Detecting structured light patterns in colour images using a support vector machine

3D reconstruction from multiple cameras is challenging in some environments because of ambiguous matches between similar-looking features. These ambiguities can be resolved by projecting a structured light pattern into the scene, and detecting points in the light pattern in each image. Robust detection of the structured light pattern is hard because of variations in object colour and lighting within the scene, however for specific applications, training data can easily be collected and labelled, enabling the detection problem to be solved using machine learning techniques. We demonstrate the application of a Support Vector Machine (SVM) to detect laser light patterns projected into images of vines, using Feature Subset Selection to design a feature descriptor. A descriptor is computed for every candidate pixel, and the SVM determines if each descriptor is part of the laser line pattern. On test images, the proposed detector achieves 99.4% precision at 90% recall, outperforming a detector which uses only one pixel´s colour.