共查询到20条相似文献,搜索用时 953 毫秒
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提出了基于类间方差参数活动轮廓模型图像分割法.该方法将气球力参数活动轮廓模型中的恒定气球力替换为包含区域信息的变力,最大化目标和背景两区域类间方差,引导轮廓曲线进化.实验结果表明:对于初始轮廓位置不论是处于目标区域内部,或者是处于背景区域内部,还是与目标和背景区域相交,该模型都能获得正确分割结果. 相似文献
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合成孔径雷达(SAR)成像是跟踪探测机动目标的基础,传统的SAR雷达成像基础采用数据融合和图像配准的雷达成像方案,在获得具有相似测度的边缘轮廓特征时成像效果较好,当运动目标边缘为断裂的非相似度特征时,无法准确对快速运动目标成像.引入虹膜边缘轮廓曲线分割技术,提出一种基于虹膜边缘函数计算和区域灰度轮廓曲线分割的SAR雷达成像技术.构建合成孔径雷达回波模型,得到虹膜轮廓曲线分割的边缘函数的演化方程,实现SAR雷达准确成像.对4类高速飞行目标进行SAR成像仿真,结果表明采用该算法能避免因距离色散和多普勒时变出现的成像散焦,边缘轮廓特征能全面提取,成像分辨高.在雷达目标识别等领域具有较好的应用性. 相似文献
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提出一种基于边缘方向信息的改进型主动轮廓算法MACA(MorifiedActiveContourAlgorithm).在Snake模型的基础上加入2项基于匹配要求的新的能量函数,用FGA(FastGreadyAlgorithm)迭代能量函数使其快速收敛.对轮廓曲线的一阶和二阶导数、搜索区域、轮廓的重抽样作锁定.实验结果表明MACA比Snake更具有凹状目标轮廓提取和目标同一性判别的能力 相似文献
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本文提出了一种新的基于距离局部信息的活动轮廓摸型。该模型的能量函数将区域可扩展能量项(region scalable fitting, RSF)和Hausdorff距离项结合,其中RSF项在目标边缘附近起主导作用,用来吸引水平集函数曲线到达目标边界;而Hausdorff距离由于包含了局部区域的相似信息,可以提高分割方法的稳定性。在保证分割精度的情况下,相对于区域可伸缩拟合及局部巴氏距离的活动轮廓模型RSFB方法,本文方法具有更快的收敛速度和更好的参数选择鲁棒性,对于解决图像分割中的边界模糊和噪声问题效果显著。实验结果显示本文提出的方法在超声图像和不均匀图像的分割中都有非常好的效果,且计算量较小。 相似文献
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传统Snake模型存在着对轮廓的初始化敏感,对高噪声图像易陷入局部极小值,以及对具有狭长深度凹陷区域的图像无法获得正确轮廓等问题.本文提出了一种基于边缘与区域信息的主动轮廓模型R-Snake(Region Snake).该模型通过文中设计的图像变换算子,并结合区域积分与曲线积分间转化的Green公式,导出了包含目标图像区域信息的区域力.然后由力平衡方程将该区域信息自然直接地引入到主动轮廓提取模型中,从而实现图像的轮廓提取.由于该模型同时利用了图像的区域信息和梯度信息来引导轮廓曲线的演化,使得本文方法不仅扩大了轮廓初始化的范围,降低了对图像噪声的敏感性,而且还增加了轮廓曲线收敛到真实边界的能力.实验结果表明,本文方法具有很强的适应性和鲁棒性,尤其是对高噪声图像和具有狭长深度凹陷的图像获得了优于传统Snake模型的结果. 相似文献
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局部变分有效地增强图像的轮廓信息,但不可避免地模糊图像的细节并在平滑区域产生阶梯效应。非局部变分能有效重构图像的纹理信息,但同时会破坏图像的结构轮廓信息。考虑到局部与非局部变分的互补性,提出了一种基于图像局部梯度与非局部梯度的复合变分模型,并通过Bregman交替迭代极小化图像的局部梯度与非局部梯度的L1范数,使去噪后的图像在去除噪声的同时更好地保留图像的结构与细节信息。对比实验证明,提出的复合变分模型有效地利用了图像的局部变分与非局部变分的优点,在图像评价的主客观方面都表现出了更好的性能。 相似文献
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Somkantha K Theera-Umpon N Auephanwiriyakul S 《IEEE transactions on bio-medical engineering》2011,58(3):567-573
Finding the correct boundary in noisy images is still a difficult task. This paper introduces a new edge following technique for boundary detection in noisy images. Utilization of the proposed technique is exhibited via its application to various types of medical images. Our proposed technique can detect the boundaries of objects in noisy images using the information from the intensity gradient via the vector image model and the texture gradient via the edge map. The performance and robustness of the technique have been tested to segment objects in synthetic noisy images and medical images including prostates in ultrasound images, left ventricles in cardiac magnetic resonance (MR) images, aortas in cardiovascular MR images, and knee joints in computerized tomography images. We compare the proposed segmentation technique with the active contour models (ACM), geodesic active contour models, active contours without edges, gradient vector flow snake models, and ACMs based on vector field convolution, by using the skilled doctors' opinions as the ground truths. The results show that our technique performs very well and yields better performance than the classical contour models. The proposed method is robust and applicable on various kinds of noisy images without prior knowledge of noise properties. 相似文献
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Active contours and active shape models (ASM) have been widely employed in image segmentation. A major limitation of active contours, however, is in their 1) inability to resolve boundaries of intersecting objects and to 2) handle occlusion. Multiple overlapping objects are typically segmented out as a single object. On the other hand, ASMs are limited by point correspondence issues since object landmarks need to be identified across multiple objects for initial object alignment. ASMs are also are constrained in that they can usually only segment a single object in an image. In this paper, we present a novel synergistic boundary and region-based active contour model that incorporates shape priors in a level set formulation with automated initialization based on watershed. We demonstrate an application of these synergistic active contour models using multiple level sets to segment nuclear and glandular structures on digitized histopathology images of breast and prostate biopsy specimens. Unlike previous related approaches, our model is able to resolve object overlap and separate occluded boundaries of multiple objects simultaneously. The energy functional of the active contour is comprised of three terms. The first term is the