A Deformation Analysis Method for Artificial Maps Based on Geographical Accuracy and Its Applications

University of Hyogo, JapanDaisuke Kitayama, Kazutoshi Sumiya

WebQuality2012

Background

• Artificial maps on the real space and the Web space• Signs on the city, leaflets for sightseeing, guiding information

on the Web, ...• Emphasized and weaken geographical information for

a certain purpose• Confirming information easily and quickly

Deformation Analysis and Geographical Accuracy

• Including excess deformations in artificial maps• Too long or too short distances• Inconsistent positional relations

• Finding the target position difficult to detect by excess deformations• Detecting the current position on this artificial map• Showing retrieved restaurants on this artificial map

Distance is too short

Distance is too long

Blue station is not on the left of red station

Enhanced artificial map• Taking a artificial map from signboard or Web page• Detecting excess modifications• Showing the current position• Adding retrieved geographical objects

Distance is too long

You are here

• Recognizing geographical objects on a map• Reading maps by OCR techniques and detecting

mistaken OCR results by spatial features

• Translating coordinate to an artificial map from a real map based on geographical accuracy

Our approach

1. Recognizing geographical objects

2. Translating coordinate based on geographical accuracy

OCR

Artificial map

Gazetteer

Real map

1. Recognizing geographical objects on a map

• 1-1. Reading characters using OCR• 1-2. Extracting candidate geographical objects for read characters by edit distance

• 1-3. Narrowing down candidate objects• 1-4. Detecting mistaken corresponded objects

• Using a statistics method of outlier detection

Name X Y Lat. Long.

PART-DIEU 124 127 40.856 14.243

PERRACHE 96 122 40.821 14.121

FOCH 140 178 41.903 12.495

... ... ... ... ...

1-1. Reading Characters and 1-2. Candidate objects

• 1-1. Reading characters using OCR• There are some noises of characters

• 1-2. Extracting candidate geographical objects for read characters using edit distance

OCR resultsA: PABI-DYENB: PEPPACMEC: FOCM

Objects namesA: PART-DIEUB: PERRACHEC: FOCH

A: PABI-DYENB: PEPPACMEC: FOCM

Gazetteer

C1: PARIS-LYONC2: SAB-LINEC3: PART-DIEU

B1: PARIS-LYONB2: SAB-LINEB3: PART-DIEU

A1: PARIS-LYONA2: SAB-LINEA3: PART-DIEU

• Showing objects in a certain region• Concentrating correct objects in a certain region

• Deleting a candidate object that is the furthest from the center of gravity of candidate objects• Repeating until a read character related to a candidate

object

1-3. Narrowing down candidate objects

A1

A2A3

B1B2

B3C1

C2

Positions of candidate objectsArtificial map

OCR resultsA: PABI-DYENB: PEPPACMEC: FOCM

Object name X Y Lat. Long

PART-DIEU 364 15 35.039343 135.745611

PERRACHE 215 379 35.016642 135.782433

FOCH 192 56 35.011791 135.749302

1-4. Detecting mistaken objects’ information

• Mistaken information of objects are included in 1-3 results• Checking distances between a object and other objects

• Detecting outliers by statistical method as Smirnov-Grubbs test using distance difference between artificial maps and real maps

Artificial map

Positions of candidate objects

2.Translating coordinate using geographical accuracy (1/2)

• Finding corresponding points by geometrical calculations using any three objects and a certain point1. Drawing a perpendicular line to a edge from the target point

2. Mapping point of intersections to the artificial map

3. Drawing lines between each point of intersection

Artificial map Real map

2.Translating coordinate using geographical accuracy (2/2)

• Using a waited average of corresponding points for detecting the corresponding point on the artificial map

• Calculating similarity of distance and direction between an artificial map and a real map as a weight of candidate point

Artificial map Real map

Artificial map Real map

・・・・・・

△ABD = 0.8

△ACD = 0.5

・・

・

Evaluation

• Recognizing artificial maps• Number of data set: 20

• Access information of universities and sightseeing information of cities

• Checking results using correct answer made by two participants

• Translating positions to artificial maps• Number of data set: 10

• Using a half of experiment “Recognizing artificial maps”

• Number of times: 220• Determining correct result by three participants

Experimental result: Recognizing artificial maps

• Extracted characters can be corresponded to correct geographical objects

• A lot of object names didn’t read by OCR

No. #Names #Read Precision Recall

1 34 7 1.00 0.21

2 49 13 0.85 0.22

3 30 10 0.60 0.20

4 22 3 0.00 0.00

5 29 6 0.50 0.10

6 7 4 1.00 0.57

7 15 13 0.31 0.27

8 17 17 0.71 0.71

9 11 4 1.00 0.36

10 12 10 0.90 0.75

Average 21.6 6.3 0.61 0.23

Result of Kyusyu University

• Most objects are corresponded correctly

Result of Hokkaido University

• All of objects are corresponded wrong positions• We read wrong character from the artificial map

Experimental result: Translating positions to artificial maps

• Correlation coefficient between accuracy and precision is 0.62• When recognition is success, translating process

returns good results

No. True False Accuracy Precision Recall

1 24 16 60% 1.00 0.21

2 26 14 65% 0.85 0.22

3 14 26 35% 0.60 0.20

4 0 30 0% 0.00 0.00

5 7 13 35% 0.50 0.10

6 3 7 30% 1.00 0.57

7 0 10 0% 0.31 0.27

8 4 6 40% 0.71 0.71

9 1 9 10% 1.00 0.36

10 7 3 70% 0.90 0.75

Result 86 134 39% 0.62 0.38

Results of Kyusyu university

• Result• Precision of map recognition: 0.85• Accuracy of corresponding point: 0.65

• We can estimate corresponding point on this map• We get a lot of correct geographical information

Result of Himeji City

• Result• Precision of map recognition: 0.71• Accuracy of corresponding point: 0.40

• Some wrong results have wrong positional relations with a road

Conclusion

• Enhanced artificial map using translating coordinate based on geographical accuracy analysis• Recognizing artificial maps using OCR techniques• Finding corresponding points based on deformation

analysis• Confirming recognition accuracy and translation

accuracy

• Future work• Treating line objects such as

roads and rivers• Evaluating on real environment

with the mobile phone

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