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System Architecture. Intelligently controlling image processing systems. Introduction. So far Presented methods of achieving goals Integration of methods? Controlling execution Incorporating knowledge. What knowledge?. What do algorithms achieve?
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System Architecture Intelligently controlling image processing systems
Introduction • So far • Presented methods of achieving goals • Integration of methods? • Controlling execution • Incorporating knowledge Image Processing and Computer Vision: 7
What knowledge? • What do algorithms achieve? • What is known about the problem being solved? • Relationship between problem and algorithm? Image Processing and Computer Vision: 7
Knowledge representation • Implied • Feature vectors • Relational structures • Hierarchical structures • Rules • Frames Image Processing and Computer Vision: 7
Implied knowledge • Knowledge encoded in software • Usually inflexible in • Execution • Reuse • Simple to design and implement • Systems often unreliable Image Processing and Computer Vision: 7
Feature vectors • As seen in statistical representations • Vector elements can be • Numerical • Symbolic coded numerically Image Processing and Computer Vision: 7
Example: A N Image Processing and Computer Vision: 7
Relational structures • Encodes relationships between • Objects • Parts of objects • Can become unwieldy for • Large scenes • Complex objects Image Processing and Computer Vision: 7
Hierarchical structures Follow natural division of scene objects parts of object Image Processing and Computer Vision: 7
Example: scene grassland roadway building road junction grass tree edges Image Processing and Computer Vision: 7
Uses • Structure defines possible appearance of objects • Structure guides processing Image Processing and Computer Vision: 7
Rules • Rules code quanta of knowledge • Interpretation • Forwards • Backwards <antecedent> <action> <two antiparallel lines> <road> Image Processing and Computer Vision: 7
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 Image Processing and Computer Vision: 7
Backward chaining • Action is some goal to achieve • Antecedent defines how it should be achieved • Suitable for high level processing • Guides focus of system Image Processing and Computer Vision: 7
Inference engine Database Rulebase System architecture Image Processing and Computer Vision: 7
Frames A “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) Image Processing and Computer Vision: 7
Methods of control • How to control how the system’s knowledge is used. • Hierarchical • Heterarchical Image Processing and Computer Vision: 7
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 Image Processing and Computer Vision: 7
Bottom-up control Object recognition Decision making Extracted features, Attributes, Relationships Feature extraction Image Image Processing and Computer Vision: 7
Top-down control Hypothesised object Prediction Predicted features, Attributes, Relationships Directed feature extraction Features in image that Support or refute the hypothesis Image Processing and Computer Vision: 7
Critique • Inflexible methods • Errors propagate • Hybrid control • Can make predictions • Verify • Modify predictions Image Processing and Computer Vision: 7
Hybrid control Object recognition Decision making Prediction Extracted features, Attributes, Relationships Predicted features, Attributes, Relationships Feature extraction Direciction Image Image Processing and Computer Vision: 7
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 Image Processing and Computer Vision: 7
KS KS KS Blackboard architecture Blackboard scheduler Blackboard Image Processing and Computer Vision: 7
Information integration • Hypotheses boolean • True or false • Facts are real valued True certainty = 1.0 False certainty = 0.0 Unsure 0.0 < certainty < 1.0 How is this represented? Image Processing and Computer Vision: 7
Example Recognising cars Shape analyser - certainty = 0.56 Position analyser - certainty = 0.78 Texture analyser - certainty = 0.40 How to combine evidence? Image Processing and Computer Vision: 7
F1 F2 F3 Bayesian methods • Define a belief network • A tree structure • Reflects evidential support of a fact Image Processing and Computer Vision: 7
Propagation of certainty • Leaf nodes • Certainty given by basic operations • Non-leaf nodes • Combine child nodes’ certainties • Results propagate to root node Image Processing and Computer Vision: 7
Dempster-Shafer • Bayesian theory has confidence in belief only • No measure of disbelief • D-S attempts to define this Image Processing and Computer Vision: 7
Certainty interval 0 .. A = measures of belief A .. B = measures of uncertainty B .. 1 = measures of disbelief [A,B] starts large. As evidence accumulates to support or refute a hypothesis, A and B change Image Processing and Computer Vision: 7
Other formalisms • Belief calculi exist • Not yet widely used • A result is important • Confidence in result is not quantified Image Processing and Computer Vision: 7
Summary • Intelligent (vision) systems • Knowledge representation • Control strategies • Integration of belief Image Processing and Computer Vision: 7
Everything that can be invented has been invented Charles Duell, Commissioner U.S. Office of Patents, 1899 Image Processing and Computer Vision: 7
