Specular surface recovery from reflections of a planar pattern undergoing an unknown pure translation

LNCS v. 6493 entitled: Computer Vision – ACCV 2010: 10th Asian Conference on Computer Vision, Queenstown, New Zealand, November 8-12, 2010, Revised Selected Papers, Part 2This paper addresses the problem of specular surface recovery, and proposes a novel solution based on observing the reflections of a translating planar pattern. Previous works have demonstrated that a specular surface can be recovered from the reflections of two calibrated planar patterns. In this paper, however, only one reference planar pattern is assumed to have been calibrated against a fixed camera observing the specular surface. Instead of introducing and calibrating a second pattern, the reference pattern is allowed to undergo an unknown pure translation, and a closed form solution is derived for recovering such a motion. Unlike previous methods which estimate the shape by directly triangulating the visual rays and reflection rays, a novel method based on computing the projections of the visual rays on the translating pattern is introduced. This produces a depth range for each pixel which also provides a measure of the accuracy of the estimation. The proposed approach enables a simple auto-calibration of the translating pattern, and data redundancy resulting from the translating pattern can improve both the robustness and accuracy of the shape estimation. Experimental results on both synthetic and real data are presented to demonstrate the effectiveness of the proposed approach. © 2011 Springer-Verlag Berlin Heidelberg.postprintThe 10th Asian Conference on Computer Vision, Queenstown, New Zealand, 8-12 November 2010. In Lecture Notes in Computer Science, 2010, v. 6493, p. 137-14

Multiple View Geometry For Video Analysis And Post-production

Multiple view geometry is the foundation of an important class of computer vision techniques for simultaneous recovery of camera motion and scene structure from a set of images. There are numerous important applications in this area. Examples include video post-production, scene reconstruction, registration, surveillance, tracking, and segmentation. In video post-production, which is the topic being addressed in this dissertation, computer analysis of the motion of the camera can replace the currently used manual methods for correctly aligning an artificially inserted object in a scene. However, existing single view methods typically require multiple vanishing points, and therefore would fail when only one vanishing point is available. In addition, current multiple view techniques, making use of either epipolar geometry or trifocal tensor, do not exploit fully the properties of constant or known camera motion. Finally, there does not exist a general solution to the problem of synchronization of N video sequences of distinct general scenes captured by cameras undergoing similar ego-motions, which is the necessary step for video post-production among different input videos. This dissertation proposes several advancements that overcome these limitations. These advancements are used to develop an efficient framework for video analysis and post-production in multiple cameras. In the first part of the dissertation, the novel inter-image constraints are introduced that are particularly useful for scenes where minimal information is available. This result extends the current state-of-the-art in single view geometry techniques to situations where only one vanishing point is available. The property of constant or known camera motion is also described in this dissertation for applications such as calibration of a network of cameras in video surveillance systems, and Euclidean reconstruction from turn-table image sequences in the presence of zoom and focus. We then propose a new framework for the estimation and alignment of camera motions, including both simple (panning, tracking and zooming) and complex (e.g. hand-held) camera motions. Accuracy of these results is demonstrated by applying our approach to video post-production applications such as video cut-and-paste and shadow synthesis. As realistic image-based rendering problems, these applications require extreme accuracy in the estimation of camera geometry, the position and the orientation of the light source, and the photometric properties of the resulting cast shadows. In each case, the theoretical results are fully supported and illustrated by both numerical simulations and thorough experimentation on real data

Spatiotemporal oriented energies for spacetime stereo

This paper presents a novel approach to recovering tem-porally coherent estimates of 3D structure of a dynamic scene from a sequence of binocular stereo images. The approach is based on matching spatiotemporal orientation distributions between left and right temporal image streams, which encapsulates both local spatial and temporal struc-ture for disparity estimation. By capturing spatial and tem-poral structure in this unified fashion, both sources of in-formation combine to yield disparity estimates that are nat-urally temporal coherent, while helping to resolve matches that might be ambiguous when either source is considered alone. Further, by allowing subsets of the orientation mea-surements to support different disparity estimates, an ap-proach to recovering multilayer disparity from spacetime stereo is realized. The approach has been implemented with real-time performance on commodity GPUs. Empir-ical evaluation shows that the approach yields qualitatively and quantitatively superior disparity estimates in compari-son to various alternative approaches, including the ability to provide accurate multilayer estimates in the presence of (semi)transparent and specular surfaces. 1

