A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-organizing maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
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A novel approach to color image segmentation is proposed and formulated in this paper. Conventional color segmentation methods apply SOFMs - among other techniques - as a first stage clustering in hierarchical or hybrid schemes in order to achieve color reduction and enhance robustness against noise. 2-D SOFMs defined upon 3-D color space are usually employed to render the distribution of colors of an image without taking into consideration the spatial correlation of color vectors throughout various regions of the image. Clustering color vectors pertaining to segments of an image is carried out in a consequent stage via unsupervised or supervised learning. A SOFM defined upon the 2-D image plane, which is viewed as a spatial input space, as well as the output 3-D color space is proposed. Two different initialization schemes are performed, i.e. uniform distribution of the weights in 2-D input space in an ordered fashion so that information regarding local correlation of the color vectors is preserved and jointly uniform distribution of the weights in both 3-D color space and 2-D input space. A second stage of Density-Based Clustering of the nodes of the SOM (utilizing an ad hoc modification of the DBSCAN algorithm) is employed in order to facilitate the segmentation of the color image.