We propose a homology between thermodynamic systems and images for the treatment of time-varying imagery. A physical system colder than its surroundings absorbs heat from the surroundings. Furthermore, the absorbed heat increases the entropy of the system, which is closely related to its disorder as given by the definition of Clausius and Boltzmann. Because pixels of an image are viewed as a state of lattice-like molecules in a thermodynamic system, the task of reckoning the entropy variations of pixels is similar to estimating their degrees of disorder. We apply this homology to the uncalibrated stereo matching problem. The absence of calibrations alleviates user efforts to install stereo cameras and enables users to freely modify the composition of the cameras. The proposed method is also robust to differences in brightness, white balancing, and even focusing between stereo image pairs. These peculiarities enable users to estimate the depths of interesting objects in practical applications without much effort in order to set and maintain a stereo vision setup. Users can consequently utilize two webcams as a stereo camera.

No abstract

No abstract

SUMMARYWe propose a novel motion segmentation method based on a Clausius Normalized Field (CNF), a probabilistic model for treating time-varying imagery, which estimates entropy variations by observing the entropy definitions of Clausius and Boltzmann. As pixels of an image are viewed as a state of lattice-like molecules in a thermodynamic system, estimating entropy variations of pixels is the same as estimating their degrees of disorder. A greater increase in entropy means that a pixel has a higher chance of belonging to moving objects rather than to the background, because of its higher disorder. In addition to these homologous operations, a CNF naturally takes into consideration both spatial and temporal information to avoid local maxima, which substantially improves the accuracy of motion segmentation. Our motion segmentation system using CNF clearly separates moving objects from their backgrounds. It also effectively eliminates noise to a level achieved when refined post-processing steps are applied to the results of general motion segmentations. It requires less computational power than other random fields and generates automatically normalized outputs without additional post-processes.