一种基于几何活动轮廓模型的弱边界区域图象分割方法

传统的几何活动轮廓模型作为一种有效的图象分割方法，一直以来被广泛使用。但其在应用中也存在不少问题，例如对图象内弱边界区域目标不能准确的分割以及对噪声的干扰容易使曲线陷入局部极值等情况。本文提出了一种基于区域梯度流力的几何活动轮廓模型，由于区域力从全局的角度为模型提供目标边界信息，这样使该模型不但能够准确的利用区域信息分割出弱边界区域而且能够有效抵御噪声的干扰。     

A robust incremental learning framework for accurate skin region segmentation in color images

In this paper, we propose a robust incremental learning framework for accurate skin region segmentation in real-life images. The proposed framework is able to automatically learn the skin color information from each test image in real-time and generate the specific skin model (SSM) for that image. Consequently, the SSM can adapt to a certain image, in which the skin colors may vary from one region to another due to illumination conditions and inherent skin colors. The proposed framework consists of multiple iterations to learn the SSM, and each iteration comprises two major steps: (1) collecting new skin samples by region growing; (2) updating the skin model incrementally with the available skin samples. After the skin model converges (i.e., becomes the SSM), a post-processing can be further performed to fill up the interstices on the skin map. We performed a set of experiments on a large-scale real-life image database and our method observably outperformed the well-known Bayesian histogram. The experimental results confirm that the SSM is more robust than static skin models.     

A Review of Statistical Approaches to Level Set Segmentation: Integrating Color,Texture, Motion and Shape

Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90’s, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.     

安全玻璃中气泡的自动检测研究

耐环境性试验是安全玻璃强制检验的内容之一。经过耐环境性试验后的安全玻璃产品中气泡的检测和识别是判定产品是否合格的重要依据。根据玻璃气泡图像的特点，提出了一种安全玻璃气泡的自动检测算法。首先采用基于数学形态学的方法对原图像进行了目标增强;然后进行图像分割;采用孔洞填充和数学形态学法进行修正;最后进行特征提取，获得玻璃气泡的边缘信息。实验结果显示，该算法能准确的检测出安全玻璃中的气泡，具有一定的应用价值。     

SVM图像分割方法的研究

支持向量机分类方法已应用于图像分割。本文以彩色图像分割为例,通过对支持向量机图像分割方法和基于灰度直方图图像分割方法进行比较研究。研究揭示:支持向量机图像分割方法是一种在SVM图像上的全局门限分割,并能自动获得默认门限值。使用支持向量机图像方法的这一特点,很容易使其它的分割方法与支持向量机方法相结合,产生新的混和方法。     

基于图像处理的粮虫自动计数系统的研究

针对目前在线粮情测控系统中存在的不能测得粮虫种类和粮虫数量的问题,本文研究了基于图像处理技术的粮虫自动计数系统,该系统采用数字图像处理技术对粮食样本图像进行处理,并在研究其灰度值分布的基础上,提出了图像分割最佳阈值算法,最后根据粮虫对象的形态特性进行计数。在对稻谷粮虫图像的试验中证明了此方法不仅能完整的将粮虫从样本图像中分割出来,而且计数准确,是实现粮虫自动计数的一种行之有效的方法。     

高尔夫球体育视频分析系统的开发

本文将介绍针对体育运动视频图像分析的高尔夫球体育运动图像采集分析系统软件，系统的硬件部分包括视频采集卡、工业计算机、工业摄像机、触摸屏和机架；软件部分实现的功能有视频捕获、对比播放、绘图分析、智能图像处理等；软件的进一步开发视频地理的功能，即视频分割，使用背景更新方法把选手的前号图像从背景中分离出来。     

舌象裂纹提取及特征分析

舌面的裂纹是一项重要的舌诊指标。文中提出的算法创新点在于用识别器能快速判断舌面是否存在裂纹;对于存在裂纹的舌象,用改进的Confidence Connected区域生长算子能有效提取裂纹区域;最后由分析器从可见性和深浅两方面分析表征裂纹特征。总体试验结果令人满意。     

基于Java 3D的医学图像三维重建系统

系统采用分层组件化的设计思想,在逻辑上分为三层:图像预处理层、图像分割层和用户界面层,实现了医学图像的二维标注与测量、三维体数据的分割、三维重建与交互功能。介绍了系统的体系结构和系统开发的主要技术方法。最后指出了系统的实用性和对医学研究方面的价值。     

基于遗传算法和梯度算子的图象边缘检测

图象边缘检测是计算机图象处理的最基本步骤之一。由于噪声的干扰和图象光照不均匀等因素的影响,目前的图象边缘检则方法还不能有效地检测出各种不同模式的边缘。在分析了几种常用边缘检测方法及其存在的检测精度不高,抗噪声性能较差等不足基础上,给出了一种基于遗传算法和梯度算子的图象边缘检测算法,该算法明显提高了检测精度和抗噪声能力。