다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

/pdf/convex-analysisnoer-san-nojin-zhan-nituite-5dl2rbmoyr.pdf

Convex Analysisの二,三の進展について

Remote sensing image scene classification is an active and challenging task driven by many applications. More recently, with the advances of deep learning models especially convolutional neural networks (CNNs), the performance of remote sensing image scene classification has been significantly improved due to the powerful feature representations learnt through CNNs. Although great success has been obtained so far, the problems of within-class diversity and between-class similarity are still two big challenges. To address these problems, in this paper, we propose a simple but effective method to learn discriminative CNNs (D-CNNs) to boost the performance of remote sensing image scene classification. Different from the traditional CNN models that minimize only the cross entropy loss, our proposed D-CNN models are trained by optimizing a new discriminative objective function. To this end, apart from minimizing the classification error, we also explicitly impose a metric learning regularization term on the CNN features. The metric learning regularization enforces the D-CNN models to be more discriminative so that, in the new D-CNN feature spaces, the images from the same scene class are mapped closely to each other and the images of different classes are mapped as farther apart as possible. In the experiments, we comprehensively evaluate the proposed method on three publicly available benchmark data sets using three off-the-shelf CNN models. Experimental results demonstrate that our proposed D-CNN methods outperform the existing baseline methods and achieve state-of-the-art results on all three data sets.

When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs

Segmentation and Detection.- Graph Cut Optimization for the Piecewise Constant Level Set Method Applied to Multiphase Image Segmentation.- Tubular Anisotropy Segmentation.- An Unconstrained Multiphase Thresholding Approach for Image Segmentation.- Extraction of the Intercellular Skeleton from 2D Images of Embryogenesis Using Eikonal Equation and Advective Subjective Surface Method.- On Level-Set Type Methods for Recovering Piecewise Constant Solutions of Ill-Posed Problems.- The Nonlinear Tensor Diffusion in Segmentation of Meaningful Biological Structures from Image Sequences of Zebrafish Embryogenesis.- Composed Segmentation of Tubular Structures by an Anisotropic PDE Model.- Extrapolation of Vector Fields Using the Infinity Laplacian and with Applications to Image Segmentation.- A Schrodinger Equation for the Fast Computation of Approximate Euclidean Distance Functions.- Semi-supervised Segmentation Based on Non-local Continuous Min-Cut.- Momentum Based Optimization Methods for Level Set Segmentation.- Optimization of Divergences within the Exponential Family for Image Segmentation.- Convex Multi-class Image Labeling by Simplex-Constrained Total Variation.- Geodesically Linked Active Contours: Evolution Strategy Based on Minimal Paths.- Validation of Watershed Regions by Scale-Space Statistics.- Adaptation of Eikonal Equation over Weighted Graph.- A Variational Model for Interactive Shape Prior Segmentation and Real-Time Tracking.- Image Enhancement and Reconstruction.- A Nonlinear Probabilistic Curvature Motion Filter for Positron Emission Tomography Images.- Finsler Geometry on Higher Order Tensor Fields and Applications to High Angular Resolution Diffusion Imaging.- Bregman-EM-TV Methods with Application to Optical Nanoscopy.- PDE-Driven Adaptive Morphology for Matrix Fields.- On Semi-implicit Splitting Schemes for the Beltrami Color Flow.- Multi-scale Total Variation with Automated Regularization Parameter Selection for Color Image Restoration.- Multiplicative Noise Cleaning via a Variational Method Involving Curvelet Coefficients.- Projected Gradient Based Color Image Decomposition.- A Dual Formulation of the TV-Stokes Algorithm for Image Denoising.- Anisotropic Regularization for Inverse Problems with Application to the Wiener Filter with Gaussian and Impulse Noise.- Locally Adaptive Total Variation Regularization.- Basic Image Features (BIFs) Arising from Approximate Symmetry Type.- An Anisotropic Fourth-Order Partial Differential Equation for Noise Removal.- Enhancement of Blurred and Noisy Images Based on an Original Variant of the Total Variation.- Coarse-to-Fine Image Reconstruction Based on Weighted Differential Features and Background Gauge Fields.- Edge-Enhanced Image Reconstruction Using (TV) Total Variation and Bregman Refinement.- Nonlocal Variational Image Deblurring Models in the Presence of Gaussian or Impulse Noise.- A Geometric PDE for Interpolation of M-Channel Data.- An Edge-Preserving Multilevel Method for Deblurring, Denoising, and Segmentation.- Fast Dejittering for Digital Video Frames.- Sparsity Regularization for Radon Measures.- Split Bregman Algorithm, Douglas-Rachford Splitting and Frame Shrinkage.- Anisotropic Smoothing Using Double Orientations.- Image Denoising Using TV-Stokes Equation with an Orientation-Matching Minimization.- Augmented Lagrangian Method, Dual Methods and Split Bregman Iteration for ROF Model.- The Convergence of a Central-Difference Discretization of Rudin-Osher-Fatemi Model for Image Denoising.- Theoretical Foundations for Discrete Forward-and-Backward Diffusion Filtering.- L 0-Norm and Total Variation for Wavelet Inpainting.- Total-Variation Based Piecewise Affine Regularization.- Image Denoising by Harmonic Mean Curvature Flow.- Motion Analysis, Optical Flow, Registration and Tracking.- Tracking Closed Curves with Non-linear Stochastic Filters.- A Multi-scale Feature Based Optic Flow Method for 3D Cardiac Motion Estimation.- A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother.- A Scale-Space Approach to Landmark Constrained Image Registration.- A Variational Approach for Volume-to-Slice Registration.- Hyperbolic Numerics for Variational Approaches to Correspondence Problems.- Surfaces and Shapes.- From a Single Point to a Surface Patch by Growing Minimal Paths.- Optimization of Convex Shapes: An Approach to Crystal Shape Identification.- An Implicit Method for Interpolating Two Digital Closed Curves on Parallel Planes.- Pose Invariant Shape Prior Segmentation Using Continuous Cuts and Gradient Descent on Lie Groups.- A Non-local Approach to Shape from Ambient Shading.- An Elasticity Approach to Principal Modes of Shape Variation.- Pre-image as Karcher Mean Using Diffusion Maps: Application to Shape and Image Denoising.- Fast Shape from Shading for Phong-Type Surfaces.- Generic Scene Recovery Using Multiple Images.- Scale Space and Feature Extraction.- Highly Accurate PDE-Based Morphology for General Structuring Elements.- Computational Geometry-Based Scale-Space and Modal Image Decomposition.- Highlight on a Feature Extracted at Fine Scales: The Pointwise Lipschitz Regularity.- Line Enhancement and Completion via Linear Left Invariant Scale Spaces on SE(2).- Spatio-Featural Scale-Space.- Scale Spaces on the 3D Euclidean Motion Group for Enhancement of HARDI Data.- On the Rate of Structural Change in Scale Spaces.- Transitions of a Multi-scale Image Hierarchy Tree.- Local Scale Measure for Remote Sensing Images.

