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Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia's agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems. [ABSTRACT FROM AUTHOR]

Objectives: High resolution satellite images (HRSI) can provide spectral characteristics observation information of ground objects at low cost and high frequency, while light detection and ranging(LiDAR) point clouds can provide fine geometric structure. The fusion of two kinds of data can realize complementary advantages, and further improve the accuracy and automation of ground object classification and information extraction. The realization of geometric registration with sub-pixel accuracy is the premise of two kinds of data fusion. Methods: A fast registration method based on line element distance transformation model is proposed. The point clouds are used as the control source, and the typical line elements such as building edges in the point clouds are projected into the image space through the initial rational polynomial coefficient (RPC) parameters of the satellite image, and the iterative closest point registration is carried out with the line elements in the satellite image, so as to achieve geometric registration by means of refining RPC parameters. In this method, the distance transformation model is used as the search table of the iterative closest point, which greatly improves the operation efficiency. Furthermore, the latest progressive robust solution strategy is adopted at the same time of the closest point iteration, so as to ensure the robustness of registration in the case of too much noise. Registration experiments are carried out on GeoEye-2 data, Gaofen-7 data,WorldView-3 data with LiDAR point clouds data. Results: The results prove that the proposed method can achieve a registration accuracy of 0.4—0.7 m on three kinds of images by using the ground control points which are accurately measured and the operator's internal control points as checkpoints. Conclusions: It is significantly better than the strategy of mapping point clouds to 2D image and registering through multimode matching. [ABSTRACT FROM AUTHOR]

Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems.

The application research of ground change detection based on multi-temporal 3D models is attracting more and more attention. However, the conventional methods of using UAV GPS-supported bundle adjustment or measuring ground control points before each data collection are not only economically costly, but also have insufficient geometric accuracy. In this paper, an automatic geometric-registration method for multi-temporal 3D models is proposed. First, feature points are extracted from the highest resolution texture image of the 3D model, and their corresponding spatial location information is obtained based on the triangular mesh of the 3D model, which is then converted into 3D spatial-feature points. Second, the transformation model parameters of the 3D model to be registered relative to the base 3D model are estimated by the spatial-feature points with the outliers removed, and all the vertex positions of the model to be registered are updated to the coordinate system of the base 3D model. The experimental results show that the position measurement error of the ground object is less than 0.01 m for the multi-temporal 3D models obtained by the method of this paper. Since the method does not require the measurement of a large number of ground control points for each data acquisition, its application to long-period, high-precision ground monitoring projects has great economic and geometric accuracy advantages. [ABSTRACT FROM AUTHOR]

The application research of ground change detection based on multi-temporal 3D models is attracting more and more attention. However, the conventional methods of using UAV GPS-supported bundle adjustment or measuring ground control points before each data collection are not only economically costly, but also have insufficient geometric accuracy. In this paper, an automatic geometric-registration method for multi-temporal 3D models is proposed. First, feature points are extracted from the highest resolution texture image of the 3D model, and their corresponding spatial location information is obtained based on the triangular mesh of the 3D model, which is then converted into 3D spatial-feature points. Second, the transformation model parameters of the 3D model to be registered relative to the base 3D model are estimated by the spatial-feature points with the outliers removed, and all the vertex positions of the model to be registered are updated to the coordinate system of the base 3D model. The experimental results show that the position measurement error of the ground object is less than 0.01 m for the multi-temporal 3D models obtained by the method of this paper. Since the method does not require the measurement of a large number of ground control points for each data acquisition, its application to long-period, high-precision ground monitoring projects has great economic and geometric accuracy advantages.

This paper presents a novel projected pixel localization principle for online geometric registration in dynamic projection mapping applications. We propose applying a time measurement of a laser projector raster-scanning beam using a photosensor to estimate its position while the projector displays meaningful visual information to human observers. Based on this principle, we develop two types of position estimation techniques. One estimates the position of a projected beam when it directly illuminates a photosensor. The other localizes a beam by measuring the reflection from a retro-reflective marker with the photosensor placed in the optical path of the projector. We conduct system evaluations using prototypes to validate this method as well as to confirm the applicability of our principle. In addition, we discuss the technical limitations of the prototypes based on the evaluation results. Finally, we build several dynamic projection mapping applications to demonstrate the feasibility of our principle. [ABSTRACT FROM AUTHOR]

Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering "similar" gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention. [ABSTRACT FROM AUTHOR]

Non-planar projection has been widely applied in virtual reality and digital entertainment and exhibitions because of its flexible layout and immersive display effects. Compared with planar projection, a non-planar projection is more difficult to achieve because projector calibration and image distortion correction are difficult processes. This paper uses a cylindrical screen as an example to present a new method for automatically calibrating a multi-projector system in a non-planar environment without using 3D reconstruction. This method corrects the geometric calibration error caused by the screen's manufactured imperfections, such as an undulating surface or a slant in the vertical plane. In addition, based on actual projection demand, this paper presents the overall performance evaluation criteria for the multi-projector system. According to these criteria, we determined the optimal placement for the projectors. This method also extends to surfaces that can be parameterized, such as spheres, ellipsoids, and paraboloids, and demonstrates a broad applicability. [ABSTRACT FROM AUTHOR]

