Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/COMPSAC48688.2020.00-21 | 2020 IEEE 44TH ANNUAL COMPUTERS, SOFTWARE, AND APPLICATIONS CONFERENCE (COMPSAC 2020) |
Keywords | DocType | ISSN |
shadow detection, light source detection, single-image solution, deep learning, unsupervised learning | Conference | 0730-3157 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Simon Colreavy-Donnelly | 1 | 0 | 1.35 |
| Stefan Kuhn | 2 | 0 | 0.34 |
| Fabio Caraffini | 3 | 0 | 0.34 |
| Stuart O'Connor | 4 | 2 | 1.38 |
| Zacharias A. Anastassi | 5 | 0 | 0.34 |
| Simon Coupland | 6 | 0 | 0.34 |