A Deformation Analysis Method for Artificial Maps Based on Geographical Accuracy and Its...
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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
Recognize
Translate