Learn More
Atmospheric conditions induced by suspended particles, such as fog and haze, severely alter the scene appearance. Restoring the true scene appearance from a single observation made in such bad weather conditions remains a challenging task due to the inherent ambiguity that arises in the image formation process. In this paper, we introduce a novel Bayesian(More)
The appearance of an object in an image encodes invaluable information about that object and the surrounding scene. Inferring object reflectance and scene illumination from an image would help us decode this information: reflectance can reveal important properties about the materials composing an object; the illumination can tell us, for instance, whether(More)
Estimating the reflectance and illumination from a single image becomes particularly challenging when the object surface consists of multiple materials. The key difficulty lies in recovering the reflectance from sparse angular samples while correctly assigning them to different materials. We tackle this problem by extracting and fully leveraging reflectance(More)
We introduce a novel parametric bidirectional reflectance distribution function (BRDF) model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics(More)
Human gait modeling (e.g., for person identification) largely relies on image-based representations that muddle gait with body shape. Silhouettes, for instance, inherently entangle body shape and gait. For gait analysis and recognition, decoupling these two factors is desirable. Most important, once decoupled, they can be combined for the task at hand, but(More)
Recovering the radiometric properties of a scene (i.e., the reflectance, illumination, and geometry) is a long-sought ability of computer vision that can provide invaluable information for a wide range of applications. Deciphering the radiometric ingredients from the appearance of a real-world scene, as opposed to a single isolated object, is particularly(More)
The DEVS formalism defines a theory for discrete-events systems specification. It is a formal approach to build the models, using a hierarchical and modular approach. DEVS formal nature showed to be useful for easy reuse of models that have been validated. In this way, the security of the simulations can be improved, reducing the testing and maintenance(More)
  • 1