Surface characterization based upon significant topographic features

Recognition with Second-Order Topographic Surface Features

We previously[3] defined eight second-order volumetric primitives and then showed[4] that they can be extracted from range data. This paper shows that by using them model matching is more efficient, because the shape vocabulary reduces the combinatorial generation of hypotheses. With the model-to-data correspondences, accurate three dimensional model location is possible.

Surface Reconstruction Based Upon

Extracting Second-Order Topographic Surface Features From Range Data

Second-order volumetric features (e.g. ridges, dents, bumps, etc) were previously defined to extend the SMS object modeling system. Here, we show that one can extract surface features from range data that can be described in this vocabulary of second-order features. The process is based on a classification of regions found by an approach based on local surface shape, and has a natural scale str...

Hyperspectral Images Classification by Combination of Spatial Features Based on Local Surface Fitting and Spectral Features

Hyperspectral sensors are important tools in monitoring the phenomena of the Earth due to the acquisition of a large number of spectral bands. Hyperspectral image classification is one of the most important fields of hyperspectral data processing, and so far there have been many attempts to increase its accuracy. Spatial features are important due to their ability to increase classification acc...

Consistent topographic surface labelling

This paper describes work aimed at consistently labelling surface facets using topographic classes derived from mean and Gaussian curvature measurements. There are two distinct contributions. Firstly, we develop a statistical model which allows label probabilities to be assigned to the di!erent topographic classes. These probabilities capture uncertainties in the computation of surface curvatur...