Tomographic reconstruction of transparent objects

The measurement of transparent objects has always been regarded as one of the difficult problems in 3D reconstruction technology. For transparent objects conforming to the birefringence model, we propose a method for measuring the parameters of transparent objects based on multi-wavelength incident light. This paper analyzes the transparent objects where light enters the birefringence model. Because the transparent objects refract light of different wavelengths differently, when the lights with different wavelengths emitted from the same point and same direction passes through the transparent objects, the position and direction of light with different wavelength will come out differently. The position and direction of these outgoing rays are related to the refractive index and surface topography of the transparent objects. By obtaining the equations of these outgoing rays, we can derive the equations of the refractive index and surface topography of the transparent objects. In this paper, the incident light obtained by computer simulation is used as the initial data, and the simulated annealing algorithm is used to verify the model. The final results show that within the range of random error values given by the computer, the surface shape, curvature, thickness and refractive index of transparent objects with small error values can be obtained. At the same time, this paper introduces the Ceres Solver C++ nonlinear optimization library into the simulated annealing algorithm, which improves the speed and accuracy of the algorithm and greatly enhances the robustness of the algorithm.

Phase shifting profilometry is an important technique for reconstructing the three-dimensional (3D) geometry of objects with purely diffuse surfaces. However, it is challenging to measure the transparent objects due to the pattern aliasing caused by light refraction and multiple reflections inside the object. In this work, we analyze the aliasing fringe pattern formation for transparent objects and then, propose to learn the front surface light intensity distribution based on the formation principle by using the diffusion models for generating the non-aliased fringe patterns reflected from the front surface only. With the generated fringe patterns, the 3D shape of the transparent objects can be reconstructed via the conventional structured light. We show the feasibility and performance of the proposed method on the data of purely transparent objects that are not seen in the training stage. Moreover, we found it could be generalized to other cases with local-transparent and translucent objects, showing the potential capability of the diffusion based learnable framework in tackling the problems of transparent object reconstruction.

This paper introduces a novel 3D measurement scheme based on UV laser triangulation to ascertain the shape of transparent objects. Transparent objects are extremely difficult to scan with traditional 3D scanners because of the refraction problem observed in the visible range. Therefore, the object surface needs to be preliminary powdered before being digitized with commercial scanners. Our approach consists of using non contact measurement scheme while dealing with the refraction problem in visible environment. The object shape is computed by classical triangulation method based on stereovision constraint. The proposed acquisition system is composed of two classical visible range cameras and a UV laser source. The exploitation of the UV laser for triangulation system characterizes the novelty of the proposed approach. The fluorescence generated by the UV radiation enables to acquire 3D data of transparent surface with a classical stereovision scheme.

Structured light is a common tool to observe the 3D shape of objects. However, most of these sensor systems assume that the objects scatter light diffusely and that there is only a moderate amount of ambient light. They are not able to deal with specular or transparent objects or with the ambient lighting from direct sunlight. We have built a scanning laser line striper that works even in bright sunlight and is able to observe optically challenging objects. In this paper we will show how the sensor can be used to measure the 3D shape of reflective objects and objects behind a semi-transparent plane.

Fluid drag is a force that opposes relative motion between fluid layers or between solids and surrounding fluids. For a stationary solid in a moving fluid, it is the amount of force necessary to keep the object stationary in the moving fluid. In addition to fluid and flow conditions, pressure drag on a solid object is dependent on the size and shape of the object. The aim of this project is to compute the shape of a stationary 2D object of size 3.5 m2 that minimizes drag for different Reynolds numbers. We solve the problem in the context of shape optimization, making use of shape sensitivity analysis. The state variables are fluid pressure and velocity modeled by the Navier-Stokes equation with cost function given by the fluid drag which depends on the state variables. The geometric constraint is removed by constructing a Lagrangian function. Subsequent application of shape sensitivity analysis on the Lagrangian generates the shape derivative and gradient. Our optimization routine uses a variational form of the sequential quadratic programming (SQP) method with the Hessian replaced by a variational form for the shape gradient. The numerical implementation is done in Python while the open source finite element package, FEniCS, is used to solve all the partial differential equations. Remeshing of the computational domain to improve mesh quality is carried out with the open source 2D mesh generator, Triangle. Final shapes for low Reynolds numbers resemble an american football while shapes for moderate to high Reynolds numbers are more streamlined in the tail end of the object than at the front.

