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"abstract": "Extreme weather events, like extreme rainfalls, are severe weather hazards and also the triggers for other natural disasters like floods and tornadoes. Accurate forecasting of such events relies on the understanding of the spatiotemporal evolution processes in climate system. Learning from climate science data has been a challenging task, because the variations among spatial, temporal and multivariate spaces have created a huge amount of features and complex regularities within the data. In this study we developed a framework for learning patterns from the spatiotemporal system and forecasting extreme weather events. In this framework, we learned patterns in a hierarchical manner: in each level, new features were learned from data and used as the input for the next level. Firstly, we summarized the temporal evolution process of individual variables by learning the location-based patterns. Secondly, we developed an optimization algorithm for summarizing the spatial regularities, SCOT, by growing spatial clusters from the location-based patterns. Finally, we developed an instance-based algorithm, SPC, to forecast the extreme events through classification. We applied this framework to forecasting extreme rainfall events in the eastern Central Andes area. Our experiments show that this method was able to find climatic process patterns similar to those found in domain studies, and our forecasting results outperformed the state-of-art model.",
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"abstract": "For the road networks containing multiple intersections and links, the traffic flow forecasting is essentially a time series forecasting problem on graphs. The task is challenging due to (1) complex spatiotemporal dependence among traffic flows of the whole road network and (2) sharp non-linearity and dynamic nature under different conditions. In this paper, by extending the LSTM to have graph attention structure in both the input-to-state and state-to-state transitions, we propose the Graph Attention LSTM Network (GAT-LSTM) and use it to build an end-to-end trainable encoder-forecaster model to solve the multi-link traffic flow forecasting problem. Experiment results show that our GAT-LSTM network could capture spatiotemporal correlations better and has achieved improvement of 15% - 16% over state-of-the-art baseline.",
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"abstract": "In this paper, we first give three kinds of model of fuzzy forecasting: fuzzy linear regression forecasting, Quadratic polynomial fuzzy regression forecasting and fuzzy time series self-regression forecasting, then we introduce the combination forecasting of fuzzy forecast,and give two kind of combination forecasting of fuzzy forecast, one is the linear combination forecasting of fuzzy forecasting based on fuzzy least square, the another is the nonlinear combination forecasting of fuzzy forecast based on Choquet fuzzy integral.",
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"abstract": "This paper presents a concept which tackles the pose estimation problem (extrinsic calibration) for distributed, non-overlapping multi-camera networks. The basic idea is to use a visual SLAM technique in order to reconstruct the scene from a video which includes areas visible by each camera of the network. The reconstruction consists of a sparse, but highly accurate point cloud, representing a joint 3D reference coordinate system. Additionally, a set of 3D-registered keyframes (images) are used for high resolution representation of the scene which also include a mapping between a set of 2D pixels to 3D points of the point cloud. The pose estimation of each surveillance camera is performed individually by assigning 2D-2D correspondences between pixels of the surveillance camera and pixels of similar keyframes that map to a 3D point. This allows to implicitly obtain a set of 2D-3D correspondences between pixels in the surveillance camera and their corresponding 3D points in a joint reference coordinate system. Thus the global camera pose can be estimated using robust methods for solving the perspective-n-point problem.",
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"content": "This paper presents a concept which tackles the pose estimation problem (extrinsic calibration) for distributed, non-overlapping multi-camera networks. The basic idea is to use a visual SLAM technique in order to reconstruct the scene from a video which includes areas visible by each camera of the network. The reconstruction consists of a sparse, but highly accurate point cloud, representing a joint 3D reference coordinate system. Additionally, a set of 3D-registered keyframes (images) are used for high resolution representation of the scene which also include a mapping between a set of 2D pixels to 3D points of the point cloud. The pose estimation of each surveillance camera is performed individually by assigning 2D-2D correspondences between pixels of the surveillance camera and pixels of similar keyframes that map to a 3D point. This allows to implicitly obtain a set of 2D-3D correspondences between pixels in the surveillance camera and their corresponding 3D points in a joint reference coordinate system. Thus the global camera pose can be estimated using robust methods for solving the perspective-n-point problem.",
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"abstract": "Simultaneous Location and Mapping (SLAM) has been applied widely in robots and computer field. However, most traditional SLAM systems based on static scene assumption cannot meet the accuracy requirements in dynamic environments. In order to improve the positioning accuracy of the robot in the dynamic environment, this paper proposes a stable RGB-D SLAM approach based on ORB-SLAM3. First, the camera frame is divided into static area, potential dynamic area, and priori dynamic area. Then the mask of the priori dynamic area generated by pixel-level semantic segmentation is used to obtain an approximately accurate camera initial pose, and the multi-view geometry technology is combined to identify the potential dynamic area. Finally, the features in static area are used to complete the tracking trajectory and camera pose. Experiments on the public dataset TUM demonstrate that the proposed method in dynamic environment have better performance than ORB-SLAM3.",
