Most real-time terrain point cloud rendering techniques do not address the empty space between the points but rather try to minimize it by changing the way the points are rendered by either rendering ...them bigger or with more appropriate shapes such as paraboloids. In this work, we propose an alternative approach to point cloud rendering, which addresses the empty space between the points and tries to fill it with appropriate values to achieve the best possible output. The proposed approach runs in real time and outperforms several existing point cloud rendering techniques in terms of speed and render quality.
We present a combined volume and surface rendering technique with global illumination caching. Our approach uses volumetric path tracing to compute the global illumination volume and local shading ...models for rendering the isosurface. By joining both visualization approaches, we have enhanced the display and illumination of the surfaces while preserving physically realistic illumination of the participating media. To achieve real-time performance and avoid recomputing the image when the camera view changes, we compute the global illumination volume incrementally and defer the projection to a later step. We evaluated our technique by comparing different local shading models for isosurface rendering with the result of full volumetric path tracing and with the non-caching variant of our technique. Results show that the caching and non-caching variants perform comparably well, while the caching variant has the added benefit of being camera-view-independent. Additionally, we show that our approach emphasizes the surfaces within volumes better than volumetric path tracing.
Direct point-cloud visualisation is a common approach for visualising large datasets of aerial terrain LiDAR scans. However, because of the limitations of the acquisition technique, such ...visualisations often lack the desired visual appeal and quality, mostly because certain types of objects are incomplete or entirely missing (e.g., missing water surfaces, missing building walls and missing parts of the terrain). To improve the quality of direct LiDAR point-cloud rendering, we present a point-cloud processing pipeline that uses data fusion to augment the data with additional points on water surfaces, building walls and terrain through the use of vector maps of water surfaces and building outlines. In the last step of the pipeline, we also add colour information, and calculate point normals for illumination of individual points to make the final visualisation more visually appealing. We evaluate our approach on several parts of the Slovenian LiDAR dataset.
A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation ...and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones.
Automatic segmentation of intracellular compartments is a powerful technique, which provides quantitative data about presence, spatial distribution, structure and consequently the function of cells. ...With the recent development of high throughput volumetric data acquisition techniques in electron microscopy (EM), manual segmentation is becoming a major bottleneck of the process. To aid the cell research, we propose a technique for automatic segmentation of mitochondria and endolysosomes obtained from urinary bladder urothelial cells by the dual beam EM technique. We present a novel publicly available volumetric EM dataset – the first of urothelial cells, evaluate several state-of-the-art segmentation methods on the new dataset and present a novel segmentation pipeline, which is based on supervised deep learning and includes mechanisms that reduce the impact of dependencies in the input data, artefacts and annotation errors. We show that our approach outperforms the compared methods on the proposed dataset.
•A novel public volumetric data-set of cellular ultra-structure in electron microscopy volumes.•A new state-of-the-art pipeline for segmentation of mitochondria and endo-lysosomes.•Contrast enhancement with transfer learning improves segmentation of unbalanced EM data.
Cybersecurity research demands continuous monitoring of the dynamic threat landscape to detect novel attacks. Researchers and security professionals often deploy honeypot networks to intercept and ...examine real attack data. However, due to the volume and variety of the collected data, it is very challenging for security analysts to investigate the attacks, compare their characteristics and infer their potential connections. To this end, we propose a novel graph-based cyberattack model for storing, analyzing, and visualizing honeynet-captured attacks as the main contribution of our work. Our model enables attack graph analysis and presents the attack data analogous to the Cyber Kill Chain framework to enable intuitive visualizations. We construct the attack graph by decomposing the intercepted attacks into a set of unique entities (represented as nodes) and actions (represented as edges) and merge them into a global attack graph. We develop a user-centric, interactive attack analysis and visualization tool that leverages the proposed model to aid the heuristic cyberattack investigation. We describe the design and technical implementation of the developed model and visual-interactive tool in detail. Finally, we demonstrate the developed tools and validate the model in an analysis of real-world attack data captured on our own distributed honeypot platform. We use the attack model and (sub)graph visualizations to depict attack topologies, identify recurring attackers, and quantify detected malware types. We also leverage graph data science algorithms to uncover and rank malware distribution networks, reveal hidden links between the attackers, and cluster the attack entities to identify potential botnets.
