Effect Of Specular Reflection Research Articles

Effects of specular reflectance in laser-induced breakdown of metals

We investigate the effects of specular reflection on the laser-induced breakdown (LIB) of copper, iron, and tungsten using fast photography and optical emission spectroscopy. The laser parameters include spot diameter ranging from 30.89 to 1589.33 μm, irradiance from 467.10 to 0.17 GW/cm2, with a single pulse of 6 ns duration and 21 mJ energy. As the laser spot defocuses, the plasma morphology changes from a single plasma near the target surface to a separated, independently evolving two-component plasma, and then to a single plasma suspended above. The defocusing distance for this transition is significantly influenced by specular reflectance. The separate plasma, comprising of a metallic component and an air component, occurs only under high specular reflectance conditions: ≥66.7% for copper, ≥51.4% for iron, and ≥44.9% for tungsten. The spectral emission of the metallic component initially increases and then decreases with reducing specular reflectance, due to a trade-off between enhanced surface absorption and reduced irradiance caused by surface roughening. LIB threshold irradiance increases with specular reflectance, rising from 0.31 to 1.22 GW/cm2 for copper, 0.24 to 0.70 GW/cm2 for iron, and 0.38 to 0.87 GW/cm2 for tungsten. These findings show the impact of sample pretreatment on LIB ignition and subsequent plasma evolution, offering insights into potential sources of inaccuracy in LIB applications.

Coupling PROSPECT with Prior Estimation of Leaf Structure to Improve the Retrieval of Leaf Nitrogen Content in Ginkgo from Bidirectional Reflectance Factor Spectra.

Leaf nitrogen content (LNC) is a crucial indicator for assessing the nitrogen status of forest trees. The LNC retrieval can be achieved with the inversion of the PROSPECT-PRO model. However, the LNC retrieval from the commonly used leaf bidirectional reflectance factor (BRF) spectra remains challenging arising from the confounding effects of mesophyll structure, specular reflection, and other chemicals such as water. To address this issue, this study proposed an advanced BRF spectra-based approach, by alleviating the specular reflection effects and enhancing the leaf nitrogen absorption signals from Ginkgo trees and saplings, using 3 modified ratio indices (i.e., mPrior_800, mPrior_1131, and mPrior_1365) for the prior estimation of the Nstruct structure parameter, combined with different inversion methods (STANDARD, sPROCOSINE, PROSDM, and PROCWT). The results demonstrated that the prior Nstruct estimation strategy using modified ratio indices outperformed standard ratio indices or nonperforming prior Nstruct estimation, especially for mPrior_1131 and mPrior_1365 yielding reliable performance for most constituents. With the use of the optimal approaches (i.e., PROCWT_S3 combined with mPrior_1131 or mPrior_1365), our results also revealed that the optimal estimation of LNCarea (normalized root mean square error [NRMSE] = 12.94% to 14.49%) and LNCmass (NRMSE = 10.11% to 10.75%) can be further achieved, with the selected optimal wavebands concentrated in 5 common main domains of 1440 to 1539 nm, 1580 to 1639 nm, 1900 to 1999 nm, 2020 to 2099 nm, and 2120 to 2179 nm. These findings highlight marked potentials of the novel BRF spectra-based approach to improve the estimation of LNC and enhance the understanding of the impact of Nstruct prior estimation on the LNC retrieval in leaves of Ginkgo trees and saplings.

On-site road properties evaluation for dry and wet asphalt roads using an imaging luminance measurement device

A method was developed to investigate the reflected properties of LED and HPS-lighted wet roads using on-site measurement of luminance images. An ILMD was used to obtain high observation angles by placing it at a close distance to the ROI. The luminance images were analyzed to derive a distribution of reflected luminance as a function of deviation, incidence, and observation angles. Furthermore, an investigation of the reflected characteristics of a dry asphalt road illuminated by LED lighting was conducted, employing on-site measurements of the images of luminance and the distribution of illuminance. The results provide valuable insights for designing lighting systems and mitigating the adverse effects of glare and specular reflection on road safety.

Pupil Detection for Automatic Diagnosis of Eye Diseases Using Optimized Color Mapping

Introduction: Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle . Method: In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction . Results: The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors . Conclusion: The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue.

Optical reflection characteristic-based emissivity analysis of a pyramid array flat-plate blackbody for remote sensor calibration.

