Data Structures For Image Analysis

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Presentation transcript:

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 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

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

Digital Image Processing 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

Digital Image Processing 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

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

Digital Image Processing 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

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

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

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

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

Region Merging Phenomenon Region merging may create holes Digital Image Processing

Digital Image Processing Relational structure Digital Image Processing

Digital Image Processing 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

Digital Image Processing T-Pyramids Digital Image Processing

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