Projective Image Research Articles

Value of projectional imaging relative to cross-sectional imaging to assess catheter tip position in the superior vena cava: Evaluation of reader variability.

The assessment of correct positioning of central venous catheters (CVC) is of major concern to avoid complications.Vascular access associations have established the cavo-atrial junction (CAJ) as the most appropriate CVC tip location. Among the different chest X-ray (CXR) landmarks proposed for assessing tip position relative to the CAJ, only the pericardial reflection lies in the same plane as the vascular structures assessed. Although extensively used, CXR has been criticized for its difficulty in estimating catheter tip location.We aimed to evaluate the observer variability to determine tip positioning on CXR. CT imaging was used as a gold standard for tip identification. 107 CT scans of patients wearing port access catheter devices realized at the XXX University Hospital between January and December 2021 were retrospectively analyzed. Distance from tip to cavo-atrial junction (DCAJ) was measured on topogram projectional imaging (PJ) and axial cross-sectional imaging (CS) by 2 × 2 observers (within and between evaluations). Observational statistics were reported using a paired t-test, repeatability coefficients (RC), and the intraclass correlation coefficient (ICC), and they were displayed comprehensively using Bland-Altman plots. All ICC were >0.9, indicating excellent reliability. The mean difference between observers comparing CS and PJ was 0.13 ± 0.80 cm (P = 0.10) with outer 95% confidence limits of 1.92 cm and -2.17 cm and an RC of 1.79 cm. CXR tip-position reading remains an accurate method for determining CVC localization. However, the assessment variability on CXR is plus or minus 2 cm and should be considered. Chest X-ray (CXR) for the assessment of tip position is subject to intra-individual and inter-individual variation. On CT, the variability is solely linked to determining the cavoatrial junction (CAJ) and represented about 1 cm. On CXR, there is a twofold challenge: the correct determination of the CAJ and the accurate identification of the catheter tip, leading to a greater variability of 2 cm.In clinical practice, while considering the 3 cm anatomical zone around the CAJ acceptable, operators should be aware of the 2 cm variability resulting from CXR assessment. To account for this variability and avoid the risk of positioning the tip beyond 3 cm from the CAJ (theoretically up to plus or minus 5 cm away from the CAJ), operators should reduce the CXR-based acceptable zone to 1 cm around the CAJ. This change may impact up to thirty percent of procedures.

We are wales: taking the hospital to the community - an engagement experiment

Abstract In Wales we are facing two major barriers to public health engagement. The first is describing our communities as ‘hard to reach’ when in public service we make little effort to reach them, and second, we do not consider in population level research or practice how we are perpetuating the sense of, and real, inequality in our communities that our work on social determinants of health and health inequalities seeks to address. In Wales we want to take the ‘hospital to the community’; to meet people where they are at, where they live and work and where they experience barriers to inclusion, discrimination, lack of trust and fear. ‘We are Wales’ is is built on the Museum of the United Nations concept of a global we - except we will start small with a small nation that as yet has not worked out how to include and involve everyone in our communities in the policy and practice decisions that may help, or hinder us all from leading a healthier, happier life. ‘We are Wales’ is a simple concept; it is about amplifying and connecting people who would not normally do so, because they do not have the resources or capacity to travel or take time out. It is about equal power in a connection to find solutions to adversity and trauma caused by structural inequality; it is about relationships, inclusion, kindness and compassion - and listening, sharing and taking action without judgement. We have taken a dome to communities in economically deprived areas of Wales and stimulated conversation between politicians, young people and communities around the concept of home. Using a virtual reality dome to project images to encourage discussion we have provided a safe space to amplify and include the voices of those who want to be heard and deserve to be listened to. Key messages • ‘Take the hospital to the community’ - meet people where they are at and feel safe. • power sharing and an equal place at the table stimulates ideas and knowledge change in a trauma-informed way.

