基于细菌觅食优化算法的多色彩空间嘴唇分割

结合多色彩空间处理、细菌觅食优化算法(BFOA)与关键点定位算法,提出一种新的嘴唇轮廓提取算法。对嘴唇图像进行色彩空间的转换,在不同的色彩空间对嘴唇图像进行色彩增强及色彩分割处理,利用BFOA结合Kapur最大熵阈值法对嘴唇区域图像求取最佳阈值,实现唇部精确分割。提取嘴唇的边缘关键点,并以提取到的关键点为输入,运用最小二乘曲线拟合的方法拟合嘴唇边缘轮廓,准确提取嘴唇的外部轮廓。实验结果表明,该方法能够完成对嘴唇的精确分割,并获得较高的分割准确率。     

非监督、多级嘴唇区域分割方法

该文介绍了一个非监督、多级嘴唇区域分割检测方法。首先提出利用fisher变换增强嘴唇区域,然后利用嘴唇在人脸区域的分布面积比,提出利用统计阈值完成嘴唇的初步分割,最后利用嘴唇对称性和轮廓光滑性的先验知识,提出了基于局部阈值调整完成嘴唇的精细分割。实验证明该方法在不同光照条件下、对不同人、各种表情都能自动地、鲁棒地、精确地检测出嘴唇区域,并较准确提取初步的嘴唇几何参数。利用这些几何参数作为轮廓定位的初始化条件,可以大大提高变形模板和ASM模型等嘴唇轮廓定位算法的速度和准确度。     

基于马尔可夫随机场的嘴唇特征提取方法*

讨论了基于马尔可夫随机场(MRF)模型的融合颜色和边缘信息的嘴唇特征提取方法.首先进行嘴唇区域检测,再结合嘴唇形状特点建立了基于MRF的嘴唇图像分割模型,构造相应的能量函数,并采用改进的最高置信度优先(HCF)算法求解能量函数的最优解,得到图像标记场,进而提取出嘴唇轮廓.结合人脸结构信息,提出了融合鼻孔角度信息的嘴唇特征点提取方法.实验结果表明,此算法具有良好的鲁棒性.     

一种自动的唇部定位及唇轮廓提取、跟踪方法

实现一种结合CbCr颜色空间、Fisher变换及变形模板的自动唇部定位及唇轮廓提取、跟踪方法.首先在CbCr空间建立肤色模型进行人脸检测、定位,并由人脸几何特征进行唇部粗定位.然后结合唇色模型进行Fisher变换使肤、唇色差别明显化,提出根据亮度信息对变换结果预处理后用Otsu法进行图像分割,经唇色模型进一步验证后实现唇部精定位.再使用变形模板来进行嘴唇轮廓特征提取,为增强内轮廓定位的鲁棒性,本文提出对经亮度预处理和唇色模型验证得到的口腔区域边缘图进行曲线拟合来实现内轮廓定位.最后,将唇读图像序列中上一帧的唇部定位结果拓展后作为当前帧的预测区域再进行处理来实现唇动跟踪.     

神经网络方法应用于轮廓定位

关于从图像中定位物体轮廓的问题,目前所采用的活动轮廓模型和基于自组织神经网络的算法,存在能量泛函优化容易陷入局部极值和演化过程依赖于初始轮廓的选取等问题。提出了一种基于RBF神经网络的轮廓定位算法。首先,通过自适应梯度阈值方法来获取图像特征点。然后,通过特征点的聚类建立一组基函数,把图像像素点的像素值和梯度构造输入向量空间,在网络权值训练完成后,利用网络的预测功能来准确判断物体轮廓。与传统算法相比,仿真结果表明提出的轮廓定位算法可以高效地实现目标轮廓定位。     

基于Level Set方法的西夏字轮廓提取

随着国内外对西夏研究的不断深入,收藏于世界各地的大批西夏古籍文献通过影印方式陆续出版。如何将这些西夏古籍文献进行数字化、文本化则有着极其重要的意义。首先利用平滑和细化算法对西夏影印文献进行了预处理,然后利用Level set方法对影印文献中的西夏字进行了轮廓提取。Level Set演化函数在空间方向上采用了四阶紧致差分逼近式离散,计算过程中加入了窄带算法及全局优化方法。实验表明,算法在不增加计算时间的基础上可以得到较精确的西夏字轮廓。     

基于径向边缘检测和Snake模型的舌像分割

舌像分割中嘴唇和舌体部分的纹理和颜色比较接近,直接采用Snake方法提取舌体轮廓很难得到理想的轮廓。本文提出一种结合径向边缘检测与Snake模型的分割方法,首先使用径向边缘检测得到初始轮廓,使用色彩对消消除嘴唇影响,最后用Snake获取舌体轮廓。实验表明该算法取得较好的分割效果。     

