Estimation of hybrid reflectance properties and shape reconstruction using the LMS method

This paper presents a new method to estimate reflectance properties of non–Lambertian surface by the least-mean-square (LMS) algorithm. In this paper, hybrid reflectance of an object is represented by the Torrance–Sparrow model. We determine reflectance parameters which minimize the sum squared difference of the intensity distribution between the image of a sample sphere and the calculated image. The estimated reflectance parameters provide the range data with intensity distributions. Therefore, we generate three reference images of a range sphere, which has the same diameter as that of the sample, from the same viewpoint with different light directions. Direct matching of the object images to the references can precisely reconstruct the shape of the object. This paper uses a plate diffuse illumination to alleviate the effects of specular spike and highlights. The simulation results show that the proposed method can estimate reflectance properties of the hybrid surface, and also recover the object shape.

<p>In this paper, a single neuron neural network beamformer is proposed. A perceptron model is designed to optimize the complex weights of a dipole array antenna to steer the beam to desired directions. The objective is to reduce the complexity by using a single neuron neural network and utilize it for adaptive beamforming in array antennas. The selection of nonlinear activation function plays the pivotal role in optimization depends on whether the weights are real or complex. We have appropriately proposed two types of activation functions for respective real and complex weight values. The optimized radiation patterns obtained from the single neuron neural network are compared with the respective optimized radiation patterns from the traditional Least Mean Square (LMS) method. Matlab is used to optimize the weights in neural network and LMS method as well as display the radiation patterns.</p>

PROBA-V mission was conceived, and launched by ESA in 2013, to ensure continuity to the SPOT - VGT longterm data series of global daily land observations, started in 1998, bridging the gap to Sentinel-3. Nowadays, thanks to the successful and smooth operations of the PROBA- V satellite, a >20 years consistent data record of land surface reflectances, is available to the scientific community. This long-term dataset has clear relevance for trend analysis, anomaly detection and climate related applications. Despite this obvious interest, the exploitation of this dataset is still limited and mostly focused to near-real time applications, using the SPOT - VGT archive as a background average to detect anomalies, e.g., in NDVI time series. In order to fill this gap, and to exploit the full potential of this dataset, ESA has funded a new project called CISAR@MEP, whose primary goal is to set the stage for the generation of a global 20 years long-term data record of surface reflectance and aerosol properties from analysis of SPOT-VGT and PROBA-V observations. For this purpose, a recently developed algorithm, called CISAR and developed by Rayference, will be used, allowing the joint retrieval of aerosol and surface reflectance properties, together with the associated retrieval uncertainties. This new long-term data record will eventually fulfill current Global Climate Observing System (GCOS) requirements for the generation of Essential Climate Variables (ECVs), such as surface albedo and aerosol optical thickness, being 5% and 10%, respectively. Such data set can support the needs of both the atmospheric and land surface research communities and it can contribute to Copernicus operational service and ESA Climate Change Initiatives (CCI) activities. The CISAR@MEP project started during Q1 2019 with expected 2 years duration. The aim of this paper is to present this ESA funded project, to illustrate the objectives and the methodology and to provide some preliminary results. Furthermore, the technological implementation approach will be detailed, which makes full use of the PROBA-V Mission Exploitation Platform (MEP), a scalable processing environment, providing direct access to both SPOT-VGT and PROBA-V full mission archive together with the required resources and tools.

Several techniques have been developed for recovering reflectance properties of real surfaces under unknown illumination conditions. However, in most cases, those techniques assume that the light sources are located at infinity, which cannot be applied to, for example, photometric modelling of indoor environments. We propose two methods to estimate the surface reflectance property of an object, as well as the position of a light source from a single image without the distant illumination assumption. Given a color image of an object with specular reflection as an input, the first method estimates the light source position by fitting to the Lambertian diffuse component, while separating the specular and diffuse components by using an iterative relaxation scheme. Moreover, we extend the above method by using a single specular image as an input, thus removing its constraints on the diffuse reflectance property and the number of light sources. This method simultaneously recovers the reflectance properties and the light source positions by optimizing the linearity of a log-transformed Torrance-Sparrow model. By estimating the object's reflectance property and the light source position, we can freely generate synthetic images of the target object under arbitrary source directions and source-surface distances.

