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.
The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, ...which enables insights into the learned representation, as well as robustness and speed which make it suitable for real-world and real-time use. The model consists of multiple layers, each composed of a number of parts. The hierarchical nature of the model corresponds well to hierarchical structures in music. The parts in lower layers correspond to low-level concepts (e.g. tone partials), while the parts in higher layers combine lower-level representations into more complex concepts (tones, chords). The layers are learned in an unsupervised manner from music signals. Parts in each layer are compositions of parts from previous layers based on statistical co-occurrences as the driving force of the learning process. In the paper, we present the model's structure and compare it to other hierarchical approaches in the field of music information retrieval. We evaluate the model's performance for the multiple fundamental frequency estimation. Finally, we elaborate on extensions of the model towards other music information retrieval tasks.
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.
Trubadur je odprtokodna platforma za urjenje glasbenega posluha z avtomatiziranimi vajami ritmičnega in intervalnega nareka. Platformo smo ovrednotili z dijaki Konservatorija za glasbo in balet ...Ljubljana v šolskih letih 2018/19–2020/21. Rezultati evalvacije so pokazali, da lahko uporaba platforme poveča uspešnost pri testih in predstavlja dopolnitev učenja na daljavo.
Herein, we report a computational algorithm that follows a spectroscopist-driven elucidation process of the structure of an organic molecule based on IR, 1H and 13C NMR, and MS tabular data. The ...algorithm is independent from database searching and is based on a bottom-up approach, building the molecular structure from small structural fragments visible in spectra. It employs an analytical combinatorial approach with a graph search technique to determine the connectivity of structural fragments that is based on the analysis of the NMR spectra, to connect the identified structural fragments into a molecular structure. After the process is completed, the interface lists the compound candidates, which are visualized by the WolframAlpha computational knowledge engine within the interface. The candidates are ranked according to the predefined rules for analyzing the spectral data. The developed elucidator has a user-friendly web interface and is publicly available (http://schmarnica.si).
Troubadour platform is an open-source personalized and adaptive web platform for ear training. The platform was developed to support music theory classes with automated music-theory-related ...exercises. In this paper, we present our three-stage development methodology, which incorporated the needs and feedback from both teachers and students to build an engaging music-theory e-learning platform with gamification elements. We developed and evaluated the platform with students of the Conservatory of Music and Ballet Ljubljana. The students of the 1st and 2nd year of the programme were split into two groups-the control group used the traditional way of learning, while the test group augmented their learning with the Troubadour platform. The evaluation results show an increase in exam performance and corroborate the platform's user experience as one of the key reasons for the students' engagement.
This paper presents a model capable of learning the rhythmic characteristics of a music signal through unsupervised learning. The model learns a multi-layer hierarchy of rhythmic patterns ranging ...from simple structures on lower layers to more complex patterns on higher layers. The learned hierarchy is fully transparent, which enables observation and explanation of the structure of the learned patterns. The model employs tempo-invariant encoding of patterns and can thus learn and perform inference on tempo-varying and noisy input data. We demonstrate the model’s capabilities of learning distinctive rhythmic structures of different music genres using unsupervised learning. To test its robustness, we show how the model can efficiently extract rhythmic structures in songs with changing time signatures and live recordings. Additionally, the model’s time-complexity is empirically tested to show its usability for analysis-related applications.
We present the development and evaluation of a gamified rhythmic dictation application for music theory learning. The application was developed as a web application in the existing Troubadour ...platform for music ear training. The focus of the developed application was on user experience and engagement. With regards to the former, we developed a responsive and intuitive user interface; for the latter, we included several gamification elements and assessed the impact on the students’ engagement. We report on students’ experience analysis done through questionnaires and background data collected through the platform. We evaluated the rhythmic dictation application with the conservatory-level music theory students through A/B testing in order to independently evaluate the application’s impact. The results show a significant impact of the application on the students’ exam scores.
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