Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model

• URL: http://arxiv.org/abs/2004.05233v1
• Date: Fri, 10 Apr 2020 21:15:54 GMT
• Title: Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model
• Authors: Gang Yao, Ryan Saltus, Ashwin Dani
• Abstract summary: A B-spline chained multiple random matrices representation is proposed to model geometric characteristics of an elongated deformable object. An expectation-maximization (EM) method is derived to estimate the shape of the elongated deformable object. The proposed algorithm is evaluated for the shape estimation of the elongated deformable objects in scenarios.
• Score: 5.94069939063928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, a B-spline chained multiple random matrices representation is proposed to model geometric characteristics of an elongated deformable object. The hyper degrees of freedom structure of the elongated deformable object make its shape estimation challenging. Based on the likelihood function of the proposed model, an expectation-maximization (EM) method is derived to estimate the shape of the elongated deformable object. A split and merge method based on the Euclidean minimum spanning tree (EMST) is proposed to provide initialization for the EM algorithm. The proposed algorithm is evaluated for the shape estimation of the elongated deformable objects in scenarios, such as the static rope with various configurations (including configurations with intersection), the continuous manipulation of a rope and a plastic tube, and the assembly of two plastic tubes. The execution time is computed and the accuracy of the shape estimation results is evaluated based on the comparisons between the estimated width values and its ground-truth, and the intersection over union (IoU) metric.

Related papers

• How can representation dimension dominate structurally pruned LLMs? [17.953689537875377]
  Pruning assumes a subnetwork exists in the original deep neural network. It is unclear how the model performance varies with the different subnetwork extractions.
  arXiv  Detail & Related papers  (2025-03-06T12:28:59Z)
• Extended Object Tracking and Classification based on Linear Splines [5.672223170618134]
  This paper introduces a framework based on linear splines for 2-dimensional extended object tracking and classification. An exact likelihood is derived for the case in which noisy measurements can be scattered from any point on the contour of the extended object. Exploiting such likelihood to measure how well the observed data fit a given shape, a suitable estimator is developed.
  arXiv  Detail & Related papers  (2024-10-31T17:46:54Z)
• Latent Semantic Consensus For Deterministic Geometric Model Fitting [109.44565542031384]
  We propose an effective method called Latent Semantic Consensus (LSC) LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses. LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting.
  arXiv  Detail & Related papers  (2024-03-11T05:35:38Z)
• Towards Efficient Time Stepping for Numerical Shape Correspondence [55.2480439325792]
  Methods based on partial differential equations (PDEs) have been established, encompassing e.g. the classic heat kernel signature. We consider here several time stepping schemes. The goal of this investigation is to assess, if one may identify a useful property of methods for time integration for the shape analysis context.
  arXiv  Detail & Related papers  (2023-12-21T13:40:03Z)
• Object based Bayesian full-waveform inversion for shear elastography [0.0]
  We develop a computational framework to quantify uncertainty in shear elastography imaging of anomalies in tissues. We find the posterior probability of parameter fields representing the geometry of the anomalies and their shear moduli. We demonstrate the approach on synthetic two dimensional tests with smooth and irregular shapes.
  arXiv  Detail & Related papers  (2023-05-11T08:25:25Z)
• Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning [60.69217130006758]
  We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns. We perform shape optimization experiments on 2D airfoils and discuss the applicable scenarios for the two models.
  arXiv  Detail & Related papers  (2023-05-03T13:45:40Z)
• AutoPoly: Predicting a Polygonal Mesh Construction Sequence from a Silhouette Image [17.915067368873018]
  AutoPoly is a hybrid method that generates a polygonal mesh construction sequence from a silhouette image. Our method can alter topology, whereas the recently proposed inverse shape estimation methods using differentiable rendering can only handle a fixed topology.
  arXiv  Detail & Related papers  (2022-03-29T04:48:47Z)
• Functional additive regression on shape and form manifolds of planar curves [0.0]
  We define shape and form as equivalence classes under translation, rotation and -- for shapes -- also scale. We extend generalized additive regression to models for the shape/form of planar curves or landmark.
  arXiv  Detail & Related papers  (2021-09-06T17:43:32Z)
• Optimal radial basis for density-based atomic representations [58.720142291102135]
  We discuss how to build an adaptive, optimal numerical basis that is chosen to represent most efficiently the structural diversity of the dataset at hand. For each training dataset, this optimal basis is unique, and can be computed at no additional cost with respect to the primitive basis. We demonstrate that this construction yields representations that are accurate and computationally efficient.
  arXiv  Detail & Related papers  (2021-05-18T17:57:08Z)
• Random Matrix Based Extended Target Tracking with Orientation: A New Model and Inference [0.0]
  We propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework. It is not possible to find a closed-form analytical expression for the true posterior because of the absence of conjugacy.
  arXiv  Detail & Related papers  (2020-10-17T16:33:06Z)
• Category Level Object Pose Estimation via Neural Analysis-by-Synthesis [64.14028598360741]
  In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module. The image synthesis network is designed to efficiently span the pose configuration space. We experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone.
  arXiv  Detail & Related papers  (2020-08-18T20:30:47Z)
• Learning to predict metal deformations in hot-rolling processes [59.00006390882099]
  Hot-rolling is a metal forming process that produces a cross-section from an input through a sequence of deformations. In current practice, the rolling sequence and the geometry of their rolls are needed to achieve a given cross-section. We propose a supervised learning approach to predict a given by a set of rolls with given geometry.
  arXiv  Detail & Related papers  (2020-07-22T13:33:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.