具有鲁棒性的图像骨架提取方法

基于最大正方形的中轴变换,提出了一种二值图像的骨架提取算法。首先通过计算目标像素邻域内最大正方形的所有像素的个数来进行中轴变换,判断骨架点,并提取骨架。如果通过设定一个阈值,再提取骨架,就可以滤除背景中的噪声。实验结果表明:该方法提取的骨架效果好,通过与基于传统中轴变换和形态学提取骨架方法进行比较,该方法抗干扰能力强。     

利用模板和邻域信息的静脉骨架提取新算法

为提高骨架提取的准确性和连通性,提出了一种利用模板和邻域信息的静脉骨架提取新算法,该算法首先对二值图像进行平滑,并通过自适应方法计算静脉纹路上所有像素点邻域之和,以快速区分出边缘点和中轴点,然后遍历图像找出所有符合中轴点模板的像素点,并删除其中的孤立中轴点之后,得到一些间断的中轴线段,最后从这些中轴线段的端点开始采用最大邻域点跟踪方法提取出静脉骨架。实验结果表明,该算法提取的静脉骨架与中轴线重合且平滑稳定,且具有尺度不变性,角度不变性和良好的抗噪性能,是一种有效的骨架提取算法。     

Triangle refinement in a constrained Delaunay triangulation skeleton

This paper presents an algorithm with the purpose of improving upon the already successful constrained Delaunay triangulation (CDT) skeletonisation technique. Using such a triangulation to construct a skeleton has proven very effective, that can sometimes, however, produce triangles that do not represent the true nature of the underlying shape. The contour pixels chosen for triangulation are of significant importance, as they determine the triangle edges that define the skeleton. The algorithm described in this paper deals with this problem by inserting new triangulation points in strategic locations in end, normal and junction triangles. Results show that the skeletons produced by this algorithm are accurate, robust against noise and, above all, comply much better with a human's perception of the image than the original triangulation method.     

一种基于轮廓的图像检索算法

针对Choi Wai-pak等人提出的基于最大内切圆直方图的图像检索算法存在的不足,提出了一种改进的新的基于轮廓的图像检索算法。该算法首先用两种不同尺度的1维高斯函数分别对目标轮廓的凹陷部分和凸起部分进行进化处理来得到一个平滑简单而又能很好代表原始轮廓主要信息的进化曲线;然后利用骨架化算法提取出目标骨架;最后,利用进化后的轮廓与骨架之间的距离直方图来构造目标形状的描述符,并实现了图像检索。与Choi Wai-pak等人提出的算法(只利用了形状的骨架信息)相比,该新算法不仅利用了轮廓所表达的外围整体形状信息,还利用了骨架所表达出的形状的拓扑关系。实验结果表明,该新算法在尺度变换、旋转变换以及抗噪性能等方面具有更优的鲁棒性。     

Extraction of the Euclidean skeleton based on a connectivity criterion

The skeleton is essential for general shape representation. The commonly required properties of a skeletonization algorithm are that the extracted skeleton should be accurate; robust to noise, position and rotation; able to reconstruct the original object; and able to produce a connected skeleton in order to preserve its topological and hierarchical properties. However, the use of a discrete image presents a lot of problems that may influence the extraction of the skeleton. Moreover, most of the methods are memory-intensive and computationally intensive, and require a complex data structure.In this paper, we propose a fast, efficient and accurate skeletonization method for the extraction of a well-connected Euclidean skeleton based on a signed sequential Euclidean distance map. A connectivity criterion is proposed, which can be used to determine whether a given pixel is a skeleton point independently. The criterion is based on a set of point pairs along the object boundary, which are the nearest contour points to the pixel under consideration and its 8 neighbors. Our proposed method generates a connected Euclidean skeleton with a single pixel width without requiring a linking algorithm or iteration process. Experiments show that the runtime of our algorithm is faster than the distance transformation and is linearly proportional to the number of pixels of an image.     

Piecewise linear skeletonization using principal curves

Proposes an algorithm to find piecewise linear skeletons of handwritten characters by using principal curves. The development of the method was inspired by the apparent similarity between the definition of principal curves (smooth curves which pass through the "middle" of a cloud of points) and medial axes (smooth curves that run equidistantly from the contours of a character image). The central fitting-and-smoothing step of the algorithm is an extension of the polygonal line algorithm, which approximates principal curves of data sets by piecewise linear curves. The polygonal line algorithm is extended to find principal graphs and complemented with two steps specific to the task of skeletonization: an initialization method to capture the approximate topology of the character, and a collection of restructuring operations to improve the structural quality of the skeleton produced by the initialization method. An advantage of our approach over existing methods is that we optimize the skeleton graph by minimizing an intuitive and explicit objective function that captures the two competing criteria of smoothing the skeleton and fitting it closely to the pixels of the character image. We tested the algorithm on isolated handwritten digits and images of continuous handwriting. The results indicated that the proposed algorithm can find a smooth medial axis in the great majority of a wide variety of character templates and that it substantially improves the pixel-wise skeleton obtained by traditional thinning methods     