prior shape term, modeled on the object of interest, thereby constraining the deformation achievable by the active contour. The second term, a boundary-based term detects object boundaries from image gradients. The third term drives the shape prior and the contour towards the object boundary based on region statistics. The results of qualitative and quantitative evaluation on 100 prostate and 14 breast cancer histology images for the task of detecting and segmenting nuclei and lymphocytes reveals that the model easily outperforms two state of the art segmentation schemes (geodesic active contour and Rousson shape-based model) and on average is able to resolve up to 91% of overlapping/occluded structures in the images. 相似文献
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A novel geodesic active contour model based on optical flow information is proposed to segment and detect the moving object for monocular robots. More specifically, an active contour is formulated using the level set method, which eliminates the need of re-initialisation. The developed scheme alleviates the effect of optical flow noise, increasing the robustness of the detection of moving objects. Experimental results show that this algorithm can successfully track a moving target, e.g. a human being. 相似文献
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为了准确分割出视频场景中的运动对象,该文提出了一种基于边缘特征的运动对象分割及跟踪算法。首先对相邻帧进行自适应变化检测,得到相邻帧二值差分图像。结合当前帧Canny算子检测的边缘图像,获得运动对象的初始边缘模板。其次对运动对象的运动分为快变和慢变两部分进行跟踪并更新运动对象的边缘模板。最后对运动对象的边缘模板进行数学形态学处理得到运动对象的外轮廓,使用梯度向量流场作为外力的改进活动轮廓算法收缩获得运动对象准确的闭合轮廓曲线。该算法对运动对象的整体运动和局部形变都有很强的鲁棒性, 能够得到运动对象准确的轮廓,并且对复杂背景有很好的适应性。 相似文献
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可变形物体的轮廓的提取 总被引:6,自引:0,他引:6
边缘信息一直被认为是计算机视觉的重要特性。因而,边缘的检测与轮廓的提取是图象分析的重要步骤。对于边缘的检测,近年来,人们的研究兴趣更多的转向了局部能量的方法,这是一种基于局部相位的方法,它的特点是通过局部能量的最大值可以同时得到不同类型的边缘。在初步得到物体边缘后,本文应用M.Kass1987年提出的蛇行模型的方法获取物体轮廓,蛇行模型比较适合于可变形物体的轮廓的提取,如:红血球。 相似文献
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Michal Holtzman-Gazit Ron Kimmel Nathan Peled Dorith Goldsher 《IEEE transactions on image processing》2006,15(2):354-363
We present a new segmentation method for extracting thin structures embedded in three-dimensional medical images based on modern variational principles. We demonstrate the importance of the edge alignment and homogeneity terms in the segmentation of blood vessels and vascular trees. For that goal, the Chan-Vese minimal variance method is combined with the boundary alignment, and the geodesic active surface models. An efficient numerical scheme is proposed. In order to simultaneously detect a number of different objects in the image, a hierarchal approach is applied. 相似文献
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Jiading Gai Robert L. Stevenson 《Journal of Visual Communication and Image Representation》2011,22(1):33-47
This paper presents a new general framework for contour tracking based on the synergy of two powerful segmentation tools, namely, spatial temporal conditional random fields (CRFs) and geodesic active contours (GACs). The contours of targets are modeled using a level set representation. The evolution of the level sets toward the target contours is formulated as one of the joint region-based (CRF) and boundary-based (GAC) segmentations under a unified Bayesian framework. A variational inference technique is used to solve this otherwise intractable inference problem, leading to approximate MAP solutions of both the new 3D spatial temporal CRF and the GAC model. The tracking result of the previous frame is used to initialize the curve in the current frame. Typical contour tracking problems are considered and experimental results are given to illustrate the robustness of the method against noise and its accurate performance in moving objects boundary localization. 相似文献
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Active contours without edges 总被引:358,自引:0,他引:358
We propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah (1989) functional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by the gradient. We minimize an energy which can be seen as a particular case of the minimal partition problem. In the level set formulation, the problem becomes a "mean-curvature flow"-like evolving the active contour, which will stop on the desired boundary. However, the stopping term does not depend on the gradient of the image, as in the classical active contour models, but is instead related to a particular segmentation of the image. We give a numerical algorithm using finite differences. Finally, we present various experimental results and in particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected. 相似文献
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