This paper examines the problem of reconstructing a voxelized representation of 3D space from a series of images. An iterative algorithm is used to find the scene model which jointly explains all the observed images by determining which region of space is responsible for each of the observations. The current approach formulates the problem as one of optimization over estimates of these responsibilities. The process converges to a distribution of responsibility which accurately reflects the constraints provided by the observations, the positions and shape of both solid and transparent objects, and the uncertainty which remains. Reconstruction is robust, and gracefully represents regions of space in which there is little certainty about the exact structure due to limited, non-existent, or contradicting data. Rendered images of voxel spaces recovered from synthetic and real observation images are shown.

Background and Objectives: Due to the soft-field effect of the electromagnetic field and the limit of detection, image reconstruction of magnetic induction tomography has to recover more complex electrical characteristics from very few signals. These cause a problem which have underdetermination, nonlinearity, and ill-posed characteristics, and therefore lead to many difficulties in finding its solution. Although many regularization image reconstruction methods exist, they are not suitable for MIT applications due to regularization parameter selection. The purpose of this paper is to study the principle of particle swarm optimization with simulated annealing, and to propose a regularization method for reconstruction, which will provide a new way to solve the MIT image problems. Methods and Models: Firstly, the regularization principle of image reconstruction of MIT will be analyzed. Then the hybrid regularization algorithm, including Tikhonov and NOSER regularization, will be developed, using the dimension of the Hessian matrix as a penalty term respecting the prior knowledge. PSO-SA algorithm will be applied to obtain an optimal solution for regularization parameters. Finally, six typical numerical models and approximately symmetrical cerebral hemorrhage models by COMSOL will be carried out, and the voltage signals obtained from the simulation will be used to verify the proposed reconstruction method. Results: Through the simulation results, the proposed imaging method has the average CC values of 0.9932, 0.8286 and the average RE values of 0.4982, 0.8320 for simple and complex models, respectively. Moreover, when the SNR changes from 55 dB to 35 dB, the CC value of the cerebral hemorrhage model reduced by 0.1034. The results demonstrate the effectiveness and high theoretical feasibility of the proposed method in MIT image reconstruction. Conclusions: This study indicates the potential application of PSO-SA algorithm in regularization imaging problem. Compared with traditional regularization imaging methods, the proposed method has the advantages of better accuracy, robustness and noise resistance, showing the certain application value in other similar ill-ness imaging problems.

