Shape from Lambertian Photometric Flow Fields

A new idea for the analysis of shape from reflectance maps is introduced in this paper. It is shown that local surface orientation and curvature constraints can be obtained at points on a smooth surface by computing the instantaneous rate of change of reflected scene radiance caused by angular variations in illumination geometry. The resulting instantaneous changes in image irradiance values across an optic sensing array of pixels constitute what is termed a photometric flow field. Unlike optic flow fields which are instantaneous changes in position across an optic array of pixels caused by relative motion, there is no correspondence problem with respect to obtaining the instantaneous change in image irradiance values between successive image frames. This is because the object and camera remain static relative to one another as the illumination geometry changes. There are a number of advantages to using photometric flow fields. One advantage is that local surface orientation and curvature at a point on a smooth surface can be uniquely determined by only slightly varying the incident orientation of an illuminator within a small local neighborhood about a specific incident orientation. Robot manipulators and rotation/positioning jigs can be accurately varied within small ranges of motion. Conventional implementation of photometric stereo requires the use of three vastly different incident orientations of an illuminator requiring either much calibration and/or gross and inaccurate robot arm motions. Another advantage of using photometric flow fields is the duality that exists between determining unknown local surface orientation from a known incident illuminator orientation and determining an unknown incident illuminator orientation from a known local surface orientation. The equations for photometric flow fields allow the quantitative determination of the incident orientation of an illuminator from an object having a known calibrated surface orientation. Computer simulations will be shown depicting photometric flow fields on a Lambertian sphere. Simulations will be shown depicting how photometric flow fields quantitatively determine local surface orientation from a known incident orientation of an illuminator as well as determining incident illuminator orientation from a known local surface orientation

Single-image RGB Photometric Stereo With Spatially-varying Albedo

We present a single-shot system to recover surface geometry of objects with spatially-varying albedos, from images captured under a calibrated RGB photometric stereo setup---with three light directions multiplexed across different color channels in the observed RGB image. Since the problem is ill-posed point-wise, we assume that the albedo map can be modeled as piece-wise constant with a restricted number of distinct albedo values. We show that under ideal conditions, the shape of a non-degenerate local constant albedo surface patch can theoretically be recovered exactly. Moreover, we present a practical and efficient algorithm that uses this model to robustly recover shape from real images. Our method first reasons about shape locally in a dense set of patches in the observed image, producing shape distributions for every patch. These local distributions are then combined to produce a single consistent surface normal map. We demonstrate the efficacy of the approach through experiments on both synthetic renderings as well as real captured images.Comment: 3DV 2016. Project page at http://www.ttic.edu/chakrabarti/rgbps

Photometric stereo for three-dimensional leaf venation extraction

© 2018 Elsevier B.V. Leaf venation extraction studies have been strongly discouraged by considerable challenges posed by venation architectures that are complex, diverse and subtle. Additionally, unpredictable local leaf curvatures, undesirable ambient illuminations, and abnormal conditions of leaves may coexist with other complications. While leaf venation extraction has high potential for assisting with plant phenotyping, speciation and modelling, its investigations to date have been confined to colour image acquisition and processing which are commonly confounded by the aforementioned biotic and abiotic variations. To bridge the gaps in this area, we have designed a 3D imaging system for leaf venation extraction, which can overcome dark or bright ambient illumination and can allow for 3D data reconstruction in high resolution. We further propose a novel leaf venation extraction algorithm that can obtain illumination-independent surface normal features by performing Photometric Stereo reconstruction as well as local shape measures by fusing the decoupled shape index and curvedness features. In addition, this algorithm can determine venation polarity – whether veins are raised above or recessed into a leaf. Tests on both sides of different leaf species with varied venation architectures show that the proposed method is accurate in extracting the primary, secondary and even tertiary veins. It also proves to be robust against leaf diseases which can cause dramatic changes in colour. The effectiveness of this algorithm in determining venation polarity is verified by it correctly recognising raised or recessed veins in nine different experiments

Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network

This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribution is the compact representation of the surface information achieved by combining photometric stereo images into a RGB image that is fed to a convolutional segmentation network trained for surface defect detection. We demonstrate the advantage of this compact surface imaging representation over the use of each photometric imaging source of information in isolation. An empirical analysis of the performance of the segmentation network on imaging samples of materials with diverse surface reflectance properties is carried out, achieving Dice performance index values above 0.83 in all cases. The results support the potential of photometric stereo in conjunction with our semantic segmentation network

Image Understanding and Robotics Research at Columbia University

Over the past year, the research investigations of the Vision/Robotics Laboratory at Columbia University have reflected the interests of its four faculty members, two staff programmers, and 16 Ph.D. students. Several of the projects involve other faculty members in the department or the university, or researchers at AT&T, IBM, or Philips. We list below a summary of our interests and results, together with the principal researchers associated with them. Since it is difficult to separate those aspects of robotic research that are purely visual from those that are vision-like (for example, tactile sensing) or vision-related (for example, integrated vision-robotic systems), we have listed all robotic research that is not purely manipulative. The majority of our current investigations are deepenings of work reported last year; this was the second year of both our basic Image Understanding contract and our Strategic Computing contract. Therefore, the form of this year's report closely resembles last year's. Although there are a few new initiatives, mainly we report the new results we have obtained in the same five basic research areas. Much of this work is summarized on a video tape that is available on request. We also note two service contributions this past year. The Special Issue on Computer Vision of the Proceedings of the IEEE, August, 1988, was co-edited by one of us (John Kender [27]). And, the upcoming IEEE Computer Society Conference on Computer Vision and Pattem Recognition, June, 1989, is co-program chaired by one of us (John Kender [23])