Alignment of spatial genomics and histology data using deep Gaussian processes

Spatially-resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of the local interactions between cells. However, it remains difficult to precisely align spatial observations across slices, samples, scales, individuals, and technologies. Here, we propose a probabilistic model that aligns a set of spatially-resolved genomics and histology slices onto a known or unknown common coordinate system into which the samples are aligned both spatially and in terms of the phenotypic readouts (e.g., gene or protein expression levels, cell density, open chromatin regions). Our method consists of a two-layer Gaussian process: the first layer maps the observed samples' spatial locations into a common coordinate system, and the second layer maps from the common coordinate system to the observed readouts. Our approach also allows for slices to be mapped to a known template coordinate space if one exists. We show that our registration approach enables complex downstream spatially-aware analyses of spatial genomics data at multiple resolutions that are impossible or inaccurate with unaligned data, including an analysis of variance, differential expression across the z-axis, and association tests across multiple data modalities.

Depth-camera-based rebar detection and digital reconstruction for robotic concrete spraying

In this paper, we tackle the challenge of detection and accurate digital reconstruction of steel rebar meshes using a set of industrial depth cameras. A construction example under investigation in this paper is robotic concrete spraying, where material is sprayed onto double-curved single layered rebar meshes. Before the spraying process can start, the location and geometry of the rebar mesh needs to be accurately know. We present an automatic image-based processing approach of depth images for grid point extraction at an accuracy of a few mm. Furthermore, we propose a sequence of execution steps in a robotic setup, including the hand–eye calibration, which enables the direct georeferencing of multiple data sets acquired from various poses into a common coordinate system. With the proposed approach we are able to digitally reconstruct a mesh of an unknown geometry in under 10 min with an accuracy better than 5 mm. The digitally reconstructed mesh allows for computation of material needed for its construction, enabling sustainable use of concrete in digital fabrication. The accurately reconstructed digital mesh, generated based on the proposed approach in this paper, is the input for the following spraying step, allowing for generation of accurate spray trajectories.

Unify The View of Camera Mesh Network to a Common Coordinate System

The demand for object tracking (OT) applications has been increasing for the past few decades in many areas of interest, including security, surveillance, intelligence gathering, and reconnaissance. Lately, newly-defined requirements for unmanned vehicles have enhanced the interest in OT. Advancements in machine learning, data analytics, and AI/deep learning have facilitated the improved recognition and tracking of objects of interest; however, continuous tracking is currently a problem of interest in many research projects. [1] In our past research, we proposed a system that implements the means to continuously track an object and predict its trajectory based on its previous pathway, even when the object is partially or fully concealed for a period of time. The second phase of this system proposed developing a common knowledge among a mesh of fixed cameras, akin to a real-time panorama. This paper discusses the method to coordinate the cameras' view to a common frame of reference so that the object location is known by all participants in the network.

Different Approaches to Coordinate Transformation Parameters Determination of Nonhomogeneous Coordinate Systems

During reconstruction and restoration of city geodetic networks, there is quite a common problem that is related to the nonhomogeneity of existing geodetic networks. In any city, local authorities operate with their coordinate systems. Such conditions lead to inconsistency between data of different services. There is only one way how to overcome the problem that lies in the creation and deployment of the new common coordinate system for the whole city. But such an approach has a lack connected with the necessity of transformation parameters acquisition for the latest and old coordinate systems. Insofar as old coordinate systems had been created with different accuracy, using various equipment, and measuring technologies, it is not possible to consider them as homogeneous. It means that we cannot use a classical conformal Helmert transformation to link different coordinate systems. In the presented paper were studied the different approaches for transformation parameters acquisition. A case study of the Almaty city coordinate system was researched and compared the following methods: Helmert transformation, bilinear transformation, the second and third-order regression transformation, and the fourth-order conformal polynomial transformation. It was found out that neither of the considered methods maintains the necessary transformation accuracy (>5 cm). That is why the creation of the transformation field using the finite element method (FEM) was suggested. The whole city was divided into triangles using Delaunay triangulation. For each triangle, the transformation parameters were found using affine transformation with the necessary accuracy.

A Sheaf Theoretical Approach to Uncertainty Quantification of Heterogeneous Geolocation Information

Integration of multiple, heterogeneous sensors is a challenging problem across a range of applications. Prominent among these are multi-target tracking, where one must combine observations from different sensor types in a meaningful and efficient way to track multiple targets. Because different sensors have differing error models, we seek a theoretically justified quantification of the agreement among ensembles of sensors, both overall for a sensor collection, and also at a fine-grained level specifying pairwise and multi-way interactions among sensors. We demonstrate that the theory of mathematical sheaves provides a unified answer to this need, supporting both quantitative and qualitative data. Furthermore, the theory provides algorithms to globalize data across the network of deployed sensors, and to diagnose issues when the data do not globalize cleanly. We demonstrate and illustrate the utility of sheaf-based tracking models based on experimental data of a wild population of black bears in Asheville, North Carolina. A measurement model involving four sensors deployed among the bears and the team of scientists charged with tracking their location is deployed. This provides a sheaf-based integration model which is small enough to fully interpret, but of sufficient complexity to demonstrate the sheaf’s ability to recover a holistic picture of the locations and behaviors of both individual bears and the bear-human tracking system. A statistical approach was developed in parallel for comparison, a dynamic linear model which was estimated using a Kalman filter. This approach also recovered bear and human locations and sensor accuracies. When the observations are normalized into a common coordinate system, the structure of the dynamic linear observation model recapitulates the structure of the sheaf model, demonstrating the canonicity of the sheaf-based approach. However, when the observations are not so normalized, the sheaf model still remains valid.

