Feature-Sensitive and Adaptive Mesh Generation of Grayscale Images

Mei, Xu; Gao, Zhanheng; Yu, Zeyun

doi:10.1007/978-3-319-09994-1_18

Cited by 2 publications

(2 citation statements)

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“…Therefore, the process can be formulated as an optimization problem which tries to optimize some energy functional that can be defined as in (1). Due to the random distribution of image features, we propose to use a sparser representation of both input images using a content adaptive mesh generator such as the one described in [12].…”

Section: Proposed Methodsmentioning

confidence: 99%

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Fast Mesh-Based Medical Image Registration

Baghaie

D’Souza

2014

Advances in Visual Computing

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In this paper a fast triangular mesh based registration method is proposed. Having Template and Reference images as inputs, the template image is triangulated using a content adaptive mesh generation algorithm. Considering the pixel values at mesh nodes, interpolated using spline interpolation method for both of the images, the energy functional needed for image registration is minimized. The minimization process was achieved using a mesh based discretization of the distance measure and regularization term which resulted in a sparse system of linear equations, which due to the smaller size in comparison to the pixel-wise registration method, can be solved directly. Mean Squared Difference (MSD) is used as a metric for evaluating the results. Using the mesh based technique, higher speed was achieved compared to pixel-based curvature registration technique with fast DCT solver. The implementation was done in MATLAB without any specific optimization. Higher speeds can be achieved using C/C++ implementations.

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Section: Proposed Methodsmentioning

confidence: 99%

“…For generating the content adaptive mesh needed for our algorithm, the method proposed by Ming et al [12] is used. Based on the discussion given in this paper, the main difference between various mesh generating methods rises from different methods used for node placement.…”

Section: Content Adaptive Mesh Generationmentioning

confidence: 99%