基于非均匀热扩散的交互式图像分割算法

交互式图像分割是图像分割中的重要分支,在现实生活和医学领域都有着广泛的应用。该文基于计算测地距离的热方法,引入了热扩散系数,提出了一种基于非均匀热扩散的交互式图像分割算法。该算法利用图像的颜色信息构造三角网格作为热扩散的媒介,首先由热方程找到距离增加的方向,再利用泊松方程还原测地距离。将前景中人工交互区域上的热流扩散速度增加,则前景不同部分之间的测地距离变小,消除了内部边界,通过设置外部边界分割限制条件,即可实现完整的前景分割。算法仅需求解两个稀疏线性方程组,鲁棒性强、精度高且更易于操作。同时,拉普拉斯算子和梯度算子的预计算可以被多次重用,减少了内存占用和时间消耗。大量交互式图像分割实验结果表明:该算法无需过多的用户交互信息,即可将现实图像中的复杂前景快速准确地分割出来。     

Space‐Time Co‐Segmentation of Articulated Point Cloud Sequences

Consistent segmentation is to the center of many applications based on dynamic geometric data. Directly segmenting a raw 3D point cloud sequence is a challenging task due to the low data quality and large inter‐frame variation across the whole sequence. We propose a local‐to‐global approach to co‐segment point cloud sequences of articulated objects into near‐rigid moving parts. Our method starts from a per‐frame point clustering, derived from a robust voting‐based trajectory analysis. The local segments are then progressively propagated to the neighboring frames with a cut propagation operation, and further merged through all frames using a novel space‐time segment grouping technqiue, leading to a globally consistent and compact segmentation of the entire articulated point cloud sequence. Such progressive propagating and merging, in both space and time dimensions, makes our co‐segmentation algorithm especially robust in handling noise, occlusions and pose/view variations that are usually associated with raw scan data.     

结合拓扑持续性和热扩散理论的3维模型分割

目的 针对已有的3维模型分割方法人为设定过多参数的问题,提出了一种基于拓扑持续性和热亲和度矩阵的3维模型分割方法,只需给定分割部件数即可自动完成分割。方法 首先通过拓扑持续性处理3维模型的热核签名,选取生存期最长的几个特征点作为模型被分割部件的显著特征点,对于模型躯干等无法通过生长周期选取特征点的部件,则选取热核签名的最小值所对应的顶点作为显著特征点,从而获得模型的初始聚类中心;然后使用不同的扩散时间所对应的热亲和度矩阵进行k-means聚类,并根据聚类中心的偏移距离等参数筛选聚类结果,从而获得3维模型的分割结果。结果 选取人体模型进行分割实验,并与其他方法进行对比分析。结果表明,所提出的热亲和度的计算时间明显优于常用的测地距离和幂指数核;相比基于拓扑持续性和基于测地距离的聚类,本文方法可以正确分割模型的各个部件并获得恰当的分割边界。此外,本文方法针对姿态不同的同一非刚体3维模型可以取得一致性的分割结果,而且对模型表面噪声具有较好的鲁棒性。结论 和已有方法相比,本文的基于拓扑持续性和热亲和度矩阵的3维模型分割方法可以在给定分割部件的前提下自动选定聚类中心并获得恰当的分割边界,并广泛适用于常见动物模型的分割。     

Watershed segmentation using prior shape and appearance knowledge

Watershed transformation is a common technique for image segmentation. However, its use for automatic medical image segmentation has been limited particularly due to oversegmentation and sensitivity to noise. Employing prior shape knowledge has demonstrated robust improvements to medical image segmentation algorithms. We propose a novel method for enhancing watershed segmentation by utilizing prior shape and appearance knowledge. Our method iteratively aligns a shape histogram with the result of an improved k-means clustering algorithm of the watershed segments. Quantitative validation of magnetic resonance imaging segmentation results supports the robust nature of our method.     

Estimating Color and Texture Parameters for Vector Graphics

Diffusion curves are a powerful vector graphic representation that stores an image as a set of 2D Bezier curves with colors defined on either side. These colors are diffused over the image plane, resulting in smooth color regions as well as sharp boundaries. In this paper, we introduce a new automatic diffusion curve coloring algorithm. We start by defining a geometric heuristic for the maximum density of color control points along the image curves. Following this, we present a new algorithm to set the colors of these points so that the resulting diffused image is as close as possible to a source image in a least squares sense. We compare our coloring solution to the existing one which fails for textured regions, small features, and inaccurately placed curves. The second contribution of the paper is to extend the diffusion curve representation to include texture details based on Gabor noise. Like the curves themselves, the defined texture is resolution independent, and represented compactly. We define methods to automatically make an initial guess for the noise texure, and we provide intuitive manual controls to edit the parameters of the Gabor noise. Finally, we show that the diffusion curve representation itself extends to storing any number of attributes in an image, and we demonstrate this functionality with image stippling an hatching applications.     