On-the-Fly Camera and Lidar Calibration

Sensor fusion is one of the main challenges in self driving and robotics applications. In this paper we propose an automatic, online and target-less camera-Lidar extrinsic calibration approach. We adopt a structure from motion (SfM) method to generate 3D point clouds from the camera data which can be matched to the Lidar point clouds; thus, we address the extrinsic calibration problem as a registration task in the 3D domain. The core step of the approach is a two-stage transformation estimation: First, we introduce an object level coarse alignment algorithm operating in the Hough space to transform the SfM-based and the Lidar point clouds into a common coordinate system. Thereafter, we apply a control point based nonrigid transformation refinement step to register the point clouds more precisely. Finally, we calculate the correspondences between the 3D Lidar points and the pixels in the 2D camera domain. We evaluated the method in various real-life traffic scenarios in Budapest, Hungary. The results show that our proposed extrinsic calibration approach is able to provide accurate and robust parameter settings on-the-fly.

CARTOMETRIC ANALYSIS OF THE ACCURACY OF PLAN OF LVIV IN 1878

The article considers the topic about assessing the accuracy of the plan of Lviv in 1878, which was published by Artaria & Co in a separate sheet from the administrative map of the Austrian cartographer and engineer Karl Richter van Kummersberg. This cartographic work was compiled on the basis of the Second Military Topographic Survey conducted in the Austrian Empire during 1855-1863, and occupies an important place in the study of architectural and urban planning of Lviv in Austrian times, before the great construction changes of the XIX century. Analysis of the accuracy of the old plans of Lviv is an important aspect in the study of these works, which allows you to assess their geometric features and obtain valuable information about the methods of their creation and processing techniques. Thus, it makes it possible to compare the cartographic, documentary and semantic value of ancient plans. The accuracy assessment methodology is based on the transformation and geometric analysis of sets of identical points on the old plan and the reference. Sets of control points are used to bring two cartographic products into a common coordinate system. The Helmert transformation with four parameters is used for such transformation. Identical points should be distributed over the entire area, ideally evenly, so that the resulting transformation key has a global character. According to the transformation key, multiquadratic interpolation is performed to construct a continuous surface from discrete data. The results of the latter make it possible to graphically visualize the errors of the old plan in the form of displacement vectors, isolines of scale and rotation, which significantly speeds up and simplifies the study of the accuracy of the old plans. In addition, using the method of least squares a value that characterizes the positional accuracy of the ancient plan was obtained. All calculations and constructions were performed in the MapAnalyst software product. The presented technique can be used for similar research on other cartographic works, and the obtained numerical results and graphical visualizations - to compare old plans with each other.

A Method of Fast and Simultaneous Calibration of Many Mobile FMCW Radars Operating in a Network Anti-Drone System

A market for small drones is developing very fast. They are used for leisure activities and exploited in commercial applications. However, there is a growing concern for accidental or even criminal misuses of these platforms. Dangerous incidents with drones are appearing more often, and have caused many institutions to start thinking about anti-drone solutions. There are many cases when building stationary systems seems to be aimless since the high cost does not correspond with, for example, threat frequency. In such cases, mobile drone countermeasure systems seem to perfectly meet demands. In modern mobile solutions, frequency modulated continuous wave (FMCW) radars are frequently used as detectors. Proper cooperation of many radars demands their measurements to be brought to a common coordinate system—azimuths must be measured in the same direction (preferably the north). It requires calibration, understood as determining constant corrections to measured angles. The article presents the author's method of fast, simultaneous calibration of many mobile FMCW radars operating in a network. It was validated using 95,000 numerical tests. The results show that the proposed method significantly improves the north orientation of the radars throughout the whole range of the initial errors. Therefore, it can be successfully used in practical applications.

Online Correction of the Mutual Miscalibration of Multimodal VIS–IR Sensors and 3D Data on a UAV Platform for Surveillance Applications

Unmanned aerial vehicles (UAVs) are widely used to protect critical infrastructure objects, and they are most often equipped with one or more RGB cameras and, sometimes, with a thermal imaging camera as well. To obtain as much information as possible from them, they should be combined or fused. This article presents a situation in which data from RGB (visible, VIS) and thermovision (infrared, IR) cameras and 3D data have been combined in a common coordinate system. A specially designed calibration target was developed to enable the geometric calibration of IR and VIS cameras in the same coordinate system. 3D data are compatible with the VIS coordinate system when the structure from motion (SfM) algorithm is used. The main focus of this article is to provide the spatial coherence between these data in the case of relative camera movement, which usually results in a miscalibration of the system. Therefore, a new algorithm for the detection of sensor system miscalibration, based on phase correlation with automatic calibration correction in real time, is introduced.