融合边缘检测与区域生长的交通图像分割方法

在交通监控中,如何从复杂的背景中分割运动物体是至关重要的一步,针对车辆的运动阴影对图像分割产生的不利影响,提出了一种新的融合边缘检测与区域生长的彩色图像分割算法,算法同时考虑了图像的彩色信息和空间信息.该算法首先对彩色图像边缘检测,并根据检测结果设置种子像素;再基于颜色相似性生长准则,结合边缘检测结果,对每个种子点进行区域生长;最后,利用区域合并算法对剩余的像素进行合并.实验结果表明该算法很大程度上克服了阴影给图像分割带来的不利影响.     

基于HSI空间的模糊C均值彩色图像分割方法

给出了一种在HSI空间上基于模糊C均值的彩色图像分割方法．首先对每个像素根据H分量和I分量计算出4个隶属度,然后将其中的两个隶属度结合形成一个二雏特征矢量来表征像素的全部颜色特征,最后对二维矢量运用模糊C均值聚类算法得到最终的彩色图像分割结果．     

基于改进模糊算子和形态学的舌体分割方法

舌诊是中医望诊中的重要内容,为了自动分析舌体的各项指标,必须将舌体从背景区域中分割出来.由于舌体与脸部等背景区域颜色较为接近,提出一种基于改进模糊算子和形态学的舌体分割方法,该方法根据由大津法确定的阈值定义隶属函数,取得了令人满意的分割效果.     

一种基于MRF的多层遗传递减视频对象分割算法

提出一种视频对象分割方法,将时域信息、空域信息和图像颜色特征有效地结合起来,采用MRF遗传递减式分类来提取视频对象.利用视频帧的颜色特征,在初始分割中采用时空域分水岭方法,建立一个时空域毗连图(ST-RAG);基于时空域毗连图建立马尔可夫随机场(MRF)模型,在分割过程中采用遗传递减式方法对区域进行合理分类,利用形态学进行后处理,分割出感兴趣的视频对象.实验结果表明所获取的分割方法具有灵活性,且精度高.     

基于改进YCrCb颜色空间的肤色分割

人脸识别是计算机视觉、模式识别、生物特征识别、图像处理等多学科交叉的一门科学,肤色分割是人脸识别研究中一个重要的步骤。针对基于YCrCb颜色空间的肤色分割在强光下分割不准确的现状,提出了一种基于改进的YCrCb空间的肤色分割方法,本文利用改进YCrCb颜色空间提高了强光下肤色分割的准确性和适应性。实验证明该算法减弱了光线强弱对人脸肤色分割的影响,更加准确的找到人脸区域,误检率较低。     

基于改进YCrCb颜色空间的肤色分割

人脸识别是计算机视觉、模式识别、生物特征识别、图像处理等多学科交叉的一门科学,肤色分割是人脸识别研究中一个重要的步骤。针对基于YCrCb颜色空间的肤色分割在强光下分割不准确的现状,提出了一种基于改进的YCrCb空间的肤色分割方法,本文利用改进YCrCb颜色空间提高了强光下肤色分割的准确性和适应性。实验证明该算法减弱了光线强弱对人脸肤色分割的影响,更加准确的找到人脸区域,误检率较低。     

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车辆阴影分割是智能交通领域中车辆识别的一项重要内容,阴影分割的好坏直接影响到车辆识别的准确性以及整个智能交通监控系统的性能。针对当前基于RGB和HSV颜色空间的车辆阴影分割算法缺陷与不足,本文提出了一种新的基于YCbCr空间的车辆阴影分割算法。首先选取图像中的运动区域,运动区域包括车辆以及阴影;然后根据阴影区域出现的特点,选择初始阴影数据;最后,通过本文提出的阴影分割算法最终确定阴影区域的形状与位置。经过实际道路运行测试,该算法能提取出的车辆阴影完整性好,具有较好的鲁棒性,在智能交通领域具有一定的应用价值与前景。车辆阴影分割是智能交通领域中车辆识别的一项重要内容,阴影分割的好坏直接影响到车辆识别的准确性以及整个智能交通监控系统的性能。针对当前基于RGB和HSV颜色空间的车辆阴影分割算法缺陷与不足,本文提出了一种新的基于YCbCr空间的车辆阴影分割算法。首先选取图像中的运动区域,运动区域包括车辆以及阴影;然后根据阴影区域出现的特点,选择初始阴影数据;最后,通过本文提出的阴影分割算法最终确定阴影区域的形状与位置。经过实际道路运行测试,该算法能提取出的车辆阴影完整性好,具有较好的鲁棒性,在智能交通领域具有一定的应用价值与前景。     

基于球面图像的三角网格模型分割

为了从局部形状的角度实现对三角网格模型的管理和重用,提出了一种基于球面图像的三角网格模型分割方法.通过球面参数化及球面划分,将三角网格模型的表面属性信息映射到球面图像中;利用成熟的区域生长、区域合并图像分割算法对球面图像进行分割;将球面图像的分割结果转换为三角网格模型的分割结果.实验结果表明:该分割方法可以对不均匀的低分辨率三角网格模型进行有效分割,降低了几何属性估算对分割结果的影响,不会发生过分割现象,不需要进行分割的后续处理.     

基于人头颜色空间和轮廓信息的行人检测方法研究

为提高智能视频监控中行人统计的实时性,提出了一种基于人头颜色空间和轮廓特征的行人检测方法.该方法首先根据人脸肤色、发色在YCbCr和RGB颜色空间的聚类情况,建立人头颜色模型,分割人头候选区域,并针对行人运动的特征,采用多帧差法提取运动信息,剔除背景噪声,修正候选区域的精度;然后根据改进的Canny 算子提取候选区域的轮廓,融合形态学对边缘进行修正,提取候选区域轮廓信息;最后根据人头轮廓的几何特征,剔除“伪候选”区域,并进行连通域信息标记,检测人头图像,从而对行人进行检测和统计信息.结果表明,该方法能快速有效地检测出人头,在动态场景下的行人检测取得了较好的效果.     

