阈值分割与颜色聚类相结合的色度学图象分割方法

根据真彩色图象象素在3D彩色空间呈现聚类分布的特点,可利用基于直方图的多维空间滤波技术得到目标图象,但会产生丢失目标信息的过度分割现象。采用颜色聚类方法改善此现象,在保证算法速度的基础上,大大提高了真彩色图象的分割效果,并对不同彩色模型和聚类中心有较好的鲁棒性,在医学染色图象处理中得到很好应用。     

基于改进迭代收缩阈值算法的微观3D重建方法

迭代收缩阈值算法（ISTA）求解离焦深度恢复动态优化问题时,采用固定迭代步长,导致算法收敛效率不佳,使得重建的微观3D形貌精度不高。为此,提出一种基于加速算子梯度估计和割线线性搜索的方法优化ISTA——FL-ISTA。首先,在每一次迭代中,由当前点和前一个点的线性组合构成加速算子重新进行梯度估计,更新迭代点;其次,为了改变迭代步长固定的限制,引入割线线性搜索,动态确定每次最优迭代步长;最后,将改进的迭代收缩阈值算法用于求解离焦深度恢复动态优化问题,加快算法的收敛速度、提高微观3D形貌重建的精度。在对标准500 nm尺度栅格的深度信息重建实验中,与ISTA、快速ISTA （FISTA）和单调快速ISTA （MFISTA）相比,FL-ISTA收敛速度均有所提升,重建的深度信息值下降了10个百分点,更接近标准500 nm栅格尺度;与ISTA相比,FL-ISTA重建的微观3D形貌均方差（MSE）和平均误差分别下降了18个百分点和40个百分点。实验结果表明,FL-ISTA有效提升了求解离焦深度恢复动态优化问题的收敛速度,提高了微观3D形貌重建的精度。     

Entropy based region selection for moving object detection

This article addresses a problem of moving object detection by combining two kinds of segmentation schemes: temporal and spatial. It has been found that consideration of a global thresholding approach for temporal segmentation, where the threshold value is obtained by considering the histogram of the difference image corresponding to two frames, does not produce good result for moving object detection. This is due to the fact that the pixels in the lower end of the histogram are not identified as changed pixels (but they actually correspond to the changed regions). Hence there is an effect on object background classification. In this article, we propose a local histogram thresholding scheme to segment the difference image by dividing it into a number of small non-overlapping regions/windows and thresholding each window separately. The window/block size is determined by measuring the entropy content of it. The segmented regions from each window are combined to find the (entire) segmented image. This thresholded difference image is called the change detection mask (CDM) and represent the changed regions corresponding to the moving objects in the given image frame. The difference image is generated by considering the label information of the pixels from the spatially segmented output of two image frames. We have used a Markov Random Field (MRF) model for image modeling and the maximum a posteriori probability (MAP) estimation (for spatial segmentation) is done by a combination of simulated annealing (SA) and iterated conditional mode (ICM) algorithms. It has been observed that the entropy based adaptive window selection scheme yields better results for moving object detection with less effect on object background (mis) classification. The effectiveness of the proposed scheme is successfully tested over three video sequences.     

一种光斑图像的阈值分割和光斑中心坐标的计算方法

工业测量中经常需要从获取的光斑图像中提取光斑区域,计算光斑中心的坐标。针对光斑图像具有背景区域较暗且面积大,目标(光斑)区域较亮且面积小的特点,提出了一种适用于光斑图像的阈值分割方法,计算单个光斑中心坐标的重心法,以及采用连通区域标记和区域大小排序计算多个光斑中心坐标的方法。与其他阈值分割方法比较后的实验结果表明,所提出的方法可更好地分割光斑图像,与期望的人工选取的阈值最接近,计算得到的光斑中心坐标准确,且运行时间较短。     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.     

An analysis of the transition proportion for binarization in handwritten historical documents

In this paper, we will present a mathematical analysis of the transition proportion for the normal threshold (NorT) based on the transition method. The transition proportion is a parameter of NorT which plays an important role in the theoretical development of NorT. We will study the mathematical forms of the quadratic equation from which NorT is computed. Through this analysis, we will describe how the transition proportion affects NorT. Then, we will prove that NorT is robust to inaccurate estimations of the transition proportion. Furthermore, our analysis extends to thresholding methods that rely on Bayes rule, and it also gives the mathematical bases for potential applications of the transition proportion as a feature to estimate stroke width and detect regions of interest. In the majority of our experiments, we used a database composed of small images that were extracted from DIBCO 2009 and H-DIBCO 2010 benchmarks. However, we also report evaluations using the original (H-)DIBCO?s benchmarks.     

利用云模型和数据场的图像分割方法

针对图像自动分割中的最优阈值选择问题,提出一种基于云模型和数据场的图像分割方法。 该方法引入数据场实现图像灰度值特征空间到数据场势值空间的非线性映射,设定两个不同的质量函数分别形成相对数据场和绝对数据场。利用两类数据场的特点,结合全局和局部统计特征获得自适应势阈值对图像像素进行划分,产生图像潜在的背景或目标像素集合。进一步由逆向云发生器算法产生图像背景和目标的云模型表示,根据图像像素隶属于背景、目标云模型的程度,采用极大判定法则得到最终的分割结果。 实验结果表明,该方法的分割效果较好、性能稳定,具有合理性和有效性。     

A noise attribute thresholding method for document image binarization

A new thresholding method, called the noise attribute thresholding method (NAT), for document image binarization is presented in this paper. This method utilizes the noise attribute features extracted from the images to make the selection of threshold values for image thresholding. These features are based on the properties of noise in the images and are independent of the strength of the signals (objects and background) in the image. A simple noise model is given to explain these noise properties. The NAT method has been applied to the problem of removing text and figures printed on the back of the paper. Conventional global thresholding methods cannot solve this kind of problem satisfactorily. Experimental results show that the NAT method is very effective. Received July 05, 1999 / Revised July 07, 2000     

红外图像统计闭值分割方法

经典的统计阈值方法采用某种形式的类方差和作为阈值选择的准则,未考虑实际图像的特性,对目标和背景具有相似统计分布的图像的分割效果不甚理想。为此,利用阈值分割后两个类的标准偏差定义了一个新的阈值选择准则,并通过最小化此准则选择出最佳分割阈值。通过一系列实际图像上的实验结果表明,与现有的几种经典阈值分割方法相比,本方法分割图像的效果更好,尤其是对红外图像分割的效果更为明显。     

Segmentation of the carotid intima-media region in B-mode ultrasound images

This paper proposes a new approach for the segmentation of both near-end and far-end intima-media regions of the common carotid artery in ultrasound images. The method requires minimal user interaction and is able to segment the near-end wall in arteries with large, hypoechogenic and irregular plaques, issues usually not considered previously due to the increased segmentation difficulty.