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"abstract": "Systems that rely on Machine Learning (ML systems) have differing demands on quality—non-functional requirements (NFRs)— compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope—and of the extent of existing research—is lacking compared to our understanding in traditional domains.Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results— as well as inter-coder reliability on a sample of NFRs—to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model). These initial findings form the groundwork for future research in this emerging domain.CCS CONCEPTS • Software and its engineering → Extra-functional properties; Requirements analysis; • Computing methodologies → Machine learning.",
"abstracts": [
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"content": "Systems that rely on Machine Learning (ML systems) have differing demands on quality—non-functional requirements (NFRs)— compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope—and of the extent of existing research—is lacking compared to our understanding in traditional domains.Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results— as well as inter-coder reliability on a sample of NFRs—to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model). These initial findings form the groundwork for future research in this emerging domain.CCS CONCEPTS • Software and its engineering → Extra-functional properties; Requirements analysis; • Computing methodologies → Machine learning.",
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"normalizedAbstract": "Systems that rely on Machine Learning (ML systems) have differing demands on quality—non-functional requirements (NFRs)— compared to traditional systems. NFRs for ML systems may differ in their definition, scope, and importance. Despite the importance of NFRs for ML systems, our understanding of their definitions and scope—and of the extent of existing research—is lacking compared to our understanding in traditional domains.Building on an investigation into importance and treatment of ML system NFRs in industry, we make three contributions towards narrowing this gap: (1) we present clusters of ML system NFRs based on shared characteristics, (2) we use Scopus search results— as well as inter-coder reliability on a sample of NFRs—to estimate the number of relevant studies on a subset of the NFRs, and (3), we use our initial reading of titles and abstracts in each sample to define the scope of NFRs over parts of the system (e.g., training data, ML model). These initial findings form the groundwork for future research in this emerging domain.CCS CONCEPTS • Software and its engineering → Extra-functional properties; Requirements analysis; • Computing methodologies → Machine learning.",
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"Machine Learning",
"Learning Artificial Intelligence",
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"__typename": "ArticleAuthorType"
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{
"affiliation": "University of Gothenburg,Chalmers,Gothenburg,Sweden",
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"abstract": "Virtual Reality is a budding new medium of interaction, but ways in which to use it for data analytics are not immediately obvious. Through our research, we investigated new and useful ways to view multidimensional data in a virtual environment. In this paper, we present a virtual laboratory with collaborative visualizations in different spaces. We focused on geographic view in the center of the room by utilizing spatial freedom in Virtual Reality, which informed both the geographic visualization and time series data. Through this laboratory and the collaborative visualizations housed within, we believe we've found a novel way to interpret data in Virtual Reality.",
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"abstract": "Textual document image denoising is the main issue of this work. Therefore, we introduce a comparative study between two state-of-the-art denoising frameworks : local and non-local smoothing filters. The choice of both of these frameworks is directly related to their ability to deal with local data corruption and to process oriented patterns, a major characteristic of textual documents. Local smoothing filters incorporate anisotropic diffusion approaches where as non-local filters introduce non-local means. Experiments conducted on synthetic and real degraded document images illustrate the behaviour of the studied frameworks on the visual quality and even on the optical recognition accuracy rates.",
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"abstract": "Wireless capsule endoscopy (WCE) is useful to detect abnormal area in human digestive tract. However, many WCE images have low quality such as rather dark and noisy. Smoothing is an effective way to removing noise and to enhance the images. However, smoothing has a drawback since it can damage the image features. Studies had shown that anisotropic diffusion is a technique used to reduce image noise without removing image features such as edges, lines, textures or other details. These significant parts of the image content are important for the interpretation of the image. To further enhance WCE images, contrast diffusion is used by using Hessian matrix. The original method proposed by B. Li has weakness where it degrades the quality of image when number of iteration goes up. To overcome this problem, standard variance and Gaussian filter is employed in our proposed scheme. Experimental and comparative results of the proposed method are presented in this paper.",
