区域生长

• Video object segmentation techniques are discussed , theories and techniques related to video segmentation are introduced and the existing typical algorithms of motion - based and spatiotemporal segmentation are analyzed and compared with the emphasis on analysis of spatiotemporal segmentation algorithms based on 3d region growing
  本文以视频对象分割技术为研究课题，首先介绍视频分割相关的理论与技术，然后对现有的基于运动和基于时空域相关两大类分割算法进行对比研究，并把重点放在基于3d区域生长的时空域分割算法的分析上。
• Using the feature : there exists distinct edge contour between the characters and the background in color image , we give a color image text extraction algorithm - tea ( text extraction algorithm ) , which makes the use of cedo to process edge detection of color image , then , adopts morphologic dilation technology to form region image , and combines the area - growth algorithm to mark the candidate text blocks , finally , determinates the correct text block by analyzing the density of text block and the space frequency feature
  利用彩色图像文本区域与背景之间存在明显边缘轮廓的特点，本文给出了彩色图像文本提取算法? tea ( textextractionalgorithm ) 。该算法首先利用cedo进行彩色图像边缘检测，然后采用形态学膨胀技术生成区域图像，并结合区域生长算法标记候选文本块，最后通过分析文本块密度和空间频率特性来判定真正的文本块，从而较好地完成了彩色图像的文本提取。
• This paper presents a new face detection algorithm for color video images based on skin color and multimodal information fusion . first , this paper presents a new means for selecting skin samples ; and then comparing skin distribution in the eight color spaces and analyzing the adaptability for different skin patterns , poses a face initial orientation ' s method which uses the single gaussian model in the tsl color spaces , and calculates skin probability images ; afterwards comprehensive comparing three typical threshold value separating algorithms , put forwards a face separating method which bases on region growing and fuses multimodal informations ; final , raises a face confirming algorithm which fuses three shape features
  首先提出了?种新的肤色样本选取方法；然后通过对八种色空间肤色分布的比较以及不同肤色模型适应性的分析，提出了在tsl色空间上用单峰高斯模型模拟肤色分布，求得肤色概率图进行人脸初定位的方法；随后在综合比较三个典型阈值化分割算法的基础上，提出了融合多源信息进行区域生长分割人脸的算法；最后提出了融合三个形状特征的人脸确认算法。
• Proceeding with several key problems about spatiotemporal segmentation , this paper discusses the generation and distribution of seeds in 3d region growing , provide the similarity measurement between pixel and volume , design the post processing and construct spatiotemporal data structure to support the algorithm . homogeneous video components with similar color feature are obtained
  从时空域分割要着重解决的几个关键问题入手，本文探讨了3d区域生长的种子分布和生成方法，给出了区域生长过程中的像素和元素之间的相似度准则和后处理过程，并构建了相应的时空域数据结构来支持生长算法的进行。
• The image segmentation is done with the approach of region growing based on block mean and variance . considering the size of extracted objects and their relative position information , get rid of the " noise " objects and merge the meaningful fragmentary regions into their corresponding bigger ones . finally all pixels in non - object blocks are classified into their corresponding adjacent objects
  图像首先被分成合适大小的子块，子块的色彩均值和方差值作为像素群的属性，用基于子块的区域生长来进行图像分割；根据提取出的对象大小以及它们的空间位置关系，去除掉过小的噪声对象同时将有意义的小对象合并到其所属的大对象中；最后处于边界的子块将逐像素地归类到对应的相邻对象中。
• In image segmentation , the paper improves some common algorithm of gradual segmentation according to its shortcomings . in the first segmentation step , the paper separates one fingerprint image to four parts at first , then confirms foreground board regarded as seeds by gray - level statistical features and apply region growing method , at last combines the four parts to one image that is the first segmentation image . in the second segmentation step , the paper signs the irrecoverable region from the first segmentation image by gray - level and directional statistical features
  在指纹图像分割方面,本文针对一些普通的指纹图像分级分割算法的缺点,对它们进行了一些改进:在第一级分割中首先把指纹图像等分为四个部分,然后利用灰度统计特性分别确定各个部分的前景区小块,并作为种子块,再利用灰度及方向等统计特性定义种子区域生长规则并进行前景区域生长,最后将四个部分合成一幅图像即得到了一幅被分割掉背景的指纹图像;在第二级分割中利用灰度及方向等统计特性分割掉第一级分割图像中的不可恢复的模糊区。
• Firstly , a fourth - orders statistic hypothesis testing in inter - frame difference is used to automatically separate the moving areas from the background in a general video sequence . secondly , change areas are extracted as markers of the corresponding moving object . using the marker , region - growing algorithm can locate the precise boundary of moving object
  ( 3 )基于轮廓跟踪；首先介绍了提取初始模板方法?基于高次统计的判决算法，通过选择合适的背景噪声阈值，最终得到较好的结果，然后对于后续帧中的边界提取，提出将灰度图像所使用的区域生长方法进行改进，即将它们运用到彩色图像中，通过选择合适的生长规则，得到较好边界图。
• The main research content of the article is involved as follows : ( 1 ) the research and discussion of the quantitative metallographic analysis methods and the measuring methods of micro hardness . ( 2 ) the application of digital image technique in metallographic image preprocess such as gray level transformation , dichotomy , noise eliminating , dilation and erosion , image enhancement , boundary detection , etc . the application of the wavelet and multi - resolution analysis in metallographic image procession to improve the measuring accuracy and efficiency . the application of the region growth and mathematical morphology in analyzing image parameters to improve the flexibility and exaction
  本文的主要研究内容： ( 1 )定量金相分析和显微硬度测量的方法研究； ( 2 )利用数字图像处理技术，实现金相图像的灰度转换、二值化、噪音消除、膨胀收缩、图像增强、边缘提取等预处理；引入小波理论、基于数学形态学的区域生长法对采样图像进行分析，实现了对采集图像边缘的有效提取，从而提高了测量精度； ( 3 )开发了金相图像分析系统的主体结构(硬件结构和软件结构) ； ( 4 )采用windows开发平台的面向对象程序设计语言microsoftvisualc + +进行系统的模块化设计； ( 5 )提出了采用多模式的知识表示方法建立知识库，应用正反推理、模糊数学模型、基于规则的模式匹配模型建立金相分析专家系统。
• In this paper , the conception and classification of image segmentation technology are first introduced , and then , the key point , the advantages and disadvantages of different methods are proposed . with the models of video series , the motion - changed regions testing theory are introduced . after the discussion and analysis of regional growth , regional separatism and regional integration - separatism three major region - based segmentation algorithms , this paper suggests an approach for image segmentation by combining region based on tree - branches growing method
  本文首先介绍了图像分割技术的概念、分类并指出了其中的关键技术及不同方法的优缺点,然后围绕视频序列的模型,介绍了分割预处理的运动变化区域检测的相关理论,接着重点研究了图像分割技术中基于区域的分割算法,在讨论和分析了区域生长、区域分裂、区域合并分裂三种主要区域分割技术的基础上,提出了基于树状结构的区域生长改进算法,实验结果证明了本文所提出算法的有效性和可行性,最后介绍了图像分割算法的评价体系并提出了算法的不足和改进方向。