彩色图像色度距离权值的图论分割算法

提出利用色度距离特征权重的图论分割算法,对彩色图像进行区域分割分析。利用图论和HSI模型,解决自然灾害图像的分割问题。针对复杂的自然图像,将图像像素转换为图论中的节点,构造基于像素点HSI模型的带权无向图;构建带权无向图的图论分割权函数及分割准则,形成区域相似度判别方法;结合实际分割需求,对图论分割后的离散区域进行二次吸收与合并运算,获取连续兴趣区域;对分割的结果与其他算法进行了比较与分析。     

基于HSI空间彩色差的快速水平集分割方法*

为提高分割速度、改善分割效果,从两方面进行改进:首先对传统的快速水平集分割进行改进,得到基于CV模型的快速水平集;其次,通过引入HSI空间中的彩色差概念,建立一种新的、尤其适合彩色图像的快速水平集分割方法。实验表明,新提出的基于HSI彩色差的快速水平集分割方法,对彩色图像具有良好的分割效果。与传统水平集分割相比,新方法在分割性能和运算时间两方面均有明显提高。     

Color image segmentation using tensor voting based color clustering

A novel color image segmentation method using tensor voting based color clustering is proposed. By using tensor voting, the number of dominant colors in a color image can be estimated efficiently. Furthermore, the centroids and structures of the color clusters in the color feature space can be extracted. In this method, the color feature vectors are first encoded by second order, symmetric, non-negative definite tensors. These tensors then communicate with each other by a voting process. The resulting tensors are used to determine the number of clusters, locations of the centroids, and structures of the clusters used for performing color clustering. Our method is based on tensor voting, a non-iterative method, and requires only the voting range as its input parameter. The experimental results show that the proposed method can estimate the dominant colors and generate good segmented images in which those regions having the same color are not split up into small parts and the objects are separated well. Therefore, the proposed method is suitable for many applications, such as dominant colors estimation and multi-color text image segmentation.     

基于HSI颜色模型的特征分类方法研究及应用

在方便面包装过程中经常出现三种调味包丢失的情况,目前主要依靠人工检测识别,因而提出了一种基于HSI颜色模型特征分类方法的识别技术。该技术已在方便面生产流水线上试运行成功。经过8个小时,6万包的现场测试,结果表明,该方法实时性好,准确率高,完全能满足生产工艺要求,提高了整个流水线的生产速度,减轻了工人劳动量。     

基于HSI颜色空间的单幅图像去雾算法

针对有雾图像对比度差、能见度低的情况,结合HSI颜色空间特点,提出一种单幅图像去雾算法。首先,将有雾图像从RGB颜色空间转换到HSI颜色空间;然后,依据HSI颜色空间中色度、饱和度和亮度各分量受雾影响程度的差异,建立相应的去雾模型;最后,通过分析图像饱和度,得到饱和度模型中权重的取值范围,再对亮度模型中权重进行估计,从而实现去雾效果。与其他几种算法的实验结果比较表明,所提算法运算效率提高1倍左右。同时该算法能有效增强图像清晰度,能很好地运用于单幅图像去雾。     

基于HSI颜色空间与灰度波动相结合的复杂桥梁蜂窝麻面的图像分割

针对桥梁蜂窝麻面图像经常存在光照不均、多背景并存的干扰问题,提出了基于HSI颜色空间与灰度波动相结合的复杂桥梁蜂窝麻面的图像分割算法。首先,绘制S分量灰度变化曲线;其次,搜索曲线所有潜在的波峰波谷,并求相邻波峰波谷的高度差;然后,基于灰度像素个数差分值的标准差筛选出部分高度差;最后,基于部分高度差的标准差搜索最佳阈值完成图像的阈值分割。实验结果表明,与二维OTSU法、Niblack法、二维Tsallis熵法等几种算法相比,该算法的分割效果和实时性更好。     

An image segmentation method using histograms and the human characteristics of HSI color space for a scene image

Image segmentation is an important subject for image recognition. Here, we propose a new image segmentation method for scene images. The proposed segmentation method classifies images into several segments based on the human visual sense and achromatic color. We calculate the histograms of the image for each component of the hue, saturation, and intensity (HSI) color space, and obtain three results of image segmentation from each histogram. We consider achromatic colors in order to decrease the number of regions. We compare the results of the proposed method with those of the k-means methods for the effectiveness of the proposed method. This work was presented, in part, at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

Color image segmentation based on homogram thresholding and region merging

In this paper, a color image segmentation approach based on homogram thresholding and region merging is presented. The homogram considers both the occurrence of the gray levels and the neighboring homogeneity value among pixels. Therefore, it employs both the local and global information. Fuzzy entropy is utilized as a tool to perform homogram analysis for finding all major homogeneous regions at the first stage. Then region merging process is carried out based on color similarity among these regions to avoid oversegmentation. The proposed homogram-based approach (HOB) is compared with the histogram-based approach (HIB). The experimental results demonstrate that the HOB can find homogeneous regions more effectively than HIB does, and can solve the problem of discriminating shading in color images to some extent.     

