Scene Radiance Estimation from Human Corneal Reflections

Separating specular reflection components from diffuse reflection components is an important subject in computer vision. Hence, the author's group has developed a method to separate a diffuse image from a high-speed-video image with specular reflection components. The method uses an estimation technique based on luminance variation owing to flicker of a strobe. However, an issue with the method is that the strobe produces a new specular reflection component. In this study, we get around this limitation by introducing a method for combining two images produced based on flickering information. Experimental results show the validity of the proposed method.

Reflectance Analysis for 3D Computer Graphics Model Generation

A numerical investigation of radiative heat transfer constructed by curved surfaces with specular and diffuse reflection components is carried out. The ray tracing method is adopted for the calculation of view factors, in which a new ray emission model is proposed. The second-degree radiation ring elements are introduced, which are of engineering importance and numerical efficiency. The accuracy of the method is analyzed and verified using a simple configuration. The present computation using the proposed ray emission model is in good agreement with the analytical solution. As a numerical example and engineering application, the effects of the specular reflection and the meniscus of the melt surface in Czochralski (CZ) crystal growth are investigated. A marked temperature decrease in the melt surface is found by introducing specular reflection and the meniscus. The combined effects of the specular reflection and the meniscus should be considered in precision heat transfer control of a CZ apparatus.

The problem of estimating an illumination distribution from images is called inverse lighting. For inverse lighting, three approaches have been developed based on specular reflection components, diffuse reflection components, and cast shadows. The present study provides theoretical insights as to why the approach based on cast shadows works in a reliable manner, and discusses what kind of basis functions are appropriate to be used for recovering illumination from cast shadows. First, we formalize the approach based on cast shadows by using spherical harmonics. Then, we analyze the approach in the frequency domain and show the advantages and the limitations of the approach. Second, motivated by the observations in the frequency domain, we propose an efficient method using Haar wavelets that provide compact supports and sparsity of coefficients. Finally, we report the results of experiments that compared the method using spherical harmonics and the method using Haar wavelets.

We present a fully automated system which unites CCD camera technology with liquid crystal technology to create a polarization camera capable of sensing the polarization of reflected light from objects at pixel resolution. As polarization affords a more general physical description of light than does intensity, it can therefore provide a richer set of descriptive physical constraints for the understanding of images. Recently, it has been shown that polarization cues can be used to perform dielectric/metal material identification, specular and diffuse reflection component analysis, as well as complex image segmentations that would be immensely more complicated or even infeasible using intensity and color alone. Such analysis has so far been done with a linear polarizer mechanically rotated in front of a CCD camera. The full automation of resolving polarization components using liquid crystals not only affords an elegant application, but reduces the amount of optical distortion present in the wobbling of a mechanically rotating polarizer. In our system two twisted nematic liquid crystals are placed in front of a fixed polarizer placed in front of a CCD camera. The application of a series of electrical pulses to the liquid crystals in synchronization with the CCD camera video frame rate produces a controlled sequence of polarization component images that are stored and processed on Datacube boards. We present a scheme for mapping polarization states into hue, saturation, and intensity which is a very convenient representation for a polarization image. Our polarization camera outputs such a color image which can then be used in polarization- based vision methods. The unique vision understanding capabilities of our polarization camera system are demonstrated with experimental results showing polarization-based dielectric/metal material classification, specular reflection, and occluding contour segmentations in a fairly complex scene, and surface orientation constraints for object recognition.

Separation of the diffuse and specular components of observed reflectance has been an active area of research in computer graphics and vision, with major applications in reflectance modeling and scene analysis. Traditionally, researchers have investigated diffusespecular separation under point or directional illumination conditions while employing polarization and, in the case of dielectric materials, color space analysis techniques. Recently, Ma et al. [2007] introduced a technique for estimating high quality diffuse and specular normals and albedo maps (see Fig. 1, (a) & (d)) of a specular object using polarized spherical gradient illumination. However, the employed polarization technique imposes view-point restriction, and results in insufficient light levels for performance capture with high speed acquisition. Hence, in this work, we look into an alternate diffuse-specular separation technique for spherical gradients based on a data-driven reflectance model. Traditional separation techniques based on color space analysis focus on removing specular reflections from the observation for scene analysis [Mallick et al. 2005]. In contrast, we focus on obtaining high quality estimates of both the diffuse and the specular reflectance components.