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"title": "Image Processing, International Conference on",
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"doi": "10.1109/ICIP.1997.648003",
"title": "Anisotropic smoothing of posterior probabilities",
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"abstract": "Teo et al. (see IEEE Trans. on Medical Imaging, 1997) proposed an efficient image segmentation technique that anisotropically smoothes the homogeneous posterior probabilities before independent pixel wise MAP classification is carried out. In this paper we develop the mathematical theory underlying the technique. We demonstrate that prior anisotropic smoothing of the posterior probabilities yields the MAP solution of a discrete MRF with a non-interacting, analog discontinuity field. In contrast, isotropic smoothing of the posterior probabilities is equivalent to computing the MAP solution of a single, discrete MRF using continuous relaxation labeling. Combining a discontinuity field with a discrete MRF is important as it allows the disabling of clique potentials across discontinuities. Furthermore, explicit representation of the discontinuity field suggests new algorithms that incorporate properties like hysteresis and non-maximal suppression.",
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"keywords": [
"Image Segmentation Independent Pixel Wise MAP Classification Anisotropic Smoothing Image Segmentation Homogeneous Posterior Probabilities Mathematical Theory MAP Solution Discrete MRF Analog Discontinuity Field Noninteracting Discontinuity Field Isotropic Smoothing Continuous Relaxation Labeling Discontinuity Field Representation Algorithms Hysteresis Nonmaximal Suppression"
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"authors": [
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"affiliation": "Dept. of Comput. Sci., Stanford Univ., CA, USA",
"fullName": "P.C. Teo",
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"affiliation": "Dept. of Comput. Sci., Stanford Univ., CA, USA",
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"affiliation": "Dept. of Comput. Sci., Stanford Univ., CA, USA",
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"title": "Character Shape Restoration of Binarized Historical Documents by Smoothing via Geodesic Morphology",
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"abstract": "We propose a method which performs an isotropic morphological dilation via implicit smoothing for the purpose of restoring the degraded character shapes of binarized images. Exploiting the idea of geodesic morphology that the binary image and its distance transformed image are interconvertible, we apply a smoothing method not to the binary image but to the distance transformed image, and then reconvert it by binarization. This allows us to apply conventional smoothing methods for continuous intensity, i.e., gray scale, images to the discrete intensity, i.e., binary, image implicitly. For instance, by using an isotropic diffusion together with geodesic dilation, an isotropic dilation along the stroke direction is obtained and brings better results.",
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"abstract": "This paper presents a cellular GPU model for structured mesh generation according to an input stereo-matching disparity map. Here, the disparity map stands for a density distribution that reflects the proximity of objects to the camera in 3D space. The meshing process consists in covering such data density distribution with a topological structured hexagonal grid that adapts itself and deforms according to the density values. The goal is to generate a compressed mesh where the nearest objects are provided with more details than objects which are far from the camera. The solution we propose is based on the Kohonen's Self-Organizing Map learning algorithm for the benefit of its ability to generate a topological map according to a probability distribution and its ability to be a natural massive parallel algorithm. We propose a GPU parallel model and its implantation of the SOM standard algorithm, and present experiments on a set of standard stereo-matching disparity map benchmarks.",
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"doi": "10.1109/CVPR.2012.6247829",
"title": "Finite Element based sequential Bayesian Non-Rigid Structure from Motion",
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"abstract": "Navier's equations modelling linear elastic solid deformations are embedded within an Extended Kalman Filter (EKF) to compute a sequential Bayesian estimate for the Non-Rigid Structure from Motion problem. The algorithm processes every single frame of a sequence gathered with a full perspective camera. No prior data association is assumed because matches are computed within the EKF prediction-match-update cycle. Scene is coded as a Finite Element Method (FEM) elastic thin-plate solid, where the discretization nodes are the sparse set of scene points salient in the image. It is assumed a set of Gaussian forces acting on solid nodes to cause scene deformation. The EKF combines in a feedback loop an approximate FEM model and the frame rate measurements from the camera, resulting in an efficient method to embed Navier's equations without resorting to expensive non-linear FEM models. Classical FEM modelling has implied an interactive identification of boundary points to constrain the scene rigid motion, in this work this dissatisfying prior knowledge is no longer needed. The scene and camer rigid motion are combined in a unique pose vector and the estimation is coded relative to the camera. Additionally, the deforming effect of the Gaussian forces on the thin-plate is computed by means of the Moore-Penrose pseudoinverse of the FEM stiffness matrix. The proposed algorithm is validated with three real sequences gathered with hand-held camera observing isometric and non-isometric deformations. It is also shown the consistency of the EKF estimation with respect to ground truth computed from stereo.",
