基于Otsu阈值法与形态学自适应修正的舌像分割方法

为了从原始舌图像中精确分割出舌体以实现中医舌诊的客观化,提出了一种基于Otsu阈值法与形态学自适应修正的舌像分割方法。该方法提取原始舌像的RGB色彩空间与HSV色彩空间中G、B、V通道的信息,运用Otsu阈值法对舌像进行二值化,基于先验知识对目标区域进行聚类,采用形态学运算方法,利用舌体当量直径自适应选取结构元素的大小,对聚类结果进行开启运算修正得到舌像分割结果。基于200幅不同形态、不同舌色类别原始舌像的实验结果表明,该方法能稳定精确地分割出舌体,与传统的Otsu方法及灰度投影-Otsu方法相比,准确率有了很大的提升。     

基于人脸肤色和特征的实时检测跟踪算法

针对实时视频中的运动人体目标,提出一种基于人脸肤色和特征的快速人脸检测和跟踪方法.首先运用帧差法和形状信息检测出视频中的运动人体目标;然后在YCbVCr色彩空间中,根据肤色的色度聚类特性建立Gaussian模型,分割出肤色区域,去除噪声后,结合人脸的几何特征和器官独有的颜色特征滤除非人脸肤色区域,准确定位人脸在图像中的位置;利用控制策略驱动摄像机,根据人脸信息使人的头肩部位始终处于视频图像的中心,从而实现运动跟踪.为了增强系统对光线变化的适应性,提出了适当的Gaussian模型参数更新策略.实验结果表明,该算法能够适应复杂背景下的人脸检测,具有速度快、准确率高、鲁棒性好的特点,实现了运动人脸的可靠跟踪.     

HSV空间中具有调整因子的肤色增强方法

为使显示器件所显示的肤色符合人们的喜好色,提出了一种基于喜好色,且具有权重调节因子的肤色增强方法。该方法首先通过心理学实验分析确定了肤色和喜好色在HSV色彩空间中的分布,得到肤色和喜好色在HSV彩色空间分布的简化模型;然后对肤色增强中的彩色畸变和反转进行了分析,利用肤色增强调整因子来抑制彩色畸变和反转;最后根据肤色和喜好色区域的偏离程度,自适应调节肤色的增强幅度。仿真和显示实验结果表明,增强后的肤色不同程度地逼近喜好色的区域,不存在彩色畸变和反转,符合视觉和心理的先验理解。     

基于K均值聚类和HSV颜色模型的烟雾图像分割

烟雾作为火灾发生前的最大特征之一,所以对火灾的检测可依靠于烟雾的检测,而煅雾图像分割是烟雾图像检测分析中最为困难且不可缺少的步骤,为有效排除非烟雾图像的干扰,降低周围环境对真实烟雾的误检,提出一种基于K均值聚类和HSV颜色模型的烟雾图像分割,该方法首先从RGB空间转换到HSV色彩空间,采用K均值聚类的烟雾图像分割,之后把分割的图像进行shen滤波去噪和区域标记去噪。实验结果表明,对烟雾图像的分割,该建议方法能够将烟雾从背景中分割出来。     

偏好肤色的获取与再现研究

目的基于人眼长期观察实践对色彩形成的认知以及识别肤色时的主观偏好记忆性,对偏好记忆的肤色进行获取以及再现。方法选取代表人群拍摄肤色数据,并通过心理物理试验获取偏好肤色数据库,采用YCb Cr空间建立椭圆模型对肤色和偏好肤色分布进行模拟。利用椭圆模型,提出一种基于仿射变换的偏好肤色再现算法。结果实现了输入图像中肤色到偏好肤色的转换和再现,并使用瑟斯顿成对比较法对仿射变换再现算法和其他算法的再现效果进行了评价。结论算法具有较高的观察者偏好度,更符合人们的主观感受,肤色再现效果较好。     

基于HDD算法的头发区域提取算法研究与实现

针对人脸头发提取的问题对光照及环境背景色彩相似性的影响,本文提出一种基于HSV色彩空间与ASM的自动头发检测（HSV—Hair-Detect．HHD）算法。实验结果表明该算法对光照和背景色彩相似性有很好的鲁棒性。     

基于改进高斯混合模型的运动物体的图像检测

传统的高斯混合模型在RGB色彩空间只对孤立像素建模,检测结果不够准确,存在拖影现象,检测到的运动物体内部容易出现空洞.针对这些问题,本文提出了一种改进的高斯混合模型.该方法从更符合人眼视觉特性的HSV色彩空间对中心像素和周边像素构成的向量进行建模,改善了原算法的性能;利用彩色分割算法提取连通区域,充分地利用了运动物体的彩色信息,并基于Phong物体光照模型进行了阴影抑制,提高了传统高斯混合模型检测的准确性.实验结果表明,与传统高斯混合模型相比,本算法能更精确地检测出运动物体,对光照变化和阴影具有鲁棒性.     

