Introduction to Vectorization of Engineering Drawings

1
Introduction to Vectorization of Engineering
Drawings

• Song Jiqiang
• 18/9/2001

2
Concept

• Vectorization the conversion from a raster image
  to its vector-form file.

3
Why do vectorization?

• A lot of old drawings to be reused, and CAD files
  are more editable than images.
• Preprocess of automatic drawing understanding
  systems (information statistic, 3D
  reconstruction).
• Save the storage space.

4
History

• Begin research on vectorization at late 70s
• Begin that for engineering drawings at late 80s
• Related organizations publications
• IAPR TC-10, IEEE
• IEEE T.PAMI, PR, PRL, CVIU, CVGIP, etc.
• ICPR, ICDAR, IEEE CVPR, etc.

5
State of arts

• K. Tombre (LNCS vol.1389, 1998)
• None of these methods works. Actually, the
  methods do work, but none of them is perfect.
• CADALYSTperforms the annual evaluation on
  commercial vectorization systems.

6
Review of existing methods

• Thinning based
• CT(Contour Tracking) based
• RLE(Run Length Encoding) based
• SPT(Sparse Pixel Tracking) based

7
Thinning-based methods
8
CT-based methods
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RLE-based methods
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SPT-based methods
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Existing difficulties

• Lines with intersections ? broken into pieces.
• Texts touch lines ? misrecognition.
• The interference of recognized objects ?
  repetitive detection, false detection.

12
Our research

• Analysis the vectorization model of existing
  methods.
• Propose an efficient vectorization model for
  engineering drawings.
• Propose a group of new graphical object
  recognition algorithms.

13
Common model of existing methods(2PV - 2 Phase
Vectorization)
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Motivation of 2PV

• Internal memory (RAM)
• Used to be high price limit capacity
• High pixel access frequency cause swap
• Pixel tracking algorithm
• No guide direction
• Repetitive tracking

15
Object-Oriented Progressive-Simplification based
Vectorization Model

• 1 phase model
• Imitate the way that humans read drawings
• Recognize a graphical object in its entirety
• Object-oriented feature
• Simplify the image data as the recognition goes
  on
• Progressive-simplification feature

16
Workflow of OOPSV
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Graphical object recognition

• Get the intrinsic characteristic of individual
  type of graphical object.
• Use the characteristic as a guide to track the
  graphic object in complex environment.

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Straight line recognition

• Seed segment detection

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Straight line recognition

• Direction guided tracking
• based on the Bresenham algorithm

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Straight line recognition

• Dynamic adjustment to tracking direction

21
Line net recognition

• A line net is a group of intersecting lines.
• Take advantage of the intersecting relationship
  to accelerate recognition.
• Example

22
Circle/Arc recognition

• Arc segment detection
• get initial arc center, radius, thickness
• Circular tracking
• based on the Bresenham algorithm for circle

23
Circular tracking
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Curve tracking

• Tracking result a sequence of polyline.

25
Image simplification

• Intersection-preserving pixel deletion
• Based on the contour detection of the
  intersecting branches

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Symbol recognition

• Common symbols
• Cartography-based recognition
• Domain-specific symbols
• Template-based recognition

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Cartography
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Symbol template
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Text recognition

• Text segmentation
• Difficulties text touches line, similar size
• Character recognition
• Stroke-based recognition algorithm

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Image before the line recognition
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Image after the line recognition
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Suspension-Release mechanism
Condition 1 Size( ?Box(li) ) lt Tl Condition 2
?p, p?l ? C(p,L) gtgt C( FP(l,L), L)
33
Stroke-based character template

• Stroke definition
• Black position
• White position
• Aspect ratio scope
• Complexity level

34
Character recognition
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Separate connected characters

• Base on the analysis of the rightmost stroke and
  the vertical projection.

36
Experimental result

• Implemented a complete vectorization system
  running on Windows platform using VC6.0.
• Automatic vectorization of an A0-size drawing
  (15M) takes about 5 minutes. (PIII500/128M)
• Line vectorization takes less than 1 minute (1600
  lines), faster than performing a thinning
  operation (3.5 mins).

37
CDI evaluation

• This protocol was proposed in Machine Vision
  Application, (1997)

38
Manual editing cost evaluation

• This protocol was proposed in LNCS V.1389, (1998)

39
Comparison of editing cost with VPStudio

• Conclusion Object-oriented recognition
  algorithms produce less misrecognition, therefore
  the editing cost has decreased.

40
Conclusion

• Progressive simplification decreases both the
  complexity and workload of vectorization.
• The object-oriented recognition algorithms
  recognize graphical objects fast and entirely.

41
Related papers

• 7 journal papers 4 conference papers
• IEEE Trans. PAMI reviewers comment
• An efficient model is very important to
  recognize engineering drawings .
• This paper suggested an object-oriented
  progressive-simplification based vectorization
  system for engineering drawings.
• It would bring an impact in this area.