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.     

Automatical detection of face features and exact face contour

The approach based on the mathematical morphology and the variational calculus is presented for the detection of an exact face contour in still grayscale images. The facial features (eyes and lips) are detected by using the mathematical morphology and the heuristic rules. Using these features an image is filtered and an edge map is prepared. The face contour is detected by minimizing its internal and external energy. The internal energy is defined by the contour tension and the rigidity. The external energy is defined by using the generalized gradient vector flow field of the image edge map. Initial contour is calculated using the detected face features. The contour detection experiments were performed using the database of 427 face images. Automatically detected contours were compared with manually labeled contours using an area and the Euclidean distance-based error measures.     

放射治疗计划系统中基于边缘模板的体廓自动提取

放射治疗中,治疗计划系统对CT、MRI等医学影像进行处理,获得体表、肿瘤等感兴趣组织的轮廓,是完成治疗规划的重要基础。论文针对CT/MRI图像的特点,提出并实现了一种基于边缘模板的体廓自动提取方法。该方法对图像进行平滑处理和锐化后,由Guass-Laplace算法进行边缘检测和轮廓提取,然后手工编辑和修改第一张图片的体廓数据,以此作为边缘模板,自动提取后续图片的体廓。该方法实现方便,效果好,满足了治疗计划系统对体廓自动提取的要求。     

基于几何活动轮廓模型的人脸轮廓提取方法

针对在结构性噪声较严重的情况下 ,常规几何活动轮廓模型无法获得理想分割效果的问题 ,提出一种基于几何活动轮廓模型的人脸轮廓提取方法 ,该方法首先将人脸形状的椭圆性约束作为算子嵌入到几何活动轮廓模型中 ,并利用几何活动轮廓模型提取任意轮廓的优势来快速抽取出图象中类似椭圆的目标边缘 ;然后根据图象中人脸的先验知识 ,通过对检测到的椭圆目标进行进一步验证来找出最终人脸轮廓 .由于采用变分水平集方法做数值计算 ,因此该方法不仅能够自然地处理曲线的拓扑变化和能较精确地提取出图象中的人脸轮廓 ,而且同时可以给出人脸水平旋转的大致角度等信息 .实验结果表明 ,该方法是有效的 .     

新颖的物体轮廓提取：基于双边滤波的多级边缘检测*

提出了一种新颖的物体轮廓提取方法,即通过多级边缘检测来提取物体的主要边界。双边滤波器用来建立多级,同时Canny边缘算子相应地产生边缘图,组合边缘图构造出一幅多级图。次段被定义为边缘像素的连接结构,提取并连接这些次段可以构成闭合轮廓。最终图像中最相关的闭合轮廓被判定为真实的物体轮廓。实验结果表明,该物体轮廓提取方法具有较高的可靠性并且受噪声影响较小。     

基于几何活动轮廓模型初始轮廓自动分割方法的研究

提出一种几何活动轮廓模型初始轮廓自动分割的方法。此方法综合利用了分水岭算法以及几何活动轮廓模型算法,通过利用人脸的肤色特征、结构特征等逐级判别原理,从而实现了自动分割的目的。实验结果表明,采用此方法很好地实现了目标对象轮廓的自动分割,基本可达到与人工干预相近的效果。此方法同样可以广泛应用干人脸检测、图像自动分割、视频监控系统等领域。     

刀具轮廓亚像素精度阈值分割算法研究

摘 要：提取亚像素精度边缘轮廓是完成刀具几何参数精确测量的重要环节。该文研究了一种简单有效的方法,该算法基于灰度阈值分割定位像素级边缘,并经双线性插值法细分完成亚像素边缘轮廓的提取。最后设计实验对比分析了该算法以及基于Canny和Sobel算子的亚像素边缘检测算法所提取轮廓的特点。结果表明在准确的前提下,对高对比度图像应用该算法后,能比后两者更快速地完成边缘轮廓提取。     

一种改进的薄壁文物碎片特征轮廓线提取技术

针对使用一般的边界提取方法提取三维网格模型特征轮廓线不完整问题,提出一 种新的薄壁文物碎片特征轮廓线提取的综合算法。区别了特征轮廓线和轮廓线的概念,引入主 轮廓线和次轮廓线以及二级邻接生长曲面的概念。主轮廓线的提取使用改进的基于边重数判断 的提取方法;提出次轮廓线的一种新的提取方法：首先对三维网格曲面分割并识别断裂面,然 后对断裂面的二级邻接生长曲面进行曲面扫描,提取次轮廓线;最后从主轮廓线和次轮廓线中 得到三维模型的特征轮廓线。使用该算法准确地提取了文物碎片的特征轮廓线,实验结果表明 此方法稳定且准确。     

