重要性图约束的均值漂移图像分割

针对已有算法结果分割区域过多问题,提出采用边缘正交场构造重要性图,通过边缘特征稳定性约束分割区域,从而有效地提高分割质量。构造边缘正交场,通过高斯积分提高边缘线的连续性和稳定性。采用边缘特征进行距离变换,生成图像的重要性图。采用均值漂移进行图像预分割,根据相邻区域边界上的重要性强度对分割区域结果进行合并。实验结果表明,和原有分割方法相比较,算法在保持原始图像重要区域的同时,对细节区域进行有效合并,明显提高分割质量。     

基于细胞显微图像特征的ROI自动提取算法

为了在细胞显微图片中快速有效地自动提取细胞聚集的区域,提出了一种基于细胞显微图像特征的感兴趣区域(ROI)自动提取算法.该算法以图像的自动阈值分割为基础,利用图像膨胀后边缘的融合和重叠消亡原理,以图像特征的相似度为判别手段,最后用八邻域轮廓跟踪法取得轮廓线.对大量细胞显微图片的实验表明,该算法成功实现了对细胞显微图像的感兴趣区域沿边缘全自动快速提取.     

Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes

Vision-based road extraction is essentially important in many fields, such as for intelligent traffic and robot navigation. However, the road detection in urban or ill-structured roads is still very challenging at current stage, and the existing methods often suffer from high computational complexity. This paper reports a novel and efficient method for road detection in challenging scenes. First, the dark channel based image segmentation is proposed to distinguish a rough road region from complex background noise, which is envisioned to reduce the workload of road detection. Furthermore, instead of using the conventional pixel-wise soft voting, a new voting strategy based on the vanishing point and the properties of the segmented regions is proposed to further reduce the computation time of road extraction stage. Finally, the segmented region which has the maximum voting value is selected as the road region. Experimental results demonstrated that the proposed algorithm shows superior performance in different kinds of road scenes. It can remove the interference from pedestrians, vehicles and other obstacles. Our method is about 40 times faster in detection speed, when compared to a recently well-known approach.     

Preserving objects in Markov Random Fields region growing image segmentation

This paper proposes an algorithm that preserves objects in Markov Random Fields (MRF) region growing based image segmentation. This is achieved by modifying the MRF energy minimization process so that it would penalize merging regions that have real edges in the boundary between them. Experimental results show that the integration of edge information increases the precision of the segmentation by ensuring the conservation of the objects contours during the region-growing process.     

Extracting eyebrow contour and chin contour for face recognition

This paper proposes a novel method for extraction of eyebrow contour and chin contour. We first segment rough eyebrow regions using spatial constrained sub-area K-means clustering. Then eyebrow contours are extracted by Snake method with effective image force. For chin contour extraction, we first estimate several possible chin locations which are used to build a number of curves as chin contour candidates. Based on the chin like edges extracted by proposed chin edge detector, the curve with the largest likeliness to be the actual chin contour is selected. Finally, the credible extracted eyebrow contour and the estimated chin contours are used as geometric features for face recognition. Experimental results show that the proposed algorithms can extract eyebrow contours and chin contours with good accuracy and the extracted features are effective for improving face recognition rates.     

Player detection in field sports

We describe a method for player detection in field sports with a fixed camera setup based on a new player feature extraction strategy. The proposed method detects players in static images with a sliding window technique. First, we compute a binary edge image and then the detector window is shifted over the edge regions. Given a set of binary edges in a sliding window, we introduce and solve a particular diffusion equation to generate a shape information image. The proposed diffusion to generate a shape information image is the key stage and the main theoretical contribution in our new algorithm. It removes the appearance variations of an object while preserving the shape information. It also enables the use of polar and Fourier transforms in the next stage to achieve scale- and rotation-invariant feature extraction. A support vector machine classifier is used to assign either player or non-player class inside a detector window. We evaluate our approach on three different field hockey datasets. In general, results show that the proposed feature extraction is effective and performs competitive results compared to the state-of-the-art methods.     

