Functional Data Analysis of Tree Data Objects.

Persian Handwriting Analysis Using Functional Principal Components

Principal components analysis is a well-known statistical method in dealing with large dependent data sets. It is also used in functional data for both purposes of data reduction as well as variation representation. On the other hand "handwriting" is one of the objects, studied in various statistical fields like pattern recognition and shape analysis. Considering time as the argument,...

An Introduction to Functional Data Analysis of Populations of Tree-structured Objects

This paper proposes a new method for understanding the structure of populations of complex objects in the area of medical image analysis. The new methods require invention of approaches to the statistical analysis of a population of tree-structured objects. The approach is based on a metric in tree space. The metric provides a foundation for defining a notion of population center. In Functional...

Functional Data Analysis of Populations of Tree-structured Objects

Object Oriented Data Analysis: Sets of Trees

Object Oriented Data Analysis is the statistical analysis of populations of complex objects. In the special case of Functional Data Analysis, these data objects are curves, where standard Euclidean approaches, such as principal components analysis, have been very successful. Recent developments in medical image analysis motivate the statistical analysis of populations of more complex data objec...

VHR Semantic Labeling by Random Forest Classification and Fusion of Spectral and Spatial Features on Google Earth Engine

Semantic labeling is an active field in remote sensing applications. Although handling high detailed objects in Very High Resolution (VHR) optical image and VHR Digital Surface Model (DSM) is a challenging task, it can improve the accuracy of semantic labeling methods. In this paper, a semantic labeling method is proposed by fusion of optical and normalized DSM data. Spectral and spatial featur...