基于Canny边缘SURF特征的红外与可见光图像配准算法

利用多源传感器之间获取信息的互补性,克服单传感器的缺陷,从而提高系统整体性能指标的思想已经在军事、医疗、卫星等领域获得了广泛的应用。可见光和红外图像相融合也能提高视觉应用场景中对目标的探测能力,降低目标警报的虚警率和漏警率,提升准确率和工作效益。对于红外与可见光图像配准过程中受不同传感器图像成像原理不同,成像结果图像灰度差异大、特征难以匹配的问题,可以利用红外和可见光图像的共有特征即边缘轮廓特征,采用Canny边缘提取算法提取出图像最基本、稳定的特征,然后在边缘图中使用SURF特征检测算法进行特征点提取与匹配,最后采用RANSAC进行精准匹配。由于边缘在红外和可见光图像中都是比较稳定的特征,而且在边缘轮廓图中进行特征提取将极大减少计算量和提高匹配率,因而最终能够获得较为准确的红外、可见光图像的变换关系。     

基于改进的Chan-Vese模型与边缘转换的图像分割算法

为了实现物联网环境下果园飞鸟的自动驱离，使其复杂条件下能够准确驱赶空中的飞鸟，提出了一种基于改进的Chan-Vese模型与边缘转换的空中飞鸟分割算法。通过准确识别飞鸟，为系统自动发出超声波驱离飞鸟提供准确的信息。利用Canny算子获取飞鸟图像的边缘信息；使用欧氏距离计算得到二进制边缘的距离映射；引入S形函数，构建边缘转换图；引入自动局部比，对Chan-Vese模型进行改进，以准确分割边缘映射图。实验结果表明：与SBGFRLS算法、G-CV算法和FAST EDGE算法相比，该算法具有更高的分割精度，在面对单目标图像分割时，其区域匹配率最高，约为70%，而均方根误差比率只有13%；对于含双目标的图像分割时，其区域匹配率最高，约为85%，而均方根误差比率只有5%。     

Accurate object contour tracking based on boundary edge selection

In this paper, a novel method for accurate subject tracking, by selecting only tracked subject boundary edges in a video stream with a changing background and moving camera, is proposed. This boundary edge selection is achieved in two steps: (1) removing background edges using edge motion, and from the output of the previous step, (2) selecting boundary edges using a normal direction derivative of the tracked contour. Accurate tracking is based on reduction of the effects of irrelevant edges, by only selecting boundary edge pixels. In order to remove background edges using edge motion, the tracked subject motion is computed and edge motions and edges having different motion directions from the subjects are removed. In selecting boundary edges using the normal contour direction, the image gradient values on every edge pixel are computed, and edge pixels with large gradient values are selected. Multi-level Canny edge maps are used to obtain proper details of a scene. Multi-level edge maps allow tracking, even though the tracked object boundary has complex edges, since the detail level of an edge map for the scene can be adjusted. A process of final routing is deployed in order to obtain a detailed contour. The computed contour is improved by checking against a strong Canny edge map and hiring strong Canny edge pixels around the computed contour using Dijkstra's minimum cost routing. The experimental results demonstrate that the proposed tracking approach is robust enough to handle a complex-textured scene in a mobile camera environment.     

A background robust active appearance model using active contour technique

This paper proposes an active contour-based active appearance model (AAM) that is robust to a cluttered background and a large motion. The proposed AAM fitting algorithm consists of two alternating procedures: active contour fitting to find the contour sample that best fits the face image and then the active appearance model fitting over the best selected contour. We also suggest an effective fitness function for fitting the contour samples to the face boundary in the active contour technique; this function defines the quality of fitness in terms of the strength and/or the length of edge features. Experimental results show that the proposed active contour-based AAM provides better accuracy and convergence characteristics in terms of RMS error and convergence rate than the existing robust AAM. The combination of the existing robust AAM and the proposed active contour-based AAM (AC-R-AAM) had the best accuracy and convergence performances.     

基于混合能量活动轮廓模型的人脸分割方法

由于受到面部五官、饰物等因素的影响,传统几何活动轮廓模型获取人脸外轮廓会产生凹陷、分片等现象.针对人脸图像的特点,将边缘外张力能量及肤色能量与全局能量结合,提出一种基于混合能量泛函的几何活动轮廓模型,有效地避免了这些问题.首先,根据演化曲线的邻域信息赋予边缘点向外的张力,使曲线能够克服面部特征及面部饰物的干扰,引导其向外轮廓方向演化.鉴于肤色是面部最重要的特征,提出肤色能量,进一步提高了模型的鲁棒性.此外,提出一种基于单高斯模型的改进算法,能够估计出接近实际人脸外轮廓的初始位置,为轮廓演化奠定了基础.在两个公共人脸库上进行测试,该方法能够得到准确的人脸分割效果;以手工分割的结果为基准,该算法定位精度明显优于传统的全局能量模型和局部能量模型.还用日常照片创建一个包含不同姿态、光照、复杂背景等因素、复杂的人脸库,分割结果表明,该方法能够克服这些因素的影响,取得了准确而稳定的人脸分割结果.     