一种基于边缘生长的灰度和彩色图象分割方法

边缘检测可以快速准确地提供区域分割的边缘点,是图象处理的一个重要领域。但由于边缘点不连续和难以把存在大量碎边缘点的高细节区提取出来这两个原因,而不能直接实现完整意义上的图象分割。为此提出用边缘生长的方法来解决不连续的边缘点链接问题和通过找出高细节区周围的区域,以便间接地将高细节区围成一个区域。该算法是边缘检测的后续处理,适合于多种应用目的,同时还可以嵌入到其它利用边缘信息的分割算法中。     

Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake

A Radiating Gradient Vector Flow (RGVF) Snake aiming at accurate extraction of both the nucleus and cytoplasm from a single-cell cervical smear image is proposed. After preprocessing, the areas in the image are roughly clustered into nucleus, cytoplasm and the background by a spatial K-means clustering algorithm. After initial contours are extracted, the image is segmented using RGVF. RGVF involves a new edge map computation method and a stack-based refinement, and is thus robust to contaminations and can effectively locate the obscure boundaries. The boundaries can also be correctly traced even if there are interferences near the cytoplasm and nucleus regions. Experiments performed on the Herlev dataset, which contains 917 images show the effectiveness of the proposed algorithm.     

融合多特征的运动一致性图像分割

目的:在彩色图像分割中,光流法能够得到运动区域,但难以获得运动目标准确的分割边界,而常用的算法往往会产生过分割。为了克服光流法的不足,在保留显著性区域的同时抑制过分割,从而获得具有运动一致性区域的分割结果,提出融合多特征的运动一致性图像分割算法。方法:首先通过Mean Shift算法获取图像的初始分割,然后利用空域信息（包括颜色、边缘和区域面积）对视觉感知上具有相似性的区域进行合并,再利用时域信息进行运动一致性区域合并,最终得到分割结果。结果:实验结果表明通过结合时空信息,该方法能够有效抑制过分割,不仅弥补了光流场不能准确提取目标边缘的不足,而且提高了分割目标的完整性。结论:与两种流行的彩色图像分割算法相比,所提方法获得了更加理想的结果。     

基于改进的Mean Shift方法的高分辨率遥感影像道路提取

遥感影像的道路提取长期以来一直是数字摄影测量未能解决的难题之一。本文在探讨了均值漂移、区域分割、边缘检测、轮廓跟踪的基础上,提出一种基于均值漂移和利用统计面积去除和合并小区域的道路提取算法,并采用数学形态学方法消除错误道路,利用轮廓跟踪法获取道路的边缘,实现道路的提取;通过与基于区域分割、边缘检测算法提取结果对比,结果表明,本文的算法进行道路提取的效果更好。     

一种新的高分辨率SAR图像道路提取算法

针对高分辨率SAR图像中道路目标难以有效提取的问题,提出一种新的高分辨率SAR图像道路提取算法,它结合了参数化内核图割和数学形态学算法。利用参数化内核图割对高分辨率SAR图像中的道路目标进行初级分割,用数学形态学填充空洞,平滑道路边缘;基于道路的几何特征,使用矩阵度、改进的长宽比、复杂度等因子去除虚警;针对处理过程中出现的道路断裂情况,利用数学形态学提取道路目标的中心线,同时根据线段邻近性、方向一致性准则对其断裂部分进行连接,用数学形态学还原道路宽度,得到道路提取结果。实验结果表明该算法不用进行SAR图像预处理,也可以有效抑制相干斑噪声,并且能准确、较为完整地提取道路目标。     

方向邻域全变分图像去噪

为了弥补传统全变分（TV）算法忽略了图像边缘方向的不足, 结合梯度幅度和方向提出了基于方向全变分的去噪算法。该算法运用图像梯度幅度将图像像素划分为边缘区域和非边缘区域, 运用梯度方向对不同区域的像素选取不同的四邻域像素, 针对不同邻域对传统TV算法进行离散分析, 完成了图像的保边去噪。实验结果表明, 结合边缘方向信息改进了传统TV算法的邻域选择方式, 不仅更好地保留了图像边缘信息和重要细节, 且提高了图像的PSNR和视觉效果。     

基于Sobel算子和均匀插值的非线性缩放算法

针对图像缩放后产生的图像畸形与拉伸问题,提出一种基于Sobel算子和均匀插值的非线性缩放算法。通过图像能量区分出图像强势区域和弱势区域,在保护强势区域的同时,对弱势区域进行非线性均匀插值缩放。实验结果表明,该算法可解决图像缩放时主体区域产生的畸变问题,保证边缘区域的平稳过渡。     

基于快速连通域标记的车牌字符分割

对车牌图像的二值图进行连通域提取可以解决倾斜带来的问题.提出了一种基于快速连通域标记的字符分割方法.先用改进的Otsu法对车牌图像进行二值化分割,之后基于快速连通域标记算法进行连通域标记,最后对各个连通域进行后续处理提取出全部车牌字符连通域.实验表明,改进的快速连通域标记算法克服了倾斜问题,而且有效地提高了连通域提取的速度,获得较好的字符分割的鲁棒性和实时性.     

