Structuring a Perceptual Inference Network

The following sections are included:

• Overview of the Algorithm

• A Structural Representation of Organization

  • The Random Parametric Structural Description

  • Dissimilarity Measure

  • Structure of the Merged RPSD,

  • Constructing the Mapping between RPSDs

• Structuring the PIN

  • Dependency Detection

  • Next Level Formation

  • Root Node Decomposition

  • Expected Network Size

  • Desired Conditions

• Estimating the Conditional Probabilities

  • Estimating P(X = 1|U₁ = u₁, ⋯, U_n = u_n)

  • Computing f_LOC(l_k, l_k1,⋯, l_kn)

• An Example

  • The Target and Elementary Organizations

  • The Primitive and Relation Sets

  • Execution Statistics

  • Robustness

• Performance on Real Images

  • Comparing Automatically and Manually Structured PINs

  • Performance on Real Data

  • Performance Evaluation