In current state-of-the-art systems for object detection and classification a huge amount of data is needed. Even if large databases are available, some classes are typically underrepresented and therefore the classifier is not able to capture the variability in appearance.
In this work we present a novel method to enrich the training database with natural looking synthetic images. The method can be used to transfer the object appearance from one image (template image) to another image (base image) containing a different object of the same or a similar category. In order to preserve natural appearance and avoid artifacts we only use the gray-level values of the base image for synthesis.
The main contribution of this work is an extension of the shift-map approach. An appropriate optimization criteria for the used Markov Random Field (MRF) is defined and the MRF is embedded into a general framework for training data synthesis, which is exemplary tailored to the generation of traffic signs. The influence of using synthetic images is evaluated using a convolutional neural network (CNN).
[IEEE Xplore] Presented at IV 2017.