An algorithm which identifies discontinuities in noisy data is presented. The signal is modelled as step edges with additive normally distributed noise present. Using finite mixture analysis a variable number of distributions are identified together with the location of the respective edges separating them. The problem is solved using a dynamic programming approach which ensures globally optimal edge positions according to the signal model of a finite mixture of normal distributions. The computational complexity is of order MN/sup 2/ where M is the number of discontinuities in the mixture and N is the number of data points in the signal. The algorithm is tested on a range of signals and yields as accurate edge positions as a corresponding square error method. Among applications for this algorithm is edge detection in medical images and examples from ultrasound imaging are included.>

Control systems which enable robots to behave intelligently is a major issue in todays process of automating factories. This paper presents a hierarchical robot control system, termed CAOS for Control using Action Oriented Schemas, with ideas taken from the neurosciences. We are using action oriented schemas (called neuroschemas) as the basic building blocks in a hierarchical control structure which is being implemented on a BBN Butterfly Parallel Processor. Serial versions in C and LISP are presented with examples showing how CAOS achieves the goals of recognizing three-dimensional polyhedral objects. We also describe a simulation on how it manipulates objects. Moreover, the ongoing implementation of a parallel version of the system is discussed.