Multiscale Feature Model for Terrain Data Based on Adaptive Spatial Neighborhood

Adaptive multiscale feature extraction from range data

We present a method to extract meaningful features from range images which introduces some novel ideas: First, our primitives are a richer set than the usual one, as we not only extract high frequences events, which correspond to jump boundaries and sharp creases, but also low frequency events such as smooth ridges and ravines. These last features are generally discarded as they tend to hide in...

mortality forecasting based on lee-carter model

over the past decades a number of approaches have been applied for forecasting mortality. in 1992, a new method for long-run forecast of the level and age pattern of mortality was published by lee and carter. this method was welcomed by many authors so it was extended through a wider class of generalized, parametric and nonlinear model. this model represents one of the most influential recent d...

Neighborhood Based Multidimensional Data Model for OLAP

A novel feature extraction approach for remote sensing image Based on the shape-adaptive neighborhood

Feature extraction is a significant procedure for target recognition and classification of remotely sensed images. In this paper, the previous feature extraction methods were considered to consist of three layers: the abstract layer, the methodology layer and the feature layer. A new feature extraction approach based on the shape adaptive neighborhood (SAN) in the abstract layer was proposed. F...

Spatial Generalization: an Adaptive Lattice Model Based on Spatial Resolution

The paper presents an Adaptive Lattice Model (ALM) based on spatial constraints or resolution such as minimum object size and minimum spatial interval. An ALM represents GIS data by latticing adaptively the spatial space at a certain abstraction level or spatial resolution and omitting all details smaller or finer than the resolution. One implementation of ALM is called CELL, which considers ea...