Automatic detection of geospatial objects using multiple hierarchical segmentations

Cataloged from PDF version of article.The object-based analysis of remotely sensed imagery provides valuable spatial and structural information that is complementary to pixel-based spectral information in classi- fication. In this paper, we present novel methods for automatic object detection in high-resolution images by combining spectral information with structural information exploited by using image segmentation. The proposed segmentation algorithm uses morphological operations applied to individual spectral bands using structuring elements in increasing sizes. These operations produce a set of connected components forming a hierarchy of segments for each band. A generic algorithm is designed to select meaningful segments that maximize a measure consisting of spectral homogeneity and neighborhood connectivity. Given the observation that different structures appear more clearly at different scales in different spectral bands, we describe a new algorithm for unsupervised grouping of candidate segments belonging to multiple hierarchical segmentations to find coherent sets of segments that correspond to actual objects. The segments are modeled by using their spectral and textural content, and the grouping problem is solved by using the probabilistic latent semantic analysis algorithm that builds object models by learning the object-conditional probability distributions. The automatic labeling of a segment is done by computing the similarity of its feature distribution to the distribution of the learned object models using the Kullback–Leibler divergence. The performances of the unsupervised segmentation and object detection algorithms are evaluated qualitatively and quantitatively using three different data sets with comparative experiments, and the results show that the proposed methods are able to automatically detect, group, and label segments belonging to the same object classes

Automatic mapping of linear woody vegetation features in agricultural landscapes using very high resolution imagery

Cataloged from PDF version of article.Automatic mapping and monitoring of agricultural landscapes using remotely sensed imagery has been an important research problem. This paper describes our work on developing automatic methods for the detection of target landscape features in very high spatial resolution images. The target objects of interest consist of linear strips of woody vegetation that include hedgerows and riparian vegetation that are important elements of the landscape ecology and biodiversity. The proposed framework exploits the spectral, textural, and shape properties of objects using hierarchical feature extraction and decision-making steps. First, a multifeature and multiscale strategy is used to be able to cover different characteristics of these objects in a wide range of landscapes. Discriminant functions trained on combinations of spectral and textural features are used to select the pixels that may belong to candidate objects. Then, a shape analysis step employs morphological top-hat transforms to locate the woody vegetation areas that fall within the width limits of an acceptable object, and a skeletonization and iterative least-squares fitting procedure quantifies the linearity of the objects using the uniformity of the estimated radii along the skeleton points. Extensive experiments using QuickBird imagery from three European Union member states show that the proposed algorithms provide good localization of the target objects in a wide range of landscapes with very different characteristics

Dispatching AGVs with Battery Constraints using Deep Reinforcement Learning

This paper considers the problem of real-time dispatching of a fleet of automated guided vehicles (AGVs) with battery constraints. AGVs must be immediately assigned to transport requests, which arrive randomly. In addition, the AGVs must be repositioned and recharged, awaiting future transport requests. Each transport request has a soft time window with late delivery incurring a tardiness cost. This research aims to minimize the total costs, consisting of tardiness costs of transport requests and travel costs of AGVs. We extend the existing literature by making a distinction between parking and charging nodes, where AGVs wait idle for incoming transporting requests and satisfy their charging needs, respectively. Also, we formulate this online decision-making problem as a Markov decision process and propose a solution approach based on deep reinforcement learning. To assess the quality of the proposed approach, we compare it with the optimal solution of a mixed-integer linear programming model that assumes full knowledge of transport requests in hindsight and hence serves as a lower-bound on the costs. We also compare our solution with a heuristic policy used in practice. We assess the performance of the proposed solutions in an industry case study using real-world data

A panel of oxidative stress assays does not provide supplementary diagnostic information in Behcet's disease patients

