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. 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

5. 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

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

7. 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

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

9. Digital Image Processing
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. Digital Image Processing
Relational structure
Digital Image Processing

14. 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

15. Digital Image Processing
T-Pyramids
Digital Image Processing

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