The concise description of algorithms contains hyperlinks to further information (author, algorithms details, BIB entry, WWW external page).
The algorithm features are: f1 = classification (supervised segmentation), f2 = hiearchy result (manual selection), f3 = known number of regions, f4 = [reserved].
| scarpa's TFR |
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f1: 0 f2: 1 f3: 0 f4: 0 |
Texture Fragmentation and Reconstruction The Texture Fragmentation and Reconstruction (TFR) segmentation algorithm is based on a texture modeling particularly suited for segmentation in an unsupervised framework. A texture is regarded for each fixed spatial direction as a finite-state Markov chain where the states of the process are quantized colors. On the basis of this modeling, a simple segmentation algorithm is derived that precesses independently color and spatial information, by first performing a color-based clustering, which provides the quantized colors, and then by means of a further spatial-based clustering, which separates regions according to their transition probability profile. Finally, a region merging algorithm allows to recover the different textures, that is to recompose their internal Markov chains. List of uploaded results for 'TFR' algorithm
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