Surface Reflectance Of Object Research Articles

A 3D Reconstruction Method Based on Homogeneous De Bruijn-Encoded Structured Light

Structured light three-dimensional reconstruction is one of the important methods for non-contact acquisition of sparse texture object surfaces. Variations in ambient illumination and disparities in object surface reflectance can significantly impact the fidelity of three-dimensional reconstruction, introducing considerable inaccuracies. We introduce a robust method for color speckle structured light encoding, which is based on a variant of the De Bruijn sequence, termed the Homogeneous De Bruijn Sequence. This innovative approach enhances the reliability and accuracy of structured light techniques for three-dimensional reconstruction by utilizing the distinctive characteristics of Homogeneous De Bruijn Sequences. Through a pruning process applied to the De Bruijn sequence, a structured light pattern with seven distinct color patches is generated. This approach ensures a more equitable distribution of speckle information.

Profile-based diffuse reflectance corrections for improved optical property measurement of spherical fruit with spatial frequency domain imaging

Diffuse reflectance (Rd), demodulated from spatial frequency domain imaging (SFDI), is strongly influenced by the surface profile of target sample, and the optical properties (absorption coefficient μa and reduced scattering coefficient μ's) extracted from the Rd are prone to large estimation errors caused by curved surface of the fruit. This study proposes a profile-based Rd correction method, aiming to improve the accuracy of optical property measurement for spherical fruit, such as apples, as well as enhance fruit bruise detection through improved optical properties. Firstly, the surface height of the test object was obtained by performing the phase-measuring profilometry (PMP) in SFDI. Secondly, the object surface reflection angle was calculated using the proposed angle calculation method for spherical samples. The Modified Lambertian Correction (MLC) method was then refined to minimize the effect of the incident angle on the Rd correction. The performance of the Rd correction method was validated using standard reflector and Teflon spheres. The results showed that the average error of the PMP measurement with the height range of 0–50 mm was within 1 mm, the relative error of the corrected Rd was within 2 %, and the standard deviation of the Rd for the Teflon sphere was reduced by about 60 % after correction. Finally, the proposed Rd correction method was applied to measure the optical properties of four spherical fruit (‘Golden Delicious’, ‘Red Fuji’ apples, lemons, and oranges). The results revealed that the standard deviations of the measured μa and μ's of the spherical fruit were greatly reduced by about 70 % and 40 % after correction. The improved optical property mappings enhanced the early bruise detection of apples due to the fact that the optical property contrast between the non-bruised and bruised tissues were significantly increased.

Aerosol optical depth (AOD) retrieval for atmospheric correction in Landsat-8 imagery using second simulation of a satellite signal in the solar spectrum-vector (6SV)

Atmospheric correction has been challenging task in digital image processing. It requires several atmospheric parameters in order to obtain accurate surface reflectance of objects within the image scene. One of the most crucial parameters required for accurate atmospheric correction is aerosol optical depth (AOD). AOD can be obtained by in-situ measurement or estimated from remote sensing observation. In this experiment, atmospheric correction was performed using second simulation of a satellite signal in the solar spectrum-vector (6SV) algorithm on Landsat-8 imagery in which AOD parameter was retrieved from surface reflectance inversion involving daily-global surface reflectance product of moderate resolution imaging spectroradiometer (MODIS). Furthermore, AOD retrieved from surface reflectance inversion was also validated using ground-based sun photometer observation data from aerosol robotic network (AERONET) station in Bandung, Indonesia. Our experiment shows the consistency between AOD from surface reflectance inversion and AOD from ground-based observation. Finally, 6SV was performed on Landsat-8 imagery to obtain the surface reflectance. We further compared surface reflectance of 6SV atmospheric correction and surface reflectance of Landsat-8 Level 2 product. The atmospherically corrected image also shared agreeable result with Landsat 8 Level-2 product.

A non-Lambertian photometric stereo under perspective projection

Under the perspective projection assumption, non-Lambertian photometric stereo is a highly non-linear problem. In this study, we present an optimized framework for reconstructing the surface normal and depth with non-Lambertian reflection models under perspective projection. By decomposing the images into diffuse and specular components, we compute the surface normal and reflectance simultaneously. We also propose a variational formulation that is robust and useful for surface reconstruction. The experiments show that our method accurately reconstructs both the surface shape and reflectance of colorful objects with non-Lambertian surfaces.

