An interpretation system for ducth cadastral system

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An interpretation system for ducth cadastral system

  1. 1. Recognition system for building flop lines<br />Poojith Jain-0666444<br />
  2. 2. Introduction<br />Aim<br />Extraction of informationfrom building flop lines<br />Storing extractedinformation<br />Representinginformation in CityGML<br />
  3. 3. BasicConcepts<br />Graph<br /> Graph is an ordered pair G: = (V,E) comprising a set V of  vertices together with a set E of edges.<br />Graph is used to show connectivity of vertices.<br />Computer Representation of images<br />Pixels<br />Pixel valuebasedon the color<br />Arrayrepresentation<br />
  4. 4. The Process<br />Thresholding and NoiseRemoval<br />Labels Identification And image cleaning<br />Graphconstruction<br />Flop line image of Building<br />CityGMLRepresentation<br />
  5. 5. Building Flop Line<br />Grayscale image<br />High Resolution<br />Indication<br /><ul><li>Thicklinesownershipboundary
  6. 6. Numbersownesrhiprights
  7. 7. Labels usage type</li></li></ul><li>Assumptions made<br />Always thicklinesindicatesownershipboundary<br />Numbersalwaysenclosed in a polygon<br />Single number in a polygonrepresentsownership<br />Numbers does notovelapwithlines and symbols<br />
  8. 8. The Process<br />Thresholding and NoiseRemoval<br />Labels Identification And image cleaning<br />Graphconstruction<br />Flop line image of Building<br />CityGMLRepresentation<br />
  9. 9. Thresholding and NoiseRemoval<br />Thresholding<br />Noise<br />Gaps<br /> Missing pixels<br />Continuity is important for contour detection<br />Solution<br />ClosingOperation<br />
  10. 10. ClosingOperation<br />CLOSING<br />
  11. 11. The Process<br />Thresholding and NoiseRemoval<br />Labels Identification And image cleaning<br />Graphconstruction<br />Flop line image of Building<br />CityGMLRepresentation<br />NumberIdentification<br />Removing Labels and<br />Thin Lines<br />
  12. 12. OwnershipIdentification<br />Identify the location of the labels<br />Connected component labeling<br />Size criteria<br />Extract the labels<br />Recognize the labels<br />{3,x,y}<br />OCR<br />{4,x,y}<br />OCR<br />
  13. 13. The Process<br />Thresholding and NoiseRemoval<br />Labels Identification And image cleaning<br />Graphconstruction<br />Flop line image of Building<br />CityGMLRepresentation<br />NumberIdentification<br />Removing Labels and<br />Thin Lines<br />
  14. 14. Removing Labels and Thin Lines<br />Labels indicatepropertyusage and type<br />Thin Lines indicate sub regioninformation<br />Thicklinesindicateboundary<br />Remove labels and thinlines.<br />Connected component labeling<br />Opening operation<br />
  15. 15. The Process<br />Thresholding and NoiseRemoval<br />Labels Identification And image cleaning<br />Graphconstruction<br />Flop line image of Building<br />CityGMLRepresentation<br />Skeletonization<br />Corner Dection<br />IdentifyingOwnershipboundary<br />
  16. 16. Skeletonization<br />WhySkeletonization?<br />Reducesforegroundregions in an image to a skeleton<br />Bythinningoperation<br />Skeletonshouldbe<br />One pixel width<br />Preserves connectivity<br />Preserves Topology<br />Centered<br />
  17. 17. The Process<br />Thresholding and NoiseRemoval<br />Labels Identification And image cleaning<br />Graphconstruction<br />Flop line image of Building<br />CityGMLRepresentation<br />Skeletonization<br />Corner Detection<br />Graphconstruction<br />Face and Floor<br />identification<br />
  18. 18. Corner Detection<br />Corenrs are intersection of twoor more edges<br />Corners forms the node of the graph<br />Harris corner Detection<br />Invariant to<br />Scaling<br />Image noise<br />Rotation<br />Illuminationvariance<br />Corner Detection<br />
  19. 19. Graph Construction<br />Identify the nodes<br />Identify the edges<br />Optimization<br />
  20. 20. Graph Construction<br />Identify the nodes<br />Identify the edges<br />Optimization<br />
  21. 21. The Process<br />Thresholding and NoiseRemoval<br />Labels Identification And image cleaning<br />Graphconstruction<br />Flop line image of Building<br />CityGMLRepresentation<br />Skeletonization<br />Corner Detection<br />Graph Construction<br />Face and Floor<br />Identification<br />
  22. 22. Face Recognition<br />Eachenclosed face becomesownershipboundary<br />Associateownership<br />Store the information<br />{3,x,y}<br />3<br />{4,x,y}<br />4<br />
  23. 23. Floor Identification<br />IdentifyingFloors<br />Storing Information<br />2<br />3<br />4<br />4<br />4<br />1<br />2<br />3<br />4<br />

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