基于Graph Cuts多特征选择的双目图像分割方法

双目图像分割对后续立体目标合成与三维重建等应用至关重要。由于双目图像中包含场景深度信息,因此直接将单目图像分割方法应用于双目图像尚不能得到理想的分割结果。目前,大多数双目图像分割方法将双目图像的深度特征作为颜色特征的额外通道来使用,仅对颜色特征与深度特征做简单整合,未能充分利用图像的深度特征。文中基于多分类Graph Cuts框架,提出了一种交互式双目图像分割方法。该方法将颜色、深度和纹理等特征融合到一个图模型中,以更充分地利用不同特征信息。同时,在Graph Cuts框架中引入了特征空间邻域系统,增强了图像前景区域与背景区域内部像素点之间的关系,提高了分割目标的完整性。实验结果表明,所提方法有效提升了双目图像分割结果的精确度。

基于生成模型约束的graph cuts标签融合算法

人脑磁共振图像(magnetic resonance imaging,MRI)分割算法易受图谱错误标签的影响,为减少错误标签对标签融合准确度的影响,提高人脑磁共振图像的分割精度,在图谱预选阶段采用梯度信息和互信息筛选出相似性较大的图谱图像,对标签融合阶段提出利用生成模型(generative model,GM)约束的graph cuts标签融合方法,快速准确地分割出了人脑海马体结构。与其他的标签融合方法对比,提出的算法具有更高的分割精度。

Efficient Stereo Matching Scheme Based on Graph Cuts

A new stereo matching scheme from image pairs based on graph cuts is given,which can solve the problem of large color differences as the result of fusing matching results of graph cuts from different color spaces.This scheme builds normalized histogram and reference histogram from matching results,and uses clustering algorithm to process the two histograms.Region histogram statistical method is adopted to retrieve depth data to achieve final matching results.Regular stereo matching library is used to verify this scheme,and experiments reported in this paper support availability of this method for automatic image processing.This scheme renounces the step of manual selection for adaptive color space and can obtain stable matching results.The whole procedure can be executed automatically and improve the integration level of image analysis process.

Feature Matching via Topology-Aware Graph Interaction Model

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive outliers.This paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://github.com/YifanLu2000/TIM.

Skin Segmentation Based on Graph Cuts

Skin segmentation is widely used in many computer vision tasks to improve automated visualiza- tion. This paper presents a graph cuts algorithm to segment arbitrary skin regions from images. The detected face is used to determine the foreground skin seeds and the background non-skin seeds with the color probability distributions for the foreground represented by a single Gaussian model and for the background by a Gaussian mixture model. The probability distribution of the image is used for noise suppression to alle- viate the influence of the background regions having skin-like colors. Finally, the skin is segmented by graph cuts, with the regional parameter y optimally selected to adapt to different images. Tests of the algorithm on many real world photographs show that the scheme accurately segments skin regions and is robust against illumination variations, individual skin variations, and cluttered backgrounds.

结合GMM和Graph-Cuts的直观传输函数设计方法

提出一种新的基于笔画的传输函数设计方法。与其他基于笔画的方法相比,该方法只要求用户对感兴趣数据做少量标记,操作更简便;另外该方法更好地考虑了体数据的空间信息,分类结果更合理。首先使用贪心极大似然估计算法构建用户感兴趣数据和背景数据的高斯混合模型(GMM)模型;然后基于GMM模型使用Graph-Cuts实现对体数据的分类,并通过仔细设计Graph-Cuts能量函数的平滑项将空间信息引入分类过程。针对体数据的规模大,难以直接应用Graph-Cuts算法的难题,在此通过使用层次模型减小Graph-Cuts算法所处理问题的规模,从而获得了实时交互的效果。该方法使用多个体数据进行测试,获得令人满意的效果。

带连通性约束的快速交互式Graph-Cut算法

Graph-Cut算法是图像及视频中经典且有效的前景和背景分离算法,针对其计算量较大导致实时性不佳、前景和背景颜色相似时分割结果易出现shrinking bias现象的问题,提出一种改进算法.该算法利用Mean-Shift技术对图像进行预处理,将原图像表示成基于区域的、而不是基于像素的图结构,预处理结果还可应用于后续的前景和背景颜色分布估计过程,使得计算量大大下降;在能量函数中引入了具有自适应权值调节功能的连通性约束项,有效地改善了shrinking bias现象,提高了分割结果的精确性.实验结果表明,文中算法具有良好的实时交互性,且分割效果更加稳定和精确.