一种基于HSI空间的异类传感器假彩色图像融合算法

提出了一种基于HSI空间的假彩色异类传感器图像融合算法。该算法从边缘、色彩这两个图像的基本特征着手,在灰度融合图像的基础上,基于HSI空间,用色彩体现特殊传感器的独有信息。选用laplacian金字塔融合算法及一致性融合规则得到边缘灰度融合图像,并将其送至HSI空间的通道;根据需要,将某特殊传感器获取的图像,送至H通道进行调制;最后将HSI空间的融合图像变换到RGB空间显示。仿真结果显示：本文算法得到的彩色融合图像不仅保留了与灰度融合图像相近的空间分辨率,而且突出了独有信息,是一幅信息量比较完备的融合图像。同时,对于HSI三通道的选择是固定的,具有很好的算法稳健性。     

Color image segmentation—an innovative approach

In this paper we describe a color image segmentation system that performs color clustering in a color space and then color region segmentation in the image domain. For color segmentation, we developed a fuzzy clustering algorithm that iteratively generates color clusters using a uniquely defined fuzzy membership function and an objective function for clustering optimization. The fuzzy membership function represents belief value of a color belonging to a color cluster and the mutual interference of neighboring clusters. The region segmentation algorithm merges clusters in the image domain based on color similarity and spatial adjacency. We developed three different methods for merging regions in the image domain. Unlike many existing clustering algorithms, the image segmentation system does not require the knowledge about the number of the color clusters to be generated at each stage and the resolution of the color regions can be controlled by one single parameter, the radius of a cluster. The color image segmentation system has been implemented and tested on a variety of color images including satellite images, car and face images. The experiment results are presented and the performance of each algorithm in the segmentation system is analyzed. The system has shown to be both effective and efficient.     

Color image segmentation: advances and prospects

Image segmentation is very essential and critical to image processing and pattern recognition. This survey provides a summary of color image segmentation techniques available now. Basically, color segmentation approaches are based on monochrome segmentation approaches operating in different color spaces. Therefore, we first discuss the major segmentation approaches for segmenting monochrome images: histogram thresholding, characteristic feature clustering, edge detection, region-based methods, fuzzy techniques, neural networks, etc.; then review some major color representation methods and their advantages/disadvantages; finally summarize the color image segmentation techniques using different color representations. The usage of color models for image segmentation is also discussed. Some novel approaches such as fuzzy method and physics-based method are investigated as well.     

改进的HSI空间形态学有噪彩色图像边缘检测

针对在RGB空间中很难有效区分颜色相似性问题,选择了更加符合颜色视觉特性的HSI颜色空间进行图像处理,提出了一种改进的形态学有噪彩色图像边缘检测方法,将开闭的迭代运算和双结构元多尺度运算应用到传统形态学梯度算子中,然后计算图像H、S、I三个分量的边缘信息,根据H、S、I所占比重对三分量进行加权融合得到彩色图像边缘.实验结果表明,该方法所检测的边缘符合人眼视觉特性,在抗噪声方面的效果比传统方法及其他多种方法更佳,能够更完整地保留原彩色图像的轮廓,计算量相对较小,有很好的实用性和通用性.     

基于均值偏移的彩色图像分割算法

提出了一种基于均值偏移的彩色图像分割算法。首先阐述了在CIE LUV均匀彩色模型下均值偏移算法的基本原理,然后给出了在图像分割中的具体实现方法：选定一个像素,在适当的空间窗和色彩窗限定的特征空间中寻找模式点,实现窗口中心从选定点到模式点的偏移,重复此过程,直到找到稳定的模式点并用模式点的色彩值代替该像素,遍历所有像素,最终达到对所有像素进行聚类。通过两幅图像对算法进行检验,实验结果证明该算法对彩色图像具有良好的分割效果。     

基于尺度空间分析和概率松弛的细胞图像分割算法

提出基于直方图尺度空间分析和概率迭代松弛的混合方法分割背景复杂的细胞图像。首先根据原始图像直方图的尺度空间特性和多尺度滤波结果,选取最佳阈值将图像分为多个类。然后,利用迭代的概率松弛法对粗分结果进行优化,并在后处理阶段中切割粘连细胞。将该方法与最大方差法和区域增长法进行比较,通过实例表明该方法的有效性。     

