Spline Smoothing: A Special Case of Diffusion Smoothing

Cai, Li-Dong

doi:10.5244/c.3.47

Cited by 2 publications

(2 citation statements)

References 3 publications

(4 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In (Cai, 1989), a parallel is made between a filtering process using h 1D and a diffusion process. In particular, they discretized the diffusion process in space and time and showed, with some pre-defined time-step and spatial-step, that a diffusion process is equivalent to a filtering process using h 1D .…”

Section: Design Of the H Filter To Deal With An Anisotropic Gridmentioning

confidence: 99%

Denoising 3D Microscopy Images of Cell Nuclei using Shape Priors on an Anisotropic Grid

Bouyrie

Manfredotti

Peyriéras

et al. 2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

View full text Add to dashboard Cite

This paper presents a new multiscale method to denoise three-dimensional images of cell nuclei. The specificity of this method is its awareness of the noise distribution and object shapes. It combines a multiscale representation called Isotropic Undecimated Wavelet Transform (IUWT) with a nonlinear transform, a statistical test and a variational method, to retrieve spherical shapes in the image. Beyond extending an existing 2D approach to a 3D problem, our algorithm takes the sampling grid dimensions into account. We compare our method to the two algorithms from which it is derived on a representative image analysis task, and show that it is superior to both of them. It brings a slight improvement in the signal-to-noise ratio and a significant improvement in cell detection.

show abstract

Section: Design Of the H Filter To Deal With An Anisotropic Gridmentioning

confidence: 99%

Denoising 3D Microscopy Images of Cell Nuclei using Shape Priors on an Anisotropic Grid

Bouyrie

Manfredotti

Peyriéras

et al. 2016

Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods

View full text Add to dashboard Cite

show abstract

“…In fact only the Z coordinate need be stored in order to recover three dimensional structure at a later stage. Regularisation takes place using gaussian interpolation (approximately equivalent to spline interpolation [18]) to produce a regular depth map.…”

Section: Depth Map Generationmentioning

confidence: 99%

Reconstruction of visual appearance

North

1990

Procedings of the British Machine Vision Conference 1990

View full text Add to dashboard Cite

This paper presents a method for reconstructing the visual appearance of a scene from any viewpoint, given a collection of stereo pairs. A representation is suggested which is flexible and efficient, and algorithms to recover three dimensional structure and grey-level detail by combining a number of two-dimensional views are discussed. Some results using real and simulated data are given. This paper presents a method for reconstructing the visual appearance of a scene from any viewpoint given a collection of stereo pairs. For example, figure 1 shows two stereo pairs of the same building. The objective is to generate a new view from this data as in figure 2 (see below). The method has a number of properties:• Occlusion is dealt with explicitly and all surface fragments visible from at least one stereo pair are correctly rendered in the output.• Surface shape and texture are obtained from the best available data.• The stereo pairs are pre-compiled into a concise representation from which reconstruction is an 0(n) process (n = number of stereo pairs).Reconstruction of visual appearance has been used extensively for visualisation of terrain models [1]. Some authors have also used reconstruction to show the result of stereo algorithms [2]. The Mosaic system [3] used stereo and geometric reasoning to generate wire-frame models of urban scenes. Although domain specific to aerial photographs of buildings, some results using grey-level rendering were achieved. Feature tracking [4,5,6,7] has been used to recover three-dimensional structure on a frame-by-frame basis. model generation of continuous objects, with accurate depth information using a controlled light source. SYSTEM OVERVIEWThe system works in two parts. Steps 1, 2, 3 and 4 are performed off-line from the available stereo pairs.Step 5 is performed for each reconstruction required.1. Production of a depth map from each stereo pair. The relative camera orientation is determined and corresponding points are found for each pair. These are projected by triangulation to give a grid of threedimensional locations.2. Computation of relative positions of stereo pairs. Overlapping regions of the surfaces generated from the stereo pairs allow the relative positions of their viewpoints to be computed.3. Detection of depth map discontinuities. By comparing depth maps, discontinuities in the interpolated depth maps are located and marked to avoid unwanted interpolation during reconstruction. The result is a set of viewpoint-dependent depth maps with discontinuities, somewhat similar to Marr's concept [12] of a 2|d sketch.

show abstract

Spline Smoothing: A Special Case of Diffusion Smoothing

Cited by 2 publications

References 3 publications

Denoising 3D Microscopy Images of Cell Nuclei using Shape Priors on an Anisotropic Grid

Denoising 3D Microscopy Images of Cell Nuclei using Shape Priors on an Anisotropic Grid

Reconstruction of visual appearance

Contact Info

Product

Resources

About