Extended-depth of field random illumination microscopy, EDF-RIM, provides super-resolved projective imaging

The ultimate aim of fluorescence microscopy is to achieve high-resolution imaging of increasingly larger biological samples. Extended depth of field presents a potential solution to accelerate imaging of large samples when compression of information along the optical axis is not detrimental to the interpretation of images. We have implemented an extended depth of field (EDF) approach in a random illumination microscope (RIM). RIM uses multiple speckled illuminations and variance data processing to double the resolution. It is particularly adapted to the imaging of thick samples as it does not require the knowledge of illumination patterns. We demonstrate highly-resolved projective images of biological tissues and cells. Compared to a sequential scan of the imaged volume with conventional 2D-RIM, EDF-RIM allows an order of magnitude improvement in speed and light dose reduction, with comparable resolution. As the axial information is lost in an EDF modality, we propose a method to retrieve the sample topography for samples that are organized in cell sheets.

Fast X-ray trimodal computed tomography with an improved diffraction enhanced imaging method

The rapid acquisition of projective images with low radiation dose is essential in computed tomography with diffraction enhanced imaging to extract absorption, refraction, and scattering images from weakly absorbing specimens. This plays a critical role in applying diffraction enhanced imaging to biological and medical imaging. In this study, an improved diffraction enhanced imaging method is proposed to rapidly implement X-ray trimodal computed tomography. This method positions the sample in a specific region near the analyzer crystal, allowing the simultaneous acquisition of transmitted and diffracted images in a single exposure. When combined with computed tomography, three different sample properties, known as absorption, phase and scattering, can be reconstructed simultaneously by collecting the projective images as the sample rotates from 0° to 360°. The experimental results demonstrate that this straightforward method enables the quantitative extraction of sample information and simplifies the data acquisition procedure in computed tomography with diffraction enhanced imaging. Therefore, this novel method offers the advantages of straightforward imaging device, rapid CT data acquisition, low radiation dose and more comprehensive sample information extraction.

Method for 3D light-field reconstruction based on a spatially free camera

This study introduces an advanced algorithm for 3D light-field displays, utilizing disparity images from non-fixed cameras to achieve precise 3D reconstructions, moving beyond the complexities of traditional methods. Unlike standard approaches that depend on complex 3D reconstruction or specific camera arrangements, this algorithm uses pixel similarity across different viewpoints to determine depth accurately. It integrates camera parameters and light paths to restore light-field information meticulously, ensuring an optimal light distribution. By comparing with the projector image array (PIA) and display effects generated by traditional light-field encoding algorithms, we proved the effectiveness of our algorithm.

Charitable crowdfunding donation-intention estimation depending on emotional project images using fNIRS-based functional connectivity.

Charitable fundraising increasingly relies on online crowdfunding platforms. Project images of charitable crowdfunding use emotional appeals to promote helping behavior. Negative emotions are commonly used to motivate helping behavior because the image of a happy child may not motivate donors to donate as willingly. However, some research has found that happy images can be more beneficial. These contradictory results suggest that the emotional valence of project imagery and how fundraisers frame project images effectively remain debatable. Thus, we compared and analyzed brain activation differences in the prefrontal cortex governing human emotions depending on donation decisions using functional near-infrared spectroscopy, a neuroimaging device. We advance existing theory on charitable behavior by demonstrating that little correlation exists in donation intentions and brain activity between negative and positive project images, which is consistent with survey results on donation intentions by victim image. We also discovered quantitative brain hemodynamic signal variations between donors and nondonors, which can predict and detect donor mental brain functioning using functional connectivity, that is, the statistical dependence between the time series of electrophysiological activity and oxygenated hemodynamic levels in the prefrontal cortex. These findings are critical in developing future marketing strategies for online charitable crowdfunding platforms, especially project images.

Enhancing Real-Time Pyramid Holographic Display Through Iterative Algorithm Optimization for 3D Image Reconstruction

