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.     

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.     

Nucleus and cytoplast contour detector from a cervical smear image

In this paper, a nucleus and cytoplast contour detector (NCC detector) is presented to automatically detect the cytoplast and nucleus contours of a cell in a cervical smear image. The NCC detector uses the adaptable threshold decision (ATD) method to separate the cell from the cervical smear image, and then uses the maximal gray-level-gradient-difference (MGLGD) method, proposed in this paper, to extract the nucleus from the cell. The experimental results show that the NCC detector is superior to two existing methods, the gradient vector flow-active contour model (GVF-ACM) and the edge enhancement nucleus and cytoplast contour (ENNCC) detector, in segmenting the cytoplast and nucleus of a cell.     

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。     

C#实现彩色图像分割

针对图像分析应用日益广泛的现状,基于阈值法对彩色图像实现分割进行论述。介绍图像分割基本概念,讲述彩色图像直方图阈值分割方法,利用二维直方图方便地寻找彩色图像阈值,最后通过C#可行的程序稳定、准确地实现彩色图像阈值分割。     

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。与其他闽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闽值最接近．计算得到的光斑中心坐标准确．且运行时间较短。     

含病变肝脏CT图像三角形表面重构的实现

本文对含病变肝脏ＣＴ图像进行了特征提取,并利用一种叠代式三角形重构算法,重构出肝脏及其中病变的３－Ｄ表面,重构效果较为满意。     

Some experiments on variable thresholding

Chow and Kaneko proposed a method of variable thresholding in which an image is divided into windows; thresholds are selected for those windows that have bimodal histograms; and these thresholds are interpolated to define a variable threshold for the entire image. This method was applied to several TV images of machine parts; the results obtained appeared to be considerably better than the results of thresholding at a fixed level. An extension of the method was defined that allowed histograms to be either bimodal or trimodal; this yielded some further improvement in the results, but was also more sensitive to shadows. Finally, an adaptive quantization scheme, based on histogram peak sharpening, was applied to two of the images; the results do not seem to be as good as those obtained using variable thresholding.     

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。