The least mean square (LMS) beamformer is an extensively considered method in numerous mobile communication applications principally because of its computational efficiency, dynamic tracking ability, and accurate adaptive beamforming. However, this method requires huge iterations to reduce the mean square error (MSE) to zero for successful adaptive beamforming. To overcome this, we use a speeding unit (SU) device to speed up the rate of convergence of the LMS beamformer. The proposed method is named as the improved LMS (ILMS) method which provides accurate adaptive beamforming in six to seven iterations. Furthermore, we improve the proposed LMS method by applying Hanning, Hamming, and Kaiser Windows to notably curb the peak side lobe levels (PSLL). These methods are named HN-ILMS, HM-ILMS, and KB-ILMS respectively. Experimental results show that proposed HN-ILMS, HM-ILMS, and KB-ILMS give accurate adaptive beamforming with reduced PSLL.

Current road lighting standards and recommendations are based on luminance levels and luminance distributions on the road surface. The luminance of the road surface depends on the amount of light falling on it and the reflection properties of the road surface. Using light aggregate for road surface pavements, significant energy savings in road lighting could be achieved due to significantly higher reflectance properties of the surface. In this study several experimental pavement samples with light surface characteristics were manufactured and their reflection properties were measured. The results indicate that lighter pavement materials result in higher average road surface luminance values and lower average lighting energy usage costs per kilometer.

The method of least mean square (LMS) is the commonly used algorithm in Adaptive filter due to its simplicity and robustness in implementation. In Active Noise Control application, a filtered reference signal is used prior to LMS algorithm to overcome the constraint on stability and convergence performance of the system due to the existence of the auxiliary path. This is known as Filtered-X LMS algorithm. In conventional Filtered-X LMS algorithm, each filter weight is updated once on every audio sample. This paper proposes the improved version of Filtered-X LMS algorithm with the use of multiple iteration of filter weight on every sample of audio signal. The proposed work uses field programmable gate arrays to realize real-time simulation on hardware for the noise signal of 500 Hz. Results from the real-time hardware simulations have shown much faster error convergence and better adaptation performance for different selections of learning constant μ, as compared with the conventional method.

In augmented virtuality, estimating object surface reflectance properties is important when rendering objects under arbitrary illumination conditions. However, faithfully estimating surface reflectance properties is difficult for objects having interreflections. The present paper describes a new method for densely estimating the non-uniform surface reflectance properties of real objects constructed of convex and concave surfaces having diffuse and specular interreflections. The registered range and surface color texture images were obtained using a laser rangefinder. In the proposed method, the light positions are first determined in order to take color images, which are then used to discriminate diffuse and specular reflection components of surface reflection. Surface reflectance parameters are then estimated based on an inverse global illumination rendering. Experiments were conducted to reveal the usefulness of the proposed method.

It may at first seem strange to find “road surfaces” dealt with under the heading “Equipment”. But the luminance of the road surface is an important quality-determining factor with regard to both visual performance and visual comfort. That luminance is determined by the amount of light directed towards the road surface from the lamps and luminaires and reflected by that road surface in the direction of the motorist. So the lamp, luminaire and reflection properties of the road surface together determine the final lighting quality. By tuning the lamp, luminaire and road surface to each other, the light needed from the lamp-luminaire combination can be minimized. The reflection properties of the road surface thus influence both the lighting quality and the cost and energy effectiveness of a road-lighting installation. Unfortunately, a theoretical approach to the reflection properties of road surfaces has not led to a workable system for describing these properties. So far as road lighting is concerned, however, it is possible to work with a simplified description system, the so called Q0-S1 system. A classification system for road surfaces, based on this description system, can be used to facilitate all calculations met with in road lighting. This description and classification system will be described in this chapter. The ways in which the values of Q0 and S1 can be obtained for a particular road surface will be discussed as well.