Differential Tracking through Sampling and Linearizing the Local Appearance Manifold

Recovering motion information from input camera image sequences is a classic problem of computer vision. Conventional approaches estimate motion from either dense optical flow or sparse feature correspondences identified across successive image frames. Among other things, performance depends on the accuracy of the feature detection, which can be problematic in scenes that exhibit view-dependent geometric or photometric behaviors such as occlusion, semitransparancy, specularity and curved reflections. Beyond feature measurements, researchers have also developed approaches that directly utilize appearance (intensity) measurements. Such appearance-based approaches eliminate the need for feature extraction and avoid the difficulty of identifying correspondences. However the simplicity of on-line processing of image features is usually traded for complexity in off-line modeling of the appearance function. Because the appearance function is typically very nonlinear, learning it usually requires an impractically large number of training samples. I will present a novel appearance-based framework that can be used to estimate rigid motion in a manner that is computationally simple and does not require global modeling of the appearance function. The basic idea is as follows. An n-pixel image can be considered as a point in an n-dimensional appearance space. When an object in the scene or the camera moves, the image point moves along a low-dimensional appearance manifold. While globally nonlinear, the appearance manifold can be locally linearized using a small number of nearby image samples. This linear approximation of the local appearance manifold defines a mapping between the images and the underlying motion parameters, allowing the motion estimation to be formulated as solving a linear system. I will address three key issues related to motion estimation: how to acquire local appearance samples, how to derive a local linear approximation given appearance samples, and whether the linear approximation is sufficiently close to the real local appearance manifold. In addition I will present a novel approach to motion segmentation that utilizes the same appearance-based framework to classify individual image pixels into groups associated with different underlying rigid motions

Contributions to image-based object reconstruction : geometric and photometric aspects

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Phenomenological modeling of image irradiance for non-Lambertian surfaces under natural illumination.

