Synthetic imagery generation is not a new topic; however, it has reemerged as a major focus in recent years. This is in part due to the success achieved by modern machine learning methodologies, in particular, deep learning. One reason these technologies have succeeded is due to the wealth of available training data. A majority of the available data are of generic objects or scenes. However, there are numerous applications in which data are neither readily available nor easily obtained in large quantities. In such scenarios, synthetic imagery is an appealing choice to address this shortcoming. While still faster than the performance of data collections, physics- based models tend to have computational complexity and require extensive computational time. This work seeks to investigate the use of reduced-order modeling (ROM) of relevant objects identified by a maximally stable extremal region (MSER) detector from the entropy image of simple ideal high-fidelity, physics-based synthetic images. Specifically, this work will utilize MSERs to identify pertinent objects to be placed within the simple scene via ROM to produce a more complex scene. This approach has the benefit of rapidly increasing both the complexity of simple, ideal, high-fidelity, physics-based scenes and the amount of synthetic imagery generated via random or statistically-based placement of the objects throughout the scene.