The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Heinz Pitsch - One of the best experts on this subject based on the ideXlab platform.

  • a level Set Formulation for premixed combustion les considering the turbulent flame structure
    Combustion and Flame, 2009
    Co-Authors: Vincent Moureau, Benoit Fiorina, Heinz Pitsch
    Abstract:

    Abstract In this paper, a consistent and rigorous Formulation is developed for the coupling of the G -equation model to an LES flow solver that describes the interactions of the scales of the flame, the turbulence, and the filtering procedure from the resolved turbulence regime to the broadened preheat regions regime. A progress variable equation is introduced to describe the filtered flame structure. The models provided for the sub-filter diffusivity and the filtered reaction term appearing in this equation are consistent with the solution of the G -equation model. The solution of the progress variable equation ensures that the resolved part of the turbulent mixing in the preheat region can be described. However, the C-field is underresolved if the sub-filter Damkohler number is not much smaller than unity, and hence the solution of the C-equation cannot be expected to produce the correct flame propagation speed. The coupling with the G -equation ensures that the flame front described by the filtered reaction progress variable moves with the correct propagation velocity, independent of numerical diffusion caused by an underresolution of the flame. Formulations both for low-Mach number flow solvers and for fully compressible solvers are presented. To validate the Formulation, the model is applied in compressible LES of two turbulent flames anchored by a triangular flame-holder. For the statistically stationary case, the mean and RMS progress variable are in very good agreement with experimental data, demonstrating that the model correctly reproduces the flame anchoring and the flame–turbulence interactions in the recirculation zone. For the acoustically pulsed case, the LES fields show the same large scale fluctuations that are present in the experimental data.

  • a consistent level Set Formulation for large eddy simulation of premixed turbulent combustion
    Combustion and Flame, 2005
    Co-Authors: Heinz Pitsch
    Abstract:

    Abstract A consistent Formulation of the G -equation approach for LES is developed. The unfiltered G equation is valid only at the instantaneous flame front location. Hence, in a filtering procedure applied to derive the appropriate LES equation, only the instantaneous unfiltered flame surface can be considered. A new filter kernel is provided, which averages along the flame surface. The filter kernel is used to derive the G equation for the filtered flame front location. This equation has two unclosed terms, involving a flame front conditional averaged flow velocity, and a filtered propagation term. A model for the conditional velocity is derived, expressing this quantity in terms of the Favre-filtered flow velocity, which is typically known from a flow solver. This model leads to the appearance of a density ratio in the propagation term of the G equation. LES of combustion in the thin reaction zones regime is discussed in the LES regime diagram. A new line is identified separating the thin reaction zones regime into two parts, where the broadened flame thickness is larger and smaller than the filter size, respectively. A model for the propagation term is provided. This leads to a term including the subfilter turbulent burning velocity and an additional term proportional to the resolved flame front curvature. For the former, an algebraic model is provided from an equation for the subfilter flame front wrinkling. The latter term depends on the inverse of the subfilter Damkohler number and disappears in the corrugated flamelets regime.

Christoph Schnorr - One of the best experts on this subject based on the ideXlab platform.

  • towards recognition based variational segmentation using shape priors and dynamic labeling
    Lecture Notes in Computer Science, 2003
    Co-Authors: Daniel Cremers, Nir Sochen, Christoph Schnorr
    Abstract:

    We propose a novel variational approach based on a level Set Formulation of the Mumford-Shah functional and shape priors. We extend the functional by a labeling function which indicates image regions in which the shape prior is enforced. By minimizing the proposed functional with respect to both the level Set function and the labeling function, the algorithm selects image regions where it is favorable to enforce the shape prior. By this, the approach permits to segment multiple independent objects in an image, and to discriminate familiar objects from unfamiliar ones by means of the labeling function. Numerical results demonstrate the performance of our approach.

Yunyun Yang - One of the best experts on this subject based on the ideXlab platform.

  • level Set Formulation for automatic medical image segmentation based on fuzzy clustering
    Signal Processing-image Communication, 2020
    Co-Authors: Yunyun Yang, Ruofan Wang, Chong Feng
    Abstract:

    Abstract The level Set method is widely used in medical image segmentation, in which the performance is seriously subject to the initialization and parameters configuration. An automatic segmentation method was proposed in this paper, which integrates fuzzy clustering with level Set method through a dynamic constrained term in the new energy functional. It is able to use the results of fuzzy clustering directly, which can control the level Set evolution. Moreover, the added constrained term is changing continuously until getting the final results. Such algorithm eliminates the manual operation a lot and leads to more robust segmentation results. With the split Bregman method, the minimization of the new energy functional is fast. The proposed algorithm was tested on some medical images and also compared with other level Set models and the state-of-the-art method such as U-Net. The quantitative and qualitative experimental results show its effectiveness and obvious improvement for medical image segmentation.

  • simultaneous segmentation and correction model for color medical and natural images with intensity inhomogeneity
    The Visual Computer, 2020
    Co-Authors: Yunyun Yang, Wenjing Jia
    Abstract:

    In this paper, a new level Set Formulation that can simultaneously segment and correct color images is proposed by combining the illumination and reflectance estimation (IRE) model, the level Set method and the split Bregman method. The advantages of our model are mainly summarized in three aspects. First, our model can effectively extract the intensity change information in the images, regarded as the bias field. Based on the accurate segmentation results, our model can correct the inhomogeneous color images by removing the estimated bias field from the original images. Second, the application of the split Bregman method accelerates the iterative process and computational speed, making our model more efficient. Third, the use of the edge detection function in the energy functional makes it easier for our model to detect the target boundary. Perfectly absorbing the above three advantages, our model is applied to segment color medical and natural images with intensity inhomogeneity. Experimental results demonstrate that our model can accurately segment color images and get satisfactory correction images with intensity homogeneity. In addition, numerical comparison results further indicate that the performance of our model for segmentation and correction is significantly superior to the IRE model.

