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Polarization has been used in numerous prior studies for separating diffuse and specular reflectance components, but in this work we show that it also can be used to separate surface reflectance contributions from individual light sources. Our approach is called polarization multiplexing and it has a significant impact in appearance modeling where the image as a function of illumination direction is needed. Multiple unknown light sources can illuminate the scene simultaneously, and the individual contributions to the overall surface reflectance are estimated. Polarization multiplexing relies on the relationship between the light source direction and the intensity modulation. Inverting this transformation enables the individual intensity contributions to be estimated. In addition to polarization multiplexing, we show that phase histograms from the intensity modulations can be used to estimate scene properties including the number of light sources. Index Terms--Reflectance, surface reflectance, specular reflectance, body reflectance, diffuse reflectance, polarization, multiplexing, appearance, appearance-based modeling.

In vision and graphics, advanced object models require not only 3D shape, but also surface detail. While several scanning devices exist to capture the global shape of an object, few methods concentrate on capturing the fine-scale detail. Fine-scale surface geometry (relief texture), such as surface markings, roughness, and imprints, is essential in highly realistic rendering and accurate prediction. We present a novel approach for measuring the relief texture of specular or partially specular surfaces using a specialized imaging device with a concave parabolic mirror to view multiple angles in a single image. Laser scanning typically fails for specular surfaces because of light scattering, but our method is explicitly designed for specular surfaces. Also, the spatial resolution of the measured geometry is significantly higher than standard methods, so very small surface details are captured. Furthermore, spatially varying reflectance is measured simultaneously, i.e., both texture color and texture shape are retrieved. Index Terms--Texture, relief texture, BTF, bidirectional texture function, 3D texture, shape from specularity, curved mirror, reflectance, roughness.

In this paper, we present a method of skin imaging called bidirectional imaging that raptures significantly more properties of appearance than standard imaging. The observed structure of the skin's surface is greatly dependent on the angle of incident illumination and the angle of observation. Specific protocols to achieve bidirectional imaging are presented and used to create the Rutgers Skin Texture Database (clinical component). This image database is the first of its kind in the dermatology community. Skin images of several disorders under multiple controlled illumination and viewing directions are provided publicly for research and educational use. Using this skin texture database, we employ computational surface modeling to perform automated skin texture classification. The classification experiments demonstrate the usefulness of the modeling and measurement methods. Index Terms--Appearance-based modeling, bidirectional texture function, BTF, skin texture, texture, 3-D texture.

Computational surface modeling that underlies material recognition has transitioned from reflectance modeling using in-lab controlled radiometric measurements to image-based representations based on internet-mined single-view images captured in the scene. We take a middle-ground approach for material recognition that takes advantage of both rich radiometric cues and flexible image capture. A key concept is differential angular imaging, where small angular variations in image capture enables angular-gradient features for an enhanced appearance representation that improves recognition. We build a large-scale material database, Ground Terrain in Outdoor Scenes (GTOS) database, to support ground terrain recognition for applications such as autonomous driving and robot navigation. The database consists of over 30,000 images covering 40 classes of outdoor ground terrain under varying weather and lighting conditions. We develop a novel approach for material recognition called texture-encoded angular network (TEAN) that combines deep encoding pooling of RGB information and differential angular images for angular-gradient features to fully leverage this large dataset. With this novel network architecture, we extract characteristics of materials encoded in the angular and spatial gradients of their appearance. Our results show that TEAN achieves recognition performance that surpasses single view performance and standard (non-differential/large-angle sampling) multiview performance. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is used to evaluate deterioration of reinforced concrete bridge decks based on measuring signal attenuation from embedded rebar. The existing methods for obtaining deterioration maps from GPR data often require manual interaction and offsite processing. In this paper, a novel algorithm is presented for automated rebar detection and analysis. We test the process with comprehensive measurements obtained using a novel state-of-the-art robotic bridge inspection system equipped with GPR sensors. The algorithm achieves robust performance by integrating machine learning classification using image-based gradient features and robust curve fitting of the rebar hyperbolic signature. The approach avoids edge detection, thresholding, and template matching that require manual tuning and are known to perform poorly in the presence of noise and outliers. The detected hyperbolic signatures of rebars within the bridge deck are used to generate deterioration maps of the bridge deck. The results of the rebar region detector are compared quantitatively with several methods of image-based classification and a significant performance advantage is demonstrated. High rates of accuracy are reported on real data that includes thousands of individual hyperbolic rebar signatures from three real bridge decks. [ABSTRACT FROM PUBLISHER]

