Constraining Relative Camera Pose Estimation with Pedestrian Detector-Based Correspondence Filters

International audienceA prerequisite for using smart camera networks effectively is a precise extrinsic calibration of the camera sensors , either in a fixed coordinate system, or relatively to each other. For cameras with partly overlapping fields of view, the relative pose estimation may be directly performed on or assisted by the video content obtained during scene analysis. In typical conditions however (wide baseline, repetitive patterns, homogeneous appearance of pedestrians), the pose estimation is imprecise and very often is affected by large errors in weakly constrained areas of the field of view. In this work, we propose to rely on progressively stricter constraints on the feature association between the camera views, guided by a pedestrian detector and a re-identification algorithm respectively. The results show that the two strategies are effective in alleviating the ambiguity which is due to the similar appearance of pedestrians in such scenes, and in improving the relative pose estimation

Efficient Surface-Aware Semi-Global Matching with Multi-View Plane-Sweep Sampling

Online augmentation of an oblique aerial image sequence with structural information is an essential aspect in the process of 3D scene interpretation and analysis. One key aspect in this is the efficient dense image matching and depth estimation. Here, the Semi-Global Matching (SGM) approach has proven to be one of the most widely used algorithms for efficient depth estimation, providing a good trade-off between accuracy and computational complexity. However, SGM only models a first-order smoothness assumption, thus favoring fronto-parallel surfaces. In this work, we present a hierarchical algorithm that allows for efficient depth and normal map estimation together with confidence measures for each estimate. Our algorithm relies on a plane-sweep multi-image matching followed by an extended SGM optimization that allows to incorporate local surface orientations, thus achieving more consistent and accurate estimates in areasmade up of slanted surfaces, inherent to oblique aerial imagery. We evaluate numerous configurations of our algorithm on two different datasets using an absolute and relative accuracy measure. In our evaluation, we show that the results of our approach are comparable to the ones achieved by refined Structure-from-Motion (SfM) pipelines, such as COLMAP, which are designed for offline processing. In contrast, however, our approach only considers a confined image bundle of an input sequence, thus allowing to perform an online and incremental computation at 1Hz–2Hz

Разработка информационных систем управления рисками для предметных областей

This paper is about specifics of developing risk management information system in construction company and advertising business

Computational strategies for dissecting the high-dimensional complexity of adaptive immune repertoires

The adaptive immune system recognizes antigens via an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity in order to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic and (iv) machine learning methods applied to dissect, quantify and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology towards coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.Comment: 27 pages, 2 figure

Systemic Complement Activation in Age-Related Macular Degeneration

Dysregulation of the alternative pathway (AP) of complement cascade has been implicated in the pathogenesis of age-related macular degeneration (AMD), the leading cause of blindness in the elderly. To further test the hypothesis that defective control of complement activation underlies AMD, parameters of complement activation in blood plasma were determined together with disease-associated genetic markers in AMD patients. Plasma concentrations of activation products C3d, Ba, C3a, C5a, SC5b-9, substrate proteins C3, C4, factor B and regulators factor H and factor D were quantified in patients (n = 112) and controls (n = 67). Subjects were analyzed for single nucleotide polymorphisms in factor H (CFH), factor B-C2 (BF-C2) and complement C3 (C3) genes which were previously found to be associated with AMD. All activation products, especially markers of chronic complement activation Ba and C3d (p<0.001), were significantly elevated in AMD patients compared to controls. Similar alterations were observed in factor D, but not in C3, C4 or factor H. Logistic regression analysis revealed better discriminative accuracy of a model that is based only on complement activation markers Ba, C3d and factor D compared to a model based on genetic markers of the complement system within our study population. In both the controls' and AMD patients' group, the protein markers of complement activation were correlated with CFH haplotypes