Published onlineJournal ArticleBACKGROUND: Recent findings suggest a role of oxidative stress in the pathogenesis of Behcet's disease (BD), but the utility of oxidative stress-associated assays in offering diagnostic information or in the monitoring of disease activity is largely unassessed. OBJECTIVE AND METHODS: We aimed to measure oxidative and inflammatory markers, along with the markers of reactive nitrogen species, S-nitrosothiols and 3-nitrotyrosine, in BD patients (n = 100) and healthy volunteers (n = 50). These markers were evaluated in regard to their role in the pathogenesis of BD as well as their relation to clinical presentation, disease activity and duration. RESULTS: Median values for erythrocyte sedimentation rate (ESR), C-reactive protein, leukocyte count, and IL-18 levels, as well as myeloperoxidase (MPO) activity, were statistically higher in the patient group compared to controls. Some inflammation markers (ESR, neutrophil and leukocyte counts) were statistically higher (p 0.05 in all statistical comparisons), nor was there any difference in median levels of these oxidative stress markers in active disease versus disease remission. S-nitrosothiols and 3-nitrotyrosine were undetectable in BD plasma. CONCLUSIONS: The application of oxidative stress-associated measures to BD blood samples offered no supplemental diagnostic or disease activity information to that provided by standard laboratory measures of inflammation. S-nitrosothiols and 3-nitrotyrosine appeared not to be markers for active BD; thus the search for biochemical markers that will indicate the active period should be continued with larger studies

Diazoxide-responsive hyperinsulinemic hypoglycemia caused by HNF4A gene mutations

Objective: The phenotype associated with heterozygous HNF4A gene mutations has recently been extended to include diazoxide responsive neonatal hypoglycemia in addition to maturity-onset diabetes of the young (MODY). To date, mutation screening has been limited to patients with a family history consistent with MODY. In this study, we investigated the prevalence of HNF4A mutations in a large cohort of patients with diazoxide responsive hyperinsulinemic hypoglycemia (HH). Subjects and methods: We sequenced the ABCC8, KCNJ11, GCK, GLUD1, and/or HNF4A genes in 220 patients with HH responsive to diazoxide. The order of genetic testing was dependent upon the clinical phenotype. Results: A genetic diagnosis was possible for 59/220 (27%) patients. KATP channel mutations were most common (15%) followed by GLUD1 mutations causing hyperinsulinism with hyperammonemia (5.9%), and HNF4A mutations (5%). Seven of the 11 probands with a heterozygous HNF4A mutation did not have a parent affected with diabetes, and four de novo mutations were confirmed. These patients were diagnosed with HI within the first week of life (median age 1 day), and they had increased birth weight (median +2.4 SDS). The duration of diazoxide treatment ranged from 3 months to ongoing at 8 years. Conclusions: In this large series, HNF4A mutations are the third most common cause of diazoxide responsive HH. We recommend that HNF4A sequencing is considered in all patients with diazoxide responsive HH diagnosed in the first week of life irrespective of a family history of diabetes, once KATP channel mutations have been excluded

Real-time Classification of Vehicle Types within Infra-red Imagery

Real-time classification of vehicles into sub-category types poses a significant challenge within infra-red imagery due to the high levels of intra-class variation in thermal vehicle signatures caused by aspects of design, current operating duration and ambient thermal conditions. Despite these challenges, infra-red sensing offers significant generalized target object detection advantages in terms of all-weather operation and invariance to visual camouflage techniques. This work investigates the accuracy of a number of real-time object classification approaches for this task within the wider context of an existing initial object detection and tracking framework. Specifically we evaluate the use of traditional feature-driven bag of visual words and histogram of oriented gradient classification approaches against modern convolutional neural network architectures. Furthermore, we use classical photogrammetry, within the context of current target detection and classification techniques, as a means of approximating 3D target position within the scene based on this vehicle type classification. Based on photogrammetric estimation of target position, we then illustrate the use of regular Kalman filter based tracking operating on actual 3D vehicle trajectories. Results are presented using a conventional thermal-band infra-red (IR) sensor arrangement where targets are tracked over a range of evaluation scenarios

A rotating three component perfect fluid source and its junction with empty space-time

The Kerr solution for empty space-time is presented in an ellipsoidally symmetric coordinate system and it is used to produce generalised ellipsoidal metrics appropriate for the generation of rotating interior solutions of Einstein's equations. It is shown that these solutions are the familiar static perfect fluid cases commonly derived in curvature coordinates but now endowed with rotation. The resulting solutions are also discussed in the context of T-solutions of Einstein's equations and the vacuum T-solution outside a rotating source is presented. The interior source for these solutions is shown not to be a perfect fluid but rather an anisotropic three component perfect fluid for which the energy momentum tensor is derived. The Schwarzschild interior solution is given as an example of the approach.Comment: 14 page

Anomaly Detection for Vision-based Railway Inspection

none7nomixedRiccardo Gasparini; Stefano Pini; Guido Borghi; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita CucchiaraRiccardo Gasparini; Stefano Pini; Guido Borghi; Giuseppe Scaglione; Simone Calderara; Eugenio Fedeli; Rita Cucchiar