Recognising and Segmenting Objects in Natural Environments

This paper presents an algorithm for recognition and segmentation of natural features in unstructured environments. By providing a Bayesian solution for the density estimation problem, the algorithm needs significantly less training data than conventional techniques and is applicable to different environments. The algorithm is based on colour and wavelet convolution of image patches to model the information contained in natural features. Dimensionality reduction techniques are applied to map data points to a lower dimensional space where Bayesian density estimation is computed. Experiments were performed in underwater, aerial and terrestrial domains demonstrating the accuracy and generalisation properties of the algorithm for recognition and segmentation. Comparisons with conventional density estimation techniques are provided to illustrate the benefits of the new approach