Reflectance Hashing for Material Recognition

We introduce a novel method for using reflectance to identify materials. Reflectance offers a unique signature of the material but is challenging to measure and use for recognizing materials due to its high-dimensionality. In this work, one-shot reflectance is captured using a unique optical camera measuring {\it reflectance disks} where the pixel coordinates correspond to surface viewing angles. The reflectance has class-specific stucture and angular gradients computed in this reflectance space reveal the material class. These reflectance disks encode discriminative information for efficient and accurate material recognition. We introduce a framework called reflectance hashing that models the reflectance disks with dictionary learning and binary hashing. We demonstrate the effectiveness of reflectance hashing for material recognition with a number of real-world materials

Reflectance measurements

The productivity of spectroreflectometer equipment and operating personnel and the accuracy and sensitivity of the measurements were investigated. Increased optical sensitivity and better design of the data collection and processing scheme to eliminate some of the unnecessary present operations were conducted. Two promising approaches to increased sensitivity were identified, conventional processing with error compensation and detection of random noise modulation

Metamaterial Coatings for Broadband Asymmetric Mirrors

We report on design and fabrication of nano-composite metal-dielectric thin film coatings with high reflectance asymmetries. Applying basic dispersion engineering principles to model a broadband and large reflectance asymmetry, we obtain a model dielectric function for the metamaterial film, closely resembling the effective permittivity of disordered metal-dielectric nano-composites. Coatings realized using disordered nanocrystalline silver films deposited on glass substrates confirm the theoretical predictions, exhibiting symmetric transmittance, large reflectance asymmetries and a unique flat reflectance asymmetry.Comment: 4 pages, 4 figures, submitted to Optics Letter

Single-shot layered reflectance separation using a polarized light field camera

We present a novel computational photography technique for single shot separation of diffuse/specular reflectance as well as novel angular domain separation of layered reflectance. Our solution consists of a two-way polarized light field (TPLF) camera which simultaneously captures two orthogonal states of polarization. A single photograph of a subject acquired with the TPLF camera under polarized illumination then enables standard separation of diffuse (depolarizing) and polarization preserving specular reflectance using light field sampling. We further demonstrate that the acquired data also enables novel angular separation of layered reflectance including separation of specular reflectance and single scattering in the polarization preserving component, and separation of shallow scattering from deep scattering in the depolarizing component. We apply our approach for efficient acquisition of facial reflectance including diffuse and specular normal maps, and novel separation of photometric normals into layered reflectance normals for layered facial renderings. We demonstrate our proposed single shot layered reflectance separation to be comparable to an existing multi-shot technique that relies on structured lighting while achieving separation results under a variety of illumination conditions

The temporal dynamics of calibration target reflectance

A field experiment investigated the hypothesis that the nadir reflectance of calibration surface substrates (asphalt and concrete) remains stable over a range of time-scales. Measurable differences in spectral reflectance factors were found over periods as short as 30 minutes. Surface reflectance factors measured using a dual-field-of-view GER1500 spectroradiometer system showed a relationship with the relative proportion of diffuse irradiance, over periods when solar zenith changes were minimal. Reflectance measurements were collected over precise points on the calibration surfaces using a novel mobile spectroradiometer device, and uncertainty in terms of absolute reflectance was calculated as being < 0.05% within the usable range of the instrument (400-1000nm). Multi-date reflectance factors were compared using one-way ANOVA and found to differ significantly (p = 0.001). These findings illustrate the anisotropic nature of calibration surfaces, and place emphasis on the need to minimise the temporal delay in collection of field spectral measurements for vicarious calibration or empirical atmospheric correction purposes

An investigation of surface albedo variations during the recent sahel drought

Applications Technology Satellite 3 green sensor data were used to measure surface reflectance variations in the Sahara/Sahel during the recent drought period; 1967 to 1974. The magnitude of the seasonal reflectance change is shown to be as much as 80% for years of normal precipitation and less than 50% for drought years. Year to year comparisons during both wet and dry seasons reveal the existence of a surface reflectance cycle coincident with the drought intensity. The relationship between the green reflectance and solar albedo is examined and estimated to be about 0.6 times the reflectance change observed by the green channel

Investigation of effects of background water on upwelled reflectance spectra and techniques for analysis of dilute primary-treated sewage sludge

In an effort to improve understanding of the effects of variations in background water on reflectance spectra, laboratory tests were conducted with various concentrations of sewage sludge diluted with several types of background water. The results from these tests indicate that reflectance spectra for sewage-sludge mixtures are dependent upon the reflectance of the background water. Both the ratio of sewage-sludge reflectance to background-water reflectance and the ratio of the difference in reflectance to background-water reflectance show spectral variations for different turbid background waters. The difference in reflectance is the only parameter considered

Deep Reflectance Maps

Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constraint nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in an end-to-end learning formulation that directly predicts a reflectance map from the image itself. We show how to improve estimates by facilitating additional supervision in an indirect scheme that first predicts surface orientation and afterwards predicts the reflectance map by a learning-based sparse data interpolation. In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images. Furthermore, we show the application of our method to a range of image-based editing tasks on real images.Comment: project page: http://homes.esat.kuleuven.be/~krematas/DRM