1. Segmentation: Region Based Methods Region-based methods –iterative methods based on region merging and/or splitting based on the degree of similarity of region properties (attributes). Region growing (narůstání oblastí) –Initialization: The image is split into a large number of segments (regions). The initial segments can be even formed by individual pixels. –Iteration: The neighboring regions are grouped together if their properties (mostly intensity) are similar. –Remarks: It is wise to pose certain constrains on the merging process. The constrains can be quite complex.

2. Segmentation: Region Based Methods Split and Merge (štěpení a slučování) –Input: 1) The original image (one region). 2) Individual pixels (many regions). 3) A moderate number of regions. –Iteration: those regions that are not homogenous are split into several smaller regions and the neighboring regions that have similar properties are merged together. –Remarks: The conditions for splitting and merging can be quite complex. Often quad-tree method is used – the non-homogenous regions are being split into 4 equal subregions (quadrants) and 4 neighboring regions of similar properties are being merged together as far as possible. After that, region merging follows applied to regions in different pyramid levels or having a different parent.

3. Segmentation: Region Based Methods Quad-tree method source: Sonka, Hlavac, Image Processing, Analysis and Machine Vision

4. Segmentation: Region Based Methods Quad-tree method examples source: http://verona.fi-p.unam.mx/project/quadtree.htm

5. Texture Segmentation Texture segmentation –dividing the image into regions based on the texture characteristics. Method 1 (Rosenfeld et al.) –Idea: Texture measure is defined and computed for all pixels (within a certain neighborhood of the given pixel). Thus, texture is converted to amplitude and then one of the amplitude segmentation methods is applied. –Drawback: texture boundaries are blurred. Method 2 (Thompson) –Idea: The transitions between regions of differing texture are detected. Thus, an edge map is built. Then the edge map is processed similarly to the edge-based segmentation. –Drawback: edges are not continuous.

6. Texture Segmentation Examples of different textures Curtain Dog fur Clothes 1 Clothes 2

7. Texture Segmentation Examples of texture segmentation source: http://debut.cis.nctu.edu.tw/pages/TextureStudy/segmentation.htm

8. Texture Segmentation Different scales of the texture in the curtain image Individual threads Meshes of the netFolds of the curtain Consequence of this example If the image contains textures of different scales, it is necessary to perform multiscale texture analysis, i.e. to analyze textures in a hierarchical manner (at different scales).

9. Segmentation: Clustering Methods Clustering methods –Methods based on cluster analysis. –Idea: At each pixel of the image a vector x = [x 1,...,x N ] of N different measurements is computed (typically N is about 10). Different user- defined characteristics are measured. Then cluster analysis in the N- dimensional space is performed, i.e. those pixels which form a cluster in the N-dimensional space are segmented into one region. –Advantage: easy. –Drawback: computationally intensive.

10. Segmentation: Template Matching Template matching (srovnávání se vzorem) –Searching for a template (pattern) in the image by computing the correlation between the pattern and the searched image data. –Idea: Evaluate a match criterion for each location and rotation of the pattern in the image. Local maxima of this criterion exceeding a preset threshold represent pattern locations in the image. –Disadvantages: Very time consuming, especially for large patterns. Sensitive to geometrical distortions of the image.

11. Possible matching criteria: f(x,y) – image h(x,y) – pattern S – set of all possible pixel coordinates in the image h Segmentation: Template Matching