基于对称点对序列的带状图骨架化算法研究

文中提出了一种基于离散轮廓的对称点对序列的骨架化算法。首先提出带状图像的对称点对序列的概念,它是通过轮廓离散点的Delaunay三角剖分而获得的,并将这个序列应用于带状图的骨架化算法中。在带状图的交叉区域利用三角剖分对偶图模型对该序列进行归并重构,从而得到交叉区域不畸变的骨架。实验表明,该算法充分利用了带状图的全局和局部信息,产生的骨架准确地反映了带状图像间的拓扑关系。     

Image Thinning by Neural Networks

An image thinning technique using a neural network is proposed. Using different activation functions at different layers, the proposed neural network removes the boundary pixels from four directions in such a manner that the general configuration of the input pattern is unaltered and the connectivity is preserved. The resulting object, called a skeleton, provides an abstraction of the global shape of the object. The skeleton is often useful for geometrical and structural analysis of the object. The output skeleton here satisfies the basic properties of a skeleton, namely connectivity and unit thickness. The proposed method is experimentally found to be more efficient in terms of better medial axis representation and robustness to boundary noise over a few existing algorithms. ID="A1" Correspondence and offprint requests to: Dr A. Datta, Indian Statistical Institute, 203 B. T. Road, Calcutta – 700 108, India. Email: amitava@isical.ac.in     

脱机手写签名图像的细化研究

针对脱机手写签名图像的细化存在的问题如细化后转折处出现的分叉笔画、两笔画不垂直相交部分的畸变，提出了新的细化算法。此算法采用串行处理的方式，首先对签名图像进行边缘提取，再针对边缘像素点进行可删除条件的判断，经过几次循环，最后形成细化图像。实验结果表明，此方法能较好地解决以上提到的问题，尤其是分叉笔画。     

Effective Piecewise Linear Skeletonization of Sparse Shapes

Conventional image skeletonization techniques implicitly assume the pixel level connectivity. However, noise inside the object regions destroys the connectivity and exhibits sparseness in the image. We present a skeletonization algorithm designed for these kinds of sparse shapes. The skeletons are produced quickly by using three operations. First, initial skeleton nodes are selected by farthest point sampling with circles containing the maximum effective information. A skeleton graph of these nodes is imposed via inheriting the neighborhood of their associated pixels, followed by an edge collapse operation. Then a skeleton tting process based on feature-preserving Laplacian smoothing is applied. Finally, a re nement step is proposed to further improve the quality of the skeleton and deal with noise or different local shape scales. Numerous experiments demonstrate that our algorithm can effectively handle several disconnected shapes in an image simultaneously, and generate more faithful skeletons for shapes with intersections or different local scales than classic methods.     

Skeletonisation of three-dimensional object using generalized potential field

In this paper, the potential-based skeletonization approach for 2D medial axis transform (MAT), which identifies object skeleton as potential valleys using a Newtonian potential model in place of the distance function, is generalized to three dimensions. The generalized potential functions given by Chung (1998), which decay faster with distance than the Newtonian potential, is used for the 3D case. The efficiency of the proposed approach results from the fact that these functions and their gradients can be obtained in closed forms for polyhedral surfaces. According to the simulation results, the skeletons obtained with the proposed approach are closely related to the corresponding MAT skeletons. While the medial axis (surface) is 2D in general for a 3D object, the potential valleys, being one-dimensional, form a more realistic skeleton. Other desirable attributes of the algorithm include stability against perturbations of the object boundary, the flexibility to obtain partial skeleton directly, and low time complexity.     

一个SAR目标边缘自动检测与定位算法

在SAR目标边缘提取的基础上,为了获取目标边缘详细信息并对目标准确定位,该文针对线性目标设计了目标重心提取算法,对环状目标设计了轮廓线追踪算法,并给出了对目标范围、中心点标识、目标横向、纵向“直径”等的计算方法。这些算法在不同图像上取得了良好的检测结果。     

一种彩色图像区域分割及轮廓矢量化新算法

提出了一种逆时针追踪轮廓线的彩色图像区域分割算法,其思想是按逆时针顺序追踪轮廓线,在追踪过程中避免了像素点的重复判断,采用回溯搜索解决崎点问题,从而保证了追踪过程的连续性和正性性,在此基础上,运用递归方法分割出图像的所有不同色彩区域,直接提取出具有一定色极特征的区域,具有较高的实用性,也是此算法的优点之一,本文亦从直线像素点排列的特点出发,提出了一种轮廓点列快速矢量化算法,极大地减沲轮廓需存储的像素点个数,节省了内存空间,并为进一步算是如形状匹配,编码等提供了基础,实施测试的结果表明,该算法效果较好,达到了预测的要求。     