Various vision tasks are usually confronted by appearance variations due to changes of illumination. For instance, in a recognition system, it has been shown that the variability in human face appearance is owed to changes to lighting conditions rather than person\u27s identity. Theoretically, due to the arbitrariness of the lighting function, the space of all possible images of a fixed-pose object under all possible illumination conditions is infinite dimensional. Nonetheless, it has been proven that the set of images of a convex Lambertian surface under distant illumination lies near a low dimensional linear subspace. This result was also extended to include non-Lambertian objects with non-convex geometry. As such, vision applications, concerned with the recovery of illumination, reflectance or surface geometry from images, would benefit from a low-dimensional generative model which captures appearance variations w.r.t. illumination conditions and surface reflectance properties. This enables the formulation of such inverse problems as parameter estimation. Typically, subspace construction boils to performing a dimensionality reduction scheme, e.g. Principal Component Analysis (PCA), on a large set of (real/synthesized) images of object(s) of interest with fixed pose but different illumination conditions. However, this approach has two major problems. First, the acquired/rendered image ensemble should be statistically significant vis-a-vis capturing the full behavior of the sources of variations that is of interest, in particular illumination and reflectance. Second, the curse of dimensionality hinders numerical methods such as Singular Value Decomposition (SVD) which becomes intractable especially with large number of large-sized realizations in the image ensemble. One way to bypass the need of large image ensemble is to construct appearance subspaces using phenomenological models which capture appearance variations through mathematical abstraction of the reflection process. In particular, the harmonic expansion of the image irradiance equation can be used to derive an analytic subspace to represent images under fixed pose but different illumination conditions where the image irradiance equation has been formulated in a convolution framework. Due to their low-frequency nature, irradiance signals can be represented using low-order basis functions, where Spherical Harmonics (SH) has been extensively adopted. Typically, an ideal solution to the image irradiance (appearance) modeling problem should be able to incorporate complex illumination, cast shadows as well as realistic surface reflectance properties, while moving away from the simplifying assumptions of Lambertian reflectance and single-source distant illumination. By handling arbitrary complex illumination and non-Lambertian reflectance, the appearance model proposed in this dissertation moves the state of the art closer to the ideal solution. This work primarily addresses the geometrical compliance of the hemispherical basis for representing surface reflectance while presenting a compact, yet accurate representation for arbitrary materials. To maintain the plausibility of the resulting appearance, the proposed basis is constructed in a manner that satisfies the Helmholtz reciprocity property while avoiding high computational complexity. It is believed that having the illumination and surface reflectance represented in the spherical and hemispherical domains respectively, while complying with the physical properties of the surface reflectance would provide better approximation accuracy of image irradiance when compared to the representation in the spherical domain. Discounting subsurface scattering and surface emittance, this work proposes a surface reflectance basis, based on hemispherical harmonics (HSH), defined on the Cartesian product of the incoming and outgoing local hemispheres (i.e. w.r.t. surface points). This basis obeys physical properties of surface reflectance involving reciprocity and energy conservation. The basis functions are validated using analytical reflectance models as well as scattered reflectance measurements which might violate the Helmholtz reciprocity property (this can be filtered out through the process of projecting them on the subspace spanned by the proposed basis, where the reciprocity property is preserved in the least-squares sense). The image formation process of isotropic surfaces under arbitrary distant illumination is also formulated in the frequency space where the orthogonality relation between illumination and reflectance bases is encoded in what is termed as irradiance harmonics. Such harmonics decouple the effect of illumination and reflectance from the underlying pose and geometry. Further, a bilinear approach to analytically construct irradiance subspace is proposed in order to tackle the inherent problem of small-sample-size and curse of dimensionality. The process of finding the analytic subspace is posed as establishing a relation between its principal components and that of the irradiance harmonics basis functions. It is also shown how to incorporate prior information about natural illumination and real-world surface reflectance characteristics in order to capture the full behavior of complex illumination and non-Lambertian reflectance. The use of the presented theoretical framework to develop practical algorithms for shape recovery is further presented where the hitherto assumed Lambertian assumption is relaxed. With a single image of unknown general illumination, the underlying geometrical structure can be recovered while accounting explicitly for object reflectance characteristics (e.g. human skin types for facial images and teeth reflectance for human jaw reconstruction) as well as complex illumination conditions. Experiments on synthetic and real images illustrate the robustness of the proposed appearance model vis-a-vis illumination variation. Keywords: computer vision, computer graphics, shading, illumination modeling, reflectance representation, image irradiance, frequency space representations, {hemi)spherical harmonics, analytic bilinear PCA, model-based bilinear PCA, 3D shape reconstruction, statistical shape from shading

Simulation of Visual Servoing in Grasping Objects Moving by Newtonian Dynamics

Robot control systems and other manufacturing equipment are traditionally closed systems. This circumstance has hampered system integration of manipulators, sensors as well as other equipment, and such system integration has often been made at an unsuitably high hierarchical level. With the aid of vision, visual feedback is used to guide the robot manipulator to the target. This hand-to-target task is fairly easy if the target is static in Cartesian space. However, if the target is dynamic in motion, a model of the dynamics behaviour is required in order for the robot to track and intercept the target. The purpose of this project is to simulate in a virtual environment to show how to organise robot control systems with sensor integration. This project is a simulation that involves catching a thrown virtual ball using a six degree-of-freedom virtual robot and two virtual digital cameras. Tasks to be executed in this project include placement of virtual digital cameras, segmentation and tracking of the moving virtual ball as well as model-based prediction of the virtual ball's trajectory. Consideration have to be given to the placement of the virtual digital cameras so that the whole trajectory of the ball can be captured by both the virtual digital cameras simultaneously. In order to track the trajectory of the virtual ball, the image of the ball captured by the digital cameras has to be segmented from its background. Then a model is to be developed to predict the trajectory of the virtual ball so that the virtual robot can be controlled to align itself to grasp the moving virtual ball

Functionalization and Characterization of Cell Membrane Models towards Controlling Cell Differentiation in Pluripotent Tissue Explants

