应用Snake模型提取彩色图象目标轮廓线的研究

为了更好地利用Snake模型来提取彩色图象中的物体轮廓,因而对Snake原型提出两点主要改进,即针对snake模型的手工初值设置问题,通过引入彩色聚类预处理过程来减少对人的依赖,首先,采用色彩聚类算法对原始图象进行分割,然后用改进的边缘追踪算法提取有意义区域的边缘,并用这一结果作为Snake模型的初值;然后针对Snake原型应用于彩色图象时出现的失真问题,通过对出错原因的分析,重新设计了Snake的外部能量函数,同时用像素在加权HSI颜色空间中的欧氏距离代替传统方法中常用的像素灰度的差分来近似图象梯度;最后,进行了对比实验,实验结果证明,改进后的算法,特别是在处理彩色图象时,大大优于原始方法．     

基于视频的改进的火焰目标分割方法

本文提出了一种改进的基于RGB与HSI颜色模型的火焰目标分割方法,能完整地提取出火焰目标。这种方法是在实时视频流中,采用对抽取出的两帧图像进行帧间差;然后根据RGB与HSI火焰颜色模型进行筛选,获取火焰目标边缘部分像素作为火焰种子;进行以火焰种子遍历八邻域区域生长的方式,提取出完整的火焰。相比以往的火焰分割方法,改进后的方法能更完整地对火焰目标实现分割,也更满足实时视频监控的要求。实验结果证实,提出的改进方法能完整地分割出火焰目标。     

均匀颜色空间中的免疫荧光图像分析

分析彩色免疫荧光图像可以获得细胞免疫信息,用于辅助研究和医疗诊断。视觉上图像前景荧光区域和背景之间存在明显的颜色差异,在CIE（L^＊,a^＊,b^＊）均匀颜色空间中利用该颜色差异给出了一种像素生长算法用于获取荧光区域。在区域提取的基础上,根据同一细胞不同区域之间的相关性初步识别细胞并计数。     

HSI颜色模型下基于空间信息的FCM算法

针对提花毛皮样片的花型识别技术,在HSI颜色模型下提出了一种基于空间信息的FCM图像分割算法。算法在HSI颜色模型下获得FCM算法的初始聚类中心,并采用了基于空间信息的模糊C均值聚类方法对图像进行分割。经C++编程验证,算法能有效去除花型图像中的噪声,获得较理想的花型识别结果。     

一种人脸表情分类的新方法——Manhattan距离

提出了一种利用Manhattan距离进行人脸表情分类的新方法。Manhattan距离计算出具有不同模式的两个对象的距离更大。在实验中,比较了Manhattan距离、欧氏距离、余弦距离在人脸表情分类中的性能,得出Manhattan距离比另外两类距离有着更好的识别效果。     

基于三角网格模型的局部纹理映射

局部纹理映射可以增添三维模型的局部细节,加强模型的真实感。为了实现对三维模型的局部特征描写,增强局部纹理映射的用户可交互性,提出一种基于区域增长和平面投影的方法来实现三角网格模型的局部纹理映射。区域增长是以指定的三角面片为初始种子面片,搜索与种子面片共顶点的增长规则扩散出待映射区域。算法不仅保证了选取区域的完整性,不会出现缺角的情况;同时用户可以改变待映射区域的位置和大小。采用平面投影法对待映射区域进行纹理映射,将三维顶点投影到基准平面上,建立二维坐标系与纹理坐标系的关系,从而确定三维顶点和纹理坐标的对应关系。算法成功应用于实验,表明该方法的可行性。     

Fingerprint classification using a fuzzy multilayer perceptron

A fuzzy multilayer perceptron is used for the classification of fingerprint patterns. The input vector consists of texturebased features along with some directional features. The output vector is defined in terms of membership values to the three classes, viz.Whorl, Left Loop and Right Loop. Perturbation is produced randomly at pixel locations to generate noisy patterns. This helps to demonstrate the ability of the model in handling distorted fingerprint images. A study is made on the effect of reducing the number of input features while increasing the size of the network on its recognition performance.     

抗噪的有监督局部保留投影降维算法

针对数据降维中的噪声干扰问题,提出基于L1-norm有监督局部保留投影算法SLPP-L1。SLPP-L1利用L1-norm替代了L2-norm;因为欧式距离比绝对值距离对噪声更加敏感,使得SLPP-L1抗噪性方面非常有效。实验结果表明,该方法可以有效地剔除噪声的影响并且提高分类的识别率。     

魔棒选择工具的实现

提出一种魔棒选择工具的具体实现方法,它已在最新推出的图象处理软件中得到实际的应用。     

基于HSI颜色空间的彩色图像边缘检测

为得到质量较高的彩色图像边缘信息,基于符合人眼视觉特性的 HSI颜色空间,提出一种新的彩色图像边缘检测算法。融合色度、饱和度和亮度分量得到新分量V,根据色度和饱和度的相关性改进色差度量方法,设计边缘生长方法以保证边缘连续性,结合4个分量的边缘信息得到最终边缘检测结果。实验结果表明,该算法可有效消除噪声影响,提高边缘信息的准确性。     

K均值和最大加权熵在彩色图像分割中的应用

K均值算法属于聚类方法的一种,常用于图像分割。针对如何确定最优聚类数K这一关键问题,在彩色图像的HSI颜色空间中,以马氏距离为距离测度进行K均值聚类,从信息论的角度出发,利用最大加权熵定义了一个目标函数,将最优聚类个数K的求取转换为目标函数的寻优,实现了彩色图像的无监督分割。该方法原理简单,易于实现,能获得比传统方法更好的分割效果。     

Texture classification using fuzzy uncertainty texture spectrum

A new method using fuzzy uncertainty, which measures the uncertainty of the uniform surface in an image, is proposed for texture analysis. A grey-scale image can be transformed into a fuzzy image by the uncertainty definition. The distribution of the membership in a measured fuzzy image, denoted by the fuzzy uncertainty texture spectrum (FUTS), is used as the texture feature for texture analysis. To evaluate the performance of the proposed method. supervised texture classification and rotated texture classification are applied. Experimental results reveal high-accuracy classification rates and show that the proposed method is a good tool for texture analysis.     