Combined heat transfer in floating zone growth of large silicon crystals with radiation on diffuse and specular surfaces

One of the origins of error in the angle of arrival estimation of a signal is the response of the ground to the reflected waves from the target. In low-height target situations, this phenomenon can be so harrowing that it may result in much more error than thermal noise. In this article, two methods of fixed beam and double null are used as preliminary techniques to overcome the problem of low-altitude target angle estimation, and the optimum weights for beam-forming array radar are proposed. These beam-forming methods enable the radar to estimate the target angle precisely without the exact knowledge of the surface slope, whereas they are robust against diffuse and specular ground reflection components.

Deep-learning-based polarization 3D imaging techniques, which train networks in a data-driven manner, are capable of estimating a target’s surface normal distribution under passive lighting conditions. However, existing methods have limitations in restoring target texture details and accurately estimating surface normals. Information loss can occur in the fine-textured areas of the target during the reconstruction process, which can result in inaccurate normal estimation and reduce the overall reconstruction accuracy. The proposed method enables extraction of more comprehensive information, mitigates the loss of texture information during object reconstruction, enhances the accuracy of surface normal estimation, and facilitates more comprehensive and precise reconstruction of objects. The proposed networks optimize the polarization representation input by utilizing the Stokes-vector-based parameter, in addition to separated specular and diffuse reflection components. This approach reduces the impact of background noise, extracts more relevant polarization features of the target, and provides more accurate cues for restoration of surface normals. Experiments are performed using both the DeepSfP dataset and newly collected data. The results show that the proposed model can provide more accurate surface normal estimates. Compared to the UNet architecture-based method, the mean angular error is reduced by 19%, calculation time is reduced by 62%, and the model size is reduced by 11%.

The diffuse and specular components of leaf reflection are both important to determine the leaf optical properties as well as to describe the leaf bidirectional reflectance distribution function (BRDF). However, the specular component is usually ignored in practice in numerous canopy reflectance models that describe the interaction between solar light and vegetated scene components. To evaluate the impact of leaf-specular reflection on canopy bidirectional reflectance factor (BRF) characteristics, we introduce a leaf BRDF model into the radiosity-graphics combined model (RGM; a 3-D scene model) to calculate canopy BRFs with nondiffuse leaves. The modified RGM is validated by comparing simulated BRFs against in situ measured BRFs over real maize canopies. The results show that ignorance of leaf-specular reflection can result in up to 50% of relative error in the blue band (435.8 nm). A series of maize canopies with different leaf angle distributions (LADs) is reconstructed to investigate the effect of five major biophysical/geometrical parameters such as leaf area index, LAD, leaf surface property, view direction, and solar zenith angle on leaf-specular reflection contributions to the canopy BRF. It is demonstrated that increasing the incident solar zenith angle and decreasing the mean leaf angle impact the angular distribution of the canopy BRF more significantly than other factors. The cumulative hemispherical relative and absolute errors of canopy BRF caused by the leaf-specular reflection are often too large to be ignored, even for canopies with rough surface leaves. Moreover, the relative error of BRF in visible waveband shows that, in general, leaf-specular reflection has a large impact than that in near-infrared waveband. However, such impact can be sufficiently accounted for by even just consideration of the first-order leaf-specular reflection in canopy reflectance calculation, leading to a substantial improvement in simulation accuracy for most vegetation canopies.

A method is described for reducing the large error that often occurs in the measurement of three-dimensional (3D) objects using the photometric stereo method when there is a specular component in te reflection characteristics of the object’s surface. First, the change in the luminance distribution caused by the component is estimated based on the correlation between the modeled distribution and a gray-level image of the object, created by numerical experiment. Next, the shape measurement of the hemisphere object was carried out. Nurmerical experiment of three dimensional measurement of a hemispherical object showed that the mean error of normal vector can be reduced by as much as 90%. This method is applicable to machine vision, automatic manufacturing, and other industrial processes.

