An interpretation system for ducth cadastral system
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Transcript of An interpretation system for ducth cadastral system
![Page 1: An interpretation system for ducth cadastral system](https://reader033.fdocuments.net/reader033/viewer/2022061203/547d0d8e5806b50d408b48a8/html5/thumbnails/1.jpg)
Recognition system for building flop lines
Poojith Jain-0666444
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Introduction
• Aim• Extraction of information from building flop lines• Storing extracted information• Representing information in CityGML
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Basic Concepts
• Graph• Graph is an ordered pair G: =
(V,E) comprising a set V of vertices together with a set E of edges.
• Graph is used to show connectivity of vertices.
• Computer Representation of images• Pixels• Pixel value based on the color• Array representation
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The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
CityGML Representation
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Building Flop Line
• Gray scale image• High Resolution• Indication
Thick lines ownership boundaryNumbers ownesrhip rightsLabels usage type
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Assumptions made
• Always thick lines indicates ownership boundary• Numbers always enclosed in a polygon• Single number in a polygon represents ownership• Numbers does not ovelap with lines and symbols
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The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
CityGML Representation
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Thresholding and Noise Removal
• Thresholding• Noise
• Gaps • Missing pixels
• Continuity is important for contour detection
• Solution• Closing Operation
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Closing Operation
CLOSING
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The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
CityGML Representation
Removing Labels and
Thin Lines
Number Identification
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Ownership Identification
• Identify the location of the labels• Connected component
labeling• Size criteria
• Extract the labels• Recognize the labels
OCR
OCR
{3,x,y}
{4,x,y}
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The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
Removing Labels and
Thin Lines
Number Identification
CityGML Representation
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Removing Labels and Thin Lines
• Labels indicate property usage and type
• Thin Lines indicate sub region information
• Thick lines indicate boundary
• Remove labels and thin lines.• Connected component
labeling• Opening operation
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The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
CityGML Representation
Corner Dection
Identifying Ownership boundary
Skeletonization
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Skeletonization
• Why Skeletonization?• Reduces foreground
regions in an image to a skeleton• By thinning operation
• Skeleton should be• One pixel width• Preserves connectivity• Preserves Topology• Centered
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The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
CityGML Representation
Corner Detection
Graph construction
Face and Floor
identification
Skeletonization
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Corner Detection
• Corenrs are intersection of two or more edges
• Corners forms the node of the graph
• Harris corner Detection• Invariant to
• Scaling• Image noise• Rotation• Illumination variance
Corner Detection
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Graph Construction
• Identify the nodes• Identify the edges• Optimization
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Graph Construction
• Identify the nodes• Identify the edges• Optimization
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The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
CityGML Representation
Corner Detection
Graph Construction
Face and Floor
Identification
Skeletonization
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Face Recognition
• Each enclosed face becomes ownership boundary
• Associate ownership• Store the information
{3,x,y}
{4,x,y}
3
4Ownership
Ownership Right
Point co-ordinate
…
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Floor Identification
• Identifying Floors• Storing Information
2 3 4 44
1 2 3 4
Ownership
Ownership Right
Floor Number
Co-ordninates
. . .