Open AccessOBJECT CLASSIFICATION VIA PLANAR ABSTRACTION
Author(s) -
Sven Oesau,
Florent Lafarge,
Pierre Alliez
Publication year - 2016
Publication title -
isprs annals of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
eISSN - 2194-9042
pISSN - 2196-6346
DOI - 10.5194/isprsannals-iii-3-225-2016
Subject(s) - planar , artificial intelligence , computer science , robustness (evolution) , orientation (vector space) , pattern recognition (psychology) , benchmark (surveying) , point (geometry) , abstraction , object orientation , computer vision , object (grammar) , class (philosophy) , set (abstract data type) , process (computing) , random forest , mathematics , object oriented programming , geometry , computer graphics (images) , biochemistry , chemistry , philosophy , geodesy , epistemology , gene , programming language , geography , operating system
We present a supervised machine learning approach for classification of objects from sampled point data. The main idea consists in first abstracting the input object into planar parts at several scales, then discriminate between the different classes of objects solely through features derived from these planar shapes. Abstracting into planar shapes provides a means to both reduce the computational complexity and improve robustness to defects inherent to the acquisition process. Measuring statistical properties and relationships between planar shapes offers invariance to scale and orientation. A random forest is then used for solving the multiclass classification problem. We demonstrate the potential of our approach on a set of indoor objects from the Princeton shape benchmark and on objects acquired from indoor scenes and compare the performance of our method with other point-based shape descriptors.