Showing papers by "Christine Fernandez-Maloigne published in 2000"
TL;DR: The generic model concept is focused on, an evolving methods to encapsulate generic information in a generic 3D mesh and a topological relationship between these non-related geometrical and topological models is described, opening a framework to transfer different kinds of generic information, ranging from algorithmic simplification and computation hints to anatomical features or learning material.
Abstract: Surgery simulation is a growing field of research comprising the efforts of various disciplines including Computer Graphics, Computer Vision, Medicine, Mechanics, Robotics and even Animation. We try to combine, adapt and extended different solutions to this problem and re-assemble them using mid-range 3D graphics hardware, modern object-oriented methods and free visualization toolkits. Reconstructions of the physical based realistic 3D models are achieved form CT scans. In particular, we focus on the generic model concept, an evolving methods to encapsulate generic information in a generic 3D mesh. This model is subsequently deformed using multivariate scattered data interpolation technique show that it matches the reconstructed model of the patient being studied, under the control of common landmark points. We describe a wy to build a topological relationship between these non-related geometrical and topological models, thus opening a framework to transfer different kinds of generic information, ranging from algorithmic simplification and computation hints to anatomical features or learning material. Starting from several uncalibrated photographs, we also show how 2D features points may be used to recover the camera parameters and employ them as control points to deformed our model in a similar fashion, so that the texture information is retrieved after projecting the mode into the photographic pose.
11 citations
05 Jun 2000
TL;DR: An estimation technique of the 2D log-spectrum based on 2D undecimated wavelet is developed, and a new technique is derived, easy to apply, which gives information about the2D frequential components of an image.
Abstract: We study the problem of estimating the log-spectrum of a stationary Gaussian time series by thresholding the wavelet coefficients. We propose the use of the undecimated wavelet transform to denoise the log-periodogram. For this, we review a denoising method based on undecimated wavelet transform, and we propose a level-dependent threshold which considers that one undecimated scale has N/b coefficients "repeating" b times. The result corresponds to the average of all log-peridogram circulant shifts denoised by a decimated wavelet transform. The purpose of this undecimated thresholding is to make the reconstructed log-spectrum as nearly noise-free as possible, but with a keep of all small frequential components. Since the wavelet denoising method can be generalized to images, we develop an estimation technique of the 2D log-spectrum based on 2D undecimated wavelet. We derive a new technique, easy to apply, which gives information about the 2D frequential components of an image.
5 citations
TL;DR: The multi-scale gradient decomposition schema proposed by Mallat is modified thus enabling a computation of uncorrelated partial derivatives, and a selection method of multi- scale contours is put forward, having a lesser algorithmic complexity than processings based on the gradient norm.
4 citations
04 Dec 2000
TL;DR: This new 'totally' non-uniform transform, more adapted than Malvar, Packets or dyadic double-tree decomposition, allows the study of all possible time-frequency partitions with the only restriction that the blocks are rectangular.
Abstract: In this paper, we propose a new decomposition scheme for spatially adaptive wavelet packets. Contrary to the double tree algorithm, our method is non-uniform and shift- invariant in the time and frequency domains, and is minimal for an information cost function. We prose some-restrictions to our algorithm to reduce the complexity and permitting us to provide some time-frequency partitions of the signal in agreement with its structure. This new 'totally' non-uniform transform, more adapted than Malvar, Packets or dyadic double-tree decomposition, allows the study of all possible time-frequency partitions with the only restriction that the blocks are rectangular. It permits one to obtain a satisfying Time-Frequency representation, and is applied for the study of EEG signals.
1 citations