On Using Anisotropic Diffusion for Skeleton Extraction

We present a novel and effective skeletonization algorithm for binary and gray-scale images, based on the anisotropic heat diffusion analogy. We diffuse the image in the direction normal to the feature boundaries and also allow tangential diffusion (curvature decreasing diffusion) to contribute slightly. The proposed anisotropic diffusion provides a high quality medial function in the image: it removes noise and preserves prominent curvatures of the shape along the level-sets (skeleton features). The skeleton strength map, which provides the likelihood of a point to be part of the skeleton, is defined by the mean curvature measure. Finally, thin and binary skeleton is obtained by non-maxima suppression and hysteresis thresholding of the skeleton strength map. Our method outperforms the most related and the popular methods in skeleton extraction especially in noisy conditions. Results show that the proposed approach is better at handling noise in images and preserving the skeleton features at the centerline of the shape.     

基于直方图重构的极大交叉熵图像分割方法

针对图像分割阈值选择问题,提出用动态参数将原始图像直方图分成两部分,构造两个新的相关直方图,分别对应于同原始图像等尺寸的虚拟图像,其中等概率像素是原始图像的相似像素。聚集计算两个构造直方图概率分布的交叉熵,分析其函数曲线极大值的峰谷关系,实现图像最佳多阈值分割。实验结果表明该方法的有效性。     

基于人体骨骼和深度图像信息的指尖检测方法

针对复杂环境下的指尖检测,从手部图像分割和指尖检测方法两方面进行改进,提出了一种基于人体骨骼和深度图像信息的指尖检测方法。首先采用Kinect获取人体骨骼和深度图像信息,通过人体骨骼信息锁定目标用户,利用锁定用户的手部节点位置从深度图像中提取手部区域图像;然后从手部骨架中搜索骨架端点,提出局部最优查找方法对轮廓凸包计算结果进行优化;最后结合手部轮廓特征找到指尖位置。实验结果表明,该方法具有良好的检测效果,满足实时性要求,能够实现复杂环境下的鲁棒检测。     

基于三角剖分对偶图的结构表示模型及其算法

通过提取中轴骨架并对其进行跟踪而获得层次结构图是模式识别中最常用的一种方法。论文提出一种相对骨架的新型结构表示模型“三角剖分对偶图”。利用这种结构表示模型可以从二值图像中直接获得层次结构图,并表达为树的最基本的数据结构。此外,论文还给出了三角剖分对偶图的提取和优化算法,并与骨架表示模型的层次结构图作了比较。     

A fast algorithm for cellular logic operations on sequential machines

This paper describes an algorithm to efficiently realize repeated Cellular Logic operations. These Cellular Logic operations include erosions and dilations as well as skeletonization (topology-preserving shrinking) and propagation procedures. In the proposed method only those image pixels which might change in an iteration are processed, based on the changed pixels in the previous iteration. Processing times for solid objects (maximum diameter 40 to 200 pixels, 256 × 256 image) on a 10 Mhz Motorola 68000 system range from 250 ms to 400 ms per iteration. A simpler suboptimal method is also described which is 2 to 3 times slower.     

Analysis of non-local image denoising methods

Image denoising is probably one of the most studied problems in the image processing community. Recently a new paradigm on non-local denoising was introduced. The non-local means method proposed by Buades, Morel and Coll computes the denoised image as a weighted average of pixels across the whole image. The weight between pixels is based on the similarity between neighborhoods around them. This method attracted the attention of other researchers who proposed improvements and modifications to it. In this work we analyze those methods trying to understand their properties while connecting them to segmentation based on spectral properties of the graph that represents the similarity of neighborhoods of the image. We also propose a method to automatically estimate the parameters which produce the optimal results in terms of mean square error and perceptual quality.     

An Iterative Thinning Algorithm for Binary Images Based on Sequential and Parallel Approaches

Thinning is an important process in several applications of computer vision. It aims to find the onepixel midline of the pattern in binary image. In spite of different thinning methods that have been proposed, the existing methods are not capable of solving all thinning problems. In this work, a new iterative thinning method for binary images was proposed based on a hybrid technique of sequential and parallel approaches. It consists of three stages. The first pre-processing stage determined and prepared the contour. Next, the peeling stage tested and removed unwanted pixels. When the first two stages did not meet more pixels, the postprocessing stage prepared the skeleton to produce the final one-pixel width skeleton. In this work, the first and last stages adopted the sequential approach, while the second stage was based on the parallel approach. To evaluate the performance of the proposed method, a selected and DIBCO2010-H_DIBCO2010_GT benchmark datasets were used with benchmark measurement techniques for thinning processing. The results were compared with Huang, Zhang, K3M, and Abu-Ain methods. The experiments show that the proposed method is implemented well with all types of thinning problems, better than other methods. It is simple to design, its result skeleton has one-pixel width, and it preserves the topology and connectivity.