Holography, a crucial technology for 3D visualization, strives to create realistic relief images. This research aims to enhance hologram quality and viewer experience by optimizing the image-processing pipeline. Conventional holographic displays face challenges due to their bulkiness and limited viewing angles. To overcome these limitations, this study proposes a novel approach that integrates digital holography with holographic pyramid technology. Digital holography uses computer algorithms for hologram generation, while holographic pyramid technology projects images onto a reflective pyramid for 3D display. The drawback of holographic pyramid displays in low-light environments is addressed through increased diffraction to enhance image resolution. This integrated approach involves comprehensive research, including an examination of existing methods. The anticipated outcome is holograms with improved visibility and resolution from multiple angles. The research presents an initial image preprocessing phase, succeeded by sophisticated processing employing iterative algorithms. This aims to diminish the image size while upholding its quality, thereby achieving an image suitable for pyramidal display. The fusion of digital holography and holographic pyramid display shows promise for immersive visual experiences. However, advancements in processing techniques may lead to increased material complexity, posing a challenge. Through this research, the system aims to unlock creative potentials and pave the way for enhanced holographic displays in various applications.

DESIGN OF CORPORATE CLOTHING: AESTHETIC AND ERGONOMIC ASPECTS

The article is devoted to the process of designing corporate clothing for employees and students of higher education of the Khmelnytskyi National University, which is considered as a system of "corporate style → corporate image → corporate clothing and its identity", including logos, colors, graphic elements and graphic standards of visual identification of the KhNU and their structure. Scientific novelty. The most characteristic artistic and compositional solutions and the typology of graphic elements used in the corporate style of KhNU as a means of communication for the creation of the image and brand of the higher education institution were analyzed and highlighted. Practical meaning. The developed systematization of the graphic elements of the corporate style of KhNU made it possible to generalize their artistic and compositional features and use them in the design of men's and women's corporate clothing for employees and students of higher education of the Khmelnytskyi National University for visual representation and broadcasting of the values and main characteristics of the educational institution. The process of creating a sketch and harmonizing the project image of corporate clothing with the help of an information model, which allows to present it as a single symbolic system with the help of modern practices of visual presentation of fashion innovations, is demonstrated. The design stages of corporate clothing samples are presented with the help of computer technologies, which provide clothing designers with wide visual and artistic opportunities in the context of modern communication practices. Artistic and design, constructive and technological solutions for men's and women's corporate clothing models were developed, which were produced under the conditions of the Ukrainian fashion brand ARTY Sport using a calender thermopress for sublimation. Testing of experimental samples of products took place at the exhibition dedicated to the Day of Science (Khmelnytskyi, KhNU, 2022). Research materials can be used in the development of corporate style and design of modern corporate clothing. The results of the study can be used in further scientific research on the design of other types of corporate clothing for employees and students of higher education of the Khmelnytskyi National University.

Robust deep fuzzy [formula omitted]-means clustering for image data

Image clustering is a difficult task with important application value in computer vision. The key to this task is the quality of images features. Most of current clustering methods encounter the challenge. That is, the process of feature learning and clustering operates independently. To address this problem, several researchers have been dedicated to performing feature learning and deep clustering together. However, the obtained features lack discriminability to address high-dimensional data successfully. To deal with this issue, we propose a novel model named as robust deep fuzzy K-means clustering (RD-FKC), which efficiently projects image samples into a representative embedding space and precisely learns membership degrees into a combined framework. Specifically, RD-FKC introduces Laplacian regularization technique to preserve locality properties of data. Moreover, by using an adaptive loss function, the model becomes more robust to diverse types of outliers. Furthermore, to avoid the latent space being distorted and make the extracted features retain the original information as much as possible, the model introduces reconstruction error and adds regularization to network parameters. Finally, an effective algorithm is derived to solve the optimization model. Numerous experiments have been conducted, illustrating the advantages and superiority of RD-FKC over existing clustering approaches.

Dense viewpoint encoding of 3D light fields based on neural graphics primitives

The enormous rendering time is a challenge for generating the Projector Image Array (PIA) using parallax images. Based on Neural Radiance Fields (NeRF), a novel method for directly rendering sub-images of PIA is proposed to accelerate light field encoding. The method is a unique combination of 3D light field coding and recent instant Neural Graphics Primitives (NGP) technique, showing how to obtain high-quality PIA quickly. The NGP is used to accelerate the training and rendering of the NeRF, and to improve the quality of the rendered parallax images by utilizing a multi-resolution hash grid container. The light ray of the traditional light field coding method is viewed as the key object linking the display system and the implicit radiance field. By encoding the light field of the actual display system as input to the NGP network, the proposed method can directly obtain the sub-PIA displayed in a single projector. For a projector with a resolution of 720×1280, the sub-PIA rendering time is only 25ms. The PIA is then synthesized from all the sub-PIAs that have been rendered. Numerical simulation results show that this method runs faster than the traditional method with high-quality displays. Light field display experiments are conducted to demonstrate the method's feasibility.