基于OD表的广域运输通道分割

针对通道所连接的区域之间的关联度随距离的延长而逐渐弱化的特点,从系统的整体与局部关系出发,提出了广域运输通道的分割理论.分析了区域关联度计算的复杂性.区域关联度计算的基础是OD表,OD对间的客货流量代表了区域间关联关系的强弱.建立了基于OD表的通道分割方法,并探讨了通道分割时OD表的应用.用大西南通道分割的实例验证了方法的可行性.     

基于图像分割的图像检索新方法

利用信息熵理论,提出了一种新的相似性图像检索方法.首先,分割图像,抽取图像的分块颜色信息;再计算图像之间的颜色互信息来确定它们的相似度.与其他的颜色特征描述方法相比,该方法该算法具有抗噪声能力强、精确度高等优点.     

A Bintree Energy Approach for Colour Image Segmentation Using Adaptive Channel Selection

A new hierarchical approach called bintree energy segmentation was presented for color image seg-mentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images,from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the "best" chan-nel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.     

颜色在识别中的应用及关键技术研究

研究颜色在识别中的应用以及所涉及到的关键技术.主要技术包括色度图、颜色库、自适应阈值、颜色模版匹配、模糊神经网络、颜色分类器等.重点研究了适于遥感图像分类的正交坐标系的YHS(亮度,色调,饱和度)变换技术.     

A bintree energy approach for colour image segmentation using adaptive channel selection

A new hierarchical approach called bintree energy segmentation was presented for color image segmentation. The image features are extracted by adaptive clustering on multi-channel data at each level and used as the criteria to dynamically select the best chromatic channel, where the segmentation is carried out. In this approach, an extended direct energy computation method based on the Chan-Vese model was proposed to segment the selected channel, and the segmentation outputs are then fused with other channels into new images, from which a new channel with better features is selected for the second round segmentation. This procedure is repeated until the preset condition is met. Finally, a binary segmentation tree is formed, in which each leaf represents a class of objects with a distinctive color. To facilitate the data organization, image background is employed in segmentation and channels fusion. The bintree energy segmentation exploits color information involved in all channels data and tries to optimize the global segmentation result by choosing the ＂best＂ channel for segmentation at each level. The experiments show that the method is effective in speed, accuracy and flexibility.     

Color image segmentation by edge linking and region grouping

A novel method toward color image segmentation is proposed based on edge linking and region grouping. Firstly, the edges extracted by the Canny detector are linked to form regions. Each of the end points of edges is connected by a direct line to the nearest pixel on another edge segment within a sub-window. A new distance is defined based on the feature that the edge tends to preserve its original direction. By sampling the lines to the image, the image is over-segmented to labeled regions. Secondly, the labeled regions are grouped both locally and globally. A decision tree is constructed to decide the importance of properties that affect the merging procedure. Finally, the result is refined by user’s selection of regions that compose the desired object. Experiments show that the method can effectively segment the object and is much faster than the state-of-the-art color image segmentation methods.     

Automatic identification of cracks from borehole image under complicated geological conditions

Identifying cracks from the spread image of a borehole wall is one of the most common usages of borehole imaging method. The manual identification of cracks is time-consuming and can be easily influenced by objective judgment. In this study, firstly, the image translation from RGB color model to HSV color model is done to highlight the structural plane region, which is closer to the color recognition of human sight; secondly, the Saturation component is filtered for further processing and a twice segmentation method is proposed to improve the accuracy of automatic identification. The primary segmentation is based on the statistics of saturation over a longer borehole section and can give a rough estimation of a crack. Then, the pixels are shifted in the reverse direction to the sine curve estimated and make the centerline of the crack flat. Based on the shifted image, the secondary segmentation is done with a small rectangle region that takes the baseline of the roughly estimated crack as its centerline. The result of the secondary segmentation can give a correction to the first estimation. Through verifying this method with actual borehole image data, the result has shown that this method can identify cracks automatically under very complicated geological conditions.     

基于多标记像素匹配的高分辨率遥感图像道路提取

针对高分辨率遥感图像道路提取方法存在提取不完整和误提取问题，提出一种基于多标记像素匹配的高分辨率遥感图像道路提取方法. 将待提取图像由RGB颜色空间转换到 Lab颜色空间，选取与照明强度弱相关的色相特征作为初始匹配项. 以矩形框的方式标记不同类型的道路，利用t 检验法剔除其匹配项的异常值，从而确定阈值来匹配道路像素点，利用局部纹理算子对匹配结果进行筛选. 利用道路区域的形态特征对匹配结果进行优化处理. 为验证所提方法的可行性和优越性，对不同传感器获取的高分辨遥感图像进行测试，与现有道路提取方法进行对比. 定性和定量精度评价结果表明，所提方法对不同类型道路的提取具有较高的精度.     

Unsupervised color-texture image segmentation

The measure J in J value segmentation （JSEG） fails to represent the discontinuity of color, which degrades the robustness and discrimination of JSEG. An improved approach for JSEG algorithm was proposed for unsupervised color-texture image segmentation. The texture and photometric invariant edge information were combined, which results in a discriminative measure for color-texture homogeneity. Based on the image whose pixel values are values of the new measure, region growing-merging algorithm used in JSEG was then employed to segment the image. Finally, experiments on a variety of real color images demonstrate performance improvement due to the proposed method.