The flat-plate blackbody (FPB) is the core device in infrared remote sensing radiometric calibration for providing accurate infrared radiation energy. The emissivity of an FPB is an important parameter that directly affects calibration accuracy. This paper uses a pyramid array structure based on the regulated optical reflection characteristics to analyze the FPB's emissivity quantitatively. The analysis is accomplished by performing emissivity simulations based on the Monte Carlo method. The effects of specular reflection (SR), near-specular reflection (NSR), and diffuse reflection (DR) on the emissivity of an FPB with pyramid arrays are analyzed. In addition, various patterns of normal emissivity, small-angle directional emissivity, and emissivity uniformity are examined under different reflection characteristics. Further, the blackbodies with the NSR and DR are fabricated and tested experimentally. The experimental results show a good agreement with the corresponding simulation results. The emissivity of the FPB with the NSR can reach 0.996 in the 8-14 µm waveband. Finally, the emissivity uniformity of FPB samples at all tested positions and angles is better than 0.005 and 0.002, respectively. The standard uncertainty of experimental measurement of waveband emissivity and spectral emissivity are 0.47% and 0.38% respectively, and the simulation uncertainty is 0.10%.

Minimizing the Effect of Specular Reflection on Object Detection and Pose Estimation of Bin Picking Systems Using Deep Learning

The rapid evolution towards industrial automation has widened the usage of industrial applications, such as robot arm manipulation and bin picking. The performance of these applications relies on object detection and pose estimation through visual data. In fact, the clarity of those data significantly influences the accuracy of object detection and pose estimation. However, a majority of visual data corresponding to metal or glossy surfaces tend to have specular reflections that reduce the accuracy. Hence, this work aims to improve the performance of industrial bin-picking tasks by reducing the effects of specular reflections. This work proposes a deep learning (DL)-based neural network model named SpecToPoseNet to improve object detection and pose estimation accuracy by intelligently removing specular reflections. The proposed work implements a synthetic data generator to train and test the SpecToPoseNet. The conceptual breakthrough of this work is its ability to remove specular reflections from scenarios with multiple objects. With the use of the proposed method, we could reduce the fail rate of object detection to 7%, which is much less compared to specular images (27%), U-Net (20%), and the basic SpecToPoseNet model (11%). Thus, it is claimable that the performance improvements gained are positive influences of the proposed DL-based contexts such as bin-picking.

Air Moving Target Indication in Nadir Region for Spaceborne Surveillance Radar Systems

For air moving target indication in nadir region, due to the fact that a spaceborne radar beam can illuminate the top of fuselage, the target radar cross section is usually high, which is beneficial for the detection of a low-observable target. However, due to the short slant range, specular reflection effect, and relatively low radar ground resolution, the power of clutter component from nadir region is comparatively high, leading to the insufficient clutter suppression and the degradation of target detection performance. Fortunately, when an air moving target is adequately high, the target echo is able to be separated from the main clutter echoes due to a shorter time delay, making it possible to be only mixed with low-power ambiguous clutter echoes. Based on these considerations, this paper analyzes the performance of air moving target indication in nadir region for a spaceborne surveillance radar system. It analyzes the target minimum detectable velocities with different target heights and beam center elevation angles. Also, an effective sample selection method based on adaptive range segmentation is proposed to solve the power heterogeneity issue between the main clutter area and the range ambiguous clutter area. As a conclusion, the larger the elevation angle of an air moving target is, the higher the minimum target detectable height is.

A CNN Based Approach for the Point-Light Photometric Stereo Problem

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach also outperforms the competition on this dataset as well. Data and test code are available at the project page.

An advanced bidirectional reflectance factor (BRF) spectral approach for estimating flavonoid content in leaves of Ginkgo plantations

