远程交互式虚拟内窥镜系统

给出了虚拟内窥镜系统的组成框架，并详细分析了其中的关键技术，为了满足远程医疗的需求，设计了一个基于远程医疗的交互式虚拟内窥镜系统，同时给出了实现该系统的技术要点和方法，文中还提出了一种轮廓数据简化算法和一种简便的轮廓点对应方法。     

一种身份证图像自动识别中文字定位与分割的方法

针对扫描后的身份证图像存在纹理及背景等强烈干扰，详细探讨了利用数字图像处理技术对身份证上的文字信息进行定位与分割的一种方法。该方法通过对身份证的特征的研究，利用水平投影与垂直投影相结合的方法，首先确定出文字的大致区域，然后利用模板框准确的定位出文字的位置。实例表明所提出的方法行之有效。     

Hypergraph imaging: an overview

Hypergraph theory as originally developed by Berge (Hypergraphe, Dunod, Paris, 1987) is a theory of finite combinatorial sets, modeling lot of problems of operational research and combinatorial optimization. This framework turns out to be very interesting for many other applications, in particular for computer vision. In this paper, we are going to survey the relationship between combinatorial sets and image processing. More precisely, we propose an overview of different applications from image hypergraph models to image analysis. It mainly focuses on the combinatorial representation of an image and shows the effectiveness of this approach to low level image processing; in particular to segmentation, edge detection and noise cancellation.     

Fusion of Region and Boundary/Surface-Based Computer Vision and Pattern Recognition Techniques for 2-D and 3-D MR Cerebral Cortical Segmentation (Part-II): A State-of-the-Art Review

Extensive growth in functional brain imaging, perfusion-weighted imaging, diffusion-weighted imaging, brain mapping and brain scanning techniques has led tremendously to the importance of cerebral cortical segmentation both in 2-D and 3-D from volumetric brain magnetic resonance imaging data sets. Besides that, recent growth in deformable brain segmentation techniques in 2-D and 3-D has brought the engineering community, such as the areas of computer vision, image processing, pattern recognition and graphics, closer to the medical community, such as to neuro-surgeons, psychiatrists, oncologists, neuro-radiologists and internists. In Part I of this research (see Suri et al [1]), an attempt was made to review the state-of-the-art in 2-D and 3-D cerebral cortical segmentation techniques from brain magnetic resonance imaging based on two main classes: region- and boundary/surface-based. More than 18 different techniques for segmenting the cerebral cortex from brain slices acquired in orthogonal directions were shown using region-based techniques. We also showed more than ten different techniques to segment the cerebral cortex from magnetic resonance brain volumes using boundary/surface-based techniques. This paper (Part II) focuses on presenting state-of-the-art systems based on the fusion of boundary/surface-based with region-based techniques, also called regional-geometric deformation models, which takes the paradigm of partial differential equations in the level set framework. We also discuss the pros and cons of these various techniques, besides giving the mathematical foundations for each sub-class in the cortical taxonomy. Special emphasis is placed on discussing the advantages, validation, challenges and neuro-science/clinical applications of cortical segmentation. Received: 25 August 2000, Received in revised form: 28 March 2001, Accepted: 28 March 2001     

Projection based method for segmentation of human face and its evaluation

We detect facial features and then circumscribe each facial feature with the smallest rectangle possible by using vertical and horizontal gray value projections of pixels. The result is evaluated with respect to the manually located enclosing rectangle on the images of a publicly available database.     

GEP在数字图像分割中的应用

基因表达式程序设计（简称GEP）是一种新型的遗传算法，它继承了遗传程序设计（简称GP）和遗传算法（简称GA）的优点并且具有更高效和更强的搜索能力，它是借鉴生物选择和进化机制发展起来的一种高度并行、随机、自适应的搜索算法。特别适合于处理传统搜索算法解决不好的复杂的和非线性问题。本文将在系统介绍表达式程序设计的基本理论基础上．介绍其在数字图像分割中的应用。     

基于Markov随机场的运动目标快速分割

提出了一种基于MRF模型自适应运动目标图像分割方法，该法采用高斯混合模型描述视频序列的差分图像，用模拟退火快速EM算法对高斯分布的参数进行估计，在此基础上建立MRF模型，利用此模型构建能量函数，ICM算法把图像用一个能量函数取到最小值的组态来表示。     

自适应模糊C均值聚类彩色图像分割方法

模糊C均值聚类用于彩色图像分割具有简单直观,易于实现的特点,但存在聚类性能受中心点初始化影响且计算量大等问题,为此,提出一种自适应模糊C均值分割方法.算法根据人类的视觉特性,参照NBS距离与人类视觉对颜色差别的定量关系,结合具体图像的色彩分布,自动确定初始聚类中心及聚类数目,继而进行模糊C均值聚类.实验表明,该方法无需人为的干预,分割速度快,分割效果跟人的主观视觉感知保持了良好的一致性.     

A consensus-based method for tracking: Modelling background scenario and foreground appearance

Modelling of the background (“uninteresting parts of the scene”), and of the foreground, play important roles in the tasks of visual detection and tracking of objects. This paper presents an effective and adaptive background modelling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model of the background, per pixel. SACON exploits both color and motion information to detect foreground objects. SACON can deal with complex background scenarios including nonstationary scenes (such as moving trees, rain, and fountains), moved/inserted background objects, slowly moving foreground objects, illumination changes etc.However, it is one thing to detect objects that are not likely to be part of the background; it is another task to track those objects. Sample consensus is again utilized to model the appearance of foreground objects to facilitate tracking. This appearance model is employed to segment and track people through occlusions. Experimental results from several video sequences validate the effectiveness of the proposed method.     

A segmentation approach for file broadcast scheduling

We study the broadcast scheduling problem in which clients send their requests to a server in order to receive some files available on the server. The server may be scheduled in a way that several requests are satisfied in one broadcast. When files are transmitted over computer networks, broadcasting the files by fragmenting them provides flexibility in broadcast scheduling that allows the optimization of per user response time. The broadcast scheduling algorithm, then, is in charge of determining the number of segments of each file and their order of transmission in each round of transmission. In this paper, we obtain a closed form approximation formula which approximates the optimal number of segments for each file, aiming at minimizing the total response time of requests. The obtained formula is a function of different parameters including those of underlying network as well as those of requests arrived at the server. Based on the obtained approximation formula we propose an algorithm for file broadcast scheduling which leads to total response time which closely conforms to the optimum one. We use extensive simulation and numerical study in order to evaluate the proposed algorithm which reveals high accuracy of obtained analytical approximation. We also investigate the impact of various headers that different network protocols add to each file segment. Our segmentation approach is examined for scenarios with different file sizes at the range of 100 KB to 1 GB. Our results show that for this range of file sizes the segmentation approach shows on average 13% tolerance from that of optimum in terms of total response time and the accuracy of the proposed approach is growing by increasing file size. Besides, using proposed segmentation in this work leads to a high Goodput of the scheduling algorithm.