Feature extraction and analysis for 3D point cloud-based object recognition
Khatamian Oskooei, Seyed Alireza
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Object recognition is one of the most problematic challenges in computer vision, robotics, autonomous agents and others. Image Processing and Machine Learning collaborate to solve this problem from various perspectives. Most systems operate on 2D projections to recognize 3D objects. The author proposes a novel methodology that performs on 3D point clouds to extract signatures and to recognize possible existing objects. 3D scanning devices can produce 3D point cloud of any object to collect a dataset; PDA devices such as Google Tango and scanners associated with 3D printers provide the scanning ability. Our objective is to build a system that recognizes objects utilizing properties of 3D point clouds, to prove such a system exists and to address some of the shortcomings in the commonly-used approaches. Moreover, some methods measure the features learnability and the impacts of the properties to analyze the proposed attributes geometrical or topological and to assess the recognition procedure and to emphasize the proof of concept.