Robust regression and outlier detection
Robust regression and outlier detection
Surfaces in range image understanding
Surfaces in range image understanding
BONSAI: 3D Object Recognition Using Constrained Search
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
A new definition of neighborhood of a point in multi-dimensional space
Pattern Recognition Letters
An Experimental Comparison of Range Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
MIR: An Approach to Robust Clustering-Application to Range Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of 3d Image Analysis and Synthesis
Principles of 3d Image Analysis and Synthesis
Computer and Robot Vision
Computers and Electronics in Agriculture
Spatial analyses to evaluate multi-crop yield stability for a field
Computers and Electronics in Agriculture
Performance testing of LiDAR exploitation software
Computers & Geosciences
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The high point density of airborne laser mapping systems enables achieving a detailed description of geographic objects and of the terrain. Growing experience shows, however, that extracting information directly from the data is practically impossible. This applies to basic tasks like Digital Elevation Model (DEM) generation and to more involved ones like the extraction of objects or generation of 3D city models. This paper presents an algorithm for surface clustering and for identifying structure in the laser data. The proposed approach concerns analyzing the surface texture, and via unsupervised classification identifying segments that exhibit homogeneous behavior. Clustering involves analysis of several key issues in relation to processing laser data such as different point densities, processing an irregularly distributed data set, analysis of attributes that can be derived from the data set, and ways to extract attributes. This paper provides a detailed discussion of these issues as well.