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Abstract3D imaging systems provide valuable information for autonomous robot navigation based on landmark detection in pipelines. This paper presents a method for using a time-of-flight (TOF) camera for detection and tracking of pipeline features such as junctions, bends and obstacles. Feature extraction is done by fitting a cylinder to images of the pipeline. Data in captured images appear to take a conic rather than cylindrical shape, and we adjust the geometric primitive accordingly. Pixels deviating from the estimated cylinder/cone fit are grouped into blobs. Blobs fulfilling constraints on shape and stability over time are then tracked. The usefulness of TOF imagery as a source for landmark detection and tracking in pipelines is evaluated by comparison to auxiliary measurements. Experiments using a model pipeline and a prototype robot show encouraging results.

Abstract-Classification of remotely sensed hyperspectral images calls for a classifier that gracefully handles high-dimensional data, where the amount of samples available for training might be very low relative to the dimension. Even when using simple parametric classifiers such as the Gaussian maximum-likelihood rule, the large number of bands leads to copious amounts of parameters to estimate. Most of these parameters are measures of correlations between features. The covariance structure of a multivariate normal population can be simplified by setting elements of the inverse covariance matrix to zero. Well-known results from time series analysis relates the estimation of the inverse covariance matrix to a sequence of regressions by using the Cholesky decomposition. We observe that discriminant analysis can be performed without inverting the covariance matrix. We propose defining a sparsity pattern on the lower triangular matrix resulting from the Cholesky decomposition, and develop a simple search algorithm for choosing this sparsity. The resulting classifier is used on four different hyperspectral images, and compared with conventional approaches such as support vector machines, with encouraging results.

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Abstract-A key component in most parametric classifiers is the estimation of an inverse covariance matrix. In hyperspectral images the number of bands can be in the hundreds leading to covariance matrices having tens of thousands of elements. Lately, the use of general linear regression models in estimating the inverse covariance matrix have been introduced in the time-series literature. This paper adopts and expands these ideas to ill-posed hyperspectral image classification problems. The results indicate that at least some of the approaches can give a lower classification error than traditional methods such as the linear discriminant analysis (LDA) and the regularized discriminant analysis (RDA). Furthermore, the results show that contrary to earlier beliefs, long-range correlation coefficients appear necessary to build an effective hyperspectral classifier, and that the high correlations between neighboring bands seem to allow differing sparsity configurations of the covariance matrix to obtain similar classification results.