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.     

Should I e-collaborate with this group? A multilevel model of usage intentions

The use of online collaboration tools for virtual teamwork has been studied extensively, but mainly at the individual-level. We decided to examine the effect of macro-level factors (i.e., team attributes) and applied hierarchical linear modeling analysis to a sample of data collected from 96 individuals nested in 34 virtual teams. Our results suggested that the development of behavioral e-collaboration intentions by individual virtual team members was affected by their perceptions about the system, as described by individual-level IT use theories, and macro-level factors pertaining to the team. The collaboration technology was perceived to be less useful when employed to communicate with social loafers; and collective social loafing negatively influenced the teams’ potency assessments. After controlling for individual-level perceptions of system usefulness, team potency augmented team members’ intentions to use the online collaboration technology with similar teams. It also improved team performance.     

A structured marketplace for arbitrary services

Creating simple marketplaces with common rules, that enable the dynamic selection and consumption of functionality, is the missing link to allow small businesses to enter the cloud, not only as consumers, but also as vendors. In this paper, we present the concepts behind a hybrid service and process repository that can act as the foundation for such a marketplace, as well as a prototype that allowed us to test various real-world scenarios. The advantage of a hybrid service and process repository is that, it not only holds a flat list of services, but also exposes a generic set of use cases, that it obtains information on how specific services can be used to implement the use cases as well as information to select services at run-time according to customer’s goal functions.     

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。     

基于多级顺序排除算法(MSEA)的改进

块匹配运动估计是视频编码国际标准中广泛采用的关键技术.许多快速块匹配法通过限制搜索点数来减少运算量,但与全搜索算法相比极易出现匹配误差.该文介绍了一种应用新的判别条件的多级顺序排除算法（MSEA）,并在此基础上提出一种新的算法,该算法引入了尺度化的部分失真消除（PDE）技术,用于尺度化累积部分误差和当前最小误差.实验证明,相对于一脉相承的同为穷举搜索算法的全搜索算法（FS）、顺序排除算法（SEA）、多级顺序排除算法（MSEA）等,该算法大幅度提高了搜索效率.与多级顺序排除算法相比,平均每宏块节省了大约75%的运算次数.该算法在保证图像质量的前提下,使视频编码的速度大大提高.     

新型递归多级中值滤波器

结合了Adrian Burlan和Pauli Kuosmanen提出的关于中值滤波的新的递归算法结构,在多级中值滤波器(MLMF)算法基础上提出了一种新的中值滤渡器．该滤波器克服了传统多级中值滤波器一些缺点。实验结果显示,在抑制噪声方面和细节保护方面,该滤波器比传统的多级中值滤波器有着明显的改善．