A fast connected components labeling algorithm for binary images

A fast label-equivalence-based connected components labeling algorithm is proposed in this paper.It is a combination of two existing efficient methods,which are pivotal operations in two-pass connected components labeling algorithms.One is a fast pixel scan method,and the other is an array-based Union-Find data structure.The scan procedure assigns each foreground pixel a provisional label according to the location of the pixel.That is to say,it labels the foreground pixels following background pixels and foreground pixels in different ways,which greatly reduces the number of neighbor pixel checks.The array-based Union-Find data structure resolves the label equivalences between provisional labels by using only a single array with path compression,and it improves the efficiency of the resolving procedure which is very time-consuming in general label-equivalence-based algorithms.The experiments on various types of images with different sizes show that the proposed algorithm is superior to other labeling approaches for huge images containing many big connected components.     

Extraction of coastline in high-resolution remote sensing images based on the active contour model

While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing image.Active contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction technique.Firstly,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour model.As better energy functions are developed,the power assist of snakes becomes effective.New internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of interest.After normalization processing,energies are iterated using greedy algorithm to accelerate convergence rate.The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement.     

图像增强算法综述

图像增强算法主要是对成像设备采集的图像进行一系列的加工处理, 增强图像的整体效果或是局部细节,从而提高整体与部分的对比度, 抑制不必要的细节信息, 改善图像的质量, 使其符合人眼的视觉特性. 首先, 本文从图像增强算法的基本原理出发, 归纳了直方图均衡图像增强、小波变换图像增强、偏微分方程图像增强、分数阶微分的图像增强、基于Retinex理论的图像增强和基于深度学习的图像增强算法, 并讨论了它们的改进算法. 然后,从视觉效果、对比度、信息熵等方面对几种算法进行了定性和定量的对比, 分析了它们的优势和劣势. 最后, 对图像增强算法的未来发展趋势作了简单的展望.     

基于改进Hough变换和图搜索的油库目标识别

为了识别遥感图像中圆形油库目标,首先改进了基于梯度信息的圆形检测Hough变换方法,提取出图像中的圆形油库。然后根据油库的空间分布关系,提出利用深度优先的图搜索策略对检测到的圆进行分组,剔除虚警目标,最终实现油库目标区域的定位。改进的Hough变换通过利用梯度的方向信息和降低参数空间维数的方法降低了算法执行时耗费的时间和占用的存储空间,提高了圆检测的效率,同时用图搜索技术来排除虚假目标和定位目标区域,降低了虚警率,提高了识别精度。实验表明,该方法能够快速准确地识别油库目标,适用于不同分辨率的可见光遥感影像。     

刀口法测量拉盖尔-高斯涡旋光斑的理论与实验研究

拉盖尔-高斯(LG0l)涡旋光束携带光轨道角动量,提供了额外复用自由度,可以极大提高光通信容量,实现该涡旋光束光斑半径的精确测量对其应用具有重要意义.刀口法由于廉价、方便、精度高、适用于较大功率的测量等优势,是测量光斑尺寸及束腰尺寸的理想方法.然而,以往刀口法的研究主要集中在基横模高斯光束上,尚缺少关于刀口法对LG0l涡旋光斑的测量研究.本文对基于刀口法的LG0l涡旋光斑尺寸测量进行了详细地理论分析和实验研究.理论分析中给出了刀口法测量LG0l涡旋光束的原理;实验上将螺旋相位板对基模高斯光束调制后产生的光束当作LG0l涡旋光束的近似,并采用刀口法测量了该LG0l(l=0,1和2)涡旋光的光斑;借助Mathematica程序数值拟合了实验测量的横向光场强度分布结果,得到了不同LG0l涡旋光束的光斑半径.根据光斑半径的特点可得出:经低涡旋阶数的螺旋相位板调制之后,基模高斯光束可变为相应涡旋阶数的LG0l涡旋光束,变换前后光斑半径基本保持不变.