As a key phenolic pigment concentrated in the surface tissues of leaves, flavonoids (Flav) are the major bioactive ingredients in Ginkgo leaf extracts. Flav are also marked natural antioxidants and significant indicators of biotic and abiotic stresses, critical for determining cultivation quality and enhancing Flav yield. In particular, area-based Flav (Flavarea) is related to the shortwave-blue light interaction within leaves per unit leaf area, whereas mass-based Flav (Flavmass) is useful for the quantitative assessment of Flav yield. In order to accurately estimate the contents of Flavarea and Flavmass in leaves of Ginkgo plantations, in this study, we developed an advanced bidirectional reflectance factor (BRF) spectra-based approach by reducing the effects of specular reflection and enhancing the absorption signals of Flav (in the shortwave-blue region of spectrum), using a suite of new spectral indices (SIs) (i.e., flavonoid index (FI), modified flavonoid index (mFI) and double difference index (DD)) calculated from the leaf clip equipped spectrometers-collected data. The results demonstrated that most of the SIs derived from the developed BRF spectra-based approach obtained relatively high performance for Flav estimation by alleviating adverse effects of specular reflection to different extents (CV-R2 = 0.60–0.76). In specific, DDnir434,421 selected from DD-type indices performed (CV-R2 = 0.76 for Flavarea; CV-R2 = 0.69 for Flavmass) better than other indices. These findings represent marked potentials of the developed BRF spectra-based approach for non-destructively estimating leaf Flav content, as well as improving the understanding of the mechanisms of specular effects on Flav estimations in leaves of Ginkgo plantations.

Origami Microwave Imaging Array: Metasurface Tiles on a Shape-Morphing Surface for Reconfigurable Computational Imaging.

Origami is the art of paper folding that allows a single flat piece of paper to assume different 3D shapes depending on the fold patterns and the sequence of folding. Using the principles of origami along with computation imaging technique the authors demonstrate a versatile shape‐morphing microwave imaging array with reconfigurable field‐of‐view and scene‐adaptive imaging capability. Microwave/millimeter‐wave based array imaging systems are expected to be the workhorse for sensory perception of future autonomous intelligent systems. The imaging capability of a planar array‐based systems operating in complex scattering conditions have limited field‐of‐view and lack the ability to adaptively reconfigure resolution. To overcome this, here, deviations from planarity and isometry are allowed, and a shape‐morphing computational imaging system is demonstrated. Implemented on a reconfigurable Waterbomb origami surface with 22 active metasurface panels that radiate near‐orthogonal modes across 17–27 GHz, capability to image complex 3D objects in full details minimizing the effects of specular reflections in diffraction‐limited sparse imaging with scene adaptability, reconfigurable cross‐range resolution, and field‐of‐view is demonstrated. Such electromagnetic origami surfaces, through simultaneous surface shape‐morphing ability (potentially with shape‐shifting electronic materials) and electromagnetic field programmability, opens up new avenues for intelligent and robust sensing and imaging systems for a wide range of applications.

Improving the statistical analysis of antihydrogen free fall by using near-edge events

An accurate evaluation of the gravity acceleration from the timing of free fall of anti-hydrogen atoms in the GBAR experiment requires to account for obstacles surrounding the anti-matter source. These obstacles reduce the number of useful events but may improve accuracy since the edges of the shadow of obstacles on the detection chamber depends on gravity, bringing additional information on the value of $g$. We perform Monte Carlo simulations to obtain the dispersion and give a qualitative understanding of the results by analysing the statistics of events close to an edge. We also study the effect of specular quantum reflections of anti-hydrogen on surfaces and show that they do not degrade the accuracy that much.

A New Polarization-Based Vegetation Index to Improve the Accuracy of Vegetation Health Detection by Eliminating Specular Reflection of Vegetation

Monitoring chlorophyll content changes in the plant via remote sensing is of great significance for understanding plant growth, monitoring vegetation pests and diseases, which is an important method to study the global climate change. However, the monitored information is often interfered by leaf specular reflection, resulting in reduced accuracy of chlorophyll content inversion. In this article, to eliminate the interference of specular reflection in vegetation remote sensing, a polarized multispectral imaging system (PMSIS) used in the different-light-level situation to observe vegetation was developed, and a new specular reflection removal vegetation index (NSRVI) was proposed to better detect the vegetation health condition under specular reflection interference. Based on previous studies, several vegetation indices (Simple ratio index (SR), Normalized difference vegetation index (NDVI); mSR, mNDVI (ref. [41]); pSR, pNDVI (ref. [46]); and NSRVI) were established, and the impact of specular reflection on vegetation health detection was evaluated. Correlation analysis was done on Relative Chlorophyll content (SPAD), SR, NDVI, mSR, mNDVI, pSR, pNDVI and NSRVI to understand their potential ability to eliminate specular interference. The results show that SR and NDVI have the highest sensitivity to specular reflection, and the other three methods can alleviate the adverse effects of specular reflection to varying degrees. It was observed that NSRVI was well correlated with SPAD (coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) = 0.899, RMSE=6.16), highlighting the potential of NRSVI in eliminating specular reflection interference and identifying vegetation health condition. In summary, this method can effectively eliminate specular interference and improve the detection accuracy of vegetation health condition.