小麦冠层图像H分量的K均值聚类分割

大田环境下小麦冠层图像具有光照不均匀、背景复杂及阴影遮挡等特点,经典图像分割算法存在精度低、过分割等问题,提出一种基于HSI空间下H分量的K均值聚类算法。使用[R+G-B]归一化处理RGB空间下的彩色图像,以抑制其B分量;将归一化图像进行RGB到HSI的颜色空间转化;根据光照是否均匀,使用K均值聚类算法对彩色图像的H分量进行不同的聚类处理,经形态学开运算及去噪处理获得最终目标图像。实验表明,该方法对不同施氮量、不同光照、不同生长时期小麦冠层图像的分割效果较好,相对基于Lab空间的K-means聚类分割,该方法可一定程度避免过分割现象;相对基于H分量的Otsu算法,对光照不均匀图像分割更完整,对复杂背景图像分割更精确。     

语义分割和HSV色彩空间引导的低光照图像增强

目的 低光照图像增强是图像处理中的基本任务之一。虽然已经提出了各种方法,但它们往往无法在视觉上产生吸引人的结果,这些图像存在细节不清晰、对比度不高和色彩失真等问题,同时也对后续目标检测、语义分割等任务有不利影响。针对上述问题,提出一种语义分割和HSV（hue,saturation and value）色彩空间引导的低光照图像增强方法。方法 首先提出一个迭代图像增强网络,逐步学习低光照图像与增强图像之间像素级的最佳映射,同时为了在增强过程中保留语义信息,引入一个无监督的语义分割网络并计算语义损失,该网络不需要昂贵的分割注释。为了进一步解决色彩失真问题,在训练时利用HSV色彩空间设计HSV损失;为了解决低光照图像增强中出现细节不清晰的问题,设计了空间一致性损失,使增强图像与对应的低光照图像尽可能细节一致。最终,本文的总损失函数由5个损失函数组成。结果 将本文方法与LIME（low-light image enhancement）、RetinexNet（deep retinex decomposition）、EnlightenGAN（deep light enhancement using generative adversarial networks）、Zero-DCE（zero-reference deep curve estimation）和SGZ（semantic-guided zero-shot learning）5种方法进行了比较。在峰值信噪比（peak signal-to noise ratio,PSNR）上,本文方法平均比Zero-DCE（zero-reference deep curve estimation）提高了0.32dB;在自然图像质量评价（natural image quality evaluation,NIQE）方面,本文方法比EnlightenGAN提高了6%。从主观上看,本文方法具有更好的视觉效果。结论 本文所提出的低光照图像增强方法能有效解决细节不清晰、色彩失真等问题,具有一定的应用价值。     

Color image segmentation using histogram thresholding - Fuzzy C-means hybrid approach

This paper presents a novel histogram thresholding - fuzzy C-means hybrid (HTFCM) approach that could find different application in pattern recognition as well as in computer vision, particularly in color image segmentation. The proposed approach applies the histogram thresholding technique to obtain all possible uniform regions in the color image. Then, the Fuzzy C-means (FCM) algorithm is utilized to improve the compactness of the clusters forming these uniform regions. Experimental results have demonstrated that the low complexity of the proposed HTFCM approach could obtain better cluster quality and segmentation results than other segmentation approaches that employing ant colony algorithm.     

HSI鲁棒彩色图像数字水印算法*

提出了一种彩色图像数字水印算法,先将图像从RGB色彩空间变换到HSI色彩空间,在HSI模型中取I（强度）分量,结合JND自适应地嵌入水印,然后再反变换回RGB色彩空间,而且水印提取不需要原始图像和原始水印。实验结果表明该算法可以有效抵抗有损压缩、滤波、添加噪声等常见图像处理操作,尤其是对色深变换具有较好的鲁棒性,是一个有实用价值的算法。     

Color image segmentation using parallel OptiMUSIG activation function

Segmentation of the different feature based data in a dataset is a challenging proposition in the image processing community. There exist different techniques to solve this problem satisfactorily. A color image is an example of three-dimensional dataset and it consists of a collection of three primary color intensity features. In this article, we focus on the segmentation of true color test images, based on all possible combination of color intensity features. A multilevel sigmoidal (MUSIG) activation function that is applied in the self-organizing neural network architecture is quite efficient enough to segment multilevel gray level intensity images. The function uses equal and fixed class responses, ignoring the heterogeneity of image information content. The optimized version of MUSIG (OptiMUSIG) activation function for the self-organizing neural network architecture can be generated with the optimized class responses from the image content and can be used to effectively segment multilevel gray level intensity images as well.