This review introduces a novel deformable image registration paradigm that exploits Markov random field formulation and powerful discrete optimization algorithms. We express deformable registration as a minimal cost graph problem, where nodes correspond to the deformation grid, a node's connectivity corresponds to regularization constraints, and labels correspond to 3D deformations. To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem. The two graphs share interconnected variables, leading to a modular, powerful, and flexible formulation that can account for arbitrary image-matching criteria, various local deformation models, and regularization constraints. To cope with the corresponding optimization problem, we adopt two optimization strategies: a computationally efficient one and a tight relaxation alternative. Promising results demonstrate the potential of this approach. Discrete methods are an important new trend in medical image registration, as they provide several improvements over the more traditional continuous methods. This is illustrated with several key examples where the presented framework outperforms existing general-purpose registration methods in terms of both performance and computational complexity. Our methods become of particular interest in applications where computation time is a critical issue, as in intraoperative imaging, or where the huge variation in data demands complex and application-specific matching criteria, as in large-scale multimodal population studies. [ABSTRACT FROM AUTHOR]

We describe a display system that automatically registers an array of casually positioned projectors and drives the display with a PC cluster. The display system uses a camera to compute the necessary transformations to geometrically align multiple overlapping projectors. Geometric alignment is realized through real-time corrective warping applied in the rendering pipeline. This combination of camera-aided display alignment with the PC-cluster rendering engine provides a display system that is easy to configure and reconfigure, accommodates casually tiled projectors and arbitrary display surfaces, and can be operational in a very short period of time. [ABSTRACT FROM AUTHOR]

Multi-projector display systems are widely used in virtual reality, flight simulators, and other entertainment systems. Geometric distortion and color inconsistency are two key problems to be solved. In this paper a geometric correction principle is theoretically demonstrated and a consistency principle of geometric correction is first proposed. A new method of automatic registration of a multi-projector on a curved screen is put forward. Two pairs of binocular-cameras are used to reconstruct the curved screen. To capture feature points of the curved screen precisely, a group of red-blue coded structured light images is designed to be projected onto the screen. Geometric homography between each projector and the curved screen is calculated to gain a pre-warp template. Work which can gain a seamless display is illustrated by a six-projector system on the curved screen. [ABSTRACT FROM AUTHOR]

In this paper, we present a binary image registration strategy for the registration of segmented CT scan data of the human head. For the characterization of the 3D skull binary images we adopt a powerful representation of the binary images: the geometric moment invariants (GMIs). They provide pertinent and discriminant information related to the geometry of the binary objects, thereby leading to match points which have similar geometric properties, and also to enhance the quality of the matching process. For the registration algorithm we propose to use the topology-preserving B-spline-based registration method proposed by Noblet et al. for the registration of MRI head images. The algorithm has proven its efficiency and high registration precision. Since the information carried out by the different GMIs is complementary, we study and discuss the importance of combining various GMIs to better guide the matching process. Results obtained using synthetic deformation fields highlight the promising performance of the strategy. [ABSTRACT FROM AUTHOR]

In this paper, we present the first method for the geometric autocalibration of multiple projectors on a set of CAVE-like immersive display surfaces including truncated domes and 4 or 5-wall CAVEs (three side walls, floor, and/or ceiling). All such surfaces can be categorized as swept surfaces and multiple projectors can be registered on them using a single uncalibrated camera without using any physical markers on the surface. Our method can also handle nonlinear distortion in the projectors, common in compact setups where a short throw lens is mounted on each projector. Further, when the whole swept surface is not visible from a single camera view, we can register the projectors using multiple pan and tilted views of the same camera. Thus, our method scales well with different size and resolution of the display. Since we recover the 3D shape of the display, we can achieve registration that is correct from any arbitrary viewpoint appropriate for head-tracked single-user virtual reality systems. We can also achieve wallpapered registration, more appropriate for multiuser collaborative explorations. Though much more immersive than common surfaces like planes and cylinders, general swept surfaces are used today only for niche display environments. Even the more popular 4 or 5-wall CAVE is treated as a piecewise planar surface for calibration purposes and hence projectors are not allowed to be overlapped across the corners. Our method opens up the possibility of using such swept surfaces to create more immersive VR systems without compromising the simplicity of having a completely automatic calibration technique. Such calibration allows completely arbitrary positioning of the projectors in a 5-wall CAVE, without respecting the corners. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel technique to calibrate multiple casually aligned projectors on fiducial-free piecewise smooth vertically extruded surfaces using a single camera. Such surfaces include cylindrical displays and CAVEs, common in immersive virtual reality systems. We impose two priors to the display surface. We assume the surface is a piecewise smooth vertically extruded surface for which the aspect ratio of the rectangle formed by the four corners of the surface is known and the boundary is visible and segmentable. Using these priors, we can estimate the display's 3D geometry and camera extrinsic parameters using a nonlinear optimization technique from a single image without any explicit display to camera correspondences. Using the estimated camera and display properties, the intrinsic and extrinsic parameters of each projector are recovered using a single projected pattern seen by the camera. This in turn is used to register the images on the display from any arbitrary viewpoint making it appropriate for virtual reality systems. The fast convergence and robustness of this method is achieved via a novel dimension reduction technique for camera parameter estimation and a novel deterministic technique for projector property estimation. This simplicity, efficiency, and robustness of our method enable several coveted features for nonplanar projection-based displays. First, it allows fast recalibration in the face of projector, display or camera movements and even change in display shape. Second, this opens up, for the first time, the possibility of allowing multiple projectors to overlap on the corners of the CAVE—a popular immersive VR display system. Finally, this opens up the possibility of easily deploying multiprojector displays on aesthetic novel shapes for edutainment and digital signage applications. [ABSTRACT FROM PUBLISHER]