Three-dimensional scanning of specular and diffuse metallic surfaces using an infrared technique

For the past two decades, the need for three-dimensional (3-D) scanning of industrial objects has increased significantly and many experimental techniques and commercial solutions have been proposed. However, difficulties remain for the acquisition of optically non-cooperative surfaces, such as transparent or specular surfaces. To address highly reflective metallic surfaces, we propose the extension of a technique that was originally dedicated to glass objects. In contrast to conventional active triangulation techniques that measure the reflection of visible radiation, we measure the thermal emission of a surface, which is locally heated by a laser source. Considering the thermophysical properties of metals, we present a simulation model of heat exchanges that are induced by the process, helping to demonstrate its feasibility on specular metallic surfaces and predicting the settings of the system. With our experimental device, we have validated the theoretical modeling and computed some 3-D point clouds from specular surfaces of various geometries. Furthermore, a comparison of our results with those of a conventional system on specular and diffuse parts will highlight that the accuracy of the measurement no longer depends on the roughness of the surface

On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities

Current object recognition methods fail on object sets that include both diffuse, reflective and transparent materials, although they are very common in domestic scenarios. We show that a combination of cues from multiple sensor modalities, including specular reflectance and unavailable depth information, allows us to capture a larger subset of household objects by extending a state of the art object recognition method. This leads to a significant increase in robustness of recognition over a larger set of commonly used objects.Comment: 12 page

AirCode: Unobtrusive Physical Tags for Digital Fabrication

We present AirCode, a technique that allows the user to tag physically fabricated objects with given information. An AirCode tag consists of a group of carefully designed air pockets placed beneath the object surface. These air pockets are easily produced during the fabrication process of the object, without any additional material or postprocessing. Meanwhile, the air pockets affect only the scattering light transport under the surface, and thus are hard to notice to our naked eyes. But, by using a computational imaging method, the tags become detectable. We present a tool that automates the design of air pockets for the user to encode information. AirCode system also allows the user to retrieve the information from captured images via a robust decoding algorithm. We demonstrate our tagging technique with applications for metadata embedding, robotic grasping, as well as conveying object affordances.Comment: ACM UIST 2017 Technical Paper

Sensitivity of GNSS-R spaceborne observations to soil moisture and vegetation

Global navigation satellite systems-reflectometry (GNSS-R) is an emerging remote sensing technique that makes use of navigation signals as signals of opportunity in a multistatic radar configuration, with as many transmitters as navigation satellites are in view. GNSS-R sensitivity to soil moisture has already been proven from ground-based and airborne experiments, but studies using space-borne data are still preliminary due to the limited amount of data, collocation, footprint heterogeneity, etc. This study presents a sensitivity study of TechDemoSat-1 GNSS-R data to soil moisture over different types of surfaces (i.e., vegetation covers) and for a wide range of soil moisture and normalized difference vegetation index (NDVI) values. Despite the scattering in the data, which can be largely attributed to the delay-Doppler maps peak variance, the temporal and spatial (footprint size) collocation mismatch with the SMOS soil moisture, and MODIS NDVI vegetation data, and land use data, experimental results for low NDVI values show a large sensitivity to soil moisture and a relatively good Pearson correlation coefficient. As the vegetation cover increases (NDVI increases) the reflectivity, the sensitivity to soil moisture and the Pearson correlation coefficient decreases, but it is still significant.Postprint (author's final draft

Classification of Material Surfaces Using the Polarization of Specular Highlights

Recently there has been interest, in computer vision research, in the segmentation of images based upon the actual material makeup of the objects or object parts that constitute image regions. The idea is to identify image characteristics which can be used to predict the material properties of objects that are being imaged. A majority of object surfaces can be simply classified according to their basic electrical properties; metal objects (e.g. Aluminum, Copper) conduct electricity rather well while dielectric objects (e.g. Rubber, Plastic, Ceramic) conduct electricity poorly. Distinguishing image regions according to whether they correspond to metal or dielectric material can provide important information for scene understanding especially in industrial machine vision. One such major application is circuit board inspection where the presence of dielectric or metal material in the wrong place can cause trouble. A previous approach to the problem of identifying metal or dielectric material in images is based upon careful spectral (i.e. color) analysis of reflected light from material objects. This paper presents a technique for identifying the material properties of objects in an image using a polarizing lens (i.e. Polaroid filter). Two images of the same scene are taken with a polarizing lens placed in front of a camera in two different respective orientations. Effectively these two images represent two linearly independent polarization components of the reflected light. It is shown that when the linearly independent components of polarization are taken parallel and perpendicular with respect to the plane in which specular rays travel that dielectric objects can be distinguished from metallic objects when specular highlights are present. In particular the two polarization components are very similar at specular highlights on metals while the two polarization components for specular highlights on dielectrics are very different, the perpendicular component having much larger magnitude than the parallel component. This is shown to hold regardless of whether the surface is polished or rough. Results for coated surfaces will be presented at a future date

Fundamental remote sensing science research program. Part 1: Scene radiation and atmospheric effects characterization project

Brief articles summarizing the status of research in the scene radiation and atmospheric effect characterization (SRAEC) project are presented. Research conducted within the SRAEC program is focused on the development of empirical characterizations and mathematical process models which relate the electromagnetic energy reflected or emitted from a scene to the biophysical parameters of interest

Segmentation of Specular Highlights from Object Surfaces

A major hindrance to image segmentation tasks are the presence of specular highlights on object surfaces. Specular highlights appear on object surfaces where the specular component of reflection from illuminating light sources is so dominant that most detail of the object surface is obscured by a bright region of reflected light. Specular highlights are very common artifacts of most lighting environments and are not part of the intrinsic visible detail of an object surface. As a result, in addition to obscuring visible detail, specular highlight regions of an image can easily deceive image understanding algorithms into interpreting these regions as separate objects or regions on an object with high albedo. Recently, a couple of approaches to identifying specular highlight regions in images of object surfaces have produced some good results using color analysis. Unfortunately these methods work only for dielectric materials (e.g. plastic, rubber etc.) and require that the color of the object be different from the color of the light source. In this paper a technique is presented exploiting the polarization properties of reflected light to identify specular highlight regions. This technique works for both dielectric and metal surfaces regardless of the color of the illuminating light source, or the color detail on the object surface. In addition to separating out diffuse and specular components of reflection, the technique presented here also as a bonus can identify whet her certain image regions correspond to a dielectric or metal object surface. Extensive experimentation will be presented for a variety of dielectric and metal surfaces, both polished and rough. Experimentation with coated surfaces using the technique presented here have not yet been studied