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Learned geometric features of 3D range data for human and tree recognition
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Learned geometric features of 3D range data for human and tree recognition
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Journal Article
Learned geometric features of 3D range data for human and tree recognition
2014
Overview
A new method of obtaining geometric features of three-dimensional range data for human and tree recognition in an off-road environment is described. The learning algorithm AdaBoost is used to select a set of discriminative features from a very large set of potential geometric features. The proposed geometric feature can be considered as a generalisation of the geometric feature used in previous studies. The experimental results for human and tree recognition show that the proposed method outperforms the other methods.
Publisher
The Institution of Engineering and Technology,Institution of Engineering and Technology,John Wiley & Sons, Inc
Subject
/ Computer science; control theory; systems
/ Data processing. List processing. Character string processing
/ Exact sciences and technology
/ geometry
/ Ground, air and sea transportation, marine construction
/ Human
/ Image and vision processing and display technology
/ Learning
/ learning (artificial intelligence)
/ Memory organisation. Data processing
/ Pattern recognition. Digital image processing. Computational geometry
/ Road transportation and traffic
/ Software
/ three‐dimensional range data
/ Trees
