【作者】 郭丽；

【导师】 高立群；

【作者基本信息】 东北大学 ， 模式识别与智能系统， 2009， 博士

【摘要】 图像分割是一种基础的图像处理技术,也是图像处理和计算机视觉领域中的难点问题。近年来,交互式分割方法受到了各领域学者的广泛关注。本文在整理、归纳和总结了几种交互式图像分割理论、方法的基础上,针对Live Wire算法中的最短路径求解、随机游走分割方法和滑降算法等问题进行深入研究,主要工作如下：针对传统Live Wire算法运行速度较慢的缺点,提出了一种基于脉冲耦合神经网络(PCNN)的Live Wire算法,利用PCNN求解用户标记的边缘种子点之间的最短路径,提高了Live Wire的运算速度。尤其是当处理大分辨率图像时,速度优势明显。在赤足足迹跟区压痕边缘检测的研究中,针对跟区压痕呈现弱边缘特性,提出了一种基于蚁群(ACO)寻找最短路径的Live Wire分割足迹跟区压痕提取的方法,并利用Sobel算子建立像素点间的代价函数,通过最小二乘算法进行椭圆拟合得到足迹参数,从而有效地提取了足迹跟区压痕边缘。为提高传统随机游走算法的有效性和分割速度,提出了一种基于滑降算法的随机游走图像分割算法。首先利用滑降算法将图像进行初始分割,将每个小区域作为一个节点,然后采用万有引力思想代替传统节点间权值定义方式,最后,利用随机游走算法完成最终分割。该算法明显提高了图像分割的速度和精度。针对智能交通系统中多车辆检测问题,提出了基于边缘检测的随机游走算法,对视频中车辆目标进行精确检测和分割。首先将背景差分与边缘信息相结合来检测运动车辆区域,然后根据检测车辆区域信息提取骨架结构,从中获取随机游走所需的有效标记点,最后采用随机游走算法实现车辆自动检测和精确分割。针对滑降算法过分割现象,本文从两个方面对滑降算法进行了改进。一方面为多尺度形态学梯度滑降分割算法,利用多尺度形态学梯度,通过大小不同的结构元素提取图像梯度特征,获得梯度图像；然后利用滑降算法进行图像分割；同时,为了减少滑降算法的过分割现象,利用区域面积和区域相似性规则进行区域合并。另一方面将反馈脉冲耦合神经网络与滑降分割相结合,提出了一种新的MRI图像分割的特征提取算法。更多还原

【Abstract】 Image Segmentation is a basic technique of image processing, and it is always one of the most difficult techniques in image processing and computer vision. In recent years, the researchers pay more and more attention to the interactive segmentation. On the base of arranging, summarizing and concluding interactive segmentation algorithm, some key theories and approaches in interactive segmentation and its applications are researched in this dissertation. In this dissertation, we attempt to have an in-depth investigation on the Live Wire algorithm, random walk and toboggan method. The main work is as follows:Considering the speed disadvantage of the traditional Live Wire algorithm, a novel Live Wire algorithm based on Pusle Coupling Neural Network (PCNN) is proposed. PCNN is used to obtain the shortest path between the two points by user. The speed of operation is increased by proposed method, especially when dealing with a larger image, the rate reflected a clear advantage.On the study of detection the footprint heel impression, this paper presents an improved Live Wire algorithm by considering the feature of weak edge for heel impression. Ant colony optimization (ACO) algorithm is used to find the shortest path. A new cost function is defined by Sobel operator. The least square method was used for ellipse fitting to obtain the parameters. The improved methods can effectively extract the information of the footprint heel impression.In order to improve the efficiency and speed of random walk algorithm, a new random walk method based on toboggan is presented. Firstly, a graph is created which decomposes the image in scale and space using the concept of toboggan. In this way, we consider each of the regions as the nodes, the weights between graph-nodes is estimated by using the law of universal gravity. Then the label for object and background is drawn by user. Finally, this paper uses the theory of random walk algorithm to segment the image.According to the multi-vehicle problem in Intelligence Transportation System, a vehicle detection algorithm is presented based on the combination of edge feature and random walk techniques. We used background subtraction and edge detection to obtain the moving area, then used morphological operations for vehicle skeleton extraction and get the seeds for random walk. Finally the accurate boundary of moving vehicles is detection by random walk.Considering the over-segmentaion of the toboggan algorithm, two improve toboggan algorithms are proposed in this thesis. Firstly, a new toboggan is presented based on multi-scale morphological gradient. The gradient image is computed by using the multi-scale morphological gradient operation. It was obtained through the difference size of the structure elements from images gradient features. In order to reduce the over-segmentation of toboggan, the approach of region combination is used after toboggan segmentation. Secondly, combining the feedback pulse coupling neural network (FPCNN) with toboggan segmentation algorithm, this thesis presents a new MRI image segmentation feature extraction methods.更多还原