Qualitative Spatial Reasoning (QSR) is an exceptionally powerful tool in the fields of computer cognition and automated computer reasoning. Recent results have shown the potential feasibility of pairing image processing techniques with basic principles of physics that humans inherently understand in order to allow the computer to extrapolate additional information about the environment in which it exists. Initial results showed that, while using the tenets of conservation of mass, conservation of energy, and inertia allowed the computer to gain more information than was initially apparent, noise in the perceived input data resulted in the software erroneously reasoning about the state of the system. Hence improving the image processing techniques used in analyzing the data should ameliorate the errors in reasoning. In this paper, the authors investigate this claim, and present a system that allows a more precise and correct computational view of the environment.