  • level Set Formulation based on edge and region information with application to accurate lesion segmentation of brain magnetic resonance images
    Journal of Optimization Theory and Applications, 2019
    Co-Authors: Yunyun Yang, Wenjing Jia, Xiu Shu
    Abstract:

    Magnetic resonance images have great significance for doctors’ analysis and diagnosis of diseases. One difficulty in segmenting magnetic resonance images is associated with the intensity inhomogeneity. In this paper, we propose an improved active contour model combining local and global information dynamically to segment images with intensity inhomogeneity. Besides, the atlas term is added into our energy functional, which improves the segmentation accuracy by restricting the segmented range around the location of the given atlas and making the contour move toward a position near the atlas. In this paper, we first present the multi-phase Formulation of our model. Then, our model is applied to segment a total of 35 different brain magnetic resonance images with lesions. We also compare the performance of our model with other models, which can handle inhomogeneous images to some extent. Experimental results demonstrate that our model has promising performance for these challenging brain magnetic resonance images. Accuracy, efficiency and robustness of the proposed model have also been demonstrated by the numerical results and comparisons with other models.

  • multi atlas segmentation and correction model with level Set Formulation for 3d brain mr images
    Pattern Recognition, 2019
    Co-Authors: Yunyun Yang, Wenjing Jia, Yunna Yang
    Abstract:

    Abstract We present an efficient multi-atlas segmentation and correction model with level Set Formulation for 3D brain MR images in this paper. We define a new energy functional by combining a weighted label fusion term, a bias field based image information fitting term and a regularization term together. More image information is taken into consideration in the new image data term to substantially improve the segmentation accuracy, especially when serious inhomogeneity and bias field exist in regions of interest in MR images. We introduce a spatially weight function and incorporate it into the label fusion term to increase the robustness of our segmentation algorithm to atlases with different registration accuracy. The new energy functional is in the form of L1 regularization problems, and we minimize it with the split Bregman method to ensure the segmentation efficiency. We apply the proposed model to segment six tissues in 3D brain MR images, including the amygdala, caudate, hippocampus, pallidum, putamen and thalamus. Experimental results have shown that our model can segment regions of interest accurately and eliminate bias field simultaneously. Quantitative comparisons with related methods have demonstrated the superiority of our model in terms of accuracy, efficiency and robustness.

Wijerupage Sardha Wijesoma - One of the best experts on this subject based on the ideXlab platform.

  • A random Set Formulation for Bayesian SLAM
    2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008
    Co-Authors: John Mullane, Martin D. Adams, Ba-ngu Vo, Wijerupage Sardha Wijesoma
    Abstract:

    This paper presents an alternative Formulation for the Bayesian feature-based simultaneous localisation and mapping (SLAM) problem, using a random finite Set approach. For a feature based map, SLAM requires the joint estimation of the vehicle location and the map. The map itself involves the joint estimation of both the number of features and their states (typically in a 2D Euclidean space), as an a priori unknown map is completely unknown in both landmark location and number. In most feature based SLAM algorithms, so-called dasiafeature managementpsila algorithms as well as data association hypotheses along with extended Kalman filters are used to generate the joint posterior estimate. This paper, however, presents a recursive filtering algorithm which jointly propagates both the estimate of the number of landmarks, their corresponding states, and the vehicle pose state, without the need for explicit feature management and data association algorithms. Using a finite Set-valued joint vehicle-map state and Set-valued measurements, the first order statistic of the Set, called the intensity, is propagated via the probability hypothesis density (PHD) filter, from which estimates of the map and vehicle can be jointly extracted. Assuming a mildly non-linear Gaussian system, an extended-Kalman Gaussian Mixture implementation of the recursion is then tested for both feature-based robotic mapping (known location) and SLAM. Results from the experiments show promising performance for the proposed SLAM framework, especially in environments of high spurious measurements.

Yun Zhu - One of the best experts on this subject based on the ideXlab platform.

  • a generalized level Set Formulation of the mumford shah functional with shape prior for medical image segmentation
    International Conference on Computer Vision, 2005
    Co-Authors: Lishui Cheng, Jie Yang, Xian Fan, Yun Zhu
    Abstract:

    Image segmentation is an important research topic in medical image analysis area. In this paper, we firstly propose a generalized level Set Formulation of the Mumford-Shah functional by a sound mathematical definition of line integral. The variational flow is implemented in level Set framework and thus implicit and intrinsic. By embedding a weighted length term to the original Mumford-Shah functional, the paper presents a generic framework that integrates region, gradient and shape information of an image into the segmentation process naturally. The region force provides a global criterion and increases the speed of convergence, the gradient information allows for a better spatial localization while the shape prior makes the model especially useful to recover objects of interest whose shape can be learned through statistical analysis. The shape prior is represented by the zero-level Set of signed distance maps of images and is well consistent with level Set based variational framework. Experiments on 2-D synthetic and real images validate this novel method.