Differential processing of binocular and monocular gloss cues in human visual cortex.

The visual impression of an object's surface reflectance (“gloss”) relies on a range of visual cues, both monocular and binocular. Whereas previous imaging work has identified processing within ventral visual areas as important for monocular cues, little is known about cortical areas involved in processing binocular cues. Here, we used human functional MRI (fMRI) to test for brain areas selectively involved in the processing of binocular cues. We manipulated stereoscopic information to create four conditions that differed in their disparity structure and in the impression of surface gloss that they evoked. We performed multivoxel pattern analysis to find areas whose fMRI responses allow classes of stimuli to be distinguished based on their depth structure vs. material appearance. We show that higher dorsal areas play a role in processing binocular gloss information, in addition to known ventral areas involved in material processing, with ventral area lateral occipital responding to both object shape and surface material properties. Moreover, we tested for similarities between the representation of gloss from binocular cues and monocular cues. Specifically, we tested for transfer in the decoding performance of an algorithm trained on glossy vs. matte objects defined by either binocular or by monocular cues. We found transfer effects from monocular to binocular cues in dorsal visual area V3B/kinetic occipital (KO), suggesting a shared representation of the two cues in this area. These results indicate the involvement of mid- to high-level visual circuitry in the estimation of surface material properties, with V3B/KO potentially playing a role in integrating monocular and binocular cues.

Image Color Registration for Illumination-Robust Pattern Recognition

Image-based pattern recognition techniques have attracted much attention in vision-based applications. Color-based methods have shown several benefits. However, due to color variations resulting from illumination changes, many color-based techniques have yet to demonstrate stable performance. For illumination-robust pattern recognition, we propose an image color registration method based on an image acquisition model. Since the image acquisition model is created using the variables related to an illumination condition, camera characteristics, and an object's surface reflectance, the proposed method normalizes the image's color by taking into account both the illumination and camera characteristics. To evaluate the performance of the proposed method in terms of illumination-robust pattern recognition, we perform both an image similarity test and a feature similarity test between images acquired under different illumination conditions. Through the experiments, the superiority and the usefulness of the proposed method was validated.

ARにおける任意物体の反射特性推定を用いた光学的整合性の実現

ARにおける任意物体の反射特性推定を用いた光学的整合性の実現

A Bayesian model of lightness perception that incorporates spatial variation in the illumination.

The lightness of a test stimulus depends in a complex manner on the context in which it is viewed. To predict lightness, it is necessary to leverage measurements of a feasible number of contextual configurations into predictions for a wider range of configurations. Here we pursue this goal, using the idea that lightness results from the visual system's attempt to provide stable information about object surface reflectance. We develop a Bayesian algorithm that estimates both illumination and reflectance from image luminance, and link perceived lightness to the algorithm's estimates of surface reflectance. The algorithm resolves ambiguity in the image through the application of priors that specify what illumination and surface reflectances are likely to occur in viewed scenes. The prior distributions were chosen to allow spatial variation in both illumination and surface reflectance. To evaluate our model, we compared its predictions to a data set of judgments of perceived lightness of test patches embedded in achromatic checkerboards (Allred, Radonjić, Gilchrist, & Brainard, 2012). The checkerboard stimuli incorporated the large variation in luminance that is a pervasive feature of natural scenes. In addition, the luminance profile of the checks both near to and remote from the central test patches was systematically manipulated. The manipulations provided a simplified version of spatial variation in illumination. The model can account for effects of overall changes in image luminance and the dependence of such changes on spatial location as well as some but not all of the more detailed features of the data.