Properties of Cirrus Clouds over the European Arctic (Ny-Ålesund, Svalbard)

Cirrus is the only cloud type capable of inducing daytime cooling or heating at the top of the atmosphere (TOA) and the sign of its radiative effect highly depends on its optical depth. However, the investigation of its geometrical and optical properties over the Arctic is limited. In this work the long-term properties of cirrus clouds are explored for the first time over an Arctic site (Ny-Ålesund, Svalbard) using lidar and radiosonde measurements from 2011 to 2020. The optical properties were quality assured, taking into account the effects of specular reflections and multiple-scattering. Cirrus clouds were generally associated with colder and calmer wind conditions compared to the 2011–2020 climatology. However, the dependence of cirrus properties on temperature and wind speed was not strong. Even though the seasonal cycle was not pronounced, the winter-time cirrus appeared under lower temperatures and stronger wind conditions. Moreover, in winter, geometrically- and optically-thicker cirrus were found and their ice particles tended to be more spherical. The majority of cirrus was associated with westerly flow and westerly cirrus tended to be geometrically-thicker. Overall, optically-thinner layers tended to comprise smaller and less spherical ice crystals, most likely due to reduced water vapor deposition on the particle surface. Compared to lower latitudes, the cirrus layers over Ny-Ålesund were more absorbing in the visible spectral region and they consisted of more spherical ice particles.

XGest: Enabling Cross-Label Gesture Recognition with RF Signals

Extensive efforts have been devoted to human gesture recognition with radio frequency (RF) signals. However, their performance degrades when applied to novel gesture classes that have never been seen in the training set. To handle unseen gestures, extra efforts are inevitable in terms of data collection and model retraining. In this article, we present XGest, a cross-label gesture recognition system that can accurately recognize gestures outside of the predefined gesture set with zero extra training effort. The key insight of XGest is to build a knowledge transfer framework between different gesture datasets. Specifically, we design a novel deep neural network to embed gestures into a high-dimensional Euclidean space. Several techniques are designed to tackle the spatial resolution limits imposed by RF hardware and the specular reflection effect of RF signals in this model. We implement XGest on a commodity mmWave device, and extensive experiments have demonstrated the significant recognition performance.

Unsupervised colonoscopic depth estimation by domain translations with a Lambertian-reflection keeping auxiliary task.

A three-dimensional (3D) structure extraction technique viewed from a two-dimensional image is essential for the development of a computer-aided diagnosis (CAD) system for colonoscopy. However, a straightforward application of existing depth-estimation methods to colonoscopic images is impossible or inappropriate due to several limitations of colonoscopes. In particular, the absence of ground-truth depth for colonoscopic images hinders the application of supervised machine learning methods. To circumvent these difficulties, we developed an unsupervised and accurate depth-estimation method. We propose a novel unsupervised depth-estimation method by introducing a Lambertian-reflection model as an auxiliary task to domain translation between real and virtual colonoscopic images. This auxiliary task contributes to accurate depth estimation by maintaining the Lambertian-reflection assumption. In our experiments, we qualitatively evaluate the proposed method by comparing it with state-of-the-art unsupervised methods. Furthermore, we present two quantitative evaluations of the proposed method using a measuring device, as well as a new 3D reconstruction technique and measured polyp sizes. Our proposed method achieved accurate depth estimation with an average estimation error of less than 1 mm for regions close to the colonoscope in both of two types of quantitative evaluations. Qualitative evaluation showed that the introduced auxiliary task reduces the effects of specular reflections and colon wall textures on depth estimation and our proposed method achieved smooth depth estimation without noise, thus validating the proposed method. We developed an accurate depth-estimation method with a new type of unsupervised domain translation with the auxiliary task. This method is useful for analysis of colonoscopic images and for the development of a CAD system since it can extract accurate 3D information.

Analysis and Radiometric Calibration for Backscatter Intensity of Hyperspectral LiDAR Caused by Incident Angle Effect.