基于多尺度特征的Graph Cut图像分割

传统Graph Cut算法忽略不同尺度的特征信息,为了解决这一问题,将多尺度分解模型与Graph Cut相结合提出基于多尺度特征的分割模型。在多尺度分解部分,设计新的变分正则化函数,在平滑非一致性区域的同时保护边缘,对弱边缘有一定的增强效果。在分割部分,根据相邻尺度平滑图像分割结果的相似度设计自动停止迭代分割条件,在迭代分割过程中自动选取具有适当尺度的平滑图像。实验结果表明所提出的分割模型,其分割性能优于传统算法和DEXTR(CNN)算法。

3D Multiphase Piecewise Constant Level Set Method Based on Graph Cut Minimization

Segmentation of three-dimensional(3D) complicated structures is of great importance for many real applications.In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmentation based on the Mumford-Shah model.Compared with the traditional approach for solving the Euler-Lagrange equation we do not need to solve any partial differential equations.Instead,the minimum cut on a special designed graph need to be computed.The method is tested on data with complicated structures.It is rather stable with respect to initial value and the algorithm is nearly parameter free.Experiments show that it can solve large problems much faster than traditional approaches.

Image Dehazing Based on Pixel Guided CNN with PAM via Graph Cut

Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is the estimation of transmittance,which is the key element of haze-affected imaging models.Conventional methods are based on a set of assumptions that reduce the solution search space.However,the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image.In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method.The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions.This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks(CNNs)with a Parallax Attention Mechanism(PAM).It uses the differential pixel-based variance in order to estimate transmittance.The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map.The transmission map is also improved based on improved Markov random field(MRF)energy functions.We used different images to test the proposed algorithm.The entropy value of the proposed method was 7.43 and 7.39,a percent increase of4.35%and5.42%,respectively,compared to the best existing results.The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics.The proposed approach’s drawback,an over-reliance on real ground truth images,is also investigated.The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.

Leveraging Graph Cut’s Energy Function for Context Aware Facial Recognition in Indoor Environments

Context-aware facial recognition regards the recognition of faces in association with their respective environments.This concept is useful for the domestic robot which interacts with humans when performing specific functions in indoor environments.Deep learning models have been relevant in solving facial and place recognition challenges;however,they require the procurement of training images for optimal performance.Pre-trained models have also been offered to reduce training time significantly.Regardless,for classification tasks,custom data must be acquired to ensure that learning models are developed from other pre-trained models.This paper proposes a place recognition model that is inspired by the graph cut energy function,which is specifically designed for image segmentation.Common objects in the considered environment are identified and thereafter they are passed over to a graph cut inspired model for indoor environment classification.Additionally,faces in the considered environment are extracted and recognised.Finally,the developed model can recognise a face together with its environment.The strength of the proposed model lies in its ability to classify indoor environments without the usual training process(es).This approach differs from what is obtained in traditional deep learning models.The classification capability of the developed model was compared to state-of-theart models and exhibited promising outcomes.

基于聚合通道特征的防震锤锈蚀缺陷识别算法

高压输电线路中防震锤锈蚀会危害输电线路的安全运行。基于图像处理技术提出了一种基于聚合通道特征的防震锤检测和锈蚀缺陷识别的算法。该算法首先引入聚合通道特征(Aggregate Channel Features,ACF)分别提取无人机拍摄的输电线路图像中的颜色、梯度幅值和梯度方向直方图,构建多尺度ACF金字塔;利用滑窗法和Adaboost分类器检测图像中的防震锤,并使用非极大抑制操作得到最佳防震锤的位置;再结合Graph Cuts算法实现防震锤图像的分割;最后采用RGB颜色模型识別防震锤锈蚀缺陷。实验结果表明:该算法对防震锤位置的检测和锈蚀识别的精度较高。

基于图像纹理合成的特征可调控的数字岩心重构方法

为弥补现有的数字岩心重构方法难以进行特征调控的不足,文章提出一种新的基于特征子图且能够自由调控特征比例的数字岩心重构方法。该方法从训练图像中挑选出有代表性的特征子图,根据事先设置的比例进行匹配块挑选,再利用Graph Cut算法合成重叠区域,一直到全图完成。结果表明该方法能够提高合成速度,控制重构岩心图像中矿石、有机质等组分比例,并且能够生成新的模式,具有良好的应用前景。

Stepwise approach for view synthesis

This paper presents some techniques for synthesizing novel view for a virtual viewpoint from two given views cap-tured at different viewpoints to achieve both high quality and high efficiency. The whole process consists of three passes. The first pass recovers depth map. We formulate it as pixel labelling and propose a bisection approach to solve it. It is accomplished in log2n(n is the number of depth levels) steps,each of which involves a single graph cut computation. The second pass detects occluded pixels and reasons about their depth. It fits a foreground depth curve and a background depth curve using depth of nearby fore-ground and background pixels,and then distinguishes foreground and background pixels by minimizing a global energy,which involves only one graph cut computation. The third pass finds for each pixel in the novel view the corresponding pixels in the input views and computes its color. The whole process involves only a small number of graph cut computations,therefore it is efficient. And,visual artifacts in the synthesized view can be removed successfully by correcting depth of the occluded pixels. Experimental results demonstrate that both high quality and high efficiency are achieved by the proposed techniques.

