2D Projection Research Articles

RICT: Rotating image computed tomography with a one-to-one reversible image rotation algorithm.

The Mueller, Siddon and Joseph weighting algorithms are frequently used for projection and back-projection, which are relatively complicated when they are implemented in computer code. This study aims to reduce the actual complexity of the projection and back-projection. First, we neglect the exact shape of the pixel, so that its shadow is a rectangle projecting precisely to a detector bin, which implies that all the pixel weights are exactly 1 for each ray through them, otherwise are exactly 0. Next, a one-to-one reversible image rotation algorithm (RIRA) is proposed to compute the projection and back-projection, where two one-to-one mapping lists namely, U and V, are used to store the coordinates of a rotated pixel and its corresponding new coordinates, respectively. For each 2D projection, the projection is simply the column sum in each orientation according to the lists U and V. For each 2D back-projection, it is simply to arrange the projection to the corresponding column element according to the lists U and V. Thus, there is no need for an interpolation in the projection and back-projection. Last, a rotating image computed tomography (RICT) based on RIRA is proposed to reconstruct the image. Experiments show the RICT reconstructs a good image that is close to the result of filtered back-projection (FBP) method according to the RMSE, PSNR and MSSIM values. What's more, our weight, projection and back-projection are much easier to be implemented in computer code than the FBP method. This study demonstrates that the RIRA method has potential to be used to simplify many computed tomography image reconstruction algorithms.

On Galvanometer Laser Projection Positioning to Layups of Large Composite Material

A laser projection positioning technique for large composite production based on a scanning galvanometer is proposed in this paper. First, based on the projecting model of the scanning galvanometer, a solution is proposed for the problem which includes pose calculations of the galvanometer projection and autocorrection technology. Then, according to the solution of the perspective-n-point (PNP) problem in the control software for the pose of the scanning galvanometer relative to the projection object, an improved genetic algorithm is proposed to optimize the results of calculating the pose. Meanwhile, to account for the tangential distortion caused by the perturbation between the scanning galvanometer and the projected object during the actual manufacturing process, the projection pattern is corrected by the perspective transform method, thus ensuring the accuracy of the projection. Eventually, in order to evaluate the proposed method, a general scheme of the projection positioning system is designed, and software is developed for the projection device relative to the pose calibration of the composite material mold and projection image correction. Following that, 3D printing model projection experiment and the large composite layup projection positioning tests are conducted with the experimental prototype of the projection positioning system. The result of the 3D printing model projection experiment shows that the calculating accuracy of the relative pose based on the improved adaptive genetic algorithm achieves 0.0007 mm, which is superior to the 1.115 mm accuracy of the solution of photographing the target with the camera. In addition, after a small deformation of the mold in the actual working conditions, the influence of the target localization point in the PNP problem in 2D and 3D coordinates on the algorithm is compared, and the optimized errors are respectively scaled to 2 mm and 0.2 mm. These numerical simulations and experimental results in working conditions show that the proposed method has high accuracy, high robustness, and fast astringency, and it provides a candidate for projection positioning of large composite material layups.

Location-Guided LiDAR-Based Panoptic Segmentation for Autonomous Driving

The application of artificial intelligence in autonomous driving is becoming increasingly extensive. LiDAR-based 3D point cloud panoptic segmentation is one of the most promising and arduous tasks. Although recent methods have produced promising results, most of them ignore the prior distribution of objects in the 3D point clouds. In this paper, we first investigate the distribution of objects around the heading of vehicles and observe that several objects are severely biased. On the basis of this observation, we use the bird's eye view (BEV) representation to project the 3D point clouds into a 2D image and divide the BEV projection into eight areas. For each area, we apply input-dependent convolution kernels to extract the local feature. These local features are concatenated to the panoptic backbone for panoptic segmentation. We validate our method on the validation and test sets of the SemanticKITTI dataset. The proposed method outperforms all state-of-the-art methods based on 2D projection in terms of higher panoptic quality scores.

UnProjection: Leveraging Inverse-Projections for Visual Analytics of High-Dimensional Data.

