一种面向对象的视频分割算法

该文讨论面向对象编码的视频分割算法。由于数学形态学工具能够很好地处理诸如大小、形状、对比度和连通性等对图像分割非常重要的特征,这种技术常用来对图像进行帧内分割,得到一些具有某种相似性的区域。然后利用运动信息进行区域合并。为了实现区域合并,该文提出一种新方法来判定局部运动。设计了一个分割细化步骤,对区域边界点进行再判决,可以得到更好的结果。实验结果表明,该文提出的方法对于平稳背景和运动背景中的视频对象分割都是有效的。     

基于四叉树结构的图像分割技术

基于四叉树结构的图像分割方法是区域增长技术和人工智能技术的结合,它的分割速度比区域增长方法快得多,在分割结束时,算法同时得到图像目标大小、目标灰度、目标个数、目标边缘等结果。对多目标图像分割有更好的适应性。文章最后激光烧孔图像、细胞图像和海上舰船图像进行了实验,说明算法有很好的适应性。     

改进的分水岭算法在超声肿瘤图像分割中的应用

在高强度超声聚焦(HIFU)治疗中,图像自动导航是整个治疗过程的关键步骤.针对超声肿瘤图像分割提出了两种算法,分别为梯度阈值法和区域合并法.其中梯度阈值法针对分水岭过分割的缺陷,选取小于设定的梯度阈值的点作为分水岭变换的种子点,从而很好地抑制了过分割现象;区域合并法首先通过分水岭变换将图像过分割成许多具有区域均质性的超像素,然后基于最小描述(MDL)原则进行合并,将拥有相似纹理特征的小区域聚类在一起,达到抑制过分割的目的.实验结果表明这两种算法有效地解决了分水岭变换过分割的问题,同时能够很好地应用到超声肿瘤图像分割中.     

一种改进的分水岭分割的研究

用分水岭方法进行图像分割时,容易造成图像的过度分割.为了克服这种缺点,本文提出了改进的图像分水岭分割的方法.该方法先用改进的中值滤波对图像进行预处理,在去除噪声的同时很好的保持物体轮廓和细节;以传统标记提取为基础,以标记点为区域极小值对图像进行分水岭分割.实验结果显示,该方法能很好地抑制过度分割,使分割得到了较好的效果.     

一种改进的基于小波变换的相干斑去噪方法

对于合成孔径雷达(SAR)图像中含有的相干斑噪声的抑制一直是SAR图像处理的热点和难点。相干斑噪声的存在对SAR图像的后续相关的图像处理,例如图像的分割、图像的特征提取以及目标的分类与识别有很大影响。提出了一种基于平稳小波变换的相干斑去噪方法,通过平稳小波变换对图像进行小波分解,对于子图像的高频区域进行阈值分割和双边滤波,利用平稳小波更好的冗余性和平稳不变性更好地去除了SAR图像的相干斑噪声。实验结果表明,这种改进的去噪方法对SAR图像的相干斑噪声抑制有很好的效果,并且尽可能地保留了图像的边缘和纹理细节。     

一种基于感兴趣区域的红外目标提取方法

针对复杂背景下的红外图像的小目标提取问题,提出了一种新的红外图像自动分割方法.为了更好地控制计算复杂性及目标提取的准确性,本方法建立在通过自适应Butterworth高通滤波器找到的感兴趣区域之上并充分地利用了该区域中背景信息比较简单、目标易被分割的特性.同时,考虑到像素灰度是红处图像区分目标与背景的重要因素,而像素间的相邻度则能较好的防止虚警的产生,结合像素的灰度和相邻度的综合关系提出了一种新的分割原则.实验证明,新算法取得了很好的效果.     

一种适合医学图像分割的改进分水岭算法

对分水岭算法提出两点改进:先对原始图像的高帽和低帽形态作滤波预处理,滤除原始图像中的噪声和非感知信息,保留原始图像的结构信息;再采用修改了标记符的梯度图像进行分水岭分割,整个分割过程无须进行分割后的区域合并处理,降低了分割的复杂性.这种改进的方法不仅很好地抑制过分割现象,还能使医学图像中的病变区域被有效分割出来,该方法还支持任意形状,与新的静态图像压缩标准JPEG2000兼容,效果很好.     

基于多阈值分割的红外与可见光图像融合

对基于NSCT分解的可见光和红外图像的融合算法进行了优化技术研究.在场景复杂或有多个区域需要同时关注时,低频子图融合阶段采用简单的二值化区域分割方法并不能将不同区域的感兴趣目标有效地反映到融合结果中.所以本文采用基于最大熵的多阈值分割方法,并对每一个分割区域计算其区域能量比指导融合系数的求取;高频融合阶段采用最能保持图像细节的梯度最大规则.实验结果表明本文算法能够将多个区域中的感兴趣目标很好地反映到融合结果中,并保留了可见光图像丰富的细节信息和分辨率高特征,融合图像具有更好的视觉效果.     