An Improved Method for Edge Detection and Image Segmentation Using Fuzzy Cellular Automata

Image segmentation is one of the most important and challenging problems in image processing. The main purpose of image segmentation is to partition an image into a set of disjoint regions with uniform attributes. In this study, we propose an improved method for edge detection and image segmentation using fuzzy cellular automata. In the first stage, we introduce a new edge detection method based on fuzzy cellular automata, called the texture histogram, and empirically demonstrate the efficiency of the proposed method and its robustness in denoising images. In the second stage, we propose an edge detection algorithm by considering the mean values of the edges matrix. In this algorithm, we use four fuzzy rules instead of 32 fuzzy rules reported earlier in the literature. In the third and final stage, we use the local edge in the edge detection stage to more accurately accomplish image segmentation. We demonstrate that the proposed method produces better output images in comparison with the separate segmentation and edge detection methods studied in the literature. In addition, we show that the method proposed in this study is more flexible and efficient when noise is added to an image.     

Heat method of non-uniform diffusion for computing geodesic distance on images and surfaces

This paper presents a non-uniform heat method to calculate geodesic distance and geodesic curves on the images and surfaces. Different from the varadhan’s formula-based heat method, our non-uniform heat method first finds the direction of distance increases by heat diffusion, and then recovers the geodesic distance by solving a Poisson equation. Various heat diffusion metrics obtained from different potentials and tensors, such as intensity-based metrics, gradient-based metrics, and anisotropy metrics et., describe the differences of geodesic distances in various regions. Combined with automatic geodesic segmentation technology, our heat method can be effectively and quickly applied to centerlines extraction and salient curves detection in images, skeleton extraction of shapes, and 3D path planning on surfaces. Two categories of discretization algorithms on scattered points and triangle meshes are more flexible and can often be used to more complicated cases. The algorithm is robust and simple to implement since it is based on solving a pair of standard sparse linear systems. Pre-calculation also greatly reduces time consumption and memory footprint.

     

快速鲁棒核空间模糊聚类分割

目的 传统模糊C-均值聚类应用于图像分割仅考虑像素本身的聚类问题,无法克服噪声干扰对图像分割结果的影响,不利于受到噪声干扰的工业图像、医学影像和高分遥感影像等进行目标提取、识别和解译。嵌入像素空间邻域信息或局部信息的鲁棒模糊C-均值聚类分割算法是近年来图像分割理论研究中的热点课题。为此,针对现有的鲁棒核空间模糊聚类算法非常耗时且抑制噪声能力弱、不适合强噪声干扰下大幅面图像快速分割等问题,提出一种快速鲁棒核空间模糊聚类分割算法。方法 利用待分割图像中像素邻域的灰度信息和空间位置等信息构建线性加权滤波图像,对其进行鲁棒核空间模糊聚类。为了进一步提高算法实时性,引入当前聚类像素与其邻域像素均值所对应的2维直方图信息,构造一种基于2维直方图的鲁棒核空间模糊聚类快速分割最优化数学模型,采用拉格朗日乘子法获得图像分割的像素聚类迭代表达式。结果 对大幅面图像添加一定强度的高斯、椒盐以及混合噪声,以及未加噪标准图像的分割测试结果表明,本文算法比基于邻域空间约束的核模糊C-均值聚类等算法的峰值信噪比至少提高1.5 dB,误分率降低约5%,聚类性能评价的划分系数提高约10%,运行速度比核模糊C-均值聚类和基于邻域空间约束的鲁棒核模糊C-均值聚类算法至少提高30%,与1维直方图核空间模糊C-均值聚类算法具有相当的时间开销,所得分割结果具有较好的主观视觉效果。结论 通过理论分析和实验验证,本文算法相比现有空间邻域信息约束的鲁棒核空间模糊聚类等算法具有更强的抗噪鲁棒性、更优的分割性能和实时性,对大幅面遥感、医学等影像快速解译具有积极的促进作用,能更好地满足实时性要求较高场合的图像分割需要。     

