<title>Image segmentation using neural tree networks</title>

Abstract

We present a technique for Image Segmentation using Neural Tree Networks (NTN). We also modify the NTN architecture to let is solve multi-class classification problems with only binary fan-out. We have used a realistic case study of segmenting the pole, coil and painted coil regions of light bulb filaments (LBF). The input to the network is a set of maximum, minimum and average of intensities in radial slices of a circular window around a pixel, taken from a front-lit and a back-lit image of an LBF. Training is done with a composite image drawn from images of many LBFs. Each node of the NTN is a multi-layer perceptron and has one output for each segment class. These outputs are treated as probabilities to compute a confidence value for the segmentation of that pixel. Segmentation results with high confidence values are deemed to be correct and not processed further, while those with moderate and low confidence values are deemed to be outliers by this node and passed down the tree to children nodes. These tend to be pixels in boundary of different regions. The results are favorably compared with a traditional segmentation technique applied to the LBF test case.