基于MATLAB仿真软件实现的图像分割

在图像识别系统中,图像分割起着重要的作用,直接影响到图像的识别效果,但是图像分割技术没有通用的办法,只要能把图像从背景区域很好的分割出来就是有意义的方法,本文研究了基于MATLAB实现的具有不同特点图像的分割技术,实验表明,基于MATLAB实现的图像分割算法,既简单快速,又能得到很好的分割效果。     

基于两方法博弈的马尔可夫随机场红外图像分割算法

电路板红外图像芯片提取是电路板红外故障检测系统中的重要环节,已成为红外图像分割领域关注的一个重点。针对红外图像的特性及传统分割算法效率和精度不足的缺陷,提出一种基于两种优化策略博弈的马尔可夫随机场红外图像分割方法。首先通过OTSU算法对图像进行初始分割;然后利用马尔可夫随机场理论建立图像分割模型;最后,通过SA、ICM优化策略间的博弈对图像进行分割,将两种优化策略视为博弈的两个局中人,通过寻找博弈的纳什均衡点来实现分割;实验结果表明,算法能够无人工干预地准确提取电路板红外图像所有芯片发热区域,并且很好地抑制噪声,准确处理边缘信息,具有一定的实用性和鲁棒性。     

基于无需重新初始化CV模型的医学图像分割

图像分割是计算机医疗辅助诊断中关键技术。文中为内窥镜辅助诊断系统提供较好的图像分割技术。使之应用于内窥镜图像的自动分割,进而进行病症的识别、筛选。再对无需重新初始化的CV模型（CV Model Without Re—initialization,CVWR）在医学图像分割中的应用进行了研究,并进一步实现了对医学视频流的的逐帧分割,方便了医生诊断。最后总结了CVWR在实际医学图像分割中的优缺点。     

A wrapper-based approach to image segmentation and classification.

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest from the background image. It is extremely difficult to obtain a reliable segmentation without any prior knowledge about the object that is being extracted from the scene. This is further complicated by the lack of any clearly defined metrics for evaluating the quality of segmentation or for comparing segmentation algorithms. We propose a method of segmentation that addresses both of these issues, by using the object classification subsystem as an integral part of the segmentation. This will provide contextual information regarding the objects to be segmented, as well as allow us to use the probability of correct classification as a metric to determine the quality of the segmentation. We view traditional segmentation as a filter operating on the image that is independent of the classifier, much like the filter methods for feature selection. We propose a new paradigm for segmentation and classification that follows the wrapper methods of feature selection. Our method wraps the segmentation and classification together, and uses the classification accuracy as the metric to determine the best segmentation. By using shape as the classification feature, we are able to develop a segmentation algorithm that relaxes the requirement that the object of interest to be segmented must be homogeneous in some low-level image parameter, such as texture, color, or grayscale. This represents an improvement over other segmentation methods that have used classification information only to modify the segmenter parameters, since these algorithms still require an underlying homogeneity in some parameter space. Rather than considering our method as, yet, another segmentation algorithm, we propose that our wrapper method can be considered as an image segmentation framework, within which existing image segmentation algorithms may be executed. We show the performance of our proposed wrapper-based segmenter on real-world and complex images of automotive vehicle occupants for the purpose of recognizing infants on the passenger seat and disabling the vehicle airbag. This is an interesting application for testing the robustness of our approach, due to the complexity of the images, and, consequently, we believe the algorithm will be suitable for many other real-world applications.     

基于Gabor小波的无边缘活动围道纹理分割方法

该文提出了一种基于Gabor小波的活动围道纹理分割新方法。该方法先用Gabor小波提取图像的纹理特征,再用Chan-Vese模型进行分割。与其它基于Chan-Vese模型的纹理分割方法相比,基于Gabor小波的活动围道的纹理分割方法有两个优点:一是同时使用纹理特征和灰度信息演化围道,可分割纹理图像和非纹理图像,分割方法的灵活性好;二是在分割多类目标时,采用多级分层式曲线演化方法解决了初始围道难以选择的问题。对自然界真实图像和遥感图像的分割实验结果说明,该文提出的分割方法精度高。     

An automatic segmentation method for moving objects based on the spatial-temporal information of video

The new MPEG-4 video coding standard enables content-based functions. In order to support the new standard, frames should be decomposed into Video Object Planes （VOP）, each VOP representing a moving object. This paper proposes an image segmentation method to separate moving objects from image sequences. The proposed method utilizes the spatial-temporal information. Spatial segmentation is applied to divide each image into connected areas and to find pre~：ise object boundaries of moving objects. To locate moving objects in image sequences, two consecutive image frames in the temporal direction are examined and a hypothesis testing is performed with Neyman-Pearson criterion. Spatial segmentation produces a spatial segmentation mask, and temporal segmentation yields a change detection mask that indicates moving objects and the background. Then spatial-temporal merging can be used to get the final results. This method has been tested on several images. Experimental results show that this segmentation method is efficient.     