LMD: Faster Image Reconstruction with Latent Masking Diffusion

As a class of fruitful approaches, diffusion probabilistic models (DPMs) have shown excellent advantages in high-resolution image reconstruction. On the other hand, masked autoencoders (MAEs), as popular self-supervised vision learners, have demonstrated simpler and more effective image reconstruction and transfer capabilities on downstream tasks. However, they all require extremely high training costs, either due to inherent high temporal-dependence (i.e., excessively long diffusion steps) or due to artificially low spatial-dependence (i.e., human-formulated high mask ratio, such as 0.75). To the end, this paper presents LMD, a faster image reconstruction framework with Latent Masking Diffusion. First, we propose to project and reconstruct images in latent space through a pre-trained variational autoencoder, which is theoretically more efficient than in the pixel-based space. Then, we combine the advantages of MAEs and DPMs to design a progressive masking diffusion model, which gradually increases the masking proportion by three different schedulers and reconstructs the latent features from simple to difficult, without sequentially performing denoising diffusion as in DPMs or using fixed high masking ratio as in MAEs, so as to alleviate the high training time-consumption predicament. Our approach allows for learning high-capacity models and accelerate their training (by 3x or more) and barely reduces the original accuracy. Inference speed in downstream tasks also significantly outperforms the previous approaches.

A Coupled Calibration Method for Dual Cameras-Projector System with Sub-Pixel Accuracy Feature Extraction.

Binocular structured light systems are widely used in 3D measurements. In the condition of complex and local highly reflective scenes, to obtain more 3D information, binocular systems are usually divided into two pairs of devices, each having a Single Camera and a Projector (SCP). In this case, the binocular system can be seen as Dual Cameras-Projector (DCP) system. In the DCP calibration, the Left-SCP and Right-SCP need to be calibrated separately, which leads to inconsistent parameters for the same projector, thus reducing the measurement accuracy. To solve this problem and improve manoeuvrability, a coupled calibration method using an orthogonal phase target is proposed. The 3D coordinates on a phase target are uniquely determined by the binocular camera in DCP, rather than being calculated separately in each SCP. This ensures the consistency of the projector parameters. The coordinates of the projector image plane are calculated through the unwrapped phase, while the parameters are calibrated by the plane calibration method. In order to extract sub-pixel accuracy feature points, a method based on polynomial fitting using an orthogonal phase target is exploited. The experimental results show that the reprojection error of our method is less than 0.033 pixels, which improves the calibration accuracy.

Enhancing legibility in educational settings: Optimizing projection illuminance for varied indoor ambient illuminance

Projectors are common display devices in the educational setting. However, dim projector lightbulbs or well-lit classrooms may cause blurriness in the projected image. To determine the optimal projection light under different ambient light conditions, the conjoint effects of projection illuminance and ambient illuminance on the legibility of projection images indoors were investigated. Participants (N = 96) were randomly assigned to one of six indoor ambient light conditions (0, 40, 80, 120, 160, and 200 lx) and performed a visual search task under several projection illuminance conditions (200, 250, 300, 350, and 400 lx). The accuracy and correct response time on the task were collected to evaluate the participants’ visual performance to represent legibility. The optimal projection illuminance (high visual accuracy and fast reaction) was 400 lx (944 ANSI lumen) under all ambient light conditions. To avoid low legibility (accuracy<0.6) and maintain acceptable legibility (accuracy>0.7), the projection illuminance should be increased as the indoor ambient light increases.

