4 April 1997 Graph structure for image segmentation
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Abstract
A unified framework for grey value and texture segmentation has been developed. It makes use of a special graph structure (feature similarity graph - FSG) which is based on a feature similarity criterion and a feature smoothing procedure applied in each layer of the network. The feature similarity criterion reflects the fact that not the features themselves but their differences are responsible for segmentation. Furthermore, it takes also into account that the separability of regions depends on the feature variation inside the regions. The segments are the connected components of the FSG. Therefore, the method can be understood as a clustering or grouping procedure of features. Starting with grey value segmentation one obtains segments which, for textured images, represent texture elements or parts of texels and background, respectively. The texels can be described by certain features, namely position, orientation, size, grey value or color, and shape descriptors. Studying position, orientation and size, spatial frequency phenomena and important observations made by investigators of human perception can be explained. The method also gives an explanation of the old Gestalt laws of proximity and similarity. Therefore, it can serve as a model for pre-attentive vision. On the other side, as a highly parallel method with local, regional and global processing ability it might be an approach for future technical vision systems. But to demonstrate this, comprehensive work, especially for a proper shape description for pre-attentive vision, is necessary in the future.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Herbert Jahn, Herbert Jahn, } "Graph structure for image segmentation", Proc. SPIE 3026, Nonlinear Image Processing VIII, (4 April 1997); doi: 10.1117/12.271122; https://doi.org/10.1117/12.271122
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