一种基于彩色路面区域分割的候选车辆区域视频检测方法

提出了一种基于彩色路面区域分割的候选车辆视频检测方法.该方法首先根据路面部分颜色的分布特点从单帧彩色图像中分割出路面部分并完成路面区域背景的初始化,然后通过对路面区域运用背景差方法和相关后处理过程进行动静态候选车辆的检测和分割.由于采用一种双背景策略能够在车辆检测的同时完成背景的训练和替换更新,该算法克服了传统背景差算法背景更新时容易存在误差累积以及对环境光线变化敏感的缺点,实现简单、稳健性好,可以满足交通视频监控系统中背景更新和车辆检测的实时性处理要求,实验结果证明了该方法的有效性.     

一种Vague集相似度量的方案决策方法

在现有Vague集相似度量的基础上,提出一种改进的Vague集相似度量方法,并应用于工程方案的决策中。在改进的Vague集相似度量方法中考虑了Vague集的隶属度的三维含义、不确定隶属度对肯定与否定隶属程度的影响以及欧氏距离的表示。在基于Vsgue集相似度量的工程方案决策中,Vsgue集之间的相似度量是评价设计方案接近期望方案（理想方案）的度量,相似度量值越大,设计方案接近期望方案越好。这种方案决策的本质是一种模式识别方法。通过实例阐明所提出的相似度量法比现有方法有较强的分辨率,更为合理的工程方案决策,方案评价取得了满意的结果。     

中文文本的关键词自动抽取和模糊分类

本文提出了中文文本分类的两种模糊方法,一种基于模糊集间的语义距离,一种基于本文中提出的‘模糊分类网络’。两者都必须首先从文本中抽取关键词集合,本文给出了一种主要采用统计方法结合受限自然语言理解技术的模糊关键词集合提取方法,它与模糊分类方法结合,可望达到文本信息的自动分类。所提出的方法同样适合于模式识别之类问题的解决。     

SEGMENTATION OF NATURAL IMAGES USING ANISOTROPIC DIFFUSION AND LINKING OF BOUNDARY EDGES

We present a segmentation method of natural images that uses an anisotropic diffusion algorithm and a region growing algorithm. We propose a modified version of the anisotropic diffusion algorithm as a precise edge-preserving smoothing technique modified by using boundary edges. We incorporate a linking algorithm for boundary edges based on a directional potential function into the anisotropic diffusion algorithm to improve the ability of edge-preserving smoothing. As a result, unnecessary details of images are effectively smoothed before performing a region growing algorithm. Therefore, the proposed method is suitable for an accurate segmentation of natural images. Several simulated examples are presented that demonstrate the effectiveness of the proposed technique.     

一种基于HSI的距离联合RGB 3维场边缘探测方法

在彩色图像边缘检测中,欧氏距离方法虽对亮度变化敏感,但对色度和饱和度的差异不太敏感,而矢量角方法又仅能探测色彩差异。为了在RGB3维彩色场中获取精确的边缘信息,提出了一种基于HIS(色度、饱和度和亮度)的距离联合边缘检测方法。该方法在高亮和色差大的区域主要利用矢量角对色彩差异来进行检测,而在低亮和色差小的区域则主要利用欧氏距离的亮度差异来进行边缘检测。实验表明,该方法可有效地解决矢量角存在的问题,其与LUV欧氏距离检测方法相比,则可以避免复杂的色彩空间变换计算,其检测效果也更佳。     

Distributional-based texture classification using non-parametric statistics

Texture classification is an important problem in image analysis. In the present study, an efficient strategy for classifying texture images is introduced and examined within a distributional-statistical framework. Our approach incorporates the multivariate Wald–Wolfowitz test (WW-test), a non-parametric statistical test that measures the similarity between two different sets of multivariate data, which is utilized here for comparing texture distributions. By summarizing the texture information using standard feature extraction methodologies, the similarity measure provides a comprehensive estimate of the match between different images based on graph theory. The proposed “distributional metric” is shown to handle efficiently the texture-space dimensionality and the limited sample size drawn from a given image. The experimental results, from the application on a typical texture database, clearly demonstrate the effectiveness of our approach and its superiority over other well-established texture distribution (dis)similarity metrics. In addition, its performance is used to evaluate several approaches for texture representation. Even though the classification results are obtained on grayscale images, a direct extension to color-based ones can be straightforward.

Unknown odor recognition using Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm

To deal with unknown odor recognition problem for a developed artificial odor discrimination system, Euclidean Fuzzy similarity-based Self-Organized Network inspired by Immune Algorithm (EF-SONIA) is proposed. Euclidean fuzzy similarity enables a zero similarity calculation between an unknown odor vector and hidden unit vectors, so that the system can recognize the unknown odor. In addition, an elliptical approach for fuzziness determination is proposed. The elliptical approach can approximate an appropriate fuzziness, so that the unknown odor recognition accuracy is improved. Experiments on three datasets of three-mixture vegetal odors show that the recognition accuracy of the proposed method is 20% better than those of the conventional method. The system is very promising to be used for a real development of dog robot that enables localization and identification of dangerous natural gas.