In this contribution, a millimeter wave FMCW (frequency modulated continuous wave) radar is utilized to investigate the surface reflection of an asphalt sample at 80 GHz. Since the reflection properties of such a rough surface usually strongly depend on the characteristics of the antenna (i.e. opening angle, spot on sample) that is used in the measurement, a more generalized approach is presented here. Based on the backprojection algorithm, the measurement data, obtained by a relatively undirected antenna, are used to infer the reflection properties for different antenna systems with other radiation patterns. Exemplary measurement results are presented in this paper, proving the suitability of the proposed method.

Spherical images have the advantage of recording full scenes using only one camera exposure and have been becoming an important data source for 3D reconstruction. However, geometric distortions inevitably exist due to the spherical camera imaging model. Thus, this study proposes a reliable feature matching algorithm for spherical images via the combination of local geometric rectification and CNN (convolutional neural network) learned descriptor. First, image patches around keypoints are reprojected to their corresponding tangent planes based on a spherical camera imaging model, which uses scale and orientation data from the keypoints to achieve both rotation and scale invariance. Second, feature descriptors are then calculated from the rectified image patches by using a pre-trained separate detector and descriptor learning network, which improves the discriminability by exploiting the high representation learning ability of the CNN. Finally, after classical feature matching with the ratio test and cross check, refined matches are obtained based on an essential matrix-based epipolar geometry constraint for outlier removal. By using three real spherical images and an incremental structure from motion (SfM) engine, the proposed algorithm is verified and compared in terms of feature matching and image orientation. The experiment results demonstrate that the geometric distortions can be efficiently reduced from rectified image patches, and the increased ratio of the match numbers ranges from 26.8% to 73.9%. For SfM-based spherical image orientation, the proposed algorithm provides reliable feature matches to achieve complete reconstruction with comparative accuracy.

Pioneer Venus radar data on surface properties have been used to compare the Vega spacecraft landing sites with the northern 1/4 of Venus mapped by the orbiters Venera 15 and 16. The regions surrounding both landing sites possess surface reflectivity and small‐scale roughness properties most similar to those of mapped volcanoes and volcanic plains regions and different surface properties than those of mapped tectonic units. Regions analogous to the Vega 1 site are relatively rare, covering 2.8% of the mapped surface. Vega 2 analogs are much more common and cover 22.6% of the surface. Neither landing site is representative of the nearby highlands of Aphrodite, but the Vega 2 landing site is similar to much of the northern plains of Venus.

Knowledge of the directional and hemispherical reflectance properties of natural surfaces, such as soils and vegetation canopies, is essential for classification studies and canopy model inversion. The Multi-angle Imaging SpectroRadiometer (MISR), an instrument to be launched in 1998 onboard the EOS-AM1 platform, will make global observations of the Earth's surface at 1.1-km spatial resolution, with the objective of determining the atmospherically corrected reflectance properties of most of the land surface and the tropical ocean. The algorithms to retrieve surface directional reflectances, albedos, and selected biophysical parameters using MISR data are described. Since part of the MISR data analyses includes an aerosol retrieval, it is assumed that the optical properties of the atmosphere (i.e. aerosol characteristics) have been determined well enough to accurately model the radiative transfer process. The core surface retrieval algorithms are tested on simulated MISR data, computed using realistic surface reflectance and aerosol models, and the sensitivity of the retrieved directional and hemispherical reflectances to aerosol type and column amount is illustrated. Included is a summary list of the MISR surface products.

In this paper we describe a web-based application we have developed that allows visualization of the multi-spectral reflectance properties of three-dimensional surfaces. To develop this application, we employ a 3D computer graphics framework that enables us to combine spectral data with other object features such as topography and reflectance properties. The cross-platform application runs in any modern web browser, has an interactive interface, and does not require the installation or maintenance of any custom or proprietary software packages. The application is configured as a standard HTML/JavaScript client-server system, which provides flexibility for working with data in local or remote setups.

Reflectance Analysis for 3D Computer Graphics Model Generation