@inproceedings{WangKumarKauppetal., author = {Wang, X. Rosalind and Kumar, Suresh and Kaupp, Tobias and Upcroft, Ben and Durrant-Whyte, Hugh}, title = {Applying ISOMAP to the learning of hyperspectral image}, series = {Australian Conference on Robotics and Automation (ACRA'05)}, booktitle = {Australian Conference on Robotics and Automation (ACRA'05)}, editor = {Sammut, C.}, pages = {1 -- 8}, abstract = {In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.}, language = {en} } @inproceedings{KauppMakarenkoKumaretal., author = {Kaupp, Tobias and Makarenko, Alexei and Kumar, Suresh and Upcroft, Ben and Williams, Stefan}, title = {Operators as information sources in sensor networks}, series = {2005 IEEE/RSJ International Conference on Intelligent Robots and Systems}, booktitle = {2005 IEEE/RSJ International Conference on Intelligent Robots and Systems}, publisher = {IEEE}, doi = {10.1109/IROS.2005.1545015}, pages = {936 -- 941}, abstract = {This paper presents an approach of integrating human operators into a sensor network formed by a heterogeneous team of unmanned air and ground vehicles. Several objectives of human-network interaction are identified. The main focus of this work is on human-to-network information flow, i.e. human operators are regarded as information sources. It is argued that operators should make raw observations which are converted into the sensor network's common representation by a probabilistic model. The concepts are discussed in the context of an outdoor sensor network under development. Human operators contribute geometric feature information in the form of range and bearing observations. Visual feature properties are specified via meaningful class labels. A sensor model, represented as a Bayesian network, translates label observations into the system's representation. The model is also used to classify features as observed by robotic sensors.}, language = {en} }