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{
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"content": "Navier's equations modelling linear elastic solid deformations are embedded within an Extended Kalman Filter (EKF) to compute a sequential Bayesian estimate for the Non-Rigid Structure from Motion problem. The algorithm processes every single frame of a sequence gathered with a full perspective camera. No prior data association is assumed because matches are computed within the EKF prediction-match-update cycle. Scene is coded as a Finite Element Method (FEM) elastic thin-plate solid, where the discretization nodes are the sparse set of scene points salient in the image. It is assumed a set of Gaussian forces acting on solid nodes to cause scene deformation. The EKF combines in a feedback loop an approximate FEM model and the frame rate measurements from the camera, resulting in an efficient method to embed Navier's equations without resorting to expensive non-linear FEM models. Classical FEM modelling has implied an interactive identification of boundary points to constrain the scene rigid motion, in this work this dissatisfying prior knowledge is no longer needed. The scene and camer rigid motion are combined in a unique pose vector and the estimation is coded relative to the camera. Additionally, the deforming effect of the Gaussian forces on the thin-plate is computed by means of the Moore-Penrose pseudoinverse of the FEM stiffness matrix. The proposed algorithm is validated with three real sequences gathered with hand-held camera observing isometric and non-isometric deformations. It is also shown the consistency of the EKF estimation with respect to ground truth computed from stereo.",
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"normalizedAbstract": "Navier's equations modelling linear elastic solid deformations are embedded within an Extended Kalman Filter (EKF) to compute a sequential Bayesian estimate for the Non-Rigid Structure from Motion problem. The algorithm processes every single frame of a sequence gathered with a full perspective camera. No prior data association is assumed because matches are computed within the EKF prediction-match-update cycle. Scene is coded as a Finite Element Method (FEM) elastic thin-plate solid, where the discretization nodes are the sparse set of scene points salient in the image. It is assumed a set of Gaussian forces acting on solid nodes to cause scene deformation. The EKF combines in a feedback loop an approximate FEM model and the frame rate measurements from the camera, resulting in an efficient method to embed Navier's equations without resorting to expensive non-linear FEM models. Classical FEM modelling has implied an interactive identification of boundary points to constrain the scene rigid motion, in this work this dissatisfying prior knowledge is no longer needed. The scene and camer rigid motion are combined in a unique pose vector and the estimation is coded relative to the camera. Additionally, the deforming effect of the Gaussian forces on the thin-plate is computed by means of the Moore-Penrose pseudoinverse of the FEM stiffness matrix. The proposed algorithm is validated with three real sequences gathered with hand-held camera observing isometric and non-isometric deformations. It is also shown the consistency of the EKF estimation with respect to ground truth computed from stereo.",
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"keywords": [
"Stereo Image Processing",
"Bayes Methods",
"Finite Element Analysis",
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"Gaussian Forces",
"Scene Deformation",
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"Vectors",
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"affiliation": "Inst. de Investig. en Ing. de Aragon (I3A), Univ. de Zaragoza, Zaragoza, Spain",
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"affiliation": "Inst. de Investig. en Ing. de Aragon (I3A), Univ. de Zaragoza, Zaragoza, Spain",
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{
"affiliation": "Inst. de Investig. en Ing. de Aragon (I3A), Univ. de Zaragoza, Zaragoza, Spain",
"fullName": "A. Agudo",
"givenName": "A.",
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"title": "Logistic Regression of Point Matches for Accurate Transformation Estimation",
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"abstract": "Feature extraction and matching (FEM) has been widely used for the registration of partially overlapping 3D shapes. Due to various factors such as imaging noise, simple geometry, or clutter, it usually introduces false positive ones. To reliably estimate the underlying transformation that brings one partial shape into the best possible alignment with another, it is critical to estimate the extent to which the established point matches are correct. To this end, we propose to use the logit function for the regression of the errors of these point matches. The novel method includes three steps: (i) normalization of the errors of the point matches, (ii) logistic regression of the point matches for the estimation of their reliabilities/weights, and (iii) estimation of the underlying transformation in the weighted least squares sense. These steps are repeated until either the maximum number of iterations has been reached or the weighted average of the errors of the point matches has been below the scanning resolution. A comparative study using real data captured by different range sensors shows that the proposed method outperforms two state-of-the-art ones for more accurate estimation of the underlying transformation.",
"abstracts": [
{
