system approach to a raster-to-vector conversion · raster-to-vector conversion algorithms”....

System approach to a raster-to-vector conversion

How to design a good commercialconversion system

Eugene [email protected]

Plan of the presentation

• The main goal of vectorization system

• Automatic vectorization

• Problems of automatic vectorization

• Efficiency of data capturing

• Semi-automatic vectorization

• Rational division of labor between operator and machine

• Simplification of operator labor

• Conclusion

Data capturing

Vector post-processing

Raster pre-processing

Vectorization

Paper line drawings

DatabasePointsLines

Polygons

Digitaldata

Raster images

For head up digitizing

2. Build a centerline for each linear object

a) linear objects (elongated parts)

1. Base topology solution orclassification of pixels into 3 classes:

b) junctions

c) solids

3. Build an outline for each solid4. Resolve junctions

What is vectorization?

The main goal of a vectorization system

To produce the highest quality vectors in

the shortest amount of time

acceptable

operator time

Manual vectorization

b. Head-up digitizing

Flexible, but Labor consuming

a. Digitizing

Time consumingNot always acceptable precision

Automatic vectorization

Automatic vectorization - articles

5. L.Baotto, V.Consorti, M.Del Buono, S. Di Zenzo, V.Eramo, A.Esposito, F.Melcarne, M.Meucci, A.Morelli, M. Mosciatti, S.Scarci, M.Tucci, “An Interpretation System for Land Register Maps”, IEEE Computer, V.25, #7, 1992, pp.25-33.

7. Dori Dov, Wenyin Liu. “Sparse Pixel Vectorization: An Algorithm and Its Performance Evaluation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No.3, pp.202-215, 1999.

9. Dave Elliman. “A Really Useful Vectorization Algorithm”, Lecture Notes in Computer Science, 1941, pp.19-27, 1999.

6. Ogniewicz R.L., Kubler O, “Hierarchic Voronoi Skeletons”, Pattern Recognition, Vol. 28, No. 3,pp. 343-359, 1995.

7. Desseilligny M.P., Stamon G., Suen Ch.Y., “Veinerization: A New Shape Description for Flexoble Sceletonization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 5, pp. 505-521, 1998.

• Rosenfeld Azriel., Pfaltz John L., “Sequential Operations in Digital Picture Processing”, J. Assn. Comput. Machiery, Vol. 13, No.4, pp.471-494, 1966.

3. Theo Pavlidis. “A Vectorizer and Feature Extractor for Document Recognition”, Computer Vision,Graphics, and Image Processing, 35, pp. 111-127, 1986.

10. Song-Chung Zhu. “Stochastic Jump-Diffusion Process for Computing Medial Axis in Markov Random Fields”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No.11, pp. 1158-1169, 1999.

4. Michihiko Minoh, Toshiyuki Sakai. “Mesh-Oriented Line Drawings Theory (MOLD Theory)”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 2, pp.210-221, 1986.

• Blum H. “A transformation for extracting new descriptors of shape”, Models for the Perception of Speechand Visual Form, (W.Watjen-Dann, ed.), MIT Press, Cambrige, Mass., pp.362-380, 1967.

Automatic vectorization - methods

• Thinning

2. Distance transformation

3. Hough transformation

4. Open disks

5. Voronoi tessellation

6. RLE-vectorization

10. Pruning

7. Energy minimization

8. Bisector skeletonization

9. Veinerization

11. Annealing

• Medial axis2. Skeleton3. Continuous skeleton4. Dense skeleton5. Path of writing instrument6. Spine with generator7. Ribbon

9. Centerline

8. Hierarchic Voronoi skeleton

Application independent

solution

Application dependent

solution

Automatic vectorization - what we are looking for


Figure 2 from: Plamondon R., Bourdeau M., Chouinard C., Suen Ch.Y., “Validation of

preprocessing algorithms: A methodology and its application to the design of a thinning

algorithm for handwritten characters”, Proc. 2nd ICDAR, Tsukuba, Japan, October 1993,

pp.262-269.

Figures 19 and 20 from Deseilligny M. P., Stamon G., Suen Ching Y., “Veinerization:

A New Shape Description for Flexible Skeletonization”, IEEE Transactions on Pattern

Analysis and Machine Intelligence, Vol.20, No. 5, pp. 505-521, 1998.

Automatic vectorization - what we are looking forBefore pruning

After pruning

Figure 15 from: Zhu Song-Chun, “Stochastic Jump-Diffusion Process for Computing

Medial Axes in Markov Random Fields”, IEEE Transactions on Pattern Analysis and

Machine Intelligence, Vol. 21, No. 11, November 1999, pp. 1158-1169. (Annealing)


Figure 13 from: Ogniewicz R.L., Kubler O., “Hierarchic Voronoi Skeletons”,

Pattern Recognition, Vol. 28, No. 3, pp. 343-359, 1995.


ArcScan for ArcGIS (ESRI)


Centerlines, RLE - vectorization

ArcScan for ArcGIS (ESRI)Raster image 10451 x 14832 pixels

5050 polylines, 1350 polygons

Vectorization time < 1 min

Automatic vectorization

Automatic vectorization - problems

Results of vectorizing a fragment of a contour map.


- noise

Automatic vectorization - problems


Results of vectorizing a fragment of a road map.

