High Speed Image Segmentation using a Binary Neural Network.

In the very near future large amounts of Remotely Sensed data will become available on a daily basis. Unfortunately, it is not clear if the processing methods are available to deal with this data in a timely fashion. This paper describes research towards an approach which will allow a user to perform a rapid pre-search of large amounts of image data for regions of interest based on texture. The method is based on a novel neural network architecture (ADAM) that is designed primarily for speed of operation by making use of computationally simple pre-processing and only uses Boolean operations in the weights of the network. To facilitate interactive use of the network, it is capable of rapid training. The paper outlines the neural network, its application to RS data in comparison with other methods, and briefly describes a fast hardware implementation of the network. 1 Introduction The advent of new satellites producing tens of megabytes of image-data per day has placed a challenge on th..

An Analysis of Initiative Selection in Collaborative Task-Oriented Discourse

In this paper we propose a number of principles and conjectures for mixedinitiative collaborative dialogs. We explore some methodologies for managing initiative between conversational participants. We mathematically analyze specific initiative-changing mechanisms based on a probabilistic knowledge base and user model. We look at the role of negotiation in managing initiative and quantify how the negotiation process is useful toward modifying user models. Some experimental results using computer--computer simulations are presented along with some discussion of how such studies are useful toward building human--computer systems