CAD Data Composition Method from 3D Sensed Data Based on Feature Extraction

Resonance Based Feature Extraction of 3D Objects from RCS Data

Classification of objects based on Radar Cross Section (RCS) has several military and civilian applications. Feature extraction is an important step in the classification process. The resonances (features) of an object are unique and are independent of the look angles (aspect). A database created with the resonances of known radar targets help in the classification of targets. A method to extra...

Feature Extraction from Visual Data Feature Extraction from Visual Data

3D Inversion of Magnetic Data through Wavelet based Regularization Method

This study deals with the 3D recovering of magnetic susceptibility model by incorporating the sparsity-based constraints in the inversion algorithm. For this purpose, the area under prospect was divided into a large number of rectangular prisms in a mesh with unknown susceptibilities. Tikhonov cost functions with two sparsity functions were used to recover the smooth parts as well as the sharp ...

Heuristic Filtering and 3d Feature Extraction from Lidar Data

The need for a fast, efficient and low cost algorithm for extracting 3D features in urban areas is increasing. Consequently, research in feature extraction has intensified. In this paper we present a new technique to reconstruct buildings and other 3D features in urban areas using LIDAR data only. We have tried to show that dense LIDAR (Light detection and ranging) data is very suitable for 3D ...

Automatic 3D Feature Extraction from Structuralized LIDAR Data

Abstract: LIDAR, or laser scanning, is capable of collecting accurate 3D coordinates of scanned points densely and sub-randomly distributed on scanned object surfaces. The huge amount of 3D points implies abundant recessive spatial information which can be turned into dominant information through various data processing methods. To explore valuable spatial information from LIDAR data automatica...