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Data Structures For Image Analysis

Data Structures For Image Analysis. Levels of image data representation Traditional image data structures Hierarchical data structures. Levels Of Image Data Representation. Computer visual perception Determine the relation b/w input image and models of real world

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Data Structures For Image Analysis

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  1. Data Structures For Image Analysis Levels of image data representation Traditional image data structures Hierarchical data structures

  2. Levels Of Image Data Representation • Computer visual perception • Determine the relation b/w input image and models of real world • Iconic image – original image data • Segmented image – ROI in groups • Geometric representation – higher level of knowledge, such as shapes, etc. • Relational model – relationships among higher level abstraction Image-based Digital Image Processing

  3. Traditional Image Data Structures • Matrices or N-dimensional arrays • Chains – describing object borders • Topological data structures – graphs, maps • Relational structures Digital Image Processing

  4. Matrices • Low-level image data representation • Depict spatial relations – neighborhood, etc. • Grid – rectangular, hexagonal grids • Pixel coordinates • Brightness – intensity, gray level, color Digital Image Processing

  5. Matrices (II) • Binary image (0/1), multi-spectral image (gray-scaled, color), hierarchical image data structure (LOD: level of detail, varied resolutions) • Global information • Histogram – probabilistic density of a phenomenon • Co-occurrence matrix – measures in terms of brightness Digital Image Processing

  6. Co-occurrence Matrix The diagonal elements correspond to the histogram! Digital Image Processing

  7. Chains • Chains are used for the description of object borders in computer vision • Chains are composed of symbols in sequence – useful for syntactic pattern recognition • Chain codes (aks: Freeman codes) • Run length coding Digital Image Processing

  8. Chain Codes 0007766555555670000006444444442221111112234445652211 00077665555556600000006444444442221111112234445652211 (X) Digital Image Processing

  9. Run Length Coding ((11144)(214)(52355)) Digital Image Processing

  10. Topological Data Structures • Describe image as set of elements and their relations • Graph: G=(V,E); V denotes the set of nodes and E represents the set of edges • Evaluated graph (or weighted graph) • Region adjacency graph Digital Image Processing

  11. Region Adjacency Graph (RAG) • Nodes represent region; edges or arcs represent connectivity • Nodes of degree 1 are cavities or holes • Edges can be used to describe relations • RAG can be created from a quadtree representation or from tracing the borders of all regions in the region map (a result of segmentation) Digital Image Processing

  12. Region Merging Phenomenon Region merging may create holes Digital Image Processing

  13. Relational structure Digital Image Processing

  14. Pyramids • M-pyramid (matrix-pyramid) – a sequence of images in reducing resolutions of the original image • Disadvantage: Only one image in certain resolution is available at a time • T-pyramid (tree-pyramid) – use the tree structures to represent M-pyramid Digital Image Processing

  15. T-Pyramids Digital Image Processing

  16. Quadtree Similar to pyramid hierarchical representations. T-pyramids are balanced; the quadtree representation is not. Digital Image Processing

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