Projection techniques are often used to visualize high-dimensional data, allowing users to better understand the overall structure of multi-dimensional spaces on a 2D screen. Although many such methods exist, comparably little work has been done on generalizable methods of inverse-projection - the process of mapping the projected points, or more generally, the projection space back to the original high-dimensional space. In this article we present NNInv, a deep learning technique with the ability to approximate the inverse of any projection or mapping. NNInv learns to reconstruct high-dimensional data from any arbitrary point on a 2D projection space, giving users the ability to interact with the learned high-dimensional representation in a visual analytics system. We provide an analysis of the parameter space of NNInv, and offer guidance in selecting these parameters. We extend validation of the effectiveness of NNInv through a series of quantitative and qualitative analyses. We then demonstrate the method's utility by applying it to three visualization tasks: interactive instance interpolation, classifier agreement, and gradient visualization.

Animal Pose Estimation Based on 3D Priors

Animal pose estimation is very useful in analyzing animal behavior, monitoring animal health and moving trajectories, etc. However, occlusions, complex backgrounds, and unconstrained illumination conditions in wild-animal images often lead to large errors in pose estimation, i.e., the detected key points have large deviations from their true positions in 2D images. In this paper, we propose a method to improve animal pose estimation accuracy by exploiting 3D prior constraints. Firstly, we learn the 3D animal pose dictionary, in which each atom provides prior knowledge about 3D animal poses. Secondly, given the initially estimated 2D animal pose in the image, we represent its latent 3D pose with the learned dictionary. Finally, the representation coefficients are optimized to minimize the difference between the initially estimated 2D pose and the 2D projection of the latent 3D pose. Furthermore, we construct 2D and 3D animal pose datasets, which are used to evaluate the algorithm’s performance and learn the 3D pose dictionary, respectively. Our experimental results demonstrate that the proposed method makes good use of the 3D pose knowledge and can effectively improve 2D animal pose estimation.

3D full-field fringe projection measurement based on imaging view transformation.

To address the loss of measurement information caused by limitation of the field of view in fringe projection profilometry, a 3D full-field measurement based on imaging view transformation with a mirror auxiliary is proposed in this work. In this system, the principle of ideal specular reflection is applied to create a symmetrical structure between the real and virtual images for subsequent imaging view transformation. The phase of images is obtained by a multifrequency and multiple phase-shift sinusoidal fringe projection algorithm, and the real and virtual point clouds of measured objects are obtained by system calibration. The specular reflection matrix is obtained by calculating the relationship between virtual and real corner points. As a result, incomplete real point clouds can be compensated with virtual point clouds for the 3D full-field measurement by imaging view transformation. Compared to the standard ladder, the mean absolute error and the maximum relative error are 0.0706mm and 0.9834%, respectively.

Generating a conformational landscape of ubiquitin chains at atomistic resolution by back-mapping based sampling.

Ubiquitin chains are flexible multidomain proteins that have important biological functions in cellular signalling. Computational studies with all-atom molecular dynamics simulations of the conformational spaces of polyubiquitins can be challenging due to the system size and a multitude of long-lived meta-stable states. Coarse graining is an efficient approach to overcome this problem-at the cost of losing high-resolution details. Recently, we proposed the back-mapping based sampling (BMBS) approach that reintroduces atomistic information into a given coarse grained (CG) sampling based on a two-dimensional (2D) projection of the conformational landscape, produces an atomistic ensemble and allows to systematically compare the ensembles at the two levels of resolution. Here, we apply BMBS to K48-linked tri-ubiquitin, showing its applicability to larger systems than those it was originally introduced on and demonstrating that the algorithm scales very well with system size. In an extension of the original BMBS we test three different seeding strategies, i.e. different approaches from where in the CG landscape atomistic trajectories are initiated. Furthermore, we apply a recently introduced conformational clustering algorithm to the back-mapped atomistic ensemble. Thus, we obtain insight into the structural composition of the 2D landscape and illustrate that the dimensionality reduction algorithm separates different conformational characteristics very well into different regions of the map. This cluster analysis allows us to show how atomistic trajectories sample conformational states, move through the projection space and in sum converge to an atomistic conformational landscape that slightly differs from the original CG map, indicating a correction of flaws in the CG template.

Elastic anisotropy, thermal conductivity and tensile properties of MAX phase V2GaC, Nb2GaC and Ta2GaC: First-principles calculations

MAX phase series materials have excellent thermal, electrical and mechanical properties, and have great research potential. In this work, the structural characteristics, elastic anisotropy, thermal and phonon properties of hexagonal M2GaC (M = V, Nb and Ta) compounds were calculated using the first-principle calculation method. The stress-strain method and Voigt-Reuss-Hill approximation are used to determine the elastic properties of single crystals and polycrystals, respectively. By calculating the formation enthalpy, it is found that M2GaC compounds are stable. Meanwhile, these M2GaC are brittle materials with respect to Poisson's ratio, GH/BH and Cauchy pressure. The elastic anisotropy of M2GaC is illustrated by the elastic anisotropy index, three-dimension (3D) surface configuration and two-dimension (2D) projection, the order of elastic anisotropy is Nb2GaC > Ta2GaC > V2GaC. Finally, the sound velocity, Debye temperature, thermal conductivity and anisotropy are discussed to provide theoretical support for further study of MAX phase compounds.

