Using Self-Contradiction to Learn Confidence Measures in Stereo Vision

Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that involves manual interaction, active sensing devices and/or synthetic scenes. To overcome this problem, we propose a new, flexible, and scalable way for generating training data that only requires a set of stereo images as input. The key idea of our approach is to use different view points for reasoning about contradictions and consistencies between multiple depth maps generated with the same stereo algorithm. This enables us to generate a huge amount of training data in a fully automated manner. Among other experiments, we demonstrate the potential of our approach by boosting the performance of three learned confidence measures on the KITTI2012 dataset by simply training them on a vast amount of automatically generated training data rather than a limited amount of laser ground truth data.Comment: This paper was accepted to the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. The copyright was transfered to IEEE (https://www.ieee.org). The official version of the paper will be made available on IEEE Xplore (R) (http://ieeexplore.ieee.org). This version of the paper also contains the supplementary material, which will not appear IEEE Xplore (R

Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity

In this paper we present a scalable approach for robustly computing a 3D surface mesh from multi-scale multi-view stereo point clouds that can handle extreme jumps of point density (in our experiments three orders of magnitude). The backbone of our approach is a combination of octree data partitioning, local Delaunay tetrahedralization and graph cut optimization. Graph cut optimization is used twice, once to extract surface hypotheses from local Delaunay tetrahedralizations and once to merge overlapping surface hypotheses even when the local tetrahedralizations do not share the same topology.This formulation allows us to obtain a constant memory consumption per sub-problem while at the same time retaining the density independent interpolation properties of the Delaunay-based optimization. On multiple public datasets, we demonstrate that our approach is highly competitive with the state-of-the-art in terms of accuracy, completeness and outlier resilience. Further, we demonstrate the multi-scale potential of our approach by processing a newly recorded dataset with 2 billion points and a point density variation of more than four orders of magnitude - requiring less than 9GB of RAM per process.Comment: This paper was accepted to the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. The copyright was transfered to IEEE (ieee.org). The official version of the paper will be made available on IEEE Xplore (R) (ieeexplore.ieee.org). This version of the paper also contains the supplementary material, which will not appear IEEE Xplore (R

Prioritized Multi-View Stereo Depth Map Generation Using Confidence Prediction

In this work, we propose a novel approach to prioritize the depth map computation of multi-view stereo (MVS) to obtain compact 3D point clouds of high quality and completeness at low computational cost. Our prioritization approach operates before the MVS algorithm is executed and consists of two steps. In the first step, we aim to find a good set of matching partners for each view. In the second step, we rank the resulting view clusters (i.e. key views with matching partners) according to their impact on the fulfillment of desired quality parameters such as completeness, ground resolution and accuracy. Additional to geometric analysis, we use a novel machine learning technique for training a confidence predictor. The purpose of this confidence predictor is to estimate the chances of a successful depth reconstruction for each pixel in each image for one specific MVS algorithm based on the RGB images and the image constellation. The underlying machine learning technique does not require any ground truth or manually labeled data for training, but instead adapts ideas from depth map fusion for providing a supervision signal. The trained confidence predictor allows us to evaluate the quality of image constellations and their potential impact to the resulting 3D reconstruction and thus builds a solid foundation for our prioritization approach. In our experiments, we are thus able to reach more than 70% of the maximal reachable quality fulfillment using only 5% of the available images as key views. For evaluating our approach within and across different domains, we use two completely different scenarios, i.e. cultural heritage preservation and reconstruction of single family houses.Comment: This paper was accepted to ISPRS Journal of Photogrammetry and Remote Sensing (https://www.journals.elsevier.com/isprs-journal-of-photogrammetry-and-remote-sensing) on March 21, 2018. The official version will be made available on ScienceDirect (https://www.sciencedirect.com

Влияние насыщения на индуктивность пазового рассеяния обмотки статора ударного генератора

На основе расчета нелинейного магнитного поля в пазу статора ударного генератора методом конечных разностей получены значения коэффициентов проводимости пазового рассеяния и по коронкам зубцов при различных соотношениях ширины зубца к зубцовому делению