A Conceptual Model of Surface Reflectance Estimation for Satellite Remote Sensing Images Using in situ Reference Data

For satellite remote sensing, radiances received at the sensor are not only affected by the atmosphere but also by the topographic properties of the terrain surface. As a result, atmospheric correction alone does not yield output images that truly reflect terrain surface properties, namely surface reflectance (bidirectional reflectance factor, BRF) of objects on the earth surface. Following the concept of the radiometric control area (RCA)-based path radiance estimation method, we herein propose a statistical approach for surface reflectance estimation utilizing DEM data and surface reflectance of selected radiometric control areas. An algorithm for identification of shaded samples and a shape factor model were also developed in this study. The proposed RCA-based surface reflectance estimation method is capable of achieving good reflectance estimates in a region where elevation varies from 0 to approximately 600 m above the mean sea level. However, further study is recommended in order to extend the application of the proposed method to areas with substantial terrain variation.

Surface color perception and equivalent illumination models

Vision provides information about the properties and identity of objects. The ease with which we perceive object properties belies the difficulty of the underlying information-processing task. In the case of object color, retinal information about object reflectance is confounded with information about the illumination as well as about the object's shape and pose. There is no obvious rule that allows transformation of the retinal image to a color representation that depends primarily on object surface reflectance. Under many circumstances, however, object color appearance is remarkably stable across scenes in which the object is viewed. Here, we review a line of experiments and theory that aim to understand how the visual system stabilizes object color appearance. Our emphasis is on models derived from explicit analysis of the computational problem of estimating the physical properties of illuminants and surfaces from the retinal image, and experiments that test these models. We argue that this approach has considerable promise for allowing generalization from simplified laboratory experiments to richer scenes that more closely approximate natural viewing. We discuss the relation between the work we review and other theoretical approaches available in the literature.

A coaxial optical scanner for synchronous acquisition of 3D geometry and surface reflectance

We present a novel optical setup and processing pipeline for measuring the 3D geometry and spatially-varying surface reflectance of physical objects. Central to our design is a digital camera and a high frequency spatially-modulated light source aligned to share a common focal point and optical axis. Pairs of such devices allow capturing a sequence of images from which precise measurements of geometry and reflectance can be recovered. Our approach is enabled by two technical contributions: a new active multiview stereo algorithm and an analysis of light descattering that has important implications for image-based reflectometry. We show that the geometry measured by our scanner is accurate to within 50 microns at a resolution of roughly 200 microns and that the reflectance agrees with reference data to within 5.5%. Additionally, we present an image relighting application and show renderings that agree very well with reference images at light and view positions far from those that were initially measured.

A coaxial optical scanner for synchronous acquisition of 3D geometry and surface reflectance

We present a novel optical setup and processing pipeline for measuring the 3D geometry and spatially-varying surface reflectance of physical objects. Central to our design is a digital camera and a high frequency spatially-modulated light source aligned to share a common focal point and optical axis. Pairs of such devices allow capturing a sequence of images from which precise measurements of geometry and reflectance can be recovered. Our approach is enabled by two technical contributions: a new active multiview stereo algorithm and an analysis of light descattering that has important implications for image-based reflectometry. We show that the geometry measured by our scanner is accurate to within 50 microns at a resolution of roughly 200 microns and that the reflectance agrees with reference data to within 5.5%. Additionally, we present an image relighting application and show renderings that agree very well with reference images at light and view positions far from those that were initially measured.

物体表面の反射特性と分光反射モデルに基づいたリアルタイムレンダリング手法

物体表面の反射特性と分光反射モデルに基づいたリアルタイムレンダリング手法

The Relationship Between Language and the Environment

The surface reflectance of objects is highly variable, ranging between 4% for, say, charcoal and 90% for fresh snow. When stimuli are presented simultaneously, people can discriminate hundreds of levels of visual intensity. Despite this, human languages possess a maximum of just three basic terms for describing lightness. In English, these are white (or light), black (or dark), and gray. Why should this be? Using information theory, combined with estimates of the distribution of reflectances in the natural world and the reliability of lightness recall over time, we show that three lightness terms is the optimal number for describing surface reflectance properties in a modern urban or indoor environment. We also show that only two lightness terms would be required in a forest or rural environment.