基于卡尔曼滤波的多运动目标跟踪算法研究

针对多运动目标跟踪的实时性和鲁棒性问题,本文提出了一种基于卡尔曼滤波的多运动目标跟踪算法,该算法运用卡尔曼滤波预测目标的位置,并以目标的中心点坐标、面积和长宽比特征、一维HSV颜色直方图作为目标的特征对当前帧检测到的目标模板和预测区域内的目标进行匹配。实验证明,该算法可实时、稳定地跟踪复杂场景内的多运动目标,并能够解决目标遮挡问题。     

图片彩色参数数字滤波设计

颜色和形状信息是识别物体的重要信息．本文提出用数字滤波器将图象空间分割为H包含M和M/H空间的一种方法．该方法进一步将分析彩色图象为众所周知的测量分割部分几何特征的算法，包括通过节点的阶数确定分割部分的分布及动态特征，包括速度和移动方向的分布等．     

烫印在线检测图像识别算法的研究

应用中值滤波法对采集的烫印图像进行了噪声消除,根据烫印图案亮度值较高的特性,将彩色图像从RGB 空间转换到了 HSV 空间,然后经过 H 通道阈值分割,结合差分求和定理以及模板分块匹配,改进了 NCC算法,实现了烫印区域识别提取。 通过大量实例对算法进行了验证,结果证明此算法能够快速、有效识别烫印区域,对机器视觉在烫印质量检测中的应用具有一定的指导意义。     

Deer Body Adaptive Threshold Segmentation Algorithm Based on Color Space

In large-scale deer farming image analysis, K-means or maximum betweenclass variance (Otsu) algorithms can be used to distinguish the deer from the background. However, in an actual breeding environment, the barbed wire or chain-link fencing has a certain isolating effect on the deer which greatly interferes with the identification of the individual deer. Also, when the target and background grey values are similar, the multiple background targets cannot be completely separated. To better identify the posture and behaviour of deer in a deer shed, we used digital image processing to separate the deer from the background. To address the problems mentioned above, this paper proposes an adaptive threshold segmentation algorithm based on color space. First, the original image is pre-processed and optimized. On this basis, the data are enhanced and contrasted. Next, color space is used to extract the several backgrounds through various color channels, then the adaptive space segmentation of the extracted part of the color space is performed. Based on the segmentation effect of the traditional Otsu algorithm, we designed a comparative experiment that divided the four postures of turning, getting up, lying, and standing, and successfully separated multiple target deer from the background. Experimental results show that compared with K-means, Otsu and hue saturation value (HSV)+K-means, this method is better in performance and accuracy for adaptive segmentation of deer in artificial breeding scenes and can be used to separate artificially cultivated deer from their backgrounds. Both the subjective and objective aspects achieved good segmentation results. This article lays a foundation for the effective identification of abnormal behaviour in sika deer.     

基于k-Means算法的彩色QR码识别

为了降低彩色QR码解码过程中出现的混叠效应,提高彩色QR码解码的正确率,提出一种基于HSV颜色模型的k-Means聚类算法。为了选择适合彩色QR码的颜色空间模型,通过实验验证了在RGB,Lab,HSV 3个颜色模型下k-Means聚类算法的效果。在HSV颜色模型下,根据等欧氏距离的原则建立彩色编码模块的配色模型,最大程度地减小解码中颜色的混叠效应。彩色QR码解码预处理阶段,利用基于HSV颜色模型的光线补偿的k-Means聚类算法对彩色编码模块进行颜色分离,以提高解码的精度。研究结果表明:在HSV颜色模型下,k-Means聚类效果最好,图像区域分类效果最清晰;所建立的配色模型可以最优地为彩色编码模块配色;基于HSV颜色模型的光线补偿的k-Means聚类算法可以提高彩色QR码解码的正确率。因此,建立合理的配色模型进行彩色编码模块的颜色设置,同时采用基于HSV颜色模型的光线补偿的k-Means聚类算法进行颜色分割,可以大幅度地降低彩色QR码编码模块之间的混叠效应,从而显著提高彩色QR码解码的正确率。     

一个用于肺癌早期诊断的彩色图像处理系统

介绍了一个用于肺癌早期诊断的彩色图像处理系统，该系统能够识别痰液涂片彩色显微图像中的肺癌细胞。为了提高识别癌细胞的稳定性与有效性，系统采用了一种细胞分割与分类的分层处理结构，首先，在某一特定的归一化彩色空间中利用自适应阈值的方法对细胞核进行分割，利用细胞核的形态学特征检出可疑癌细胞，最后用细胞核区的色度学特征和纹理特征对可疑癌细胞进行分类与识别，从而确定是否存在癌细胞。     

An algorithm for finger-vein segmentation based on modified repeated line tracking

Abstract

Image segmentation is an important step for finger-vein identification technique. However, it is difficult to extract precise details of the image because of the irregular noise and shades around the finger-vein. The repeated line tracking algorithm achieves good segmentation performance for low quality images of finger-vein, but it has some drawbacks such as low robustness and efficiency. In this paper, a modified repeated line tracking algorithm is proposed for image segmentation of finger-vein. Firstly, we propose a segmentation method called threshold image to execute rough segmentation and obtain binary and skeleton image of finger-vein. Secondly, the width of finger-vein is estimated based on the binary and skeleton image. The parameters are revised according to the width. Then, the modified repeated line tracking algorithm is executed to figure out the locus space of finger-vein based on the revised parameters. Finally, processing results are obtained by using Otsu algorithm which executes exact segmentation on the locus space. Experiments show that the proposed algorithm is more robust and efficient than traditional repeated line tracking algorithm.     

