外文翻译---特征空间稳健性分析:彩色图像分割
计算机科学与技术专业使用阈值技术的图像分割等毕业论文外文文献翻译及原文
毕业设计(论文)外文文献翻译文献、资料中文题目: 1.使用阈值技术的图像分割2.最大类间方差算法的图像分割综述文献、资料英文题目:文献、资料来源:文献、资料发表(出版)日期:院(部):专业:计算机科学与技术班级:姓名:学号:指导教师:翻译日期: 2017.02.14毕业设计(论文)题目基于遗传算法的自动图像分割软件开发翻译(1)题目Image Segmentation by Using ThresholdTechniques翻译(2)题目A Review on Otsu Image Segmentation Algorithm使用阈值技术的图像分割 1摘要本文试图通过5阈值法作为平均法,P-tile算法,直方图相关技术(HDT),边缘最大化技术(EMT)和可视化技术进行了分割图像技术的研究,彼此比较从而选择合的阈值分割图像的最佳技术。
这些技术适用于三个卫星图像选择作为阈值分割图像的基本猜测。
关键词:图像分割,阈值,自动阈值1 引言分割算法是基于不连续性和相似性这两个基本属性之一的强度值。
第一类是基于在强度的突然变化,如在图像的边缘进行分区的图像。
第二类是根据预定义标准基于分割的图像转换成类似的区域。
直方图阈值的方法属于这一类。
本文研究第二类(阈值技术)在这种情况下,通过这项课题可以给予这些研究简要介绍。
阈分割技术可分为三个不同的类:首先局部技术基于像素和它们临近地区的局部性质。
其次采用全局技术分割图像可以获得图像的全局信息(通过使用图像直方图,例如;全局纹理属性)。
并且拆分,合并,生长技术,为了获得良好的分割效果同时使用的同质化和几何近似的概念。
最后的图像分割,在图像分析的领域中,常用于将像素划分成区域,以确定一个图像的组成[1][2]。
他们提出了一种二维(2-D)的直方图基于多分辨率分析(MRA)的自适应阈值的方法,降低了计算的二维直方图的复杂而提高了多分辨率阈值法的搜索精度。
这样的方法源于通过灰度级和灵活性的空间相关性的多分辨率阈值分割方法中的阈值的寻找以及效率由二维直方图阈值分割方法所取得的非凡分割效果。
图像分割综述
摘要图像分割是把图像划分为有意义的若干区域的图像处理技术,分割技术在辅助医学诊断及运动分析、结构分析等领域都有着重要的研究价值和广泛的应用发展前景。
在阅读大量文献的基础上,本文对图像分割技术的理论基础、发展历程及图像分割方法的热点、难点问题进行了分类综述,对不同分割算法优缺点进行了总结和归纳,并对图像分割的发展趋势进行了初步的展望和预测。
在此基础上,为了对图像分割理论有更直观的认识,本文选取并行边界算法和分水岭算法这两种方法,用MATLAB软件进行了基础的仿真,并对结果进行了分析和总结,本文重点对一些近年来新兴的算法,比如水平集(Level-set)算法、马尔科夫随机场算法(Markov)、模糊算法、遗传算法、数学形态学算法等进行了概略性的探讨,对这些新兴算法的特点、原理、研究动态进行了分析和总结。
关键词:图像分割;边界;区域;水平集;马尔科夫AbstractImage segmentation is an image processing technology that divides the image into a number of regions. Image segmentation has very important significance in supporting medical diagnosis, motion analysis, structural analysis and other fields.Based on recent research, a survey on the theory and development of image segmentation, hot and difficult issues in image segmentation is given in this article. And describes the characteristics of each method as well as their respective advantages and disadvantages in image segmentation .This article introduces and analyzes some basic imaging and image segmentation methods in theory and describes the development trends of medical image segmentation. To have a better understanding of image segmentation, I use MATLAB software to stimulate on images about the parallel edge algorithms and watershed algorithm. And the analysis of the segmentation results is given in the article.This article introduces and analyzes the new algorithms in recent years such as Level-set algorithm, Markov algorithm, Fuzzy algorithm, Genetic algorithm and Morphological algorithm. In this paper, the features, theory and research trends of these algorithms are analyzed and summarized.Keywords: Image segmentation; Border; Area;Level-set;Markov第1章引言1.1 图像分割的背景和重要作用图像是传达信息的一种方式,图像中含有大量的有用信息,理解图像并从图像中抽取信息以用来完成其他工作是数字图像技术中一个重要的应用领域,而理解图像的第一步就是图像的分割。
基于RGB颜色空间的彩色图像分割
基于RGB颜色空间的彩色图像分割作者:洪梦霞梁少华来源:《电脑知识与技术》2020年第34期摘要:颜色分割可用于检测身体肿瘤、从森林或海洋背景中提取野生动物的图像,或者从单一的背景图像中提取其他彩色物体,大数据时代背景下,颜色空间对于图像分析仍然非常有用,通过在RGB和HSV颜色空间可视化图像,可以看到图像颜色分布的散点图。