This paper shows that significant errors occur if a formulation of reflectance is employed that does not utilize both diffuse and specular components. It then discusses how these components can be measured, and describes in detail the construction of a suitable goniophotometer. It also shows how a series of measurements with various source sizes can be employed to calculate the specular and diffuse components of reflectance from the experimental data.

In the early stages of atherosclerosis, the luminal surface of the arterial wall becomes rough due to injury and detachment of endothelial cells. This roughening can potentially be estimated with ultrasound since the electrical echo signal from the transducer is sensitive to both the angle of incidence to an extended surface as well as the roughness of the surface. Specifically, as the roughness of an interface increases, specular reflection is substituted by scattering. We propose a method that attempts separation of reflection and backscattering components in the received echo signals. Assuming the predominant propagation directions of the reflected and scattered waves can be somewhat controlled by the emitted sound field, separation of those components was attempted using synthetic aperture imaging with a transmit beam, focused at a point more distant than the imaging depth. Specifically, two dedicated beamforming processes were used for generation of reflection-emphasized and backscattering-emphasized images. Experimental verifications on a phantom using an ultrasound system with a limited number of active transmit-receive channels yielded a difference between these two images of 8dB. The results further showed a similar (slightly improved) lateral spatial resolution size of 0.41mm for the backscattering-emphasized image compared with conventional B-mode imaging (0.47mm). A new technique for separation of the reflection and backscattering components using synthetic aperture beamforming with a transmit beam featuring a large focal distance was proposed. The technique demonstrated a partial separation of the reflection and backscattering components, which potentially may be used to estimate surface roughness.

We propose a method for imaging small-scale diffraction objects in complex environments in which Kirchhoff-based approaches may fail. The proposed method is based on a separation between the specular reflection and diffraction components of the total wavefield in the migrated surface angle domain. Reverse-time migration was utilized to produce the common image gathers. This approach provides stable and robust results in cases of complex velocity models. The separation is based on the fact that, in surface angle common image gathers, reflection events are focused at positions that correspond to the apparent dip angle of the reflectors, whereas diffracted events are distributed over a wide range of angles. The high-resolution radon-based procedure is used to efficiently separate the reflection and diffraction wavefields. In this study, we consider poststack diffraction imaging. The advantages of working in the poststack domain are its numerical efficiency and the reduced computational time. The numerical results show that the proposed method is able to image diffraction objects in complex environments. The application of the method to a real seismic dataset illustrates the capability of the approach to extract diffractions.

The intensity value recorded by terrestrial laser scanning (TLS) systems is significantly influenced by the incidence angle. The incidence angle effect is an object property, which is mainly related to target scattering properties, surface structures, and even some instrumental effects. Most existing models focus on diffuse reflections of rough surfaces and ignore specular reflections, despite that both reflections simultaneously exist in all natural surfaces. Due to the coincidence of the emitter and receiver in TLS, specular reflections can be ignored at large incidence angles. On the contrary, at small incidence angles, TLS detectors can receive a portion of specular reflections. The received specular reflections can trigger highlight phenomenon (hot-spot effects) in the intensity data of the scanned targets, particularly those with a relatively smooth or highly-reflective surface. In this study, a new method that takes diffuse and specular reflections, as well as the instrumental effects into consideration, is proposed to eliminate the specular reflection effects in TLS intensity data. Diffuse reflections and instrumental effects are modeled by a polynomial based on Lambertian reference targets, whereas specular reflections are modeled by the Phong model. The proposed method is tested and validated on different targets scanned by the Faro Focus3D 120 terrestrial scanner. Results imply that the coefficient of variation of the intensity data from a homogeneous surface is reduced by approximately 38% when specular reflections are considered. Compared with existing methods, the proposed method exhibits good feasibility and high accuracy in eliminating the specular reflection effects for intensity image interpretation and 3D point cloud representation by intensity.