ROOT-Eve (REve), the new generation of the ROOT event-display module, uses a web server-client model to guarantee exact data translation from the experiments’ data analysis frameworks to users’ ...browsers. Data is then displayed in various views, including high-precision 2D and 3D graphics views, currently driven by THREE.js rendering engine based on WebGL technology. RenderCore, a computer graphics research-oriented rendering engine, has been integrated into REve to optimize rendering performance and enable the use of state-of-the-art techniques for object highlighting and object selection. It also allowed for the implementation of optimized instanced rendering through the usage of custom shaders and rendering pipeline modifications. To further the impact of this investment and ensure the long-term viability of REve, RenderCore is being refactored on top of WebGPU, the next-generation GPU interface for browsers that supports compute shaders, storage textures and introduces significant improvements in GPU utilization. This has led to optimization of in-terchange data formats, decreased server-client traffic, and improved offloading of data visualization algorithms to the GPU. FireworksWeb, a physics analysis-oriented event display of the CMS experiment, is used to demonstrate the results, focusing on high-granularity calorimeters and targeting high data-volume events of heavy-ion collisions and High-Luminosity LHC. The next steps and directions are also discussed.
Unmanned aerial vehicles and battleships are equipped with the infrared search and tracking (IRST) systems for its mission to search and detect targets even in low visibility environments. However, ...infrared sensors are easily affected by diverse types of conditions, therefore most of IRST systems need to apply advanced contrast enhancement (CE) methods to cope with the low dynamic range of sensor output and image saturation. The general histogram equalization for infrared images has unwanted side effects such as low contrast expansion and saturation. Also, the local area processing for saturation reduction has been studied to solve the problems regarding the saturation and non-uniformity. We propose the cross fusion based adaptive contrast enhancement with three counter non-uniformity methods. We evaluate the proposed method and compare it with conventional CE methods using the discrete entropy, PSNR, SSIM, RMSE, and computation time indexes. We present the experimental results for images from various products using several datasets such as infrared, multi-spectral satellite, surveillance, general gray and color images, as well as video sequences. The results are compared using the integrated image quality measurement index and they show that the proposed method maintains its performance on various degraded datasets.
The importance of self-regulated learning (SRL) has increased during the COVID-19 pandemic and measures for assessing students' self-regulation skills and knowledge are greatly needed. We present the ...results of the first thorough adaptation of the Children's Perceived use of Self-Regulated Learning Inventory (CP-SRLI). The inventory, consisting of 15 scales measuring nine components of SRL, was administered to a sample of 541 Slovenian ninth graders. Confirmatory factor analyses supported internal structure validity of most components, but two components required some structural modifications. Internal consistency coefficients were acceptable for the majority of scale scores and were highly comparable to the original ones. While metric invariance across gender was confirmed, the scalar invariance of some scales needs further examination. Meaningful correlations with relevant externally assessed and self-reported self-regulation and school performance variables indicated good criterion validity of the inventory. The Slovenian version of the CP-SRLI thus proved to be a sufficiently valid and reliable instrument for assessing pupils' learning self-regulation.
The article presents a method for segmentation of ethnomusicological field recordings. Field recordings are integral documents of folk music performances captured in the field, and typically contain ...performances, intertwined with interviews and commentaries. As these are live recordings, captured in non-ideal conditions, they usually contain significant background noise. We present a segmentation method that segments field recordings into individual units labelled as speech, solo singing, choir singing, and instrumentals. Classification is based on convolutional deep networks, and is augmented with a probabilistic approach for segmentation. We describe the dataset gathered for the task and the tools developed for gathering the reference annotations. We outline a deep network architecture based on residual modules for labelling short audio segments and compare it to the more standard feature based approaches, where an improvement in classification accuracy of over 10% was obtained. We also present the SeFiRe segmentation tool that incorporates the presented segmentation method.
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