高斯混合函数区域匹配引导的Level Set纹理图像分割

基于高斯混合模型颜色匹配及多尺度图像增强,文中提出了有效的边缘停止函数用于引导level set函数演化,有效地解决了纹理图像的分割问题.文中首先提出基于高斯混合模型颜色分布的边缘停止函数,通过计算level set演化窄带区域与用户给定交互区域的相似性,根据其相似性来引导level set快速演化;然后,提出一个定义在多尺度图像梯度上的边缘停止甬数,使得level set能精确地分割出图像的边缘;最后,结合以上两种边缘停止函数的优点,提出一个边缘停止函数的混合模型,根据图像颜色、边缘特征自适应地引导level set函数演化.实验结果表明,文中提出的算法不仅能有效地检测出纹理目标区域,同时需要计算出纹理区域精确、光滑的边界.     

人机交互中视觉语言的灰度轮廓权向量差分唇形特征模型

该文结合函数变形模型和灰度轮廓向量模型的特点,给出了一种维数少、有效性高的视觉语言特征—灰度轮廓权向量差分唇形特征模型。该特征融合了嘴唇图像的形状变化信息和灰度信息,能够较完善地描述嘴唇的变化。同时,得出了一种新的视觉特征提取算法。仿真结果表明,该算法与传统的函数变形模型相比,总的特征提取准确率提高了5个百分点,每个发音图像序列特征提取的准确率提高了1.6至9个百分点,每帧图像的特征提取时间由4.6495秒下降到0.4455秒。对“1”至“10”数字发音的嘴唇图像序列进行识别,获得了较高的识别率。因此,灰度轮廓权向量差分唇形特征是一种精炼、描述性强、适合于唇读识别的视觉语言特征,该算法能自动地完成模型的训练和视觉特征的提取,是一种有效的特征提取算法。     

局部矩不变量轮廓图像角点检测

为解决图像轮廓特征点问题,提出了基于轮廓局部矩不变量的轮廓角点检测方法,通过计算轮廓线支撑区域内中心点两侧轮廓点的不变矩特征,从而提取轮廓曲率函数.实验结果验证了该方法具有抗干扰性好、定位准等优点.     

基于改进水平截集算法的SAR图像海岸线检测

合成孔径雷达(SAR)图像海岸线检测,在自动导航、地图绘制等海洋应用方面具有重要意义。水平截集算法是一种基于人类视觉特性的边缘检测方法。由于它具备检测效果好、抗噪能力强等优点,因而在海岸线检测方法的研究和应用两方面倍受关注。然而,水平截集算法由于迭代方式复杂等原因在应用于分辨率较大的图像时,检测速度比较慢,限制了它在工程应用中的实用性。针对SAR图像,提出一种基于水平截集算法的改进算法,先在低分辨率图像中用水平截集算法进行粗略检测,得到贴近真实海岸线的轮廓线,然后将轮廓线映射到高分辨率图像上,继续用水平截集算法进行检测,最后得到精确的结果。实验中使用Radarsat ScanSAR图像证明该方法可大大加快检测速度,通过与原水平截集算法的检测效果进行对比,新方法没有降低检测效率。     

Region-driven distance regularized level set evolution for change detection in remote sensing images

Change detection is a fundamental task in the interpretation and understanding of remote sensing images. The aim is to partition the difference images acquired from multitemporal satellite images into changed and unchanged regions. Level set method is a promising way for remote sensing images change detection among the existed methods. Unfortunately, re-initialization, a necessary step in classical level set methods is known a complex and time-consuming process, which may limits their practical application in remote sensing images change detection. In this paper, we present an unsupervised change detection approach for remote sensing image based on an improved region-based active contour model without re-initialization. In order to eliminate the process for re-initialization and reduce the numerical errors caused by re-initialization, we describe an improving level set method for remote sensing images change detection. The proposed method introduced a distance regularization term into the energy function which could maintain a desired shape of the level set function and keep a signed distance profile near the zero level set. The experimental results on real multi-temporal remote sensing images demonstrate the advantages of our method in terms of human visual perception and segmentation accuracy.     

Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method

Coastline extraction from synthetic aperture radar (SAR) data is difficult because of the presence of speckle noise and strong signal returns from the wind-roughened and wave-modulated sea surface. High resolution and weather change independent of SAR data lead to better monitoring of coastal sea. Therefore, SAR coastline extraction has taken up much interest. The active contour method is an efficient algorithm for the edge detection task; however, applying this method to high-resolution images is time-consuming. The current article presents an efficient approach to extracting coastlines from high-resolution SAR images. First, fuzzy clustering with spatial constraints is applied to the input SAR image. This clustering method is robust for noise and shows good performance with noisy images. Next, binarization is carried out using Otsu’s method on the fuzzification results. Third, morphological filters are used on the binary image to eliminate spurious segments after binarization. To extract the coastline, an active contour level set method is used on the initial contours and is applied to the input SAR image to refine the segmentation. Because the proposed approach is based on an active contour model, it does not require preprocessing for SAR speckle reduction. Another advantage of the proposed method is the ability to extract the coastline at full resolution of the input SAR image without degrading the resolution. The proposed approach does not require manual initialization for the level set method and the proposed initialization speeds up the level set evolution. Experimental results on low- and high-resolution SAR images showed good performance for coastline extraction. A criterion based on neighbourhood pixels for the coastline is proposed for the quantitative expression of the accuracy of the method.     