Hyperspectral LiDAR (HSL) is a new remote sensing detection method with high spatial and spectral information detection ability. In the process of laser scanning, the laser echo intensity is affected by many factors. Therefore, it is necessary to calibrate the backscatter intensity data of HSL. Laser incidence angle is one of the important factors that affect the backscatter intensity of the target. This paper studied the radiometric calibration method of incidence angle effect for HSL. The reflectance of natural surfaces can be simulated as a combination of specular reflection and diffuse reflection. The linear combination of the Lambertian model and Beckmann model provides a comprehensive theory that can be applied to various surface conditions, from glossy to rough surfaces. Therefore, an adaptive threshold radiometric calibration method (Lambertian–Beckmann model) is proposed to solve the problem caused by the incident angle effect. The relationship between backscatter intensity and incident angle of HSL is studied by combining theory with experiments, and the model successfully quantifies the difference between diffuse and specular reflectance coefficients. Compared with the Lambertian model, the proposed model has higher calibration accuracy, and the average improvement rate to the samples in this study was 22.67%. Compared with the results before calibration with the incidence angle of less than 70°, the average improvement rate of the Lambertian–Beckmann model was 62.26%. Moreover, we also found that the green leaves have an obvious specular reflection effect near 650–720 nm, which might be related to the inner microstructure of chlorophyll. The Lambertian–Beckmann model was more helpful to the calibration of leaves in the visible wavelength range. This is a meaningful and a breakthrough exploration for HSL.

초음파센서를 이용한 UKF 기반 로봇의 실내 위치 평가 방법

This paper proposes a UKF-Based indoor localization method that evaluates the optimal position of a robot by fusing the position information from encoders and the distance information of the obstacle measured by ultrasonic sensors. UKF is a method of evaluating the robot’s position by transforming optimal sigma points extracted using the unscented transform and is advantageous for the localization of a nonlinear system. To solve the problem of the specular reflection effect of ultrasonic sensors, we propose a validation gate that evaluates the reliability of the ranges measured by sonar sensors, that can maximize the quality of the position evaluation. The experimental results showed that the method is stable and convergence of the position error regardless of the size of the initial position error and the length of the sampling time.

Magnetic confinement at a boundary approximates specular reflection

We conjecture that for a plasma in a spatial domain with a boundary, the specular reflection effect of the boundary can be approximated by a large magnetic confinement field in the near‐boundary region. In this paper, we verify this conjecture for the 1.5D relativistic Vlasov–Maxwell (RVM) system on a bounded domain with an external confining magnetic field.

Effects of Diffuse and Specular Reflections on Detecting Embedded Defects of Foams With a Bifocal Active Imaging System at 0.22 THz

We show the diffuse and specular reflection images of defective foams placed on iron plates, which are obtained by a bifocal active imaging system at 0.22 THz. Foam materials studied are extruded polystyrene boards and acetate copolymer boards with artificially embedded defects. In the field of view of approximately 50 × 90 cm2, the incident angle of the emitting and receiving cochannel system affects the reflected signal intensity and the imaging method. We build a light reflection model, which corrects the angle-dependent intensity behavior, to make the imaging results more intuitive. Experimental results show that large-angle imaging, which collects mainly diffuse reflection light, can be used to detect defects and is actually more suitable for detecting small defects than small-angle imaging, which mainly collects specular reflection light.

Content-aware specular reflection suppression based on adaptive image inpainting and neural network for endoscopic images

In this article, a content-aware specular reflection suppression scheme was developed based on adaptive image inpainting and neural network for endoscopic images. To decrease the impact of specular reflection on visual quality, the proposed scheme consists of three parts: reflection detection, reflection region classification, and reflection concealment. To automatically locate specular reflection regions, a thresholding algorithm with a morphological dilation operation is employed. To reduce the effect of specular reflection, an adaptive image inpainting algorithm is devised to deal with different reflection regions. To achieve content-aware image inpainting, a reflection region classification algorithm is designed by analyzing the local image content to adjust the parameters in the proposed image inpainting algorithm. The experimental results demonstrate that the proposed scheme can automatically and correctly not only locate but also conceal specular reflection regions in endoscopic images. Furthermore, since the average SSIM (structural similarity index) value of the proposed scheme is higher than those of the existing methods, our specular reflection suppression scheme is superior to the existing methods.