A Neural Network for Interpolating Light-Sources




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Peer reviewed


This study combines two novel deterministic methods with a Convolutional Neural Network to develop a machine learning method that is aware of directionality of light in images. The first method detects shadows in terrestrial images by using a sliding-window algorithm that extracts specific hue and value features in an image. The second method interpolates light-sources by utilising a line-algorithm, which detects the direction of light sources in the image. Both of these methods are single-image solutions and employ deterministic methods to calculate the values from the image alone, without the need for illumination-models. They extract real-time geometry from the light source in an image, rather than mapping an illumination-model onto the image, which are the only models used today. Finally, those outputs are used to train a Convolutional Neural Network. This displays greater accuracy than previous methods for shadow detection and can predict light source-direction and thus orientation accurately, which is a considerable innovation for an unsupervised CNN. It is significantly faster than the deterministic methods. We also present a reference dataset for the problem of shadow and light direction detection.

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shadow detection, light source detection, singleimage solution, deep learning, unsupervised learning


Colreavy-Donnelly, S., Kuhn, S., Caraffini, F., O'Connor, S., Anastassi, Z., Coupland, S. (2020) A Neural Network for Interpolating Light-Sources. 4th IEEE International Workshop on Software Engineering for Smart Systems - IEEE Computer Society Signature Conference on Computers, Software and Applications (COMPSAC), Madrid, Spain, July 2020.


Research Institute

Institute of Artificial Intelligence (IAI)