@inproceedings{bbbe384693b342a6bdb05f262dbac0a0,

title = "Unsupervised image segmentation with neural networks",

abstract = "The segmentation of colour images (RGB), distinguishing clusters of image points, representing for example background, leaves and flowers, is performed in a multi-dimensional environment. Considering a two dimensional environment, clusters can be divided by lines. In a three dimensional environment by planes and in an n-dimensional environment by n-1 dimensional structures. Starting with a complete data set the first neural network, represents an n-1 dimensional structure to divide the data set into two subsets. Each subset is once more divided by an additional neural network: recursive partitioning. This results in a tree structure with a neural network in each branching point. Partitioning stops as soon as a partitioning criterium cannot be fulfilled. After the unsupervised training the neural system can be used for the segmentation of images.",

keywords = "Image segmentation, Neural networks, Recursive partitioning, Unsupervised learning",

author = "Th.H. Gieling and H.J.J. Janssen and {de Vries}, H.C. and P. Loef",

year = "2001",

language = "English",

series = "Acta Horticulturae",

number = "562",

pages = "100--108",

booktitle = "Proceedings of the Third International Symposium on Sensors in Horticulture, ISHS - Sensors in Horticulture, Tiberias (IL), August 1997",

}