Beyond ManifoldEM: geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections.

ManifoldEM is an established method of geometric machine learning developed to extract information on conformational motions of molecules from their projections obtained by cryogenic electron microscopy (cryo-EM). In a previous work, in-depth analysis of the properties of manifolds obtained for simulated ground-truth data from molecules exhibiting domain motions has led to improvements of this method, as demonstrated in selected applications of single-particle cryo-EM. In the present work this analysis has been extended to investigate the properties of manifolds constructed by embedding data from synthetic models represented by atomic coordinates in motion, or three-dimensional density maps from biophysical experiments other than single-particle cryo-EM, with extensions to cryo-electron tomography and single-particle imaging with a X-ray free-electron laser. Our theoretical analysis revealed interesting relationships between all these manifolds, which can be exploited in future work.

Cross-Domain Image-Object Retrieval Based on Weighted Optimal Transport

Given a 2D image query and a pool of 3D objects, the goal of image-object retrieval is to rank the 3D objects according to how well their content fits the query. Previous methods usually project 2D images and 3D objects into a joint embedding space and minimize the distance metric to complete the retrieval task. Since 2D images and 3D objects come from two different domains with large discrepancy, even when 3D objects and 2D images are mapped to a shared space, the gap in feature distribution remains significant, which always leads to domain misalignment. In this work, we propose a novel image-object retrieval method by leveraging optimal transport theory. Specifically, to tackle the dimensionality gap between 2D images and 3D objects, we first represent a 3D object via a sequence of its 2D projections. We then design a Cross-Domain View Attention module (CDVA) to automatically compute the optimal combination of 3D object projections given a 2D query image. Next, we exploit Weighted Optimal Transport (WOT)-based distance to depict the discrepancy between 2D images and 3D objects, and reduce the discrepancy to achieve instance-level alignment. Through this scheme, the transported 2D images and 3D objects with the same label are enforced to follow similar distributions. Finally, we design an explicit Category Centroid Alignment module (CCA) to achieve class-level alignment to improve the retrieval performance. Extensive experiments show that our method can achieve competitive performance on the MI3DOR and MI3DOR-2 benchmarks.

Surface Projection of CT Reconstructed Lung Volume for Regional Lung Mechanics Estimation: A Preliminary Analysis

Estimating regional lung volume along bedside is critical for creating a fine level of digital twins model for optimized setting of mechanical ventilation. Non-contact techniques with structured light is capable of reconstructing chest motion to incorporate with ventilator recorded volume to assess the regional lung volume changes between left and right lung, while its extension to a higher resolution of estimation requires a relation map between the surface motion and inside lung capacity distribution. To establish the relation map, a 2D projection of 3D lung CT lung volume is proposed to represent the lung capacity distribution on the chest surface plane in this study. A convolutional neural network (CNN) is designed to implement lung segmentation and reconstruct 3D lung volume via CT scans. The 2D projection map is then compared to the recorded forced vital capacity (FVC) as a surrogate of tidal volume. The test was conducted against two CT datasets for training CNN model and build the 2D surface mapping. The results show a good correlation of R squared value of 0.71 between the overall lung capacity via the proposed 2D projection map against the FVC, thus showing the potential of back calculate the regional volume distribution using measured ventilator tidal volume and structured light surface motion.

