Shape Reconstruction Technique Research Articles

Topological and shape gradient strategy for solving geometrical inverse problems

In this paper we present a technique for shape reconstruction based on the topological and shape gradients. The shape in consideration is a solution of an inverse conductivity problem. To solve such a problem numerically, we compute the topological gradient of a Kohn–Vogelius-type cost function when the domain under consideration is perturbed by the introduction of a small inclusion instead of a hole. The reconstruction is done by considering the shape as a superposition of very thin elliptic inclusions to get a first approximation. Then, we use a gradient-type algorithm to perform a good reconstruction. Various numerical experiments of single and multiple inclusions demonstrate the viability of the designed algorithm.

INVERSE SCATTERING SHAPE RECONSTRUCTION OF 3D BACTERIA USING THE LEVEL SET ALGORITHM

Bacteria exist in a variety of groups of shapes, sizes, and single or multiple cell formations. In this paper, the level set shape reconstruction technique, the method of moments, and the marching cubes methods are integrated in the high frequency band for imaging three dimensional bacteria. The time step and the resolution of the marching cubes method are investigated to smooth the error function of the level set and hence speed up the convergence at high frequencies. The numerical results demonstrate the robustness of the level set algorithm for the detection of bacteria based on their shapes. The three dimensional shape reconstructions of unknown bacteria can be utilized to classify biological warfare agents.

Temporally coherent completion of dynamic shapes

We present a novel shape completion technique for creating temporally coherent watertight surfaces from real-time captured dynamic performances. Because of occlusions and low surface albedo, scanned mesh sequences typically exhibit large holes that persist over extended periods of time. Most conventional dynamic shape reconstruction techniques rely on template models or assume slow deformations in the input data. Our framework sidesteps these requirements and directly initializes shape completion with topology derived from the visual hull. To seal the holes with patches that are consistent with the subject's motion, we first minimize surface bending energies in each frame to ensure smooth transitions across hole boundaries. Temporally coherent dynamics of surface patches are obtained by unwarping all frames within a time window using accurate interframe correspondences. Aggregated surface samples are then filtered with a temporal visibility kernel that maximizes the use of nonoccluded surfaces. A key benefit of our shape completion strategy is that it does not rely on long-range correspondences or a template model. Consequently, our method does not suffer error accumulation typically introduced by noise, large deformations, and drastic topological changes. We illustrate the effectiveness of our method on several high-resolution scans of human performances captured with a state-of-the-art multiview 3D acquisition system.

Prospects of aberration correction for lattice-resolved electron tomography

Electron tomography is a valuable 3D characterization technique, so far limited to a nanometer scale resolution. This paper intends to explore the extension of the technique to the atomic scale. Computer simulations of three HREM technologies are performed on a CeO2 crystal and the effects of the voltage, focus spread and aberration correction are assessed. Experimental challenges related to the technique are discussed and illustrated on HREM images of a CeO2 nanoparticle acquired with the JEM 2200FS aberration-corrected TEM. Finally, geometric tomography is proposed as a complementary shape reconstruction technique when only few zone-axis tilt images are used for the lattice-resolved reconstruction.

Detecting and imaging dielectric objects from real data: A shape-based approach

In this paper we investigate the performance of a shape-reconstruction technique as tested on the ‘Marseille data’. This approach, which is based on a level set technique, offers several advantages compared to other approaches, as for example well-defined boundaries and the incorporation of an intrinsic regularization in the form of a priori assumptions regarding the general structures in the medium. The level set strategy (which is an implicit representation of the shapes) frees us from topological restrictions during this reconstruction process. Our algorithm is aiming at, not only detecting the objects, but simultaneously determining their approximate locations, sizes and dielectric properties. The numerical experiments show the utility of this method.

Microwave Imaging for Early Breast Cancer Detection Using a Shape-based Strategy

In this paper, we propose and analyze a novel shape reconstruction technique for the early detection of breast cancer from microwave data, which is based on a level-set technique. The shape-based approach offers several advantages compared to more traditional pixel-based approaches when targeting the reconstruction of key characteristics of a hidden tumor such as its correct size, shape, and static permittivity value. In addition to these key characteristics of hidden tumors, we aim at estimating the correct interfaces between fatty and fibroglandular tissue in the breast and their internal permittivity profiles. The level set strategy (which is an implicit representation of the shapes) frees us from topological restrictions when reconstructing an a priori arbitrary number of tumors and the often quite complicated interfaces between fatty and fibroglandular regions. The presented strategy is able to detect and, in most cases, characterize tumors whose sizes (diameters) are much smaller than the wavelengths of the electromagnetic waves that are used for illuminating the breast. We present numerical results for a 2-D model with two distinct tissue types (fatty and fibroglandular) in the interior of the breast (in addition to a possible tumor and the surrounding skin). Our results demonstrate the performance and potential of our scheme in various simulated but realistic situations.

Non-referential, self-compared shape defect inspection for bond pads with deformed shapes

Automated visual inspection algorithms for printed circuit boards have long been focused on geometric defects of conductive paths that comprise regularly straight line and circular arc segments. In this study, we aim at defect inspection of substrate bond pads consisting of rotated and deformed shapes. A non-referential, self-comparison machine vision scheme is proposed in this paper for shape defect detection of bond pads. It is invariant to translation and orientation, and is tolerable to shape deformation of faultless objects. A shape reconstruction technique based on Fourier transforms is first applied to restore an inspection object with varying smoothness. Two discrimination features are then extracted from the point-to-point distances between the original and reconstructed shapes. The first discrimination feature measures the global irregularity of the whole boundary of the inspection object, and the second feature measures the maximum local deviation of the object boundary. Experimental results of numerous real bond pad samples have shown that the proposed self-comparison scheme with the two discrimination features can well separate the clusters of faultless and defective samples.

