Description Logic for VisionBased

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• Description Logic for Vision-Based
• Intersection Interpretation

Britta Hummel
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Motivation
Road Recognition The Model-based Approach
1. Project
Low-dim. geometry model (clothoid, )
2. Compare
3. Update Parameters

• Solved for highly constrained domains (highways)

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Motivation
Intersection Recognition HeimesNagel02
1. Project
2. Compare
3. Update Parameters

• How can we generalize to arbitrary intersections?

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Motivation
Challenges

• High-dimensional
• hypothesis space
• 2. Few features
• - Narrow field of view
• - Massive occlusions
• - Omitted features
• Presence of noise
• - Unmodelled objects
• - Bad feature quality

• Model-based approach becomes ill-posed!

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Motivation
So what now?

• More top-down information flow
• ? start higher up use conceptual knowledge!
• ? move further down parameterize feature
  detectors!
• Collective classification
• ? simultaneously reconstruct geometry and
  semantics!

• Narrow down hypothesis space!

• FOL Representation and FOL Reasoning!

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This Talk

• Motivation
• Architecture
• DL Road Network KB
• DL Inference for Scene Interpretation
• Application
• Evaluation

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Architecture
Enhance Model-Based Vision by Logic
Feature detectors, other KBs,
Project
Update Pars
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This Talk

• Motivation
• Architecture
• DL Road Network KB
• DL Inference for Scene Interpretation
• Application
• Evaluation

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Model of Geometry
Geometric Primitives
GP1
GP3
GP2
Spatial Relations
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Symbol Grounding
Geometric Primitives
Spatial Relations
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TBox
Geometric Constraints
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TBox
Constraints wrt Road Building Regulations
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ABox
Sensor Data Integration

• Partial observability
• ? OWA
• Structurally differing sensor data (e.g. from
  map, video)
• Distributed sensor data
• Non-UNA identification reasoning
• Open/Closed Domain Data
• (Nominals) / Closed domain assumption
• Conflicting/Uncertain Data
• ? BLPs/MLNs/

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This Talk

• Motivation
• Architecture
• DL Road Network KB
• DL Inference for Scene Interpretation
• Application
• Evaluation

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Inference Example I
(Collective) Classification is Abox realization
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Inference Example I
(Collective) Classification is Abox realization
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Inference Example I
(Collective) Classification is Abox realization
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Inference Example I
(Collective) Classification is Abox realization
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Inference Example I
Link Prediction is Instance Checking
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Inference Example II
Link Prediction is Instance Checking
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Inference Example III
Data Association is Identification Reasoning
Positioning Device Map Matching
Video
Digital Map
?
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Inference Example IV
Hypothesis Generation is ?

• Classical logical inference is deductive
• Bio./Mach. Vision is not deductive lots of
  hypothetical reasoning, jumping to conclusions,
  backtracking if wrong
• ? Non-deductive / non-monotonic reasoning
  needed!
• Abduction
• Poole, Shanahan, Möller
• Introducing procedurality
• NeumannMöller06
• Model construction by transformation into
  Constraint Satisfaction Pr.
• ReiterMackworth87
• Model construction under Answer Set Semantics
• We have started

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Beautiful Analogies
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This Talk

• Motivation
• Architecture
• DL Road Network KB
• DL Inference for Scene Interpretation
• Application
• Evaluation

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Application
Geometry model generated from DL ground truth
ABox
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Application
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This Talk

• Motivation
• Architecture
• DL Road Network KB
• DL Inference for Scene Interpretation
• Application
• Evaluation

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Summary

• Road Recognition ? Intersection Interpretation
• escape from toy world ? narrow down hypothesis
  space
• not only bottom-up but also top-down
  reasoning
• collective classification
• Enhance model-based vision by logical reasoning
• Expressive geometry model
• Generate generic geometric model out of logical
  configuration model
• Generate and constrain logical model through
  logical reasoning

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Evaluation

• Vision
• Sets of knowledge engineers codingmaintaining
  large, distributed, modular, semantically
  unambiguous KBs for SI
• DL
• Wish List ?
• Foundational ontologies / best practices for KB
  design for SI
• Faster Abox reasoning (gt10 individuals
  prohibitively slow on our KB)
• Language expressiveness
• Spatial Relations JEPD condition
• Feature chains
• Nominals
• Nonmonotonic reasoning

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Outlook

• Nonmonotonic reasoning with ASP
• Incremental hypothesize test
• Integration with Irina Lulchevas MLN-based
  traffic participant classificator
• Rule Learning from Training Data

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Thanks

• Thanks ?

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Description Logic

• Decidable subset of 1st order logic
• Syntax
• Semantics Set-theoretic

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Description Logic

• Axioms form sentences
• A DL Knowledge Base consists of
• Tbox Set of terminological axioms
• ? general domain knowledge
• Abox Set of assertional axioms
• ? knowledge about particular situation
• ( Rulebox )

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Description Logic

• Classical DL inference services

• Non-classical inference
• .