Short Note: Nanoscale adaptive meshing for rapid STM imaging
Journal of Computational Physics
Topological triangle characterization with application to object detection from images
Image and Vision Computing
Generating segmented meshes from textured color images
Journal of Visual Communication and Image Representation
Generating segmented quality meshes from images
Journal of Mathematical Imaging and Vision
Content adaptive mesh representation of images using binary space partitions
IEEE Transactions on Image Processing
Motion-compensated reconstruction of gated cardiac SPECT images using a deformable mesh model
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Grid smoothing: a graph-based approach
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Iterative image coding using hybrid wavelet-based triangulation
MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
Fast depth map compression and meshing with compressed tritree
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Adaptive mesh generation of MRI images for 3D reconstruction of human trunk
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Mesh modeling is an important problem with many applications in image processing. A key issue in mesh modeling is how to generate a mesh structure that well represents an image by adapting to its content. We propose a new approach to mesh generation, which is based on a theoretical result derived on the error bound of a mesh representation. In the proposed method, the classical Floyd-Steinberg error-diffusion algorithm is employed to place mesh nodes in the image domain so that their spatial density varies according to the local image content. Delaunay triangulation is next applied to connect the mesh nodes. The result of this approach is that fine mesh elements are placed automatically in regions of the image containing high-frequency features while coarse mesh elements are used to represent smooth areas. The proposed algorithm is noniterative, fast, and easy to implement. Numerical results demonstrate that, at very low computational cost, the proposed approach can produce mesh representations that are more accurate than those produced by several existing methods. Moreover, it is demonstrated that the proposed algorithm performs well with images of various kinds, even in the presence of noise.