通过阈值分割,确定要提取的所有像素的阈值,在所有像素中获取期望的像素,得到分割后的图像。
实验结果分析,使用OpenCV基于Python中的颜色从图像中分割对象,可以达到简单、快速、可靠的目的。
关键词:颜色空间;颜色分割;阈值分割中图分类号:TP3 文献标识码:A文章编号:1009-3044(2020)34-0225-03Abstract: Color segmentation can be used to detect body tumors, extract wildlife images from forest or marine background, or extract other color objects from a single background image. In the background of big data era, color space is still very useful for image analysis. By visualizing images in RGB and HSV color spaces, we can see the scatter map of image color distribution. Through threshold segmentation, the threshold of all the pixels to be extracted is determined, and the desired pixels are obtained from all pixels to obtain the segmented image. Experimental results show that using OpenCV to segment objects from images based on Python color can achieve the purpose of simple, fast and reliable.Key words: color space; color segmentation; threshold segmentation圖像分割是把图像分成若干个特定的、具有独特性质的区域并分割出感兴趣区域的过程。
图像处理中的图像分割效果评估指标研究
图像处理中的图像分割效果评估指标研究图像处理领域中的图像分割是一项重要的任务,广泛应用于医学图像分析、目标检测、图像识别等领域。
在图像分割过程中,评估其效果的指标对于算法的改进和优化具有重要意义。
本文将探讨图像处理中常用的图像分割评估指标,分析其原理和适用范围。
图像分割是将图像分成具有相似性质或特征的不重叠区域的过程。
评估图像分割的效果需要一个准确而全面的指标。
以下是几个常用的图像分割效果评估指标:1. 轮廓相似度(Contour Similarity)轮廓相似度是评估分割结果与真实分割之间轮廓接近程度的指标。
它通过计算分割边界与真实边界之间的重合程度来评估分割的准确性。
轮廓相似度的计算通常使用Jaccard系数或Dice系数,它们分别是分割轮廓区域与真实轮廓区域的交集除以它们的并集。
这些系数的取值范围为0到1,越接近1表示分割效果越好。
2. 区域相似度(Region Similarity)区域相似度是评估分割结果与真实分割之间区域匹配程度的指标。
它通过计算分割区域与真实区域之间的重合程度来评估分割的准确性。
常用的区域相似度指标包括覆盖率(Recall)和准确率(Precision)。
覆盖率表示分割中正确划分的区域与真实区域的比例,准确率表示真实区域中被正确划分的区域比例。
综合考虑覆盖率和准确率的F1得分是一个常用的评价指标,其计算公式为F1 = 2 * (Precision * Recall) / (Precision + Recall)。
3. 边界误差(Boundary Error)边界误差是评估分割结果边界与真实边界之间差异程度的指标。
它可以通过计算分割边界与真实边界之间的距离进行测量。
常用的边界误差指标包括平均绝对误差(MAE)和线段对称哈尔夫距离(LSHD)。
MAE计算所有点与最近边界之间的距离的平均值,而LSHD计算最小距离点对之间的平均距离。
4. 相似性指数(Similarity Index)相似性指数是评估分割结果与真实分割之间相似程度的指标。
基于HSI色彩坐标相似度的彩色图像分割方法
基于HSI色彩坐标相似度的彩色图像分割方法李宁;许树成;邓中亮【摘要】该文提出一种基于HSI彩色空间的图像分割方法。
欧氏距离作为图像分割中常用的衡量像素点之间彩色关系的依据,在HSI坐标系下却不能很好地反应两个像素点之间的关系。
因此,提出相似度代替欧氏距离作为一种新的衡量两个像素点之间彩色关系的依据。
算法通过确定HSI分量中占主导地位的分量,建立彩色图像分割模型,创建一个和原图尺寸一样的颜色相似度等级图,并利用相应的颜色相似度等级图的颜色信息对像素点进行聚类。
实验结果表明,所提出的分割算法具有很强的鲁棒性和准确性,在其他条件相同的情况下,基于相似度的分割方法优于基于欧氏距离为基准的彩色图像分割。
%A new method for color image segmentation which based on HSI color space is presented in this paper. Euclidean distance as a common basis of measuring the colour relationship between two pixels can not reflect the relationship between the two pixels in the HSI coordinate system. Therefore,the traditional Euclidean distance is abandoned,and the color similarity is proposed as a new basis of measuring the relationship between the two pixels. The algorithm is used to build the color image seg?mentation model by at determining the dominant component in the HSI components and create a color similarity level picture with the size same as the original picture. The color information of the corresponding color level diagram is adopted to cluster the pixel points. The experimental results show that the segmentation algorithm has strong robustness and high accuracy,and under the sameconditions,the segmentation method based on similarity is better than the segmentation method based on Euclidean dis?tance.