System Architecture Intelligently controlling image processing systems

Image Processing and Computer Vision: 72 Introduction So far Presented methods of achieving goals Integration of methods? Controlling execution Incorporating knowledge

Image Processing and Computer Vision: 73 What knowledge? What do algorithms achieve? What is known about the problem being solved? Relationship between problem and algorithm?

Image Processing and Computer Vision: 74 Knowledge representation Implied Feature vectors Relational structures Hierarchical structures Rules Frames

Image Processing and Computer Vision: 75 Implied knowledge Knowledge encoded in software Usually inflexible in Execution Reuse Simple to design and implement Systems often unreliable

Image Processing and Computer Vision: 76 Feature vectors As seen in statistical representations Vector elements can be Numerical Symbolic coded numerically

Image Processing and Computer Vision: 77 Example: strokes3 loops1 w-h ratio1 A N strokes3 loops0 w-h ratio1

Image Processing and Computer Vision: 78 Relational structures Encodes relationships between Objects Parts of objects Can become unwieldy for Large scenes Complex objects

Image Processing and Computer Vision: 79 Follow natural division of Hierarchical structures scene objects parts of object

Image Processing and Computer Vision: 710 Example: scene roadwaybuilding grassland grasstreeroadjunction edges

Image Processing and Computer Vision: 711 Uses Structure defines possible appearance of objects Structure guides processing

Image Processing and Computer Vision: 712 Rules Rules code quanta of knowledge Interpretation Forwards Backwards 

Image Processing and Computer Vision: 713 Forward chaining If is TRUE Execute Antecedent will be a test on some data Action might modify the data Suitable for low level processing

Image Processing and Computer Vision: 714 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: 715 System architecture DatabaseRulebase Inference engine

Image Processing and Computer Vision: 716 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: 717 Methods of control How to control how the system’s knowledge is used. Hierarchical Heterarchical

Image Processing and Computer Vision: 718 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: 719 Bottom-up control Object recognition Extracted features, Attributes, Relationships Image Decision making Feature extraction

Image Processing and Computer Vision: 720 Top-down control Hypothesised object Predicted features, Attributes, Relationships Features in image that Support or refute the hypothesis Prediction Directed feature extraction

Image Processing and Computer Vision: 721 Critique Inflexible methods Errors propagate Hybrid control Can make predictions Verify Modify predictions

Image Processing and Computer Vision: 722 Hybrid control Object recognition Image Decision making Feature extraction Extracted features, Attributes, Relationships Predicted features, Attributes, Relationships Prediction Direciction

Image Processing and Computer Vision: 723 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: 724 Blackboard architecture KS Blackboard scheduler

Image Processing and Computer Vision: 725 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: 726 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: 727 Bayesian methods Define a belief network A tree structure Reflects evidential support of a fact F1F2F3

Image Processing and Computer Vision: 728 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: 729 Dempster-Shafer Bayesian theory has confidence in belief only No measure of disbelief D-S attempts to define this

Image Processing and Computer Vision: 730 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: 731 Other formalisms Belief calculi exist Not yet widely used A result is important Confidence in result is not quantified

Image Processing and Computer Vision: 732 Summary Intelligent (vision) systems Knowledge representation Control strategies Integration of belief

Image Processing and Computer Vision: 733 Everything that can be invented has been invented Charles Duell, Commissioner U.S. Office of Patents, 1899