Computer Vision with Machine Learning Enabled Skin Lesion Classification Model

Recently,computer vision(CV)based disease diagnosis models have been utilized in various areas of healthcare.At the same time,deep learning(DL)and machine learning(ML)models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools.This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification(CVOML-SLDC)model.The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images.Primarily,the CVOML-SLDC model derives a gaussian filtering(GF)approach to pre-process the input images and graph cut segmentation is applied.Besides,firefly algorithm(FFA)with EfficientNet based feature extraction module is applied for effectual derivation of feature vectors.Moreover,naïve bayes(NB)classifier is utilized for the skin lesion detection and classification model.The application of FFA helps to effectually adjust the hyperparameter values of the EfficientNet model.The experimental analysis of the CVOML-SLDC model is performed using benchmark skin lesion dataset.The detailed comparative study of the CVOML-SLDC model reported the improved outcomes over the recent approaches with maximum accuracy of 94.83%.

Fast segmentation of anterior segment optical coherence tomography images using graph cut

Background:Optical coherence tomography(OCT)is a non-invasive imaging system that can be used to obtain images of the anterior segment.Automatic segmentation of these images will enable them to be used to construct patient specific biomechanical models of the human eye.These models could be used to help with treatment planning and diagnosis of patients.Methods:A novel graph cut technique using regional and shape terms was developed.It was evaluated by segmenting 39 OCT images of the anterior segment.The results of this were compared with manual segmentation and a previously reported level set segmentation technique.Three different comparison techniques were used:Dice’s similarity coefficient(DSC),mean unsigned surface positioning error(MSPE),and 95%Hausdorff distance(HD).A paired t-test was used to compare the results of different segmentation techniques.Results:When comparison with manual segmentation was performed,a mean DSC value of 0.943±0.020 was achieved,outperforming other previously published techniques.A substantial reduction in processing time was also achieved using this method.Conclusions:We have developed a new segmentation technique that is both fast and accurate.This has the potential to be used to aid diagnostics and treatment planning.

Generalization performance of graph-based semisupervised classification

Semi-supervised learning has been of growing interest over the past few years and many methods have been proposed. Although various algorithms are provided to implement semi-supervised learning,there are still gaps in our understanding of the dependence of generalization error on the numbers of labeled and unlabeled data. In this paper,we consider a graph-based semi-supervised classification algorithm and establish its generalization error bounds. Our results show the close relations between the generalization performance and the structural invariants of data graph.

Cheeger's cut, maxcut and the spectral theory of1-Laplacian on graphs

This is primarily an expository paper surveying up-to-date known results on the spectral theory of1-Laplacian on graphs and its applications to the Cheeger cut, maxcut and multi-cut problems. The structure of eigenspace, nodal domains, multiplicities of eigenvalues, and algorithms for graph cuts are collected.

Robust Video Text Detection with Morphological Filtering Enhanced MSER

Video text detection is a challenging problem, since video image background is generally complex and its subtitles often have the problems of color bleeding, fuzzy boundaries and low contrast due to video lossy compression and low resolution. In this paper, we propose a robust framework to solve these problems. Firstly, we exploit gradient amplitude map （GAM） to enhance the edge of an input image, which can overcome the problems of color bleeding and fuzzy boundaries. Secondly, a two-direction morphological filtering is developed to filter background noise and enhance the contrast between background and text. Thirdly, maximally stable extremal region （MSER） is applied to detect text regions with two extreme colors, and we use the mean intensity of the regions as the graph cuts＇ label set, and the Euclidean distance of three channels in HSI color space as the graph cuts smooth term, to get optimal segmentations. Finally, we group them into text lines using the geometric characteristics of the text, and then corner detection, multi-frame verification, and some heuristic rules are used to eliminate non-text regions. We test our scheme with some challenging videos, and the results prove that our text detection framework is more robust than previous methods.

Semi-Global Depth Estimation Algorithm for Mobile 3-D Video Applications

Three-dimensional （3-D） video applications, such as 3-D cinema, 3DTV, ana Free Viewpomt Video （FVV） are attracting more attention both from the industry and in the literature. High accuracy of depth video is a fundamental prerequisite for most 3-D applications. However, accurate depth requires computationally intensive global optimization. This high computational complexity is one of the bottlenecks to applying depth generation to 3-D applications, especially for mobile networks since mobile terminals usually have limited computing ability. This paper presents a semi-global depth estimation algorithm based on temporal consis- tency, where the depth propagation is used to generate initial depth values for the computationally intensive global optimization. The accuracy of initial depth is improved by detecting and eliminating the depth propagation outliers before the global optimization. Integrating the initial values without outliers into the global optimization reduces the computational complexity while maintaining the depth accuracy. Tests demonstrate that the algorithm reduces the total computational time by 54%-65% while the quality of the virtual views is essentially equivalent to the benchmark.