Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Pai, Gautam"'
Automating the current bridge visual inspection practices using drones and image processing techniques is a prominent way to make these inspections more effective, robust, and less expensive. In this paper, we investigate the development of a novel d
Externí odkaz:
http://arxiv.org/abs/2403.17725
The roto-translation group SE2 has been of active interest in image analysis due to methods that lift the image data to multi-orientation representations defined on this Lie group. This has led to impactful applications of crossing-preserving flows f
Externí odkaz:
http://arxiv.org/abs/2402.15322
Group equivariant convolutional neural networks (G-CNNs) have been successfully applied in geometric deep learning. Typically, G-CNNs have the advantage over CNNs that they do not waste network capacity on training symmetries that should have been ha
Externí odkaz:
http://arxiv.org/abs/2210.00935
We introduce a data-driven version of the plus Cartan connection on the homogeneous space $\mathbb{M}_2$ of 2D positions and orientations. We formulate a theorem that describes all shortest and straight curves (parallel velocity and parallel momentum
Externí odkaz:
http://arxiv.org/abs/2208.11004
Establishing a correspondence between two non-rigidly deforming shapes is one of the most fundamental problems in visual computing. Existing methods often show weak resilience when presented with challenges innate to real-world data such as noise, ou
Externí odkaz:
http://arxiv.org/abs/2203.07694
Publikováno v:
2021 International Conference on 3D Vision (3DV)
We consider the problem of computing dense correspondences between non-rigid shapes with potentially significant partiality. Existing formulations tackle this problem through heavy manifold optimization in the spectral domain, given hand-crafted shap
Externí odkaz:
http://arxiv.org/abs/2110.09994
We propose an extension of the Allen-Cahn model for pattern synthesis on two dimensional curved surfaces. This model is based on a single PDE and it offers improved ability of controlling the type of generated surface patterns via the chosen reaction
Externí odkaz:
http://arxiv.org/abs/2008.07654
A majority of shape correspondence frameworks are based on devising pointwise and pairwise constraints on the correspondence map. The functional maps framework allows for formulating these constraints in the spectral domain. In this paper, we develop
Externí odkaz:
http://arxiv.org/abs/1907.12993
A deep learning approach to numerically approximate the solution to the Eikonal equation is introduced. The proposed method is built on the fast marching scheme which comprises of two components: a local numerical solver and an update scheme. We repl
Externí odkaz:
http://arxiv.org/abs/1903.07973
Publikováno v:
In Image and Vision Computing July 2022 123
