Sampling and reconstruction with adaptive meshes

Terzopoulos, Demetri; Vasilescu, M. Alex O.

doi:10.1109/cvpr.1991.139663

Cited by 146 publications

(85 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…They have already been used in computer vision, specifically for range data reconstruction (Terzopoulos and Vasilescu, 1991) or surface mesh decimation (Gourdon, 1995). One of the advantages of this technique is to decrease the numerical complexity of a structure while preserving its accuracy.…”

Section: Adaptive Meshesmentioning

confidence: 99%

Dense Non-Rigid Motion Estimation in Sequences of 3D Images Using Differential Constraints

Benayoun

Nastar²,

Ayache

1995

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. We describe a new method for computing the displacement vector field in time sequences of 2D or 3D images (4D data). The method is energy-minimizing on the space of correspondence functions; the energy is split into two terms, with one term matching differential singularities in the images, and the other constraining the regularity of the field. In order to reduce the computational time of the motion estimation, we use an adaptive image mesh, the resolution of which depends on the value of the gradient intensity. We solve numerically the minimization problem with the finite element method which gives a continuous approximation of the solution. We present experimental results on synthetic data and on medical images and we show how to use these results for analyzing cardiac deformations.

show abstract

Section: Adaptive Meshesmentioning

confidence: 99%

Dense Non-Rigid Motion Estimation in Sequences of 3D Images Using Differential Constraints

Benayoun

Nastar²,

Ayache

1995

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Alternatively, some techniques have adopted an opposite strategy, i.e., a fine mesh is successively coarsened until the approximation error reaches a tolerance [10]. Mixed approaches that combine refinement and coarsening [11,12] as well as optimization schemes for positioning the vertices have also been developed [13,14].…”

Section: Related Workmentioning

confidence: 99%

Generating segmented meshes from textured color images

Lizier

Martins

Cuadros-Vargas

et al. 2009

Journal of Visual Communication and Image Representation

View full text Add to dashboard Cite

“…Alternatively, some techniques have adopted an opposite strategy, i.e., a fine mesh is successively coarsened until the approximation error reaches a tolerance [4]. Mixed approaches that combine refinement and coarsening [15,14] as well as optimization for re-positioning the vertices in minimum error places have also been developed [19].…”

Section: Related Workmentioning

confidence: 99%

Generating Segmented Quality Meshes from Images

et al. 2008

View full text Add to dashboard Cite

Techniques devoted to generate a triangular mesh from images either take as starting point a segmented image or generate a mesh without distinguishing different structures contained in the image. The pre-segmentation and the absence of well defined structures may impose difficulties in using the resulting mesh in some applications, as for example numerical simulations. In this work we present a new technique for mesh generation that aims at eliminating the need for pre-processing by building the segmentation into the mesh generation process. Furthermore, the proposed technique generates theoretically guaranteed quality meshes, rendering our framework very appropriated for applications in numerical simulation as well as in image modeling. In fact, the effectiveness of our approach in both applications are presented and discussed.

show abstract

Sampling and reconstruction with adaptive meshes

Abstract: Abstract

Cited by 146 publications

References 11 publications

Dense Non-Rigid Motion Estimation in Sequences of 3D Images Using Differential Constraints

Dense Non-Rigid Motion Estimation in Sequences of 3D Images Using Differential Constraints

Generating segmented meshes from textured color images

Generating Segmented Quality Meshes from Images

Contact Info

Product

Resources

About