【期刊名称】《现代电子技术》【年(卷),期】2017(040)002【总页数】5页(P30-33,38)【关键词】图像分割;HSI彩色空间;颜色相似度;欧氏距离【作者】李宁;许树成;邓中亮【作者单位】北京邮电大学,北京 100876;北京邮电大学,北京 100876;北京邮电大学,北京 100876【正文语种】中文【中图分类】TN911.73-34基于彩色信息的图像分割算法在计算机视觉中扮演着重要的角色,并广泛应用于各个领域。
基于HSI颜色空间分割彩色图像的聚类算法
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[ 摘 要] I Hs 色彩 空间是从人 的视 觉 系统 出发 , 用色调 ( e、 Hu)色饱 和度(a rt n或 ch0 和亮度 ( tni St ao u i rI m) I esy或 Br hn s来描述 n t i tes g ) 色彩 。本 文提 出 了一种 新 的彩 色 图像 分割 方 法 , 据 HS 颜 色空 间 色调 和 亮度 的 二 维 直 方 图进 行 分 割 。 根 I [ 关键词 ] 色图像分割 HS 聚类 二 维直方 图 波峰查找 彩 I
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图像分割和分析。通 常需要把 R B 色空 间变换到其他颜色空间 , G 颜 以 便进行更好地分割。 现有 的颜色空间有很多 ,其 中 HS 颜色空 间是一种和人眼的视觉 I 感知相吻合的色彩空间 , 的三个颜色分量相对独立[ 其中色调 、 和 它 4 1 , 饱 度和亮度信息分别由分量 H、 、表示。 sI 色调表示了基本 的颜色 ; 饱和度 是颜色纯度的测度 , 表示 了混入 白光的 比例 ; 亮度则 描述了图像的明亮 程度 。 把颜色信息和亮度信息分离开来 , 能更好的进行 图像分割。 HS 可以由 R B颜 色空 间转换而来, I G 转换公式 : 为
汽车车牌识别系统毕业论文(带外文翻译)解析
汽车车牌识别系统---车牌定位子系统的设计与实现摘要汽车车牌识别系统是近几年发展起来的计算机视觉和模式识别技术在智能交通领域应用的重要研究课题之一。
在车牌自动识别系统中,首先要将车牌从所获取的图像中分割出来实现车牌定位,这是进行车牌字符识别的重要步骤,定位的准确与否直接影响车牌识别率。
本次毕业设计首先对车牌识别系统的现状和已有的技术进行了深入的研究,在此基础上设计并开发了一个基于MATLAB的车牌定位系统,通过编写MATLAB文件,对各种车辆图像处理方法进行分析、比较,最终确定了车牌预处理、车牌粗定位和精定位的方法。
本次设计采取的是基于微分的边缘检测,先从经过边缘提取后的车辆图像中提取车牌特征,进行分析处理,从而初步定出车牌的区域,再利用车牌的先验知识和分布特征对车牌区域二值化图像进行处理,从而得到车牌的精确区域,并且取得了较好的定位结果。
关键词:图像采集,图像预处理,边缘检测,二值化,车牌定位ENGLISH SUBJECTABSTRACTThe subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location.Firstly, the paper gives a deep research on the status and technique of the plate license recognition system. On the basis of research, a solution of plate license recognition system is proposed through the software MATLAB,by the M-files several of methods in image manipulation are compared and analyzed. The methods based on edge map and das differential analysis is used in the process of the localization of the license plate,extracting the characteristics of the license plate in the car images after being checked up for the edge, and then analyzing and processing until the probably area of license plate is extracted,then come out the resolutions for localization of the car plate.KEY WORDS:imageacquisition,image preprocessing,edge detection,binarization,licence,license plate location目录前言 (1)第1章绪论 (2)§1.1 课题研究的背景 (2)§1.2 车牌的特征 (2)§1.3 国内外车辆牌照识别技术现状 (3)§1.4车牌识别技术的应用情况 (4)§1.5 车牌识别技术的发展趋势 (5)§1.6车牌定位的意义 (6)第2章MATLAB简介 (7)§2.1 MATLAB发展历史 (7)§2.2 MATLAB的语言特点 (7)第3章图像预处理 (10)§3.1 灰度变换 (10)§3.2 图像增强 (11)§3. 3 图像边缘提取及二值化 (13)§3. 4 形态学滤波 (18)第4章车牌定位 (21)§4.1 车牌定位的主要方法 (21)§4.1.1基于直线检测的方法 (22)§4.1.2 基于阈值化的方法 (22)§4.1.3 基于灰度边缘检测方法 (22)§4.1.4 基于彩色图像的车牌定位方法 (25)§4.2 车牌提取 (26)结论 (30)参考文献 (31)致谢 (33)前言随着交通问题的日益严重,智能交通系统应运而生。
