基于语义分割的双目场景流估计

针对现有场景流计算方法在复杂场景、大位移和运动遮挡等情况下易产生运动边缘模糊的问题,提出一种基于语义分割的双目场景流估计方法.首先,根据图像中的语义信息类别,通过深度学习的卷积神经网络模型将图像划分为带有语义标签的区域;针对不同语义类别的图像区域分别进行运动建模,利用语义知识计算光流信息并通过双目立体匹配的半全局匹配方法计算图像视差信息.然后,对输入图像进行超像素分割,通过最小二乘法耦合光流和视差信息,分别求解每个超像素块的运动参数.最后,在优化能量函数中添加语义分割边界的约束信息,通过更新像素到超像素块的映射关系和超像素块到移动平面的映射关系得到最终的场景流估计结果.采用KITTI 2015标准测试图像序列对本文方法和代表性的场景流计算方法进行对比分析.实验结果表明,本文方法具有较高的精度和鲁棒性,尤其对于复杂场景、运动遮挡和运动边缘模糊的图像具有较好的边缘保护作用.     

Left Ventricle Segmentation Using Model Fitting and Active Surfaces

A method to perform 4D (3D over time) seg mentation of the left ventricle of a mouse heart using a set of B mode cine slices acquired in vivo from a series of short axis scans is described. We incorporate previ ously suggested methods such as temporal propagation, the gradient vector flow active surface, superquadric models, etc. into our proposed 4D segmentation of the left ventricle. The contributions of this paper are incor poration of a novel despeckling method and the use of locally fitted superellipsoid models to provide a better initialization for the active surface segmentation algorithm. Average distances of the improved surface segmentation to a manually segmented surface through out the entire cardiac cycle and cross-sectional contours are provided to demonstrate the improvements pro duced by the proposed 4D segmentation.     

A fluid dynamics-based deformable model for segmentation of cervical cell images

A novel deformable model for unsupervised segmentation of cervical cells within Pap smear images is presented in this paper. The proposed method is inspired by fluid mechanics and based on the simulation of incompressible fluid flood via grid-based solution of Navier–Stokes equations. In this approach, simulation starts inside the cytoplasmic region and the simulated fluid is attracted toward the cell contours. Unlike most of the other fluid-based methods, gradient magnitude data are not used for extracting topological relief of the image. However, gradient magnitude of the image is still considered as the source for extracting particles. Direction of propagation of the flow is determined by an interaction mechanism based on the permeability rate of these particles. Interaction between fluid and particles guides the advancing fronts of the fluid toward object boundaries. Redefinition of complex topologies with particle groups provides potential of improved segmentation capability and flexibility to the model. We demonstrate the segmentation capability of our model with fully automated and unsupervised experimental setting on Pap smear sample images. Results showed that proposed method may be more adaptive than watershed algorithm and have an improved performance on recovering shape and boundary data of cervical cells.     

FRFCM聚类与深度优化的RGBD场景流计算

针对现有RGBD场景流计算模型在复杂场景、非刚性运动和运动遮挡等情况下易产生场景过度平滑和运动边缘模糊的问题,提出一种基于FRFCM（Fast and Robust Fuzzy C-Means）聚类与深度优化的RGBD场景流计算方法.首先以图像序列连续帧间光流信息为基准,利用FRFCM聚类算法对输入图像进行初始分割,然后根据深度图像的运动边缘信息优化初始分割结果,提取高置信度的运动分层信息.最后设计基于图像分割的RGBD场景流能量函数,采用金字塔变形策略计算精确的场景流结果.分别采用Middlebury和MPI-Sintel数据库所提供的测试图像集对本文方法和现有的RGBD场景流算法进行综合对比分析,实验结果表明本文方法相对于其他方法具有更好的场景流估计精度和鲁棒性,有效改善了场景过度平滑和运动边缘模糊问题.     

Motion-based object segmentation using hysteresis and bidirectional inter-frame change detection in sequences with moving camera

We present an unsupervised motion-based object segmentation algorithm for video sequences with moving camera, employing bidirectional inter-frame change detection. For every frame, two error frames are generated using motion compensation. They are combined and a segmentation algorithm based on thresholding is applied. We employ a simple and effective error fusion scheme and consider spatial error localization in the thresholding step. We find the optimal weights for the weighted mean thresholding algorithm that enables unsupervised robust moving object segmentation. Further, a post processing step for improving the temporal consistency of the segmentation masks is incorporated and thus we achieve improved performance compared to the previously proposed methods. The experimental evaluation and comparison with other methods demonstrate the validity of the proposed method.     

Novel multistage three-dimensional medical image segmentation: methodology and validation.