The major aim of this thesis is to create a novel in vitro model substrate for the controlled cell differentiation in pluripotent tissue sheets by fine-tuning the balance between cell-cell (in plane) and cell-matrix (out of plane) interactions. As the model substrate to culture tissue explants, planar lipid membranes deposited on solid substrates (supported membranes) were functionalized with the extracellular domain of Xenopus cadherin-11 (Xcad-11), expressed in embryos during the neural crest cell (NCC) differentiation. In Chapter 4, the quantitative functionalization of supported membranes with Xcad-11 was confirmed by the combination of specular X-ray reflectivity (XRR), grazing incidence X-ray fluorescence (GIXF), and quartz crystal microbalance with dissipation (QCM-D). In Chapter 5, "animal cap" tissue sheets isolated from blastula stage embryos of Xenopus laevis were placed on membranes functionalized with Xcad-11.Interactions of the isolated tissue with membranes sheets displaying Xcad-11 were investigated by the combination of reflection interference contrast microscopy (RICM) and fluorescence microscopy. The capability of such a model system to induce the NCC differentiation was demonstrated by the activation of NCC marker gene marker slug by enhanced green fluorescent signal of the fusion protein. In Chapter 6, the two opposite concentration gradients of two recombinant proteins was established by means of "membrane electrophoresis", utilizing the fluidic nature of supported membranes. This allows for the creation of one-dimensional gradients of morphogens (e.g. Wnt and BMP) on supported membranes, which can guide the development of three-dimensional tissues in vitro

Material Recognition Meets 3D Reconstruction : Novel Tools for Efficient, Automatic Acquisition Systems

For decades, the accurate acquisition of geometry and reflectance properties has represented one of the major objectives in computer vision and computer graphics with many applications in industry, entertainment and cultural heritage. Reproducing even the finest details of surface geometry and surface reflectance has become a ubiquitous prerequisite in visual prototyping, advertisement or digital preservation of objects. However, today's acquisition methods are typically designed for only a rather small range of material types. Furthermore, there is still a lack of accurate reconstruction methods for objects with a more complex surface reflectance behavior beyond diffuse reflectance. In addition to accurate acquisition techniques, the demand for creating large quantities of digital contents also pushes the focus towards fully automatic and highly efficient solutions that allow for masses of objects to be acquired as fast as possible. This thesis is dedicated to the investigation of basic components that allow an efficient, automatic acquisition process. We argue that such an efficient, automatic acquisition can be realized when material recognition "meets" 3D reconstruction and we will demonstrate that reliably recognizing the materials of the considered object allows a more efficient geometry acquisition. Therefore, the main objectives of this thesis are given by the development of novel, robust geometry acquisition techniques for surface materials beyond diffuse surface reflectance, and the development of novel, robust techniques for material recognition. In the context of 3D geometry acquisition, we introduce an improvement of structured light systems, which are capable of robustly acquiring objects ranging from diffuse surface reflectance to even specular surface reflectance with a sufficient diffuse component. We demonstrate that the resolution of the reconstruction can be increased significantly for multi-camera, multi-projector structured light systems by using overlappings of patterns that have been projected under different projector poses. As the reconstructions obtained by applying such triangulation-based techniques still contain high-frequency noise due to inaccurately localized correspondences established for images acquired under different viewpoints, we furthermore introduce a novel geometry acquisition technique that complements the structured light system with additional photometric normals and results in significantly more accurate reconstructions. In addition, we also present a novel method to acquire the 3D shape of mirroring objects with complex surface geometry. The aforementioned investigations on 3D reconstruction are accompanied by the development of novel tools for reliable material recognition which can be used in an initial step to recognize the present surface materials and, hence, to efficiently select the subsequently applied appropriate acquisition techniques based on these classified materials. In the scope of this thesis, we therefore focus on material recognition for scenarios with controlled illumination as given in lab environments as well as scenarios with natural illumination that are given in photographs of typical daily life scenes. Finally, based on the techniques developed in this thesis, we provide novel concepts towards efficient, automatic acquisition systems