Impact of ethyl cellulose variation on microstructural and electrochemical properties of spin coated YSZ electrolyte thin films for SOFCs: Slurry composition evolution

The present work meticulously highlights the physical and electrochemical properties of slurry spin coated Yttria Stabilized Zirconia (YSZ) electrolyte thin films for solid oxide fuel cell (SOFC) applications wherein the concentration of ethyl cellulose (EC) is varied from 3.5 wt% to 6.5 wt% in the EC/YSZ/terpineol slurry. The TGA of the prepared mixtures reveals the complete mass removal of EC in temperature range of 380–400 °C. The dense and crack-free YSZ thin films on NiO/YSZ anode composites are developed by performing four separate coating cycles where each cycle is preceded by baking the films at 380 °C. The prepared YSZ electrolyte thin films showed polycrystalline nature with cubic phased prominent reflection corresponding to plane (111). The crystallite size of YSZ thin films is determined from W–H and Scherrer's relations which is obtained in the range of 18–26 nm and 21–26 nm, respectively. The 3D AFM projection shows hill-like topography where the average roughness is found to be increased from 3.01 nm to 8.49 nm with an increase in the EC content. FESEM analysis unveils smooth morphology along with the presence of some abscission zones in YSZ thin films after the removal of EC. The electrochemical properties are explored by fitting equivalent electrical circuit to the Nyquist data which confirms electrolytic response of the prepared coatings. The thermal activation of ionic conduction has been verified by observing the Arrhenius plots where conductivity is found to be varied in range 10 −4 -10 −5 S/cm with the respective increase in EC content. The activation energies of YSZ electrolyte thin films are found to be in the range of 1.09–1.18 eV which are slightly higher as compared to that of bulk YSZ.

Jan Sawka: Theatre of Image and Metaphor

Jan Sawka: Theatre of Image and Metaphor

6D Phase space diagnostics based on adaptively tuned physics-informed generative convolutional neural networks

A physics-informed generative convolutional neural network (CNN)-based 6D phase space diagnostic is presented which generates all 15 unique 2D projections (x, y), (x, y′),...,(z, E) of a charged particle beam’s 6D phase space (x, y, z, x′, y′, E). The CNN is trained by supervised learning over a wide range of input beam distributions, accelerator parameters, and the associated 6D beam phase spaces at multiple accelerator locations. The CNN is applied in an un-supervised adaptive manner without knowledge of the input beam distribution or accelerator parameters and is robust to their unknown time variation. Adaptive feedback automatically tunes the low-dimensional latent space of the encoder-decoder CNN to predict the 6D phase space based only on 2D (z, E) longitudinal phase space measurements from a device such as a transverse deflecting RF cavity (TCAV). This method has the potential to provide diagnostics beyond the existing state of the art at many accelerator facilities. Studies are presented for two very different accelerators: the 5-meter-long ultra-fast electron diffraction (UED) HiRES compact accelerator at LBNL and the kilometer long plasma wakefield accelerator FACET-II at SLAC.

Circulating Emotions: UNESCO's original initiative on Artificial Intelligence

We propose to analyze how emotions and stereotypes circulate, using the example of recent UNESCO publications in artificial intelligence (AI). Yuri Lotman outlined two types of restrictions imposed by culture on human behavior, regulated by shame or fear, respectively. This distinction coincides with the division of the collective into ‘we’ and ‘they’ groups. The circulation of emotions is a semiotic process, and in the discourse on AI, the primary emotion is fear. Today, our attitude to artificial intelligence is regulated by fear and is based on dividing the collective into ‘we’ (ours, people) and ‘they’ (others, AI). However, several international organizations are discussing issues related to the ethics of AI. They propose moving from a system of relations with AI based on fear (‘we’ – ‘he’) to regulation based on shame (creating a new ethical community around ‘we’). This community should be understood in the spirit of anthroposemiotic studies of recent years: human interaction, collectives, habitats, and imaginations. In 2022, UNESCO launched an original graphic project to block the circulation of AI-associated fears and stereotypes. The graphic novel Inside AI is presented in several language versions. The versions of the title can be interpreted as illustrating types of semiotic existence: virtual (in Spanish), actual (in French), and realized (in English). The task of blocking the circulation of fear and overcoming it by immersing yourself in the world of AI is carried out through the prism of gender discourse. Readers are invited to take a journey into the world of AI in the company of four characters, three women, and one man. Specific gender features and fears mark each of them's discourse. Today, as the function of graphics as a marker of metalinguistic expression increases, the UNESCO graphic novel is intended to combat fears and stop the circulation of gender stereotypes simultaneously.