Reservoir characterization using stochastic initializations and the level set method

In this work we present a novel level set technique for shape reconstruction in history matching for reservoirs with two or more kinds of rocks (the so-called lithofacies ) using stochastic initializations. In the paper we discuss the use of sequential Gaussian simulation for the creation of geostatistical initial guesses which will then be applied to an earlier introduced level set based shape reconstruction algorithm for the history matching problem in reservoir characterization. The shapes or regions with sharp interfaces to each other are represented by a level set function which needs to be determined from the production data gathered at few locations of the reservoir. We present numerical results in 2D which demonstrate that our method is able to provide reliable estimates of this structure from these relatively few production data even though the topology of the unknown regions is a priori unknown.

A level set evolution strategy in microwave imaging for early breast cancer detection

In this paper we propose and analyse a novel shape-reconstruction technique for the early detection of breast cancer from microwave data, which is based on a level-set technique. The shape-based approach offers several advantages compared to more traditional pixel-based approaches, as, for example, well-defined boundaries and the incorporation of an intrinsic regularization (in form of a-priori assumptions regarding the general anatomical structures present in the medium) that reduces the dimensionality of the inverse problem and thereby stabilizing the reconstruction. The level set strategy (which is an implicit representation of the shapes) frees us from topological restrictions during this reconstruction process. We present numerical results in 2D which demonstrate the performance of our scheme in various simulated realistic situations.

A level set method for 3D low frequency electromagnetic imaging with applications in geophysical prospecting

AbstractWe propose a novel shape reconstruction technique for controlled source 3D low frequency electromagnetic induction tomography which uses a level set representation of the shapes. The main application which we have in mind in this paper is geophysical prospecting, in particular the monitoring of reservoir flooding processes in secondary oil recovery and the investigation of conductive geophysical structures at a depth of up to a few hundred metres. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

An experimental study on damage detection of structures using a timber beam

Using vibration methods for the damage detection and structural health monitoring in bridge structures is rapidly developing. However, very little work has so far been reported on timber bridges. This paper intends to address such shortcomings by experimental investigation on a timber beam using a vibration based method to detect damage. A promising damage detection algorithm based on modal strain energy was adopted and modified to locate/evaluate damage. A laboratory investigation was conducted on a timber beam inflicted with various damage scenarios using modal tests. The modal parameters obtained from the undamaged and damaged state of the test beam were used in the computation of damage index, were then applied using a damage detection algorithm utilising modal strain energy and a statistical approach to detect location of damage. A mode shape reconstruction technique was used to enhance the capability of the damage detection algorithm with limited number of sensors. The test results and analysis show that location of damage can be accurately identified with limited sensors. The modified method is less dependent on the number of modes selected and can detect damage with a higher degree of confidence.

A technique for shape reconstruction for nuclear electronic signals in sampled systems

During the analysis of data taken by RD16 (FERMI) 1 1 FERMI is a collaboration with the aim of designing integrated electronics for the read out of calorimeter detectors in particle physics experiments at hadron collider. It includes: CERN, Geneva, Switzerland; Department of Physics and Measurement Technology, University of Linköping, Sweden; Center for Industrial Microelectronics and Materials Technology, University of Linköping, Sweden; LPNHE Universities Paris VI-VII, Paris, France; Dipartimento di Elettronica, Politecnico di Milano, and INFN, Pavia, Italy; Dipartimento di Fisica Nucleare e Teorica dell’Università and INFN, Pavia, Italy; Dipartimento di Elettronica dell’ Università and INFN, Pavia, Italy; Manne Siegbahn Institute pf Physics, Stockhlom, Sweden; SiCon AB, Linköping, Sweden; ABB-Hafo AB, Järfälla, Sweden; Swedish Microsystem AB, Linköping, Sweden. (Allipi et al., Nucl. Instr. and Meth. A 344 (1994) 180–184; Benetta et al., CERN/LHCC/95-28), the capability of reconstructing the pulse shape from sampled data proved to be of crucial importance. We developed an original approach for the reconstruction of a nonbandwidth-limited signal shape, using as input data many realizations of the same pulse shape sampled with a phase randomly variable with respect to the clock sequence. If the phase distribution is known and spans the whole sampling period, the limitation deriving by the Nyquist criterion can be overcome and the pulse shape univocally determined.

Unification scheme for 3D surface reconstruction using physically based models

AbstractWe propose a unification framework for three‐dimensional shape reconstruction using physically based models. A variety of 3D shape reconstruction techniques have been developed in the past two decades, such as shape from stereopsis, from shading, from texture gradient, and from structured lighting. However, the lack of a general theory that unifies these shape reconstruction techniques into one framework hinders the effort of a synergistical image interpretation scheme using multiple sensors/information sources. Most shape‐from‐X techniques use an “observable” (e.g., the stereo disparity, intensity, or texture gradient) and a model, which is based on specific domain knowledge (e.g., the triangulation principle, reflectance function, or texture distortion equation) to predict the observable, in 3D shape reconstruction. We show that all these “observable–prediction‐model” types of techniques can be incorporated into our framework of energy constraint on a flexible, deformable image frame. In our algorithm, if the observable does not confirm to the predictions obtained using the corresponding model, a large “error” potential results. The error potential gradient forces the flexible image frame to deform in space. The deformation brings the flexible image frame to “wrap” onto the surface of the imaged 3D object. Surface reconstruction is thus achieved through a “package wrapping” or a “shape deformation” process by minimizing the discrepancy in the observable and the model prediction. The dynamics of such a wrapping process are governed by the least action principle which is physically correct. A physically based model is essential in this general shape reconstruction framework because of its capability to recover the desired 3D shape, to provide an animation sequence of the reconstruction, and to include the regularization principle into the theory of surface reconstruction.