PetroSurf3D - A Dataset for high-resolution 3D Surface Segmentation

The development of powerful 3D scanning hardware and reconstruction algorithms has strongly promoted the generation of 3D surface reconstructions in different domains. An area of special interest for such 3D reconstructions is the cultural heritage domain, where surface reconstructions are generated to digitally preserve historical artifacts. While reconstruction quality nowadays is sufficient in many cases, the robust analysis (e.g. segmentation, matching, and classification) of reconstructed 3D data is still an open topic. In this paper, we target the automatic and interactive segmentation of high-resolution 3D surface reconstructions from the archaeological domain. To foster research in this field, we introduce a fully annotated and publicly available large-scale 3D surface dataset including high-resolution meshes, depth maps and point clouds as a novel benchmark dataset to the community. We provide baseline results for our existing random forest-based approach and for the first time investigate segmentation with convolutional neural networks (CNNs) on the data. Results show that both approaches have complementary strengths and weaknesses and that the provided dataset represents a challenge for future research.Comment: CBMI Submission; Dataset and more information can be found at http://lrs.icg.tugraz.at/research/petroglyphsegmentation

О некоторых направлениях дальнейшей автоматизации бетатронов

The novel ScaleMP vSMP architecture employs commodity x86-based servers with an InfiniBand network to assemble a large shared memory system at an attractive price point. We examine this combined hardware- and softwareapproach of a DSM system using both system-level kernel benchmarks as well as real-world application codes. We compare this architecture with traditional shared memory machines and elaborate on strategies to tune application codes parallelized with OpenMP on multiple levels. Finally we summarize the necessary conditions which a scalable application has to fulfill in order to profit from the full potential of the ScaleMP approach

Методика прогноза дождевых паводков в бассейне Верхнего Амура (на примере р. Онон)

Актуальность работы. Бассейн Амура относится к паводкоопасному региону. Дождевые наводнения в бассейне верхнего Амура, носящие катастрофический характер, наблюдались за последнее столетие 8 раз. Они охватывали одновременно огромные территории, сопровождались человеческими жертвами, разрушением жилых и производственных зданий, инженерных коммуникаций. Эффективным способом борьбы с наводнениями является регулирование речного стока путем создания водохранилищ. Существующих водохранилищ в речной системе Амура не хватает, чтобы эффективно регулировать сток воды. Их строительство предусмотрено в планах дальнейшего освоения региона. Прогнозы притока паводковых вод являются одной из ключевых задач, позволяющих минимизировать ущерб от паводков и определить наиболее рациональный режим эксплуатации существующих и вновь создаваемых водохранилищ. Цель работы: на примере реки Онон исследовать процессы формирования наводнений и разработать методику их краткосрочного прогноза в бассейне верхнего Амура. Методы исследования: методы водного баланса, географо-гидрологические, статистические, математическое моделирование процессов формирования стока. Результаты. Для реализации прогноза ежедневных расходов (уровней) воды дождевых паводков адаптирована концептуальная модель Д.А. Буракова, используемая в сибирских подразделениях Росгидромета. В качестве ландшафтно-гидрологической основы построения модели принято деление бассейна на районы и высотные зоны. Исходной территориальной единицей осреднения гидрометеорологических характеристик в бассейнах горных рек является высотная зона. В пределах высотной зоны территориальная неравномерность распределения запасов снега и емкостного поглощения воды учитывается с помощью распределений вероятности. Отрезки времени, в течение которых суточное поступление воды на поверхность бассейна превышает суточное испарение и просачивание, образуют последовательные паводкообразующие периоды. Для каждых суток паводкообразующего периода рассчитывается водоотдача высотных зон на основе инфильтрационно-емкостной модели Е.Г. Попова, гравитационный запас воды на склонах и приток в русловую сеть. В основу модели расчета добегания притока воды по русловой сети положен интеграл свёртки (генетическая формула паводка). В результате выполненных исследований разработана методика прогноза ежедневных уровней воды в русловой системе р. Онон. Испытания методики в оперативном режиме в Читинском гидрометеорологическом центре показали ее эффективность.Relevance. The Amur basin is situated in a flood-inclined region. Over the course of the past century the disastrous pluvial flooding have occurred in the basin eight times. They covered huge territories, took peopleґs lives and caused considerable damage to residential and industrial buildings, engineering systems. One of the efficient methods to struggle the floods is to regulate the river run-off developing flood-control reservoirs. The number of existing reservoirs on the Amur river system is insignificant to control efficiently the river run-off. Their building is tied with further region development. The forecast of flood water inflow allows minimizing damage and identifying the most rational reservoir release rules for the existing and expected reservoirs. Aim of the research is to investigate the floods formation by the example of the Onon river and to develop the methods for short-term forecast of floods in the upper Amur basin. Research methods: water balance method, geographical and hydrological methods, statistical method, mathematical modeling of run-off formation. Results. The Burakovґs conceptual model is adapted to forecast daily rain floods water flows. This model is used by the Siberian department of the Federal Service for Hydrometeorology and Environmental Monitoring of Russia. The landscape and hydrological basis for this model is the basin division into areas and altitudinal zones. The altitudinal zone is an initial territorial unit of averaging the hydrological characteristics of mountain rivers. Within the altitudinal zone, territorial irregularity of snow cover distribution and capacitive water absorption are taken into account by probability distribution. The periods, when the diurnal water entry to the surface of the basin exceeds the diurnal evaporation and infiltration, compose successive flood-forming periods. For each day of a flood-forming period, the water yield is estimated using the Popovґs infiltration capacitive model. Besides, the gravitational water storage on the slopes and the inflow in the channel network are calculated. The method, describing water lag along the river channels, is based on applying the convolution integral (the genetic flood formula). As a result of the research, the author has developed the method of forecasting daily water levels in the Onon riverbed system. The method was applied by the Chita department of the Federal Service for Hydrometeorology and Environmental Monitoring of Russia and proved its efficiency