レーザーレンジファインダを用いた柱状物体の3次元形状測定と表面反射特性推定の同時実現法

レーザーレンジファインダを用いた柱状物体の3次元形状測定と表面反射特性推定の同時実現法

Reflectance-Field-Based Separation of Surface Reflection Components

Light reflected from an object to a camera is a mixture of light from specular and diffuse reflection. This has important implications for many computer vision tasks, such as image matching and understanding. Many applications, for example, digital contents production, photorealistic image synthesis, and motion analysis, may require the diffuse and specular reflection to be separated. We present an approach for separating the diffuse and specular components of object surface reflection. This approach is based on the well-known dichromatic reflection model, however it separates reflections from reflectance fields constructed for every point on a 3-D object surface. Our method can prevent having to segment image into several uniformly-colored areas. Our method can thus separate reflection from an object surface with a complicated texture. We analyzed the properties of the reflectance field constructed from original frames and showed how to separate reflection components for each 3-D point. Experiments on real scenes showed that our method was successful.

Reflectance-Field-Based Separation of Surface Reflection Components

Light reflected from an object to a camera is a mixture of light from specular and diffuse reflection. This has important implications for many computer vision tasks, such as image matching and understanding. Many applications, for example, digital contents production, photorealistic image synthesis, and motion analysis, may require the diffuse and specular reflection to be separated. We present an approach for separating the diffuse and specular components of object surface reflection. This approach is based on the well-known dichromatic reflection model, however it separates reflections from reflectance fields constructed for every point on a 3-D object surface. Our method can prevent having to segment image into several uniformly-colored areas. Our method can thus separate reflection from an object surface with a complicated texture. We analyzed the properties of the reflectance field constructed from original frames and showed how to separate reflection components for each 3-D point. Experiments on real scenes showed that our method was successful.

Surface reflectance modeling of real objects with interreflections for augmented virtuality

AbstractIn mixed reality, especially in augmented virtuality, in which real objects are virtualized, it is important to estimate object surface reflectance properties in order to render the objects under arbitrary illumination conditions. Although several methods have been explored, faithfully estimating surface reflectance parameters for complex objects having nonuniform surface reflectance properties or interreflections remains difficult. This article describes a new method for densely estimating the nonuniform surface reflectance properties of real objects constructed of convex and concave surfaces with interreflections. Registered range and surface color texture images are obtained using a laser rangefinder. In the proposed method, light source positions are first determined in order to obtain color images for discriminating diffuse and specular reflection components considering the object geometry. Surface reflectance parameters are then estimated based on radiosity and the Torrance–Sparrow model. The usefulness of the proposed method is shown experimentally. © 2005 Wiley Periodicals, Inc. Col Res Appl, 31, 38–47, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/col.20184

Projection pattern intensity control technique for 3-D optical measurement

A new projection pattern control technique is presented in an attempt to solve the problem whereby an image having an ideal intensity distribution cannot be photographed when measurement conditions, such as object color or object surface reflection, change. The proposed technique can adjust the intensity distribution of a projection pattern automatically, according to changes in the measurement conditions. An image with an ideal intensity distribution can then be obtained in a short time, approximately three projections on average. Thus, the speed, robustness, and practicality of 3-D image measurement can be improved.

An advanced rangefinder equipped with a new image sensor with the ability to detect the incident angle of a light stripe

This paper describes an advanced rangefinder equipped with a new image sensor capable of detecting the incident angle of a light stripe. The triangulation-based rangefinding approach is the one that is most widely used. However, ordinary triangulation-based rangefinders have hitherto been limited to measuring Lambertian objects because triangulation-based approaches are particularly susceptible to the object's surface reflectivity. We previously proposed a new type of rangefinder that relaxed the above-mentioned restrictions on ordinary rangefinders. However, the previous rangefinder had two unsolved problems: the image sensor used for the rangefinder was one-dimensional, and the experimental verification of the applied object was limited to specular objects and Lambertian objects. We here propose a rangefinder with an area image sensor, extending the capabilities of our previous sensor, which is capable of detecting the two-dimensional incident position and angle of incident light. We have made a prototype rangefinder, and experimentally compared it to our previous rangefinder and to a conventional rangefinder. The experimental results of the proposed rangefinder demonstrate that it measures three-dimensional (3D) shapes objects faster than the previous rangefinder and produces the same high level of accuracy in measuring 3D shapes regardless of the surface reflectance of the given objects.