Massacres in the Arnsberg Forest (abstract)

In recent years, an historical reappraisal by one of us (MW) has been carried out on one of the worst crimes outside of prisons and concentration camps committed in Germany by the SS and Wehrmacht in the final months of the Second World War. In the Arnsberg Forest near Warstein and Meschede (southern Westphalia, western Germany), 208 forced labourers were massacred by the 'Division for Vengeance' (Division zur Vergeltung) of SS General Hans Kammler in March 1945. In the course of this joint project, archaeological research conducted by the Olpe Department of the LWL-Archäologie für Westfalen (Westphalian Archaeological Heritage Service) has pinpointed the exact location of the crime scenes and provided clues for the reconstruction of the course of events in the field (Warstein Langenbachtal, Suttrop, Meschede-Eversberg). It has also enabled the recovery of finds belonging to both the victims and perpetrators. We have also been able to locate and examine the two temporary cemeteries (Warstein Melkeplätzchen, Suttrop). This has provided information about the history of exhumation of the victims in 1964, as well as clarifying the whereabouts of the Soviet obelisk from the Melkeplätzchen, the provisional cemetery of the Langenbach victims located close to the crime scene. This work has also allowed a follow-up search in the temporary cemetery in Suttrop for seven victims who, according to the exhumation documents of 1964, are presumably still lying there. Between 2018 and 2021, almost all of aspects addressed above could be clarified with new insights gained. Our results were summarised at the EAC conference in Bonn 2023 (although not published in this theme). Several publications already report on them and they are listed below. In addition to several German language publications, there is also an extensive English language publication (Baales et al. 2022) where the essential aspects of the interdisciplinary project are presented. In addition, the historical context, research project and numerous images are presented on several websites listed below.

Precast concrete project image dataset for deep learning object detection

Computer vision technology in precast concrete (PC) projects has the potential to enhance site management but faces challenges due to the lack of specialized datasets for this field. This study developed a PC components dataset for object detection (PCCODA) in four steps: component range selection, image collection, image preprocessing, and labeling. The performance of PCCODA was assessed using multiple algorithms, including You Only Look Once (YOLO), YOLO X, faster region-based convolutional neural networks (Faster R-CNN), and Double Head R-CNN. The dataset resulted in average accuracies of 0.88–0.97, average robustness of 0.84–0.96, and average detection speed of 3.64–23.3 frames per second. This performance fulfills the level required by most studies on construction automation. The applicability of the dataset was tested at a real site. This study contributes to the off-site construction project management theory and enhances productivity in PC projects by automating object detection-related tasks.

PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation

Four-dimensional conebeam computed tomography (4D CBCT) is an efficient technique to overcome motion artifacts caused by organ motion during breathing. 4D CBCT reconstruction in a single scan usually divides projections into different groups of sparsely sampled data based on the respiratory phases. The reconstructed images within each group present poor image quality due to the limited number of projections. To improve the image quality of 4D CBCT in a single scan, we propose a novel reconstruction scheme that combines prior knowledge with motion compensation. We apply the reconstructed images of the full projections within a single routine as prior knowledge, providing structural information for the network to enhance the restoration structure. The prior network (PN-Net) is proposed to extract features of prior knowledge and fuse them with the sparsely sampled data using an attention mechanism. The prior knowledge guides the reconstruction process to restore the approximate organ structure and alleviates severe streaking artifacts. The deformation vector field (DVF) extracted using deformable image registration among different phases is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction algorithm to generate 4D CBCT images. Proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Compared with previous methods, our approach exhibits significant improvements across various evaluation metrics.

Digital diplomacy against international stigmatization: the Bukele case

Abstract The article addresses the diversity of digital diplomatic strategies used by governmental actors to fight international stigmatization. Faced with structural limitations, the leaders of small states use it to defend their political project's image or reputation when they come under international criticism for deviating from or transgressing international norms. Borrowing from Erving Goffman, I draw on the idea that social networks can be conceptualized as orders of interaction, in which digital diplomatic strategies can be put in place and international politicization and normative debates can develop. An examination of Salvadoran president Nayib Bukele's Twitter posts from January 2019 to May 2022, during a period of increasing international criticism regarding the authoritarian–populist drift in El Salvador, improves our knowledge of the diversity of digital diplomatic strategies. Consequently, this enlightens us on the constitutive elements of how small states manage their relationship with external actors, their image and status, and eventually how they challenge the international order, international norms and powers.

