Estimating Infrared-to-Visible Light Ratio in Images Using Gradient-Boosted Decision Trees and Multilayer Perceptrons
This thesis investigates using machine learning for estimating the infrared-to-visible light ratio (qIR) in images, which is used for day/night synchronization in surveillance cameras. Three different models were developed, namely a regression multilayer perceptron (RMLP), a regression-by-classification MLP (RbC MLP), as well as a gradient-boosted decision tree (GBDT). Using an internal dataset co
