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Presentation transcript:

Chapter 4 – Data structures for image analysis 4.1 Levels of image data representation 4.2 Traditional image data structures 4.3 Hierarchical data structures Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Summary Level of image data representation Data structures together with algorithms are used to devise solutions to computational tasks. Data structures for vision may be loosely classified as * Iconic * Segmented * Geometric * Relational *Boundaries between these layers may not be well defined. Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Summary Traditional image data structures The matrix (2D array) is the most common data structure used for low-level representations, implemented as an array. Matrices hold image data explicitly. Spatial characteristics are implicitly available. Binary images are represented by binary matrices; multispectral images are represented by binary matrices; Hierarchical image structures are represented by matrices of different dimensions; The co-occurrence matrix is an example of global information derived from an image matrix; it is useful in describing texture. Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Summary Traditional image data structures cont. Chains may be used to describe pixel paths, especially borders. Chain codes are useful for recognition based on syntactic approaches. Run length codes are useful for simple image compression. Graph structures may be used to describe regions and their adjacency. These may be derived from a region map, a matrix of the same size as the image. Relational structures may be used to describe semantic relationships between image regions. Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.

Summary Hierarchical data structures Hierarchical structures can be used to extract large-scale features, which may be used to initialize analysis. They can provide significant computational efficiency. M-pyramids and T-pyramids provide data structures to describe multiple image resolutions. Quadtrees are a variety of T-pyramid in which selected areas of an image are stored at higher resolution than others, permitting selective extraction of detail. Many algorithms for manipulation of quadtrees are available. Quadtrees are prone to great variation from small image differences. Leaf codes provide a more efficient form of quadtree. Many ways of deriving pyramids exist, dependent on choice of reduction window. Copyright ©2008, Thomson Engineering, a division of Thomson Learning Ltd.