Scale space and variational methods in computer vision : Second International Conference, SSVM 2009, Voss, Norway, June 1-5, 2009 : proceedings

This paper proposes a general weighted l2-l0 norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.

A Weighted Dictionary Learning Model for Denoising Images Corrupted by Mixed Noise

In this paper, we propose a new variational framework to solve the Gaussian mixture model (GMM) based methods for image segmentation by employing the convex relaxation approach. After relaxing the indicator function in GMM, flexible spatial regularization can be adopted and efficient segmentation can be achieved. To demonstrate the superiority of the proposed framework, the global, local intensity information and the spatial smoothness are integrated into a new model, and it can work well on images with inhomogeneous intensity and noise. Compared to classical GMM, numerical experiments have demonstrated that our algorithm can achieve promising segmentation performance for images degraded by intensity inhomogeneity and noise.

Image Segmentation Using a Local GMM in a Variational Framework

http://wims.math.ecnu.edu.cn/ChunLi/Seminar_20110318/Used%20papers/cam11-10.pdf

A fast segmentation method based on constraint optimization and its applications: Intensity inhomogeneity and texture segmentation

To achieve high scene classification performance of high spatial resolution remote sensing images (HSR-RSIs), it is important to learn a discriminative space in which the distance metric can precisely measure both similarity and dissimilarity of features and labels between images. While the traditional metric learning methods focus on preserving interclass separability, label consistency (LC) is less involved, and this might degrade scene images classification accuracy. Aiming at considering intraclass compactness in HSR-RSIs, we propose a discriminative distance metric learning method with LC (DDML-LC). The DDML-LC starts from the dense scale invariant feature transformation features extracted from HSR-RSIs, and then uses spatial pyramid maximum pooling with sparse coding to encode the features. In the learning process, the intraclass compactness and interclass separability are enforced while the global and local LC after the feature transformation is constrained, leading to a joint optimization of feature manifold, distance metric, and label distribution. The learned metric space can scale to discriminate out-of-sample HSR-RSIs that do not appear in the metric learning process. Experimental results on three data sets demonstrate the superior performance of the DDML-LC over state-of-the-art techniques in HSR-RSI classification.

Learning a Discriminative Distance Metric With Label Consistency for Scene Classification

Hyperspectral image (HSI) super-resolution refers to enhancing the spatial resolution of a 3-D image with many spectral bands (slices). It is a seriously ill-posed problem when the low-resolution (LR) HSI is the only input. It is better solved by fusing the LR HSI with a high-resolution (HR) multispectral image (MSI) for a 3-D image with both high spectral and spatial resolution. In this article, we propose a novel nonnegative and nonlocal 4-D tensor dictionary learning-based HSI super-resolution model using group-block sparsity. By grouping similar 3-D image cubes into clusters and then conduct super-resolution cluster by cluster using 4-D tensor structure, we not only preserve the structure but also achieve sparsity within the cluster due to the collection of similar cubes. We use 4-D tensor Tucker decomposition and impose nonnegative constraints on the dictionaries and group-block sparsity. Numerous experiments demonstrate that the proposed model outperforms many state-of-the-art HSI super-resolution methods.

Jun Liu

Papers

A Weighted Dictionary Learning Model for Denoising Images Corrupted by Mixed Noise

Image Segmentation Using a Local GMM in a Variational Framework

A fast segmentation method based on constraint optimization and its applications: Intensity inhomogeneity and texture segmentation

Learning a Discriminative Distance Metric With Label Consistency for Scene Classification

Nonnegative and Nonlocal Sparse Tensor Factorization-Based Hyperspectral Image Super-Resolution