge encoded in software Usually inflexible in
Execution Reuse
Simple to design and implement Systems often unreliable
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Feature vectors As seen in statistical
representations Vector elements can be
Numerical Symbolic coded numerically
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Example:
strokes 3
loops 1
w-h ratio 1
A Nstrokes 3
loops 0
w-h ratio 1
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Relational structures Encodes relationships between
Objects Parts of objects
Can become unwieldy for Large scenes Complex objects
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Follow natural division ofHierarchical structures
scene
objects
parts of object
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Example:scene
roadway building grassland
grass treeroad junction
edges
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Uses Structure defines possible
appearance of objects Structure guides processing
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Rules Rules code quanta of knowledge
Interpretation Forwards Backwards
<antecedent> <action>
<two antiparallel lines> <road>
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Forward chaining If <antecedent> is TRUE Execute <action>
Antecedent will be a test on some data
Action might modify the data
Suitable for low level processing
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Backward chaining Action is some goal to achieve Antecedent defines how it should
be achieved
Suitable for high level processing Guides focus of system
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System architecture
Database RulebaseInferenceengine
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FramesA “data-structure for representing a
stereotyped situation”
Slot(attribute) Filler
(value: atomic, link to another frame, default or empty, call to a function to fill the slot)
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Methods of control How to control how the system’s
knowledge is used. Hierarchical Heterarchical
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Hierarchical control “Algorithm” defines control Compare other software:
Main programme calls subroutines Achieve a predefined sequence of
tasks Two extreme variants
Bottom-up Top-down
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Bottom-up controlObject
recognition
Extracted features,Attributes,
Relationships
Image
Decision making
Feature extraction
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Top-down controlHypothesised
object
Predicted features,Attributes,
Relationships
Features in image thatSupport or refute the
hypothesis
Prediction
Directed feature extraction
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Critique Inflexible methods Errors propagate
Hybrid control Can make predictions Verify Modify predictions
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Hybrid controlObject
recognition
Image
Decision making
Feature extraction
Extracted features,Attributes,
Relationships
Predicted features,Attributes,
Relationships
Prediction
Direciction
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Heterarchical control “Data” defines control via
knowledge sources KSs contribute to process image KS fires in response to presence of
data Creates new data Modifies existing data
Can be chaotic Blackboard
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Blackboard architecture
KS KS KS
Blackboard Blackboardscheduler
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Information integration Hypotheses boolean
True or false Facts are real valued
True certainty = 1.0False certainty = 0.0Unsure 0.0 < certainty < 1.0
How is this represented?
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Example
Recognising cars
Shape analyser - certainty = 0.56Position analyser - certainty = 0.78Texture analyser - certainty = 0.40
How to combine evidence?
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Bayesian methods Define a belief network A tree structure
Reflects evidential support of a fact
F1
F2 F3
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Propagation of certainty Leaf nodes
Certainty given by basic operations Non-leaf nodes
Combine child nodes’ certainties Results propagate to root node
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Dempster-Shafer Bayesian theory has confidence in
belief only No measure of disbelief D-S attempts to define this
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Certainty interval
0 .. A = measures of beliefA .. B = measures of uncertaintyB .. 1 = measures of disbelief
[A,B] starts large.As evidence accumulates to support or
refute a hypothesis, A and B change
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Other formalisms Belief calculi exist Not yet widely used
A result is important Confidence in result is not quantified
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Summary Intelligent (vision) systems
Knowledge representation Control strategies Integration of belief
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Everything that can be invented has been inventedCharles Duell, Commissioner U.S. Office
of Patents, 1899