基于改进能量泛函模型的噪声图像分割算法

针对噪声图像的分割难,分割不准确,以及现有模型无法适应多种噪声环境的问题,提出了一种基于改进的能量泛函模型的噪声图像分割算法,该算法结合各向异性扩散方程和灰度水平集算法,通过对能量泛函的改进实现对噪声图像的准确、快速分割。将非凸泛函引入能量泛函模型,并通过证明不存在全局最小值,利用获得的能量泛函模型得到光滑的目标图像边界。将各向异性扩散模型得到的光滑图像与水平集模型相结合,得到改进的能量泛函模型。通过求解在能量泛函的最小值,得到前景目标的水平集演化的最佳位置。该算法与同类模型的对比实验表明该模型对噪声图像具有较高的分割精度和鲁棒性。     

A multi-direction GVF snake for the segmentation of skin cancer images

A multi-direction gradient vector flow (GVF) snake-based scheme is proposed in this paper for the segmentation of skin cancer images. Unlike the traditional anisotropic diffusion (AD) filter, which has many disadvantages such as sensitivity to noise, a new AD filter has been developed to remove the noise. The proposed AD filter uses adaptive threshold selection and a new gradient computation method, which is robust to noise and can effectively remove the hairs. After the noise is removed from the skin cancer image, the image is segmented using a multi-direction GVF snake. The multi-direction GVF snake extends the single direction GVF snake and traces the boundary of the skin cancer even if there are other objects near the skin cancer region. Experiments performed on 11 cancer images show the effectiveness of the proposed algorithm.     

A class-independent flexible algorithm to generate region proposals

Generating a sufficient number of regions with high accuracy is an important objective in the region proposal generation techniques. This paper presents a new, robust, and effective approach, which is based on the bottom-up segmentation, to produce a pool of well-quality regions. After image segmentation, the segmented candidates are expanded into the surrounding regions. The suggested algorithm produces some enlarged regions, which better cover objects and stuff. The proposed process can be applied in three different modes, namely fixed_mode, all_mode, and efficient_mode. The fixed_mode extends each region into parts of all the adjacent regions using an extension controller, which considers adjacent sequential pixels for each point on the region boundary. In all_mode, the current region is merged with all the adjacent regions to generate a larger region. The efficient_mode is then implemented using the accumulation of the results from both the fixed_mode and all_mode. Besides, the algorithm can be repeated in the fixed_mode and all_mode by considering a variety of values for the extension controller factor. No features are required to be extracted in the proposed algorithm, except for the image segmentation stage. In this study, four challenging datasets known as MSRC, VOC2007, VOC2012, and COCO 2017 are used to compare the proposed algorithm with other segmentation and region proposal algorithms. As a significant advantage compared to well-known region proposal algorithms, our approach achieves a greater Recall with the desirable number of regions. Furthermore, the algorithm shows a good improvementin extraction of small, medium, and large objects.

     

正则化图形模糊聚类及鲁棒分割算法

针对现有图形模糊聚类算法合理性差和抗噪能力弱的问题，提出嵌入对称正则项的图形模糊聚类鲁棒算法。将样本聚类所对应的中立度与拒分度相结合构造对称正则项，嵌入现有图形模糊聚类所对应的目标函数；同时，利用像素邻域所对应的均值信息辅助当前像素聚类并构造了空间信息约束正则项，采用拉格朗日乘子法获得正则化图形模糊聚类鲁棒分割算法。不同噪声干扰图像分割结果表明，所建议的分割算法是有效的，相比现有的鲁棒模糊聚类分割算法具有更强的抑制噪声能力。     

基于梯度与形态学的低质量车牌定位

针对低质量车牌的定位问题,提出一种水平梯度信息和数学形态学操作相结合的定位方法。利用自适应梯度图阈值分割算法分割出车牌部分的梯度信息;通过形态学和区域合并操作得到连通的候选区域;结合车牌自身特点进行区域搜索,粗定位出车牌;用扫描线法去除边框,实现车牌的精确定位。实验结果表明,该方法对光线昏暗、噪声严重、车牌区域污损、倾斜等条件下的车牌定位具有较强的鲁棒性,能满足实际交通场景中的车牌定位需求。     

