In addition, most of the other researchers at NICTA hold adjunct positions at one of these universities, enabling them to teach courses and supervise Ph.D. Many of the researchers at NICTA are seconded from these universities. NICTA has close links with its university members (Australian National University, the University of New South Wales, and the University of Melbourne) as well as with its partner universities (University of Sydney, Griffith University, Queensland University of Technology, University of Queensland, and most recently Monash University). student studying at NICTA graduates every 10 days. At present and averaged over the year, one new Ph.D. There are currently around 700 staff and Ph.D. It has five laboratories in four Australian capital cities: Sydney, Canberra, Melbourne, and Brisbane. National Information and Communications Technology Australia (NICTA) is the largest ICT research center in Australia, having been established 10 years ago in 2002. This combination of a broad domain of application with the use of key technologies makes the use of imaging spectroscopy a worthwhile opportunity for researchers in the areas of computer vision and pattern recognition. For food security, health and precision agriculture it can be the basis for the development of diagnostic and surveying tools which can detect pests before symptoms are apparent to the naked eye. In computational photography, images may be enhanced taking into account each specific material type in the scene. For instance, spectroscopic scene analysis can enable advanced capabilities for surveillance by permitting objects to be tracked based on material properties. The combination of spatial and compositional information opens-up a vast number of application possibilities. This is important, since scene analysis in the scope of imaging spectroscopy involves the ability to robustly encode material properties, object composition and concentrations of primordial components in the scene. In this study, the authors explore the opportunities, application areas and challenges involving the use of imaging spectroscopy as a means for scene understanding. We also compare our results to several alternatives in the literature. We demonstrate the utility of our method on synthetic and real world imagery. Our approach is quite general in nature and can be applied to a family of reflectance models that are based on the Fresnel reflection theory. Then, by integrating the knowledge of light source and diffuse reflectance parameters, we recover shape of the scene from the diffuse component. With the estimated specular reflectance parameters, we recover the single point light source position from specular highlights by applying two novel constraints, coplanarity and Kullback-Leibler divergence. This permits the recovery of the reflection parameters through an iterative optimization scheme, which we render well posed by adopting a novel reparameterization that reduces the number of degrees of freedom in the cost function. We start from a general formulation that hinges in the notion that the light reflected from an object can be deemed to be a linear combination of specular and diffuse reflections. This paper presents a novel approach for estimating the light direction, shape and reflectance parameters from a single multispectral image.
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