Learned geometric features of 3D range data for human and tree recognition. Issue 3 (1st January 2014)

• Record Type: Journal Article
• Authors: Cho, Kuk; Kim, Chunghoon; Baeg, Seung‐Ho; Park, Sangdeok
• Is Part Of: Electronics letters. Volume 50:Issue 3(2014)
• Page Start: 173
• Page End: 175
• Publication Date: 2014-01-01
• Subjects: geometry -- learning (artificial intelligence) -- object recognition; geometric features -- 3D range data -- three‐dimensional range data -- tree recognition -- human recognition -- off‐road environment -- AdaBoost learning algorithm -- discriminative features; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2013.2761
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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.