改进全变分保边平滑分割

为了弥补活动轮廓算法对自然图像存在欠、过分割现象,根据图像不同区域对分割贡献各异,设计了一个新的保边平滑函数,该函数对不同区域自适应选择扩散方式;结合水平集建立了改进全变分保边平滑分割模型,同时依分割区域的置信度设计了平滑收敛条件,避免了因过度平滑导致分割曲线消失。相对于传统CV算法和Li算法,该算法分割测评分数提高了3%以上,且对噪声抑制能力较强。     

增强图像细节和去噪能力的改进形态学分水岭算法

为了抑制分水岭算法过分割和滤波后保持图像细节,论文提出一种改进的 形态学分水岭分割算法。首先,对图像进行多尺度小波分解得到低频系数和高频系数;对低 频系数进行基于Perona-Malik 扩散模型各向异性扩散滤波;对高频系数,引入神经网络中的 sigmoid 函数改进自适应遗传算法的变异和交叉概率生成,并用父代的最优个体替换子代中 最差的个体来保护最优个体不被破坏,克服遗传算法的局部最优现象,利用改进的自适应遗 传算法增强和去噪。然后,对梯度图像做锐化处理以突出边缘, 再做形态学运算并进行 H-minima 标记。最后,执行分水岭分割,实现改进的算法。实验结果表明,改进算法能够 有效地抑制噪声的干扰,减轻过分割,分割精度也有所提高。     

基于各向异性扩散活动轮廓模型的左心室MRI分割

结合各向异性扩散算法与梯度矢量流活动轮廓模型,提出了基于各向异性扩散活动轮廓模型并应用于心脏核磁共振图像分割;模型采用各向异性扩散方程构造活动轮廓模型的外部能量函数,得到边界更加清晰的分段平滑图像,运用梯度矢量流将边缘图梯度散射到平坦区域,可以有效抑制噪声,同时保持了目标边界;对左心室核磁共振图像的分割实验表明,该模型可以克服噪声和伪影的干扰,与原梯度矢量流模型相比具有更高的精确性和可靠性,有利于实现自动分割.     

On using an analogy to heat flow for shape extraction

We introduce a novel evolution-based segmentation algorithm which uses the heat flow analogy to gain practical advantage. The proposed algorithm consists of two parts. In the first part, we represent a particular heat conduction problem in the image domain to roughly segment the region of interest. Then we use geometric heat flow to complete the segmentation, by smoothing extracted boundaries and removing noise inside the prior segmented region. The proposed algorithm is compared with active contour models and is tested on synthetic and medical images. Experimental results indicate that our approach works well in noisy conditions without pre-processing. It can detect multiple objects simultaneously. It is also computationally more efficient and easier to control and implement in comparison with active contour models.     

Moving-edge detection via heat flow analogy

In this paper, a new and automatic moving-edge detection algorithm is proposed, based on using the heat flow analogy. This algorithm starts with anisotropic heat diffusion in the spatial domain, to remove noise and sharpen region boundaries for the purpose of obtaining high quality edge data. Then, isotropic and linear heat diffusion is applied in the temporal domain to calculate the total amount of heat flow. The moving-edges are represented as the total amount of heat flow out from the reference frame. The overall process is completed by non-maxima suppression and hysteresis thresholding to obtain binary moving-edges. Evaluation, on a variety of data, indicates that this approach can handle noise in the temporal domain because of the averaging inherent of isotropic heat flow. Results also show that this technique can detect moving-edges in image sequences, without background image subtraction.     

基于RandomWalk算法的CT图像肺实质自动分割

针对复杂情况下肺实质的分割问题,提出了一种基于Random Walk算法对肺实质自动分割的方法。首先,根据胸部组织解剖学及其计算机断层扫描(CT)图像的影像学特征,在肺实质及其周围组织分别确定目标区域种子点和背景种子点位置;然后,使用Random Walk算法对CT图像进行分割,提取近似肺区域的掩模;接下来,对掩模实施数学形态学运算,来进一步调整目标区域种子点和背景种子点的标定位置,使其适合具体的复杂情况;最后,再次使用Random Walk算法分割图像,得到最终的肺实质分割结果。实验结果显示,该方法与金标准的平均绝对距离为0.44±0.13 mm,重合率(DC)为99.21%±0.38%。与其他分割方法相比,该方法在分割精度上得到了显著提高。结果表明,提出的方法能够解决复杂情况下肺实质分割的问题,确保了分割的完整性、准确性、实时性和鲁棒性,分割结果和时间均可满足临床需求。