\( \newcommand{\E}{\mathrm{E}} \) \( \newcommand{\A}{\mathrm{A}} \) \( \newcommand{\R}{\mathrm{R}} \) \( \newcommand{\N}{\mathrm{N}} \) \( \newcommand{\Q}{\mathrm{Q}} \) \( \newcommand{\Z}{\mathrm{Z}} \) \( \def\ccSum #1#2#3{ \sum_{#1}^{#2}{#3} } \def\ccProd #1#2#3{ \sum_{#1}^{#2}{#3} }\)
CGAL 4.12 - Classification
Classification Reference

data_classif.png
Simon Giraudot, Florent Lafarge
This component implements an algorithm that classifies a data set into a user-defined set of labels (such as ground, vegetation, buildings, etc.). A flexible API is provided so that users can classify any type of data, compute their own local features on the input data set, and define their own labels.


Introduced in: CGAL 4.12
Depends on: CGAL and Solvers, dD Spatial Searching, Boost Serialization and Boost IO Streams
BibTeX: cgal:lm-clscm-12-18a
License: GPL
Windows Demo: Operations on Polyhedra
Common Demo Dlls: dlls

Classified Reference Pages

Concepts

Main Functions

Classifiers

Data Structures

Label

Feature

Predefined Features

Modules

 Concepts
 
 Main Functions
 Functions that perform classification based on a set of labels and a classifier, with or without regularization.
 
 Classifiers
 Classifiers are functors that, given a label set and an input item, associate this input item with an energy for each label.
 
 Data Structures
 Useful data structures that are used to compute features (computation of eigenvalues, for example) and to regularize classification (neighborhood).
 
 Label
 A label represents how an item should be classified, for example: vegetation, building, road, etc.
 
 Feature
 Features are defined as scalar fields that associates each input item with a specific value.
 
 Predefined Features
 CGAL provides some predefined features that are relevant for classification of point sets.