Reconstructing a 3D Medical Image from a Few 2D Projections Using a B-Spline-Based Deformable Transformation

(1) Background: There was a need for 3D image reconstruction from a series of 2D projections in medical applications. However, additional exposure to X-ray projections may harm human health. To alleviate it, minimizing the projection numbers is a solution to reduce X-ray exposure, but this would cause significant image noise and artifacts. (2) Purpose: In this study, a method was proposed for the reconstruction of a 3D image from a minimal set of 2D X-ray projections using a B-spline-based deformable transformation. (3) Methods: The inputs of this method were a 3D image which was acquired in previous treatment and used as a prior image and a minimal set of 2D projections which were acquired during the current treatment. The goal was to reconstruct a new 3D image in current treatment from the two inputs. The new 3D image was deformed from the prior image via the displacement matrixes that were interpolated by the B-spline coefficients. The B-spline coefficients were solved with the objective function, which was defined as the mean square error between the reconstructed and the ground-truth projections. In the optimization process the gradient of the objective function was calculated, and the B-spline coefficients were then updated. For the acceleration purpose, the computation of the 2D and 3D image reconstructions and B-spline interpolation were implemented on a graphics processing unit (GPU). (4) Results: When the scan angles were more than 60°, the image quality was significantly improved, and the reconstructed image was comparable to that of the ground-truth image. As the scan angles were less than 30°, the image quality was significantly degraded. The influence of the scan orientation on the image quality was minor. With the application of GPU acceleration, the reconstruction efficiency was improved by hundred times compared to that of the conventional CPU. (5) Conclusions: The proposed method was able to generate a high-quality 3D image using a few 2D projections, which amount to ~ 20% of the total projections required for a standard image. The introduction of the B-spline-interpolated displacement matrix was effective in the suppressing noise in the reconstructed image. This method could significantly reduce the imaging time and the radiation exposure of patients under treatment.

Symmetries and Dynamics of Generalized Biquaternionic Julia Sets Defined by Various Polynomials

Higher-dimensional hypercomplex fractal sets are getting more and more attention because of the discovery of more and more interesting properties and visual aesthetics. In this study, the attention was focused on generalized biquaternionic Julia sets and a generalization of classical Julia sets, defined by power and monic higher-order polynomials. Despite complex and quaternionic Julia sets, their biquaternionic analogues are still not well investigated. The performed morphological analysis of 3D projections of these sets allowed for definition of symmetries, limit shapes, and similarities with other fractal sets of this class. Visual observations were confirmed by stability analysis for initial cycles, which confirm similarities with the complex, bicomplex, and quaternionic Julia sets, as well as manifested differences between the considered formulations of representing polynomials.

Appropriate Technology-Based Project-Based Learning: 3D Printing Utilization for Learning Media Class

Indonesia has more than ten thousand islands, making it an archipelago; therefore, the distribution of learning media and facilities over the country is difficult. This condition may affect the learning material delivery and the student's understanding. Thus, the do-it-yourself (DIY) learning media helps to overcome this condition. We investigated the usefulness of applying appropriate technology through the 3D printing technique to fabricate learning media inspired by local wisdom to meet the needs of learning media. The students were guided by a project-based learning (PjBL) method to find the problem, design their learning media, fabricate it using a 3D printer, and expected to be applied to deliver the teaching material in the class. This study aimed to observe the implementation of the designing process based on theory to reality. A qualitative method and questionnaire were used to measure student's responses. We found that PjBL learning based on a 3D printing project increases students' motivation through creativity and effectiveness in delivering the teaching materials. Furthermore, 3D printed-based learning media can easily be fabricated, producing less waste.

Lyapunov Exponents and Kaplan-yorke Dimension for Fivedimensional System

This work introduces a new high-dimensional five-dimensional system with chaotic and periodic solutions. For special values of parameters, we calculate the Kaplan- Yorke dimension and we show the dynamics of Lyapunov exponents. Some definitions and propositions are given. The main intent is to use the 2D and 3D projections of the 5D trajectories on different subspaces, to construct the graphs of solutions for understanding and managing the system. Visualizations where possible, are provided.

Structural, elastic, optical and thermodynamic properties of CdGeP2 from theoretical prediction

We have studied the structural, elastic anisotropy, electronic properties and optical properties of CdGeP2 crystal by using first-principles method. The effect of pressure on the elastic constants of CdGeP2 was explored. The changes in the anisotropy of the CdGeP2 at different pressures were analyzed in detail by three-dimensional (3D) and 2D projections of bulk modulus and Young's modulus. The electronic properties, including the band structure, density of states, were investigated, and it turned out that CdGeP2 is a semiconductor. Furthermore, we analyze the dielectric function, refractive index, extinction coefficient, absorption coefficient, and electrical conductivity of CdGeP2 in detail. Finally, the influence laws of temperature and pressure on Debye temperature, heat capacity and Grüneisen parameter are obtained, respectively