Bayesian Networks for Sketch Understanding

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Bayesian Networks for Sketch Understanding

• Christine AlvaradoMIT Student Oxygen Workshop12
  September 2003

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Sketching in Design
Mechanical Engineering
Software
3
Computer Design Tools
4
A Challenge In Sketch Understanding

• Noisy Input

There is no one threshold for shapes or
constraints?Interpretation depends on context
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Naïve Approach

• Why not just try all possibilities?

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Naïve Approach

• Why not just try all possibilities?

Arrow?
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Naïve Approach

• Why not just try all possibilities?

Arrow?
8
Naïve Approach

• Why not just try all possibilities?

Arrow?
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Naïve Approach

• Why not just try all possibilities?

Arrow?
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Naïve Approach

• Why not just try all possibilities?

Arrow?
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Naïve Approach

• Why not just try all possibilities?

interpretations
Must consider
n number of strokes/segmentsS set
of shapeski subcomponents in shape Si
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Naïve Approach

• Why not just try all possibilities?

interpretations
Must consider
n number of strokes/segmentsS set
of shapeski subcomponents in shape Si
And this only considers shapes independently
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Previous Approaches Use Rigid Segmentation

• Single stroke shapes
• Palm Pilot Graffiti
• Long et. al. 1999
• Explicit Segmentation
• Quickset Cohen et. al. 2001
• Pause between strokes

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Recognition Using Partial Interpretations

• Recognition
• Build partial interpretations (PIs) as the user
  draws based on easily recognizable low-level
  shapes
• Prune unlikely PIs and use likely PIs to find
  misrecognized low-level shapes
• Evaluating PIs ? Graphical Models
• Missing data unobserved nodes
• Interpretation influenced by top-down and
  bottom-up information

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BN fragments similar to PRMs, Getoor et. al.
1999
(Define Arrow (Components (Line shaft)
(Line head1)
(Line head2)) (Constraints (connects
shaft.p1 head1.p1) (connects shaft.p1
head2.p1) ( head1.length head2.length) (head1.length shaft.length) (shaft) 90) (
(angle head1 shaft) 0) ( (angle shaft head2)
0)))
L1L2L3C1C2C3C4C5C6C7C8
Arrow

L1
L2
L3
C1
C2
C3
C8
Hammond and Davis, 2003
Instantiated and linked together as recognition
proceeds
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Specifying Conditional Probability Tables
Quadrilateral
Arrow
L1

• P(Child Parent) specified in description
• P(L1 Arrow) 1
• Probability of optional components/constraints
  lower
• Parents combine with noisy-OR (XOR)
• Priors specified for top-level shapes/patterns

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Primitive shapes/Constraints

• Observation node added when primitive linked to
  stroke
• P(ObsPrim) determined through data collection

L1
Obs
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Example
0.59
Force-pushes-body
Touches F B
0.95
Body(B)
Force(F)
0.5
Quad
0.95
Arrow
Remaining Arrow Constraints
0.97
0.99
0.99
Connects l1 l2
Line
Line
Line(l1)
Line(l2)
Line(l3)
Sq. error (Stroke a)
Sq. error (Stroke b)
Observation
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Example
0.59
Force-pushes-body
Touches F B
0.95
Body(B)
Force(F)
0.5
Quad
0.95
Arrow
Remaining Arrow Constraints
0.97
0.99
0.99
Connects l1 l2
Line
Line
Line(l1)
Line(l2)
Line(l3)
Sq. error (Stroke a)
Sq. error (Stroke b)
Sq. error (Stroke c)
Observation
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Example
0.61
Force-pushes-body
Touches F B
1
Body(B)
Force(F)
0.47
Quad
1
Arrow
Remaining Arrow Constraints
0.97
1
1
Connects l1 l2
0.95
Line
Line
Line(l1)
Line(l2)
Line(l3)
Sq. error (Stroke a)
Sq. error (Stroke b)
Sq. error (Stroke c)
Observation
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Example
0.95
Force-pushes-body
0.99
0.97
Touches F B
1
Body(B)
Force(F)
0.47
Quad
Observation
1
Arrow
Remaining Arrow Constraints
1
0.99
1
1
Connects l1 l2
1
Ellipse
Line
Line
Line(l1)
Line(l2)
Line(l3)
Sq. error (Stroke d)
Sq. error (Stroke a)
Sq. error (Stroke b)
Sq. error (Stroke c)
Observation
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Uh oh what about speed?

• Networks get very large (and ugly)
• Solutions
• Prune network
• Assert values (even if not confirmed)
• Approximate inference
• Incremental changes to inference structures

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Current/Future Work

• Expand domain/include other domains
• Gather sketches from users

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Conclusion

• Graphical models evaluation Partial
  Interpretations ?Context-guided search
• More drawing freedom More robust recognition
  More natural interfaces (i.e. The goal
  of OXYGEN)