Two graph theory based methods for identifying the pectoral muscle in mammograms

Two image segmentation methods based on graph theory are used in conjunction with active contours to segment the pectoral muscle in screening mammograms. One method is based on adaptive pyramids (AP) and the other is based on minimum spanning trees (MST). The algorithms are tested on a public data set of mammograms and results are compared with previously reported methods. In 80% of the images, the boundary of the segmented regions has average error less than 2 mm. In 82 of 84 images, the boundary of the pectoral muscle found by the AP algorithm has average error less than 5 mm.     

鲁棒的车辆行道线提取方法

提出了一种新的行道线提取方法。该方法利用均值滤波对道路图像进行亮度估计,把均值滤波后的图像和原图像进行差分从而突出白色的行道线区域,并且采用多阈值方案,对得到的差图像进行二值化。对得到的包含行道线的二值图像进行干扰去除和细化处理,并运用基于加权的Hough变换求得多条候选行道线,基于空间约束从所得的候选行道线中挑选出合适的直线对作为左右行道线。实验结果表明,该算法在复杂路况的情况下能够快速准确地提取出行道线,从而实现对驾车者偏离车道的报警。     

一种面向SAR图像的多窗口道路边缘检测算法

道路作为一种重要地物信息,在城市规划等领域中起着不可替代作用。合成孔径雷达（SAR）具有全天候等成像特点,因此基于SAR图像已有许多道路边缘检测算法。提出一种多窗口道路边缘检测算法,来解决相干斑噪声引起的道路边缘误检率高完整性差等问题。该算法首先以加权局部熵的大小为基础,评估SAR图像中像素点落在道路上的概率,然后以该概率为依据,确定每个像素点多窗口融合的权值。最后,对不同大小窗口的边缘检测结果进行加权融合。通过对不同区域的SAR图像切片进行实验,结果表明加权融合后得到的道路边缘的完整性及对噪声的抑制效果均有所提高。     

带H1正则项的C-V模型

C-V模型(CHAN T F, VESE L A. Active contours without edges. IEEE Transactions on Image Processing, 2001, 10(2): 266-277)是一个著名的基于区域的图像分割模型。它对活动轮廓的初始化和噪声不敏感,但分割的图像的范围不够广泛。因此,运用理论分析与实验相结合的方法,在C-V模型中添加H1正则项,对其进行了改进,提出了一个新颖的图像分割的能量泛函,并推导出了以偏微分方程形式表示的基于区域的自适应插值拟合的活动轮廓模型。实验表明：该模型能够分割某些原来C-V模型不适用的图像,它对初始轮廓的大小、位置的敏感性较小,抗噪性较强。     

采用高斯混合模型及树结构的立体匹配算法

针对传统立体匹配算法无法同时为图像边缘和低纹理区域提供一个合适大小的聚合窗口而导致匹配精度较低的难题,提出一种结合高斯混合模型及最小生成树结构的立体匹配算法。通过图像初始视差、像素颜色及距离信息将图像分为初始若干区域及待分割候选像素;基于高斯混合模型并行迭代更新各区域参数,得到最终的分割;在各分割上建立最小生成树计算聚合值求取视差;通过邻域内的有效视差修正误匹配点,获取精度较高的稠密视差图。与其他算法相比,该算法能有效降低误匹配率,尤其在深度不连续区域的匹配效果显著改善。     

一种新的基于图像边缘的插值算法

灰度图像放大时,插值所具有的平滑作用会退化图像的高频部分,使放大图像轮廓变得模糊,该文提出了一种新的图像插值算法,先用一阶微分运算分离出图像的平坦区域和边缘区域,对图像的平坦区域采用双线性插值法,对图像的边缘部分采用本文的插值算法,实验证明该算法有效地保持了边缘信息,得到了视觉信息较好的插值图像。