data structures for image analysis levels of image data representation traditional image data...

Data Structures For Image Analysis

Levels of image data representation

Traditional image data structures

Hierarchical data structures

Digital Image Processing 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


Traditional Image Data Structures

Matrices or N-dimensional arrays Chains – describing object borders Topological data structures – graphs,

maps Relational structures


Matrices

Low-level image data representation Depict spatial relations –

neighborhood, etc. Grid – rectangular, hexagonal grids Pixel coordinates Brightness – intensity, gray level, color


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


Co-occurrence Matrix

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The diagonal elements correspond to the histogram!


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


Chain Codes

0007766555555670000006444444442221111112234445652211

00077665555556600000006444444442221111112234445652211 (X)


Run Length Coding

((11144)(214)(52355))


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


Region Adjacency Graph (RAG)

NodesNodes represent region; edgesedges or arcsarcs represent connectivity Nodes of degree 1 are cavitiescavities or holesholes 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 mapregion map (a result of segmentation)


Region Merging Phenomenon

Region merging may create holes


Relational structure


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


T-Pyramids

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Quadtree

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

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