Surface reflectance and texture provides a unique signature for applications such as recognition and rendering. Intuition tells us that a camera captures appearance. However, a traditional camera captures intensity dependent on the environment lighting, camera position, and the surface geometry. Imaging for computational appearance recovers reflectance that is intrinsic to an object or scene and useful for recognition and other applications. Reflectance can be captured in a lab-based setting with a gonioreflectometer or domes of lights and cameras. Recent methods in computational imaging provide appearance-capture that is comprehensive, efficient, compact, or optimal depending on the task at hand. In this article, we review methods for capturing and modeling computational appearance. The impact of these appearance representations is significant with applications areas such as e-commerce, digital architecture, humancomputer interaction, intelligent vehicles, robotics, and inspection. [ABSTRACT FROM PUBLISHER]

Detection of cracks on bridge decks is a vital task for maintaining the structural health and reliability of concrete bridges. Robotic imaging can be used to obtain bridge surface image sets for automated on-site analysis. We present a novel automated crack detection algorithm, the STRUM (spatially tuned robust multifeature) classifier, and demonstrate results on real bridge data using a state-of-the-art robotic bridge scanning system. By using machine learning classification, we eliminate the need for manually tuning threshold parameters. The algorithm uses robust curve fitting to spatially localize potential crack regions even in the presence of noise. Multiple visual features that are spatially tuned to these regions are computed. Feature computation includes examining the scale-space of the local feature in order to represent the information and the unknown salient scale of the crack. The classification results are obtained with real bridge data from hundreds of crack regions over two bridges. This comprehensive analysis shows a peak STRUM classifier performance of 95% compared with 69% accuracy from a more typical image-based approach. In order to create a composite global view of a large bridge span, an image sequence from the robot is aligned computationally to create a continuous mosaic. A crack density map for the bridge mosaic provides a computational description as well as a global view of the spatial patterns of bridge deck cracking. The bridges surveyed for data collection and testing include Long-Term Bridge Performance program's (LTBP) pilot project bridges at Haymarket, VA, USA, and Sacramento, CA, USA. [ABSTRACT FROM AUTHOR]

Quantitative characterization of surface appearance is an important but difficult task. Surfaces of real world objects are detailed landscapes, with complex geometry and local optical properties. Surface appearance is strongly affected by the direction from which it is viewed and illuminated. Computational modeling of surface texture has potential uses in many applications including realistic rendering for computer graphics and robust recognition for computer vision. For recognition, the overall structure of the object is important, but fine-scale details can assist the recognition problem greatly. We develop models of surface texture and demonstrate their use in recognition tasks. We also describe a texture camera for capturing fine-scale surface details. Specifically, the texture camera measures reflectance and surface height variation using curved mirrors. We discuss why measurements and models of fine scale detail are important in modern industrial applications. [Copyright &y& Elsevier]

In vision and graphics, advanced object models require not only 3D shape, but also surface detail. While several scanning devices exist to capture the global shape of an object, few methods concentrate on capturing the fine-scale detail. Fine-scale surface geometry (relief texture), such as surface markings, roughness, and imprints, is essential in highly realistic rendering and accurate prediction. We present a novel approach for measuring the relief texture of specular or partially specular surfaces using a specialized imaging device with a concave parabolic mirror to view multiple angles in a single image. Laser scanning typically fails for specular surfaces because of light scattering, but our method is explicitly designed for specular surfaces. Also, the spatial resolution of the measured geometry is significantly higher than standard methods, so very small surface details are captured. Furthermore, spatially varying reflectance is measured simultaneously, i.e., both texture color and texture shape are retrieved. [ABSTRACT FROM AUTHOR]

We consider texture modeling techniques which are exemplar-based, i.e. techniques which use several example texture images to learn pixel arrangements. The motivation for these techniques is that the structure of a texture can be characterized by the spatial distribution of pixels in a neighborhood of the image or the multiresolution pyramid. To get another instance of the same type of texture, one can rearrange the pixels as long as certain spatial neighbor relationships are enforced. In this work, we investigate two components of exemplar-based modeling: (1) grouping examples for computational savings during analysis and (2) blending for artifact removal during synthesis. First, we employ clustering in order to group example features and this method provides a significant computational savings without compromising the quality of the texture characterization. Second, we implement techniques for blending to remove border artifacts during the placement stage of texture synthesis. We show that for many textures, the pixel rearrangements can be done without filtering or neighborhood constraints, as long as the blending of borders is done well. Specifically, random rearrangement of texture patches generates a new texture instance of the same texture type when artifacts are removed with blending. We investigate existing blending approaches and introduce a blending method based on image fusion. [Copyright &y& Elsevier]