Learning from prior geological information for geotechnical soil stratification with tree-based methods

Geotechnical subsurface stratification based on sparse measurements presents a significant challenge. Learning from prior geological information, such as learning soil layer distribution patterns from stratification results (2D images) of adjacent data-rich projects, is an emerging approach to reducing uncertainties caused by data scarcity. Existing methods rely on pixel-based techniques that require training images to have identical soil types as the testing image. Additionally, pixel-based methods are prone to error accumulation. To address these issues, this study introduces a new framework that focuses solely on learning boundary information from training image rather than soil types. This eliminates the need for matching soil types between training and testing images. Tree-based model is proposed to learn boundary information from training images. Contrary to conventional tree-based models that use coordinates as input, this study employs a set of designed distance fields (boundary dictionary) to represent complex boundary patterns. A selection process is introduced to identify the most important distance fields from the training images. Using these selected distance fields for model input results in soil stratification that aligns well with both borehole data and training image boundaries. The proposed method's efficacy is validated through multiple simulated and real-world cases. The proposed method outperforms pixel-based methods in multiple cases, achieving up to a 25% improvement in accuracy. This method is robust and it yields consistent results for a variety of training images considered. Additionally, the proposed method also provides quantification of interpolation uncertainty through the Gini impurity method.

Development of Powtoon Media on Natural Resources Material to Increase Learning Interest and Logical Thinking of Learners in Elementary School

This study aims to develop animated videos that can help project images of various natural resources with technological advances. In the era of society 5.0, educators must be able to use more interesting learning methods. This audio-visual media will increase students' interest in learning. In addition, it is also to find out the feasibility and effectiveness of the results of developing PowToon-based animated videos. This study uses the Research and Development (RnD) method. This research model uses ADDIE, namely analysis, design, development, implementation, and evaluation. Data collection techniques used are questionnaires and tests in descriptive quantitative data. The media that will be used for learning is said to be valid by the three experts as evidenced by the results: (1) material experts have a validity percentage of 81 percent (2) media experts are 84 percent, (3) language experts are 75 percent. After being declared valid, the media was tested on class IV SDN Kalanganyar students. Based on the pre-test's t-test analysis, the average value is 60.0, and the post-test is 78.5. Because the value of sig. (2-tailed) or test 1 of 0.00, which means is less than or equal to 0.05, it can be concluded that Ho is rejected, and Ha is accepted. The conclusions that can be drawn from the results of this study indicate that there is a significant influence from animated video so that can increase interest in learning and logical thinking of fourth-grade students at SDN Kalanganyar.

Detecting Adversarial Examples on Deep Neural Networks With Mutual Information Neural Estimation

Despite achieving exceptional performance, deep neural networks (DNNs) suffer from the harassment caused by adversarial examples, which are produced by corrupting clean examples with tiny perturbations. Many powerful defense methods have been presented such as training data augmentation and input reconstruction which, however, usually rely on the prior knowledge of the targeted models or attacks. A clean example and its adversarial version are very similar but have different high-level representations in a victim model. If we can obtain a space in which the representations of similar examples are also similar, then adversarial examples can be picked out by comparing the representations of input examples in this space and the high-level space of the victim model. Inspired by this, we propose a novel approach for detecting adversarial images, which can protect any pre-trained DNN classifiers and resist an endless stream of new attacks. Specifically, we first adopt a dual autoencoder to project images to a latent space. The dual autoencoder uses the self-supervised learning to ensure that small modifications to samples do not significantly alter their latent representations. Next, the mutual information neural estimation is utilized to enhance the discrimination of the latent representations. We then leverage the prior distribution matching to regularize the latent representations. To easily compare the representations of examples in the two spaces, and not rely on the prior knowledge of the targeted model, a simple fully connected neural network is used to embed the learned representations into an eigenspace, which is consistent with the output eigenspace of the targeted model. Through the distribution similarity of an input example in the two eigenspaces, we can judge whether the input example is adversarial or not. Extensive experiments on MNIST, CIFAR-10, and ImageNet show that the proposed method has superior defense performance and transferability than state-of-the-arts.