A metamaterial approach to wideband radar cross section reduction that is thin, low-cost and doesn't dictate object shaping is presented in work from Iran. The approach uses principles potentially applicable to infrared and visible light as well as radar. The wideband RCS reducing surface is produced on a commercial high-frequency circuit substrate using conventional printed circuit manufacturing techniques Reducing an object's detectability to radar systems is now a well established priority for military applications, most famously in aircraft applications. ‘Stealth’ aircraft that were once well kept secrets are now a familiar concept to the general public and even fly in air shows. The radar cross section (RCS) of an object is essentially a measure of how detectable an object is to radar systems and so reducing an object's RCS is the basic goal of radar focused ‘stealth’ technologies. There are three main approaches to reducing RCS; radar absorbing material (RAM), object shaping and reflected wave cancelling by object coating. RAM materials, as the name suggests, are designed to reduce radar reflections back to the detector by absorbing the electromagnetic energy transmitted by the radar system, converting it into heat. However, they can be bulky and high maintenance. Object shaping works by shaping the surface of the object to minimise reflections of the radar waves back to the radar system, instead reflecting them in other directions. The obvious disadvantage to this approach is that it dictates the shape of the object, which can compromise the performance of the object for its primary purpose. For example, above all an aircraft must be shaped to enable it to fly, an aircraft that is very hard to detect but cannot take off is of no use to anyone. The work reported in this issue, by researchers at Iran University of Science and Technology (IUST) uses the third approach, in which a surface coating reflects the radar waves in such a way as to reduce the amount of radar energy that returns to the detector. This approach is low profile, low bulk and doesn't dictate the shape of the object. In the IUST team's work it can also be cheaply and simply manufactured using printed circuit board manufacturing techniques. Their proposed structure consist of a chessboard arrangement of two different artificial magnetic conductor (AMC) unit cell patterns, each of which reflects the radar waves differently. The key difference in the reflections is a 180° phase shift, which causes the reflections from the two different AMC cell types to interfere destructively and so significantly reduces the useful energy returned to the radar system's detector. “Since radar systems usually send out a short pulse signal in the time domain, wide bandwidth of the RCS reducer is necessary. The proposed AMC structure achieves a RCS reduction over a very wide bandwidth, about 85%,” said Dr Seid Hasan Sedighy, one of the IUST researchers on this project. The wide bandwidth achieved is possible because the critical 180° phase difference in the reflections is produced by the AMC unit cells over a wide frequency range, from 9.4 GHz to 23.28 GHz, providing 10 dB RCS reduction in that range. The peak RCS reduction is in excess of 40 dB, at 15 GHz. Printed circuit manufacturing techniques were used to create the AMC patterns on a commercial high-frequency circuit substrate, providing a low-cost manufacturing method. As flexible circuit board technologies are now available and continue to develop, this AMC-based, effective and relatively cheap approach to RCS reductions could be applied to arbitrary shapes. AMCs are metamaterial structures that behave as perfect magnetic conductors in a limited range of frequencies. The two unit cell AMC patterns used in this work are an arrangement of ‘E’ shapes and saltire arrows. The patterns were optimised to achieve the desired 180° phase difference in their reflections by simulation before being confirmed by experiment. The RCS reducer consists of a chessboard pattern of AMC patches using two different AMC unit cell designs, which produce reflections 180° out of phase with each other. The resulting destructive interference provides the intended RCS reduction The principles of AMCs and using destructive interference to reduce reflected energy are of course applicable to other frequencies of the EM spectrum, not just the band used for radar. This means that similar effects could be achievable in very different frequencies if the structures can be manufactured. Dr Sedighy expects this to lead to developments closer to photonic frequencies “It seems that this design approach will develop in the other frequency bands such as visible and infrared frequencies, which have many civil and military applications.”

We discuss different holographic materials for thick holograms recording. For material selection we apply various holographic and technological factors. Chalcogenide glasses have good optical properties, low scattering and high resolution for IR application. Dichromated gelatic film and thick layer-gel of dichromated gelatin may be used in visible green-blue light, but these holograms are unstable. Photo polymer PMMA - for hologram recording in visible light we used as base material, which has high holographic quality and good stability. We discuss the synthesis of material and describe the method of latent image for selection of optimal composition and additives. We use the molding method for production of high quality surfaces. A special recording scheme allows the material to record large size elements with highly reproducible parameters. It is shown that in order to obtain highly uniform elements it is necessary to maintain the very strict control of synthesis process. Application of this technology is discussed.

Perceiving the shape of three-dimensional objects is essential for interacting with them in daily life. If objects are constructed from different materials, can the human visual system accurately estimate their three-dimensional shape? We varied the thickness, motion, opacity, and specularity of globally convex objects rendered in a photorealistic environment. These objects were presented under either dynamic or static viewing condition. Observers rated the overall convexity of these objects along the depth axis. Our results show that observers perceived solid transparent objects as flatter than the same objects rendered with opaque reflectance properties. Regional variation in local root-mean-square image contrast was shown to provide information that is predictive of perceived surface convexity.

Weakly electric fish can generate an electric field with their electric organ (EO). Through 14,000 electroreceptors distributed on their skin, they can sense the electric field perturbation induced by a nearby prey or object. Many researchers have studied to reveal the mechanism of electrolocalization. Simulations are typically based on an analytical model which represents the EO as a set of charge, or a model based on finite-element method (FEM) in a 2-dimensional space. In this paper, we show a 3-dimensional FEM model to test the electric perturbation of various shapes of objects. Using this model, we show that a measure of caudal relative slope or tail-side half width at half maximum can estimate the lateral distance of a target object regardless of its size, shape and rostrocaudal position.