结合显著性和边缘信息的水平集图像分割方法

针对 LBF 模型对初始轮廓的依赖性和对边缘的弱控制能力,研究了一种结合显著性和边缘信息 的水平集图像分割方法。首先,结合小波分析理论,基于视觉注意机制构造图像显著图;然后,利用小波分解 所描述的图像边缘信息,构造边缘检测函数,同自适应初始轮廓一起引入到 LBF 水平集模型中,并用有限差 分法进行数值求解。实验结果表明,提出的图像分割方法能有效降低初始轮廓位置对活动轮廓模型的影响,对 合成图像、自然图像均有较好的分割结果,相较于其他传统方法具有更高的演化效率和分割质量。     

Automatic lip contour extraction from color images

Visual information from lip shapes and movements helps improve the accuracy and robustness of a speech recognition system. In this paper, a new region-based lip contour extraction algorithm that combines the merits of the point-based model and the parametric model is presented. Our algorithm uses a 16-point lip model to describe the lip contour. Given a robust probability map of the color lip image generated by the FCMS (fuzzy clustering method incorporating shape function) algorithm, a region-based cost function that maximizes the joint probability of the lip and non-lip region can be established. Then an iterative point-driven optimization procedure has been developed to fit the lip model to the probability map. In each iteration, the adjustment of the 16 lip points is governed by three pieces of quadratic curves that constrain the points to form a physical lip shape. Experiments show that the proposed approach provides satisfactory results for 5000 unadorned lip images of over 20 individuals. A real-time lip contour extraction system has also been implemented.     

一种有效的舌体自动化分割算法*

舌体轮廓正确分割是实现中医舌诊信息化的重要前提.目前主流方法是用阈值方法或先验知识得到舌体的初始轮廓线,然后再用Snake模型使得曲线收敛到舌体边缘.但它们都存在两个问题:对比度较小的舌像,如舌面和脸部有相似的对比度的舌象,基本上没法处理;得到的初始化轮廓线无法克服嘴唇的影响,因此用Snake方法曲线常收敛于嘴唇边缘.通过对舌象的低层信息的研究,发现使用简单而有效的直方图均衡化能扩大图像的颜色分辨力,使得现有的阈值处理方法能在原有基础上很好地处理那些原来无法处理的舌体图像;然后根据舌体形状、位置等先验信息,用几何方法修正受嘴角和嘴唇影响得到的边缘.实验结果表明,该方法取得了很好的分割效果.     

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.     

基于简化Mumford-Shah模型的水平集图像分割算法

为解决传统图像分割方法受噪声和边界轮廓影响而使分割效果不佳问题，基于简化的Mumford Shah模型的水平集图像分割算法通过将曲线嵌入水平集函数，利用函数的求解以达到曲线演化和图像分割的目的。试验表明此分割算法与初始轮廓线位置无关、不受边界轮廓线连续性限制、对图像噪声具有很强的鲁棒性，对均质灰度目标分割效果良好。     

Contour evolution scheme for variational image segmentation and smoothing

An algorithm, based on the Mumford-Shah (M-S) functional, for image contour segmentation and object smoothing in the presence of noise is proposed. However, in the proposed algorithm, contour length minimisation is not required and it is demonstrated that the M-S functional without contour length minimisation becomes an edge detector. Optimisation of this nonlinear functional is based on the method of calculus of variations, which is implemented by using the level set method. Fourier and Legendre's series are also employed to improve the segmentation performance of the proposed algorithm. The segmentation results clearly demonstrate the effectiveness of the proposed approach for images with low signal-to-noise ratios.     

一种快速鲁棒的唇部建模方法研究与实现

快速提取完整的嘴唇外形是计算机人脸动画和语音动画的首要任务之一，模仿真实感的嘴唇，建立逼真的唇部模型是该文的主要目的。文中主要采用Red Exclusion与Cr色调分离相结合的唇部检测方法。该方法首先利用肤色模型快速准确确定出人脸区域及嘴唇检测区域，然后在RGB空间采用红色排除法，在已有的唇部区域中只考虑绿色和蓝色光谱将唇部从背景图像中分割出来。最后利用此法得到的唇部信息与变形模板方法相结合建立唇部模型。该算法对近百幅人脸图片进行嘴部提取实验，结果令人满意。该方法能够快速检测出完整的嘴唇外形，建立较好的唇部模型，为人脸动画提供唇部素材及唇部模型。