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"content": "Feature extraction and matching (FEM) has been widely used for the registration of partially overlapping 3D shapes. Due to various factors such as imaging noise, simple geometry, or clutter, it usually introduces false positive ones. To reliably estimate the underlying transformation that brings one partial shape into the best possible alignment with another, it is critical to estimate the extent to which the established point matches are correct. To this end, we propose to use the logit function for the regression of the errors of these point matches. The novel method includes three steps: (i) normalization of the errors of the point matches, (ii) logistic regression of the point matches for the estimation of their reliabilities/weights, and (iii) estimation of the underlying transformation in the weighted least squares sense. These steps are repeated until either the maximum number of iterations has been reached or the weighted average of the errors of the point matches has been below the scanning resolution. A comparative study using real data captured by different range sensors shows that the proposed method outperforms two state-of-the-art ones for more accurate estimation of the underlying transformation.",
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"normalizedAbstract": "Feature extraction and matching (FEM) has been widely used for the registration of partially overlapping 3D shapes. Due to various factors such as imaging noise, simple geometry, or clutter, it usually introduces false positive ones. To reliably estimate the underlying transformation that brings one partial shape into the best possible alignment with another, it is critical to estimate the extent to which the established point matches are correct. To this end, we propose to use the logit function for the regression of the errors of these point matches. The novel method includes three steps: (i) normalization of the errors of the point matches, (ii) logistic regression of the point matches for the estimation of their reliabilities/weights, and (iii) estimation of the underlying transformation in the weighted least squares sense. These steps are repeated until either the maximum number of iterations has been reached or the weighted average of the errors of the point matches has been below the scanning resolution. A comparative study using real data captured by different range sensors shows that the proposed method outperforms two state-of-the-art ones for more accurate estimation of the underlying transformation.",
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"title": "2019 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)",
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"abstract": "We propose a finite element method (FEM) based approach for surface stitching which can be integrated into existing SLAM and NRSfM pipelines for AR applications. Given individual reconstructions and camera poses at different time stamps, our stitching method incrementally completes the surface with a smooth transition between the hidden and the observed parts, so that all the observed parts can be stitched into a single surface. Thanks to the physical modelling, deformations from the observed parts are propagated to the hidden parts enabling an overall high-fidelity and realistic estimate. To keep the computational time in bounds, deformations near the observed parts are computed with FEM, and the remaining region is approximated by Laplacian deformation. We assume that no force is applied to the hidden parts. To evaluate the algorithm, we generate a synthetic dataset with ground truth. In our dataset, the camera observes only a part of the target surface in each frame and moves until the whole target surface is covered. The dataset which will be made publicly available includes the ground truth camera poses and geometries of the whole surface at each time frame. An experimental evaluation of the stitching method with accuracy metrics rounds out the draft.",
"abstracts": [
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"content": "We propose a finite element method (FEM) based approach for surface stitching which can be integrated into existing SLAM and NRSfM pipelines for AR applications. Given individual reconstructions and camera poses at different time stamps, our stitching method incrementally completes the surface with a smooth transition between the hidden and the observed parts, so that all the observed parts can be stitched into a single surface. Thanks to the physical modelling, deformations from the observed parts are propagated to the hidden parts enabling an overall high-fidelity and realistic estimate. To keep the computational time in bounds, deformations near the observed parts are computed with FEM, and the remaining region is approximated by Laplacian deformation. We assume that no force is applied to the hidden parts. To evaluate the algorithm, we generate a synthetic dataset with ground truth. In our dataset, the camera observes only a part of the target surface in each frame and moves until the whole target surface is covered. The dataset which will be made publicly available includes the ground truth camera poses and geometries of the whole surface at each time frame. An experimental evaluation of the stitching method with accuracy metrics rounds out the draft.",
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"abstract": "Recent results in sensor-based motion planning suggest that the collision avoidance task can be handled automatically, thus freeing the operator for global control. Whole-sensitive arm manipulators bodies are covered with a sensitive skin sensor to detect nearby objects are proposed for this purpose. The data from the sensitive skin is used with the sensor data processing and motion planning algorithms to avoid collisions for the entire robot arm body in an unknown or time-varying environment. The result is a highly efficient, safe, and robust hybrid system in which the shift motion control between the operator and the automatic system is done transparently and in real-time. The motion of the master arm is either executed faithfully, or, to avoid collisions, is used as general guidance for the slave arm.<>",
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