91 research outputs found

    Multiple constraints to compute optical flow

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    The computation of the optical flow field from an image sequence requires the definition of constraints on the temporal change of image features. In this paper, we consider the implications of using multiple constraints in the computational schema. In the first step, it is shown that differential constraints correspond to an implicit feature tracking. Therefore, the best results (either in terms of measurement accuracy, and speed in the computation) are obtained by selecting and applying the constraints which are best “tuned” to the particular image feature under consideration. Considering also multiple image points not only allows us to obtain a (locally) better estimate of the velocity field, but also to detect erroneous measurements due to discontinuities in the velocity field. Moreover, by hypothesizing a constant acceleration motion model, also the derivatives of the optical flow are computed. Several experiments are presented from real image sequences

    Multiple constraints to compute optical flow

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    A comparison of constitutive models for describing the flow of uncured styrene-butadiene rubber

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    Uncured styrene-butadiene rubber (SBR) can be modelled as a viscoelastic material with at least two different relaxation mechanisms. In this paper we compare multi-mode constitutive models combining two viscoelastic modes (linear and/or nonlinear) in three possible ways. Our particular choice of the two modes was inspired by models originally developed to describe the response of asphalt binders. We select the model that best fits the experimental data obtained from a modified stress relaxation experiment in the torsional configuration of the plate-plate rheometer. The optimisation of the five model parameters for each model is achieved by minimising the weighted least-squares distance between experimental observations and the computer model output using a tree-structured Parzen estimator algorithm to find an initial guess, followed by further optimisation using the Nelder-Mead simplex algorithm. The results show that the model combining the linear mode and the nonlinear mode is the most suitable variant to describe the observed behavior of SBR in the given regime. The predictive capabilities of the three models are further examined in changed experimental and numerical configurations. Full data and code to produce the figures in this article are included as supplementary material

    Articulating Space: Geometric Algebra for Parametric Design -- Symmetry, Kinematics, and Curvature

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    To advance the use of geometric algebra in practice, we develop computational methods for parameterizing spatial structures with the conformal model. Three discrete parameterizations – symmetric, kinematic, and curvilinear – are employed to generate space groups, linkage mechanisms, and rationalized surfaces. In the process we illustrate techniques that directly benefit from the underlying mathematics, and demonstrate how they might be applied to various scenarios. Each technique engages the versor – as opposed to matrix – representation of transformations, which allows for structure-preserving operations on geometric primitives. This covariant methodology facilitates constructive design through geometric reasoning: incidence and movement are expressed in terms of spatial variables such as lines, circles and spheres. In addition to providing a toolset for generating forms and transformations in computer graphics, the resulting expressions could be used in the design and fabrication of machine parts, tensegrity systems, robot manipulators, deployable structures, and freeform architectures. Building upon existing algorithms, these methods participate in the advancement of geometric thinking, developing an intuitive spatial articulation that can be creatively applied across disciplines, ranging from time-based media to mechanical and structural engineering, or reformulated in higher dimensions

    Proceedings for the ICASE Workshop on Heterogeneous Boundary Conditions

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    Domain Decomposition is a complex problem with many interesting aspects. The choice of decomposition can be made based on many different criteria, and the choice of interface of internal boundary conditions are numerous. The various regions under study may have different dynamical balances, indicating that different physical processes are dominating the flow in these regions. This conference was called in recognition of the need to more clearly define the nature of these complex problems. This proceedings is a collection of the presentations and the discussion groups

    Kinematics and Robot Design IV, KaRD2021

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    This volume collects the papers published on the special issue “Kinematics and Robot Design IV, KaRD2021” (https://www.mdpi.com/journal/robotics/special_issues/KaRD2021), which is the forth edition of the KaRD special-issue series, hosted by the open-access journal “MDPI Robotics”. KaRD series is an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2021, after the peer-review process, accepted 12 papers. The accepted papers cover some theoretical and many design/applicative aspects

    Computational Methods for Computer Vision : Minimal Solvers and Convex Relaxations

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    Robust fitting of geometric models is a core problem in computer vision. The most common approach is to use a hypothesize-and-test framework, such as RANSAC. In these frameworks the model is estimated from as few measurements as possible, which minimizes the risk of selecting corrupted measurements. These estimation problems are called minimal problems, and they can often be formulated as systems of polynomial equations. In this thesis we present new methods for building so-called minimal solvers or polynomial solvers, which are specialized code for solving such systems. On several minimal problems we improve on the state-of-the-art both with respect to numerical stability and execution time.In many computer vision problems low rank matrices naturally occur. The rank can serve as a measure of model complexity and typically a low rank is desired. Optimization problems containing rank penalties or constraints are in general difficult. Recently convex relaxations, such as the nuclear norm, have been used to make these problems tractable. In this thesis we present new convex relaxations for rank-based optimization which avoid drawbacks of previous approaches and provide tighter relaxations. We evaluate our methods on a number of real and synthetic datasets and show state-of-the-art results

    Camera Pose Estimation from Aerial Images

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    This thesis demonstrates the applicability of the digital camera as an aerial po sitioning device. The necessary theory behind digital, optical imaging systems and geometrical image formation is presented. In addition, basic image distortions and camera calibration are introduced. However, the main emphasis is on the correspondence problem between two images and on camera pose estimation. The position and orientation of the camera can be estimated relatively to previous known coordinates or absolutely to some reference coordinate system. In relative camera pose estimation, the correspondences between two consecutive images can be recognized from image derivatives. In general, di erential methods are used for low resolution images with high frame rates. For high resolution images, feature-based methods are generally more appropriate. Image features are often detected with subpixel accuracy, and their surroundings are described with feature vectors. These feature vectors are matched between two images to and the pointwise correspondences. The relative translation and orientation of the camera can be estimated from the correspondences. However, the major problem in all relative positioning methods is the error accumulation, where errors from previous estimations are accumulated to further estimations. The error accumulation can be avoided by registering sensed aerial images to previously captured georeferenced images, which coordinates are known for every pixel. In this thesis, image registration between the reference image and an aerial image is implemented manually. Position and orientation of a camera are estimated absolutely to the reference coordinate system. This thesis presents algorithms to solve the correspondence problem and to estimate the relative and absolute position and orientation of an aerial camera. The presented algorithms are verified with virtual Google Earth images and real-lif eaerial images from the test ight. In addition, the performance of the algorithms is also analyzed in terms of noise resistance
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