数字图像处理论文中英文对照资料外文翻译文献
第 1 页中英文对照资料外文翻译文献原 文To image edge examination algorithm researchAbstract :Digital image processing took a relative quite young discipline,is following the computer technology rapid development, day by day obtains th widespread application.The edge took the image one kind of basic characteristic,in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widesp application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develo the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainlyhas Robert, Laplacian, Sobel, Canny, operators and so on LOG 。
一种快速_稳健的图像分割方法
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normalized cuts and image segmentation翻译
规范化切割和图像分割摘要:为解决在视觉上的感知分组的问题,我们提出了一个新的方法。
我们目的是提取图像的总体印象,而不是只集中于局部特征和图像数据的一致性。
我们把图像分割看成一个图形的划分问题,并且提出一个新的分割图形的全球标准,规范化切割。
这一标准衡量了不同组之间的总差异和总相似。
我们发现基于广义特征值问题的一个高效计算技术可以用于优化标准。
我们已经将这种方法应用于静态图像和运动序列,发现结果是令人鼓舞的。
1简介近75年前,韦特海默推出的“格式塔”的方法奠定了感知分组和视觉感知组织的重要性。
我的目的是,分组问题可以通过考虑图(1)所示点的集合而更加明确。
Figure1:H<iw m.3Uiyps?通常人类观察者在这个图中会看到四个对象,一个圆环和内部的一团点以及右侧两个松散的点团。
然而这并不是唯一的分割情况。
有些人认为有三个对象,可以将右侧的两个认为是一个哑铃状的物体。
或者只有两个对象,右侧是一个哑铃状的物体,左侧是一个类似结构的圆形星系。
如果一个人倒行逆施,他可以认为事实上每一个点是一个不同的对象。
这似乎是一个人为的例子,但每一次图像分割都会面临一个相似的问题一将一个图像的区域D划分成子集Di会有许多可能的划分方式(包括极端的将每一个像素认为是一个单独的实体)。
我们怎样挑选“最正确”的呢?我们相信贝叶斯的观点是合适的,即一个人想要在较早的世界知识背景下找到最合理的解释。
当然,困难在于具体说明较早的世界知识一一些低层次的,例如亮度,颜色,质地或运行的一致性,但是关于物体对称或对象模型的中高层次的知识是同等重要的。
这些表明基于低层次线索的图像分割不能够也不应该旨在产生一个完整的最终的正确的分割。
目标应该是利用低层次的亮度,颜色,质地,或运动属性的一致性继续的提出分层分区。
中高层次的知识可以用于确认这些分组或者选择更深的关注。
这种关注可能会导致更进一步的再分割或分组。
关键点是图像分割是从大的图像向下进行,而不是像画家首先标示出主要区域,然后再填充细节。
图像语义分割算法最新发展趋势
图像语义分割算法最新发展趋势近年来,随着计算机视觉和深度学习的快速发展,图像语义分割算法也取得了显著的进展。
图像语义分割是指将图像中的每个像素标记为属于特定类别的过程,其在自动驾驶、智能辅助医疗、人机交互等领域具有重要的应用价值。
以下将介绍图像语义分割算法的最新发展趋势。
1. 基于深度学习的图像语义分割算法深度学习在图像语义分割任务中取得了巨大的成功。
传统的图像分割算法主要基于手工设计的特征和机器学习算法,而深度学习算法则通过神经网络自动学习特征和分类器。
最新的基于深度学习的图像语义分割算法采用了各种类型的神经网络结构,包括全卷积网络(Fully Convolutional Network, FCN)、编码器-解码器网络(Encoder-Decoder Network)、空洞卷积网络(Dilated Convolutional Network)等。
这些网络结构能够在不同尺度上有效地提取图像的语义信息,从而实现更准确的分割结果。
2. 融合多模态信息的图像语义分割算法除了利用图像本身的信息进行分割,最新的图像语义分割算法还试图将多模态信息(如深度图像、红外图像、激光雷达等)融合到分割过程中。
这种融合可以提供更丰富的输入特征,从而改善分割结果的准确性。
同时,多模态信息的融合也有助于解决部分单模态图像难以分割的问题。
例如,在自动驾驶领域,融合激光雷达和图像信息可以帮助精确分割道路和障碍物。
3. 弱监督学习的图像语义分割算法传统的图像语义分割算法通常需要大量标注的像素级标签数据来训练模型。
然而,标注大规模图像数据是一项耗时费力的工作。
最新的图像语义分割算法开始探索利用弱监督学习方法来降低对标注数据的依赖性。
弱监督学习方法通过利用较低精度的标签或辅助信息来训练模型,例如图像级标签、边界框或图像级标签估计。
这样可以大幅降低标注数据的需求,并且保持分割结果的准确性。
4. 增强学习在图像语义分割中的应用增强学习是指智能体通过与环境的交互来学习如何做出决策以最大化累积奖励的过程。
数字图像处理英文原版及翻译
