Colour Image Segmentation by Non-Parametric Density Estimation in Feature Space

A novel colour image segmentation routine, based on clustering pixels in colour space using non-parametric density estimation, is described. Although the basic methodology is well known, several important improvements to the previous work in this area are introduced. The density is estimated at a series of knot points in the colour space, and clustering is performed by hill climbing on this density function. The hill climbing is constrained such that no step crosses an intermediate Voronoid cell, ensuring that all salient clusters are detected. Most importantly, the problem of scale selection has been addressed using a statistically motivated approach, by placing the knot points according to an estimate of the noise in the original images, taking full account of error propagation in the algorithm. The algorithm has been evaluated both on synthetic data and in the context of its application in a machine vision system, specifically the calibration of velocity estimates extracted from a novel infrared sensor used in a fall detector. The application of the technique to medical images and texture recognition is also discussed.