In this paper, we propose a novel multistage method for three-dimensional (3-D) segmentation of medical images and a new radial distance-based segmentation validation approach. For the 3-D segmentation method, we first employ a morphological recursive erosion operation to reduce the connectivity between the region of interest and its surrounding neighborhood; then we design a hybrid segmentation method to achieve an initial result. The hybrid approach integrates an improved fast marching method and a morphological reconstruction algorithm. Finally, a morphological recursive dilation is employed to recover any lost structure from the first stage of the multistage method. This approach is tested on 12 CT and 3 MRI images of the brain, heart, and kidney, to demonstrate the effectiveness and accuracy of this technique across a variety of imaging modalities and organ systems. In order to validate the multistage segmentation method, a novel radial distance-based validation method is proposed that uses a global accuracy (GA) measure. The GA is calculated based on local radial distance errors (LRDE), where LRDE are calculated on the radii emitted from points along the skeleton of the object rather than the centroid, in order to accommodate more complicated organ structures. The experimental results demonstrate that the proposed multistage segmentation method is fast and accurate, with comparable performance to existing segmentation methods, but with a significantly higher execution speed.     

RAGS: region-aided geometric snake

An enhanced, region-aided, geometric active contour that is more tolerant toward weak edges and noise in images is introduced. The proposed method integrates gradient flow forces with region constraints, composed of image region vector flow forces obtained through the diffusion of the region segmentation map. We refer to this as the Region-aided Geometric Snake or RAGS. The diffused region forces can be generated from any reliable region segmentation technique, greylevel or color. This extra region force gives the snake a global complementary view of the boundary information within the image which, along with the local gradient flow, helps detect fuzzy boundaries and overcome noisy regions. The partial differential equation (PDE) resulting from this integration of image gradient flow and diffused region flow is implemented using a level set approach. We present various examples and also evaluate and compare the performance of RAGS on weak boundaries and noisy images.     

Segmentation of thin structures in volumetric medical images.

We present a new segmentation method for extracting thin structures embedded in three-dimensional medical images based on modern variational principles. We demonstrate the importance of the edge alignment and homogeneity terms in the segmentation of blood vessels and vascular trees. For that goal, the Chan-Vese minimal variance method is combined with the boundary alignment, and the geodesic active surface models. An efficient numerical scheme is proposed. In order to simultaneously detect a number of different objects in the image, a hierarchal approach is applied.     

A novel approach to motion segmentation based on gamma distribution

We provide a new motion segmentation method in image sequences based on gamma distribution. Motion segmentation is very important because it can be employed for video surveillance, object tracking, and action recognition. The Gaussian mixture model (GMM) has been widely used as a distribution model for motion segmentation. However, we found that the gamma distribution model is more suitable than the GMM for the optical flow based motion segmentation. Experimental results show that the proposed method is very effective in producing accurate motion segmentation results in image sequences.     

基于图像分解的稀疏正则化多区域图像分割方法

针对图像具有不同特征的成分,提出一种基于图像分解的多区域图像分割模型和算法.首先将图像分解项引入到图像分割模型中,递减了纹理和噪声对分割的影响;其次使用稀疏正则化方法保持分割区域的边缘几何结构;最后基于增广Lagrange乘子法,给出一种由扩散流引导的小波迭代阈值图像分割算法.一系列实验结果表明,提出的方法抗干扰能力强,对噪声具有更好的鲁棒性.提出的方法不仅能够分割结构图像,并且能够分割较复杂的纹理图像.     

基于自适应背景图像更新的运动目标检测方法

在运动目标的实时检测中常用的方法是背景图像差分法,但因其缺乏背景图像随监视场景光照变化而及时更新的合理方法,限制了本方法的适应性.对此,本文首先提出了一种基于光流场等技术的自适应背景逼近更新方法,并根据彩色差值模型得到差分图像;然后引入Gauss模型实现运动目标的自适应阈值分割.实验结果表明:本文提出的背景更新方法可随着光照条件的变化实时、准确地更新背景图像,在此基础上提出的基于Gauss模型的自适应阈值分割方法可以实现运动目标的完整分割,这为运动目标的后续识别与理解奠定了基础.     

基于参数化互信息的脑MR图像分割与偏移场矫正模型及快速算法

脑核磁共振(Magnetic Resonance简称MR)图像中存在灰度不均匀现象使得传统方法很难得到理想的分割与偏移场矫正结果.针对这一问题,本文首先提出Legendre基函数拟合偏移场下的参数化互信息度量,建立脑MR图像的分割与偏移场矫正的变分模型.最后,给出了基于分裂Bregman迭代方法的快速分割与偏移场矫正算...     

一种基于密集深度分离卷积的SAR图像水域分割算法

SAR图像的水域分割在舰船目标检测、灾害监测等军事和民用领域具有重要意义。针对传统水域分割算法鲁棒性差、难以准确进行分割等问题,该文首先建立了基于高分三号的SAR图像水域分割数据集,并基于深度学习技术提出了基于密集深度分离卷积的分割网络架构,该网络以SAR图像作为输入,通过密集分离卷积和扩张卷积提取图像高维特征,并构造基于双线性插值的上采样解码模块用于输出分割结果。在水域分割数据集上的实验结果表明,与传统方法相比,该方法不仅在分割准确度上有大幅提高,在算法的鲁棒性和分割速度上也具有部分优势,具备较好的工程实用价值。      

A Hybrid Geometric–Statistical Deformable Model for Automated 3-D Segmentation in Brain MRI

