Binary Classification of Images for Applications in Intelligent 3D Scanning

Abstract

Three-dimensional (3D) scanning techniques based on photogrammetry, also known as Structure-from-Motion (SfM), require many two-dimensional (2D) images of an object, obtained from different viewpoints, in order to create its 3D reconstruction. When these images are acquired using closed-space 3D scanning rigs, which are composed of large number of cameras fitted on multiple pods, flash photography is required and image acquisition must be well synchronized to avoid the problem of ‘misfired’ cameras. This paper presents an approach to binary classification (as ‘good’ or ‘misfired’) of images obtained during the 3D scanning process, using four machine learning methods—support vector machines, artificial neural networks, k-nearest neighbors algorithm, and random forests. Input to the algorithms are histograms of regions determined to be of interest in the detection of image misfires. The considered algorithms are evaluated based on the prediction accuracy that they achieved on our dataset. The average prediction accuracy of 94.19% is obtained using the random forests approach under cross-validation. Therefore, the application of the proposed approach allows the development of an ‘intelligent’ 3D scanning system which can automatically detect camera misfiring and repeat the scanning process without the need for human intervention.