an interpretation system for ducth cadastral system
DESCRIPTION
TRANSCRIPT
Recognition system for building flop lines
Poojith Jain-0666444
Introduction
• Aim• Extraction of information from building flop lines• Storing extracted information• Representing information in CityGML
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
The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
CityGML Representation
Building Flop Line
• Gray scale image• High Resolution• Indication
Thick lines ownership boundaryNumbers ownesrhip rightsLabels usage type
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
The Process
Flop line image of Building
Thresholding and Noise Removal
Labels Identification And image cleaning
Graph construction
CityGML Representation
Thresholding and Noise Removal
• Thresholding• Noise
• Gaps • Missing pixels
• Continuity is important for contour detection
• Solution• Closing Operation
Closing Operation
CLOSING
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
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}
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
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
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
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
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
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
Graph Construction
• Identify the nodes• Identify the edges• Optimization
Graph Construction
• Identify the nodes• Identify the edges• Optimization
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
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
…
Floor Identification
• Identifying Floors• Storing Information
2 3 4 44
1 2 3 4
Ownership
Ownership Right
Floor Number
Co-ordninates
. . .