Anatomical region segmentation method from dermoscopic images of pigmented skin lesions

Melanoma tumor can cause a serious life threatening problem in humans, if left untreated for a long time without early diagnosis. For early diagnosis of melanoma, it is more significant to develop novel methods based on biophysics analyses, molecular targets recognitions, and new image analysis criteria. In this article, anatomical region segmentation and diameter identification is proposed to detect melanoma from dermoscopic images. Four main steps of the proposed system are as follows: In the first step, the preprocessing is performed to smooth the melanoma extraction process. The region segmentation is done in the second step using watershed segmentation and Sobel operator. In the third step, the postprocessing procedures like as morphological open, canny edge detection also applied to improve the region of interest. Finally, the melanoma region is identified using color symmetry features. The proposed method is tested with two data sets to prove the performance proposed method. The proposed method achieved an accuracy of 95.31% and specificity of 98.3%, which is better than other methods. Experimental results show that the effectiveness of the proposed method and illustrate viability of real-time clinical applications.     

复杂背景下基于HSV空间和模板匹配的车牌识别方法研究

车牌识别技术作为交通管理自动化的重要手段,在交通监视和控制中占有很重要的地位.车牌识别过程可分为车牌定位、车牌校正、字符分割和字符识别四个部分.在车牌定位中,若单纯采用纹理特征或颜色特征来进行定位,往往适用于背景较为简单的场景,对复杂背景的定位效果尚有待改进.在字符分割中,目前单行车牌的分割已比较成熟,但双行车牌的分割仍不理想.提出一种在HSV空间下两次颜色标定和纹理特征相结合的定位方法和一种单双行车牌的字符分割方法.该定位方法利用车牌固定颜色搭配特性,对图片两次标记并利用投影法定位车牌,对200张不同背景图片测试,定位准确率达到98％.在字符分割部分,利用改进的模板匹配方法对字符分割,可适用于单、双行车牌分割,准确率达到95％.     

A Method of Obtaining Catchment Basins with Contour Lines for Foam Image Segmentation

Foam image segmentation, represented by watershed algorithm, is wildly used in the extraction of bubble morphology features. H-minima transformation was proved to be effective in locating the catchment basins in the traditional watershed segmentation method. To further improve the accuracy of watershed segmentation, method of top-bottom-cap filters and method of morphological reconstruction were implied to marking the catchment basins. In this paper, instead of H-minima transformation, a method of contour lines is specially proposed to obtain the catchment basins for foam image segmentation by using top-bottom-cap filters and less morphological reconstruction. Experimental results in foam segmentation show that the proposed method is equally accurate but more efficient than the method of H-minima plus morphological reconstruction, and equally efficient but more accurate than the method of H-minima plus top-bottom-cap filters.     

一种新的多特性联合阴影检测方法

针对运动阴影检测时单一阴影特征难以完全将前景和阴影正确分离,提出一种多特征联合运动阴影检测方法.考虑运动阴影的光照、色度、纹理和区域统计特性,提出采用小邻域光照的对数比值不变性来判定阴影,接着联合阴影HSV颜色空间特性和梯度方向小块合并的阴影区域统计特性来实现多特征联合运动阴影检测.为了客观评价方法性能,采用一种改进的量化方法,对不同光照和环境条件下的视频序列进行测试.实验结果表明,该方法效果好,前景检测率和阴影检测率高,可应用于智能视频监控的目标检测.     

HSV空间下改进的直觉模糊C均值聚类猪肉图像检测

利用计算机视觉技术对畜肉分级的方法种类繁多,但由于光照因素使前期图像预处理分割目标和背景的工作难度增大。针对传统的最大类间方差法分割图像效果不佳、噪声适应能力不强的问题,以及核磁共振、高光谱成像等无损检测方法大多存在检测仪器体积大、不便于携带、成本高等问题,提出利用色调、饱和度、明度(Hue,Saturation,Value,HSV)色彩空间结合聚类方法对图像像素点进行聚类分割。在对取自自然光照环境中的猪肉图像进行分割时,所提方法相对于传统聚类分割方法分割正确率平均提高1.46%;在对人工加入了0.1椒盐噪声和0.2椒盐噪声的图像进行分割时,该方法相对于传统方法表现出了更好的抗噪声能力,传统分割方法平均错误率分别升高了16.15%和38.28%,该方法平均错误率仅升高了1.57%和1.49%。该方法具有良好的分割准确率和噪声鲁棒性,提高了目标区域的检测精度,减少了图像预处理阶段的信息丢失,提高了畜肉分级方法的质量。