- symbols

Automatic vectorization - problems - many thematic layers

How to divide it into separate layers?

Automatic vectorization - problems - linear object vs. solid

Automatic vectorization - problems - selective vectorization


Data capturing – interactive process

Paper line drawing B/W raster image

DatabasePointsLines

Polygons

Vectorization



AdditionalInformation

Paper line drawing B/W raster image

DatabasePointsLines

Polygons

Vectorization



AdditionalInformation

B/W raster image

Select the mode

Raster cleaning

Verification of the result

Correction of errors

Select thresholds and parameters


Attributing

Responsibility of the operator

Jaisimha M.Y., Dori D., Haralick R. “A methodology for the characterization of the performance

of thinning algorithms”, Proc. of Int. Conf on Document Analysis and Recognition, CDAR’93,

Tsukuba, Japan, 1993, pp. 282-286.

Hori Osamu, Doermann David S. “Quantitative Measurement of the Performance of

Raster-to-Vector Conversion Algorithms”. Lecture Notes in Computer Science, GREC,

1072, pp.57 – 68, 1995.

Phillips Ihsan, Liang Jisheng, Chhabra Atul K., Haralic Robert. “A Performance Evaluation

Protocol for Graphics Recognition Systems”. Lecture Notes in Computer Science, 1389,

pp.372-389, 1998

Dori Dov, Wenyin Liu. “Sparse Pixel Vectorization: An Algorithm and Its Performance

Evaluation”. IEEE Transactions on Pattern Analysis and Machine Intelligence. 21, No.3,

pp.202-215, 1999.

Efficiency of raw vectorization: number of errors , types of errors

Efficiency of data capturing: the operator time needed to process maps

, types of errors, and the capabilities of the

raster and vector editors

Number of errors

Efficiency of data capturing: the capabilities of the raster and vector editors

What to do it if there is no a good smoothing algorithm?How long it will be and how will it influence the contour capturing?


Efficiency of vectorization is

the operator time needed to process maps

How to define priorities for research ?________________________________________________

No. Operation Duration Degree of possible

automation ofoperations

________________________________________________

• Operation_1 duration_1 D_1

• Operation_2 duration_2 D_2

… …………. ………… ….

K Operation_k duration_k D_k

… …………. ………… ….

Order durations

of operations

duration_k -- maximum

Operation_k is very important

5min

30min

= 5min

= 5hrs

= 99%

= 10%

The manual and head up digitizing are still being widely used.

Why?

The time spent on editing after automatic vectorization is

commensurate with the time spent on digitizing

The large number of vectorization errors result not from

the low quality of automatic vectorization

but from the complexity of the converted documents

and excess noise

Is it because of the bad quality of automatic vectorization?

No.

Semi-automatic vectorization methods can spare the operator

from performing monotonous routine work

and would use the operator only when

human judgment is necessary.

Semi-automatic vectorization methods

Raster Snapping(one click localization)

Compare with automatic vectorization



Tracing

29 verticesOne click --



Where is a research dedicated to interactive vectorization

methods like tracing or raster snapping?

2. Elimination of edge effects

3. On-line recognition of linear objects

4. Resolution of intersections

5. Smoothing and compression of simultaneously changing lines

1. During tracing it is not important how long it takes vectorization of the full document,

but the tracing of each linear object has to be done in real time.

Are there some specific and specific problems?

Rational division of labor between operator and machine

Humans are peculiarly adept at recognition

that is difficult for a computer program to do automatically.

text

digits

symbols

one line



Identification of various thematic layers.



Isolation and recognizing texts and symbols Try to recognize 407 with OCR or to

develop a program that will do it automatically



Recognition of critical points



Continuation of broken lines and circle recognition

circles

Rational division of labor between operator and machine Humans are peculiarly adept at recognition

Recognition of areas filled with symbols


Computers can do a lot of things

that are difficult for humans

Time ~20 sec

Image 11050 x 10486 pixels

Polylines 1656

Polygons 25


Rational division of labor between operator and machine Computers can do a lot of things

that are difficult for humans


Noise (“salt”)

Image 20000 x18000Polylines 9845Cleaning ~20 secVectorization < 2 min

Result

Helping the operator with new interactive semi-automatic procedures

One click measuring of local line width



Automatic verification

Bodansky Eugene, Pilouk Morakot, “Using Local Deviations of Vectorization to Enhancethe Performance of Raster-to-Vector Conversion Systems”, International Journalon Document Analysis and Recognition”, No. 3, 2000, pp. 67-72.


Correction of intersections with templates

IntersectionTemplates


Text emphasizing

Arvind Ganesan, “Integration of Surveying and Cadastral GIS: From Field-to-Fabric & Land Records-to-Fabric”, ESRI, 2002 User Conference Proceedings, (http://gis.esri.com/library/userconf/proc02/abstracts/a0868.html)

One click vectorization of buildings

Click on the building VS. draw it



Recognition with learning

Critical pointsArcs

Straight lineRight angles

Point to critical points, arcs, lines,

right angles VS. selection thresholds

but do not be afraid to use the operator.

Give him tasks that are simple for him.

If you can automate some operations and to obtain good

and stable result automatically DO IT,

Help him with new interactive semi-automatic procedures.

Conclusion

system approach to a raster-to-vector conversion · raster-to-vector conversion algorithms”....

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