融合分区与Canny泛函的水平集对猴脑提取的研究

The traditional level set has randomness in the location selection of the initial contour, and lacks the processing of edge information. Therefore, accurate extraction of brain tissue edges cannot be achieved. Therefore, firstly, the level set algorithm of fusion partition and Canny functional fuses the idea of partition and combines the morphological information of each region to complete the initial contour position selection, so that the initial contour contains more brain tissue, and improve the efficiency of brain tissue extraction. Secondly, the Canny operator is integrated into the energy functional, which improves the accuracy of detecting the edge of the macaque brain tissue while retaining the superiority of the traditional level set on the uneven grayscale image. Results show that the algorithm achieves accurate extraction of macaque brain tissue with an accuracy of up to 86%.     

近距大偏角圆形目标的检测

圆形目标距离图像采集设备较近且存在较大偏转角时成像会产生严重的形变,给目标图像的自动检测带来了困难。提出了一种近距大偏角圆形目标的检测算法,利用闭运算处理,去除图像背景中的噪声,并采用嵌套双层轮廓提取方法实现目标轮廓提取,通过轮廓特征提取算法获得目标轮廓的初选轮廓,利用初选轮廓的细节特征信息确认目标轮廓。为了实现目标的高效检测,采用模板匹配算法进一步提高了算法运行效率以及稳定性。实验结果表明,所提出的检测算法可以有效地适应不同的背景环境,具有较强的鲁棒性和实时性。     

Contour detection improved by frequency domain filtering of gradient image

We propose an intermediate computational step,frequency domain filtering of gradient image,to improve contour detection performance of gradient-based edge detectors.This step is inspired by analyzing the spectrum distribution of object contours and texture edges in the frequency domain of gradient image.We illustrate the principle and efect of this step by adding it to the Canny edge detector.The resulting operator can selectively retain object contours and region boundaries,and meanwhile can dramatically reduce non-meaningful elements caused by textured background.We use several types of images to compare the proposed method and other related methods qualitatively and quantitatively.Experimental results show that the proposed method can efectively enhance the contour detection of Canny edge detector and achieves similar detection performance to two other related methods but runs faster.     

强噪声干扰下的对数极坐标空间边缘提取算法

准确提取对数极坐标空间的目标边缘信息是对数极坐标变换视觉不变性获得成功应用的前提和关键。由于传统的边缘提取算法无法满足强噪声干扰下的单像素精度要求,在主动轮廓模型和水平集方法的基础上,设计了一种独特的边缘提取算法。经融合Canny算子的水平集方法全局降噪,利用能量驱动的主动轮廓模型逐次演化逼近,提取可能的边缘曲线,通过改进型跟踪寻迹剔除虚假信息,即可得到最终的目标边缘。实验表明,该算法行之有效,边缘提取特征相似度达96%以上。     

一种基于矢量方法的等高线自动标定与检验算法

等高线作为传统地图和现代地理信息系统（GIS）的重要要素，常用来表示各种地形变化的形态，基于自动方法的等高线高程标定和检验是地图自动化输入，数字高程模型（DEM）生成的重要手段。该文在基于光栅图像等高级自动标定研究成果的基础上，提出一种新的基于矢量等高线的高程自动标定与检验算法。该算法的核心是生成一种称为包含树的数据结构，然后利用这种数据结构转换成等高线自由树，从而实现对等高线高程的自动标定和检验。     

等高线图的高度恢复算法研究

用于等高线图到数字高度图（Digital Elevation Map)转换的转换算法是很多应用都需要的算法，为了提高这一转换的精度与效率，一个相应的算法－区域内插法被特别提出来，该算法利用了等高线图固有的特性，即“图象被等高线分割成多个区域，每个区域内的边界只有两个等高线值”的特性，该算法可以在计算机上快速实现，经过与现有的象限搜索法的实验比较，由于它更好地利用了等高线图结构上的特点，致使其在提高速度的同时，精度上较象限搜索法也有很大的提高，因而具有很强的实用价值。     

基于活动轮廓模型的人脸特征提取方法的研究

人脸面部特征提取是自动视觉翻译和人脸识别中的最关键的技术之一。本文对活动轮廓模型理论的基本原理、方法和技术等作了有益的探索和尝试,并在此基础上,提出了提取人脸面部特征的系列算法,特征是精确的轮廓描述而非简单的数字表达,实验中,我们用动态模板方法提取眼睛轮廓,用活动轮廓方法提取眉毛形状,结果证明,该方法具有较强的鲁棒性和自适应性。