We present a novel 3-D deformable model-based approach for accurate, robust, and automated tissue segmentation of brain MRI data of single as well as multiple magnetic resonance sequences. The main contribution of this study is that we employ an edge-based geodesic active contour for the segmentation task by integrating both image edge geometry and voxel statistical homogeneity into a novel hybrid geometric-statistical feature to regularize contour convergence and extract complex anatomical structures. We validate the accuracy of the segmentation results on simulated brain MRI scans of both single T1-weighted and multiple T1/T2/PD-weighted sequences. We also demonstrate the robustness of the proposed method when applied to clinical brain MRI scans. When compared to a current state-of-the-art region-based level-set segmentation formulation, our white matter and gray matter segmentation resulted in significantly higher accuracy levels with a mean improvement in Dice similarity indexes of 8.55% (p<0.0001) and 10.18% (p<0.0001), respectively.     

A class-adaptive spatially variant mixture model for image segmentation.

We propose a new approach for image segmentation based on a hierarchical and spatially variant mixture model. According to this model, the pixel labels are random variables and a smoothness prior is imposed on them. The main novelty of this work is a new family of smoothness priors for the label probabilities in spatially variant mixture models. These Gauss-Markov random field-based priors allow all their parameters to be estimated in closed form via the maximum a posteriori (MAP) estimation using the expectation-maximization methodology. Thus, it is possible to introduce priors with multiple parameters that adapt to different aspects of the data. Numerical experiments are presented where the proposed MAP algorithms were tested in various image segmentation scenarios. These experiments demonstrate that the proposed segmentation scheme compares favorably to both standard and previous spatially constrained mixture model-based segmentation.     

Robust, object-based high-resolution image reconstruction fromlow-resolution video

We propose a robust, object-based approach to high-resolution image reconstruction from video using the projections onto convex sets (POCS) framework. The proposed method employs a validity map and/or a segmentation map. The validity map disables projections based on observations with inaccurate motion information for robust reconstruction in the presence of motion estimation errors; while the segmentation map enables object-based processing where more accurate motion models can be utilized to improve the quality of the reconstructed image. Procedures for the computation of the validity map and segmentation map are presented. Experimental results demonstrate the improvement in image quality that can be achieved by the proposed methods.     

Two-dimensional extension of variance-based thresholding for image segmentation

Variance-based thresholding method is a very effective technology for image segmentation. However, its performance is limited in traditional one-dimensional and two-dimensional scheme. In this paper, a novel two-dimensional variance thresholding scheme to improve image segmentation performance is proposed. The two-dimensional histogram of the original and local average image is projected to one-dimensional space in the proposed scheme firstly, and then the variance-based criterion is constructed for threshold selection. The experimental results on bi-level and multilevel thresholding for synthetic and real-world images demonstrate the success of the proposed image thresholding scheme, as compared with the Otsu method, the two-dimensional Otsu method and the minimum class variance thresholding method.     

A nonparametric statistical method for image segmentation using information theory and curve evolution.

In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the image pixel intensities within each region are completely unknown a priori, and we formulate the problem based on nonparametric density estimates. Due to the nonparametric structure, our method does not require the image regions to have a particular type of probability distribution and does not require the extraction and use of a particular statistic. We solve the information-theoretic optimization problem by deriving the associated gradient flows and applying curve evolution techniques. We use level-set methods to implement the resulting evolution. The experimental results based on both synthetic and real images demonstrate that the proposed technique can solve a variety of challenging image segmentation problems. Futhermore, our method, which does not require any training, performs as good as methods based on training.     

一种基于简化PCNN的自适应图像分割方法

近年来的研究表明,脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)可有效地用于图像分割.然而对于不同图像,常需要选取适当的网络参数,以得到有效的分割结果.但是,目前网络参数的选取还主要停留在人工调整和确定阶段,尚无一种能够根据图像本身特性自动确定参数的方法,这在很大程度上限制了PCNN的应用.针对这一问题,本文提出了一种基于简化PCNN的自适应图像分割方法,通过利用图像本身空间和灰度特性自动确定网络参数,实现对不同图像的分割.实验结果表明,本文算法可以有效地对不同图像进行自动分割,具有一定的健壮性.     

联合二分思想和时空管道的监控视频运动分割

监控视频运动分割是视频浓缩、行为识别等视频智能处理的基础和前提,是计算机视觉领域的研究热点。现存运动分割方法大多步骤繁琐、计算量大,难以应用于计算能力有限的领域。为此,提出了一种联合二分思想和时空管道的监控视频运动分割方法。该方法首先使用嵌套椭圆时空管道模型计算初始累计时空流量来判断目标轨迹完整性(completeness of target trajectory, CTT);然后结合二分思想动态地调节椭圆采样线,自适应地捕捉采样区域的运动目标;最后提取采样线上的全部像素点形成自适应时空管道进行运动分割。实验结果表明,所提方法在保证精度的同时计算速度明显优于对比方法,且所提方法鲁棒性强,对运动情况多变的监控场景同样适用。