Electrical impedance tomography is a non-invasive imaging modality that uses the electrical conductivity distribution to reconstruct images based on potential measurements from the object's surface. The proposed study was to design and fabricate a low-cost and simple reconstruction method for 3D electrical impedance tomography imaging. In this study, we have been successfully developed 3 Dimensional Electrical Impedance Tomography (3D-EIT) system using 16 copper electrodes (Cu) to detect and reconstruct the presence of objects in the Phantom. 3D-EIT was developed using Phantom as a test object with PVC pipe material, with an inner diameter of 7.2 cm and a height of 5.4 cm. Electrodes were arranged in two rings, with each ring having eight electrodes arranged in a planar line. Furthermore, the Gauss-Newton algorithm and Laplace prior regularization were used to image reconstruction of objects inside the Phantom using voltage measurement produced from sequential pairs of neighboring electrodes. The voltage is obtained from the injection of a constant current of 1 mA at 20 kHz into the system's electrode pairs. The objects used in this research are PVC pipe, solid aluminum, PVC pipes filled with wax glue, and copper trusses. The results obtained show that the reconstruction results can provide information about the position, the electrical properties, and the shape of real objects. Finally, the system can detect the location, height, and electrical properties of objects for all variations of angle and height variations.

Methods for symbolic spatial reasoning requires quite often a representation of the objects in multiple resolutions. The traditional methods are in such cases less useful since the objects are either represented in a resolution of an order that is too low or they are based on a structure that does not easily allow the representation of the object in a symbolic or a qualitative way. The approach taken here has been to develop an approach that shares some of the characteristics found in resolution pyramids and quad-trees resulting in a structure represented in terms of symbolic tiles. This symbolic structure can be used for spatial reasoning in applications where, among other things, the purpose is to reason about object shape and relationships where the objects need to be represented in multiple resolutions.

The research for metadata extraction originates from the intelligent video surveillance system, which is widely used in outdoor and indoor environment for the aims of traffic monitor, security guard, and intelligent robot. Various features are extracted from the surveillance image sequences such as target detection, target tracking, object's shape and activities. However, the trend of more and more features being used and shared in video surveillance system calls for more attention to bridge the gap between specific analysis algorithms and end-user's expectation. This paper proposes a three-layer object oriented model to extract the surveillance metadata including shape, motion speed, and trajectory of the object emerging in image sequence. Meanwhile, the high-level semantic metadata including entry/exit point, object duration time is organized and stored which are provided for the further end-user queries. The paper also presents the experiment results in different indoor and outdoor surveillance scenarios. At last, a comparative analysis with another traditional method is presented.

Structured light sensors based on diffuse reflection of projected patterns are widely used to measure the 3D shape of objects, e.g., in industry, medicine, or cultural heritage. Unfortunately, there exist many objects made of uncooperative materials, i.e., materials with optical properties such as being glossy, transparent, absorbent, or translucent, which cannot be measured reliably by this measurement technique. In the last years, we presented a two-step method based on thermal pattern projection which allows the determination of the object surface of these uncooperative objects. In the first step, a multi-fringe thermal pattern is projected onto the measurement object. In the second step, a mid-wave infrared (MWIR) stereo camera records the thermal radiation that is absorbed and re-emitted by the object surface. This system allows us to measure transparent objects. However, the measurement time is with tens of seconds up to minutes quite long and the measurement accuracy should be improved. In this contribution, we present a projection principle of a fast sequentially scanning fringe leading to a significant reduction in measurement periods down to the second range or even below while increasing the measurement accuracy. The work includes a characterization of our MWIR 3D setup for both projection principles, the multifringe and sequential fringe one, regarding the measurement accuracy and speed. We show measurement results obtainted with both projection techniques for an object made of different material classes.