Association of Sociodemographic, Psychopathological and Gambling-Related Factors with Treatment Utilization for Pathological Gambling

Background/Aims: Only a small percentage of pathological gamblers utilizes professional treatment for gambling problems. Little is known about which social and gambling-related factors are associated with treatment utilization. The aim of this study was to look for factors associated with treatment utilization for pathological gambling. Methods: The study followed a sampling design with 3 different recruitment channels, namely (1) a general population-based telephone sample, (2) a gambling location sample and (3) a project telephone hotline. Pathological gambling was diagnosed in a telephone interview. Participants with pathological gambling (n = 395) received an in-depth clinical interview concerning treatment utilization, comorbid psychiatric disorders and social characteristics. Results: Variables associated with treatment were higher age [odds ratio (OR) 1.05, 95% confidence interval (CI) 1.03-1.08], an increased number of DSM-IV criteria for pathological gambling (OR 1.34, 95% CI 1.06-1.70), more adverse consequences from gambling (OR 1.10, 95% CI 1.03-1.16) and more social pressure from significant others (OR 1.17, 95% CI 1.07-1.27). Affective disorders were associated with treatment utilization in the univariate analysis (OR 1.81, 95% CI 1.19-2.73), but multivariate analysis showed that comorbid psychiatric disorders were not independently associated. Conclusion: These results indicate that individuals with more severe gambling problems utilize treatment at an older age when more adverse consequences have occurred. Further research should focus on proactive early interventions

Компьютерный статистический анализ качества инженерного образования. Текущий контроль математических знаний

Проведен компьютерный статистический анализ результатов текущего контроля математических знаний. Оценен вклад элитной составляющей технического образования. Дан сравнительный анализ результатов входного контроля математических знаний с результатами текущего контроля в целом и в зависимости от разных форм обучения в вузе в частности. Сделан вывод о статистически значимых различиях этих результатов. Обсуждены причины выявленных существенных различий. Рассмотрена корреляционная зависимость результатов текущего контроля от результатов входного контроля математических знаний