一种基于DFC聚类算法的图像分割方法

图像分割是一种重要的图像技术,在理论研究和实际应用中都得到了人们的广泛重视。图像分割的方法和种类很多,有些分割运算可直接应用于任何图像,而另一些只能适用于特殊类别的图像。目前,图像分割的方法层出不穷。其中,最具代表性的图像分割算法是基于FCM聚类算法的图像分割方法。然而FCM聚类算法从理论上来说存在着聚类数目无法自动确定及运算的开销太大的缺点,因而限制了这种方法的应用。针对其不足,本文将FCM聚类算法引入到图像分割方法中。数值实验结果显示：新方法分割图像的效果是良好的。     

基于小波变换和尺度共生矩阵的超声图像分割

超声图像所固有的特性使得图像分割比较困难,尤其是应用计算机实现超声图像的自动分割技术远不能达到实际需要,因此,超声图像的分割是亟待解决的一个难题.文中将图像树型框架小波变换、尺度共生矩阵和自组织神经网络聚类相结合应用于超声图像提出一种分割方法.实验表明,应用所提出的方法可得到比较清晰的分割结果,显著提高分割图像的对比度.     

Object localization and border detection criteria design in edge-based image segmentation: automated learning from examples

This paper provides methodology for fully automated model-based image segmentation. All information necessary to perform image segmentation is automatically derived from a training set that is presented in a form of segmentation examples. The training set is used to construct two models representing the objects--shape model and border appearance model. A two-step approach to image segmentation is reported. In the first step, an approximate location of the object of interest is determined. In the second step, accurate border segmentation is performed. The shape-variant Hough transform method was developed that provides robust object localization automatically. It finds objects of arbitrary shape, rotation, or scaling and can handle object variability. The border appearance model was developed to automatically design cost functions that can be used in the segmentation criteria of edge-based segmentation methods. Our method was tested in five different segmentation tasks that included 489 objects to be segmented. The final segmentation was compared to manually defined borders with good results [rms errors in pixels: 1.2 (cerebellum), 1.1 (corpus callosum), 1.5 (vertebrae), 1.4 (epicardial), and 1.6 (endocardial) borders]. Two major problems of the state-of-the-art edge-based image segmentation algorithms were addressed: strong dependency on a close-to-target initialization, and necessity for manual redesign of segmentation criteria whenever new segmentation problem is encountered.     

一种二型模糊可能性聚类红外图像分割算法

提出了一种新的基于二型模糊可能性聚类的红外图像分割算法。针对受概率约束的模糊聚类算法和不受概率约束的可能性聚类算法在红外图像分割时存在的问题,采用二型模糊系统融合两种分割算法的隶属度函数,将隶属度函数看作一个区间型分布,而不是单独采用两种算法输出的确定模糊值。这种处理方式不但能有效抑制噪声及野值,而且能有效防止红外图像的过分割。实验仿真结果表明,该算法较传统聚类算法能获得更好的分割效果,可有效抑制噪声对目标区域分割的干扰。     

基于自适应TV的Chan—Vese图像分割方法

自适应整体变分(Total Variation,TV)图像平滑模型能有效去除噪声,具有较强的图像保征能力.基于多相水平集的Chan-Vese图像分割模型能有效地实现多质图像的分割.将自适应TV图像平滑方法和Chan-Vese图像分割方法有机整合,提出了自适应TV的Chan-Vese图像分割方法.实验表明,该方法能得到较好的分割结果.     

Segmentation of moving objects in image sequence: A review

Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In second-generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers. The more recent applications, such as content-based image/video retrieval and image/video composition, require that the segmented objects be semantically meaningful. Indeed, the recent multimedia standard MPEG-4 specifies that a video is composed of meaningful video objects. Although many segmentation techniques have been proposed in the literature, fully automatic segmentation tools for general applications are currently not achievable. This paper provides a review of this important and challenging area of segmentation of moving objects. We describe common approaches including temporal segmentation, spatial segmentation, and the combination of temporal-spatial segmentation. As an example, a complete segmentation scheme, which is an informative part of MPEG-4, is summarized.