Digital Image Processing and Edge DetectionDigital Image ProcessingInterest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception.An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pixels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields asingle number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in be- tween image processing and computer vision.There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and high level processes. Low-level processes involve primitive opera- tions such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higher level processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting(segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.”As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some form of organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnet- ic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to theother.Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. A familiar example of enhancement is when we increase the contrast of an image because “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good”enhancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it.Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image , such as the jpg used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a longway toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for trans- forming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital image processing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig 2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where theinformation of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig 2 above by the use of double-headed arrows between the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.Although point and line detection certainly are important in any discussion on segmentation,edge detection is by far the most common approach for detecting meaningful discounties in gray level.Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:1.focal blur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local specularities or interreflections in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there maytherefore usually be one edge on each side of the line.To illustrate why edge detection is not a trivial task, let us consider the problem of detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels.If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges.Hence, to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled.There are many methods for edge detection, but most of them can be grouped into two categories,search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian of the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking out irrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges.If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholdingparameters, and suitable thresholding values may vary over the image.Some edge-detection operators are instead based upon second-order derivatives of the intensity. This essentially captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.We can come to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that point.Since we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a threshold.Thus we define a point in an image as being as being an edge point if its two-dimensional first-order derivative is greater than a specified criterion of connectedness is by definition an edge.The term edge segment generally is used if the edge is short in relation to the dimensions of the image.A key problem in segmentation is to assemble edge segments into longer edges.An alternate definition if we elect to use the second-derivative is simply to define the edge ponits in an image as the zero crossings of its second derivative.The definition of an edge in this case is the same as above.It is important to note that these definitions do not guarantee success in finding edge in an image.They simply give us a formalism to look for them.First-order derivatives in an image are computed using the gradient.Second-order derivatives are obtained using the Laplacian.数字图像处理和边缘检测数字图像处理在数字图象处理方法的兴趣从两个主要应用领域的茎:改善人类解释图像信息;和用于存储,传输,和表示用于自主机器感知图像数据的处理。
结合目标色彩特征的基于注意力的图像分割
结合目标色彩特征的基于注意力的图像分割张建兴;李军;石庆龙【摘要】An attention-based approach for image segmentation is proposed. It integrates the bottom-up and top-down attention mechanism, to form a scene-target selection method for the target objects in an image. In the multi-scale space of image, this algorithm simultaneously extractsthe intensity, color and orientation features of the scene image and the color feature of the target object to generate the scene-target saliency map. Then, it processes the saliency map by combination the multi-scale scene-target images with normalization of the image features. Finally, the target object is obtained by double-interpolation and black-white segmentation of the scene image. By applying the algorithm to the images in natural scene and indoor environment, experiment is conducted. The experimental results indicate that the algorithm can successfully segment the scene image and extract the target object in any condition, and exhibit good robustness even for the scene image with noisy objects.%提出一种基于注意力的图像分割算法,在视觉场景选择机制基础上结合目标色彩特征的任务驱动机制,形成了自下而上和自上而下的注意力集成分割机理。
基于机器学习的颜色图像分割算法研究
基于机器学习的颜色图像分割算法研究第一章:引言颜色图像分割是计算机视觉领域中的一个重要问题,其主要目的是将一个彩色图像划分成不同的区域,从而提取出图片中各种对象和区域的信息。
颜色图像分割在许多领域都有重要的应用,如医学图像分析、图像处理以及计算机视觉等。
在过去的几十年中,许多学者已经开发出了各种各样的图像分割算法,包括传统的阈值分割、边缘检测和区域生长等算法。
然而,传统的图像分割算法存在许多局限性,如对光照变化和噪声敏感性较高、对图像质量要求较高等。
因此,如何提高图像分割的准确性和稳定性成为计算机视觉领域中的一个重要课题。
近年来,随着机器学习技术的发展,机器学习的方法已经被广泛应用于图像分割领域。
机器学习技术能够从大量的数据中学习出图像的特征和模式,从而实现更加准确和稳定的图像分割。
本文将重点介绍基于机器学习的颜色图像分割算法的研究进展和应用。
第二章:基本原理2.1 颜色特征提取在颜色图像分割中,颜色特征是非常重要的特征之一。
颜色特征提取通常包括色调、饱和度和亮度等几个方面。
色调是指像素颜色的基本色彩。
饱和度是指像素颜色的纯度或鲜艳程度。
亮度是指像素颜色的明暗程度。
2.2 卷积神经网络卷积神经网络是一种广泛应用于图像识别和分类的深度学习神经网络模型。
卷积神经网络在处理图像时,能够从图像中提取出深层次的特征信息,从而实现对图像的高效识别和分类。
2.3 支持向量机支持向量机(SVM)是一种基于统计的机器学习算法,它能够通过学习一组正负样本数据,从而寻找出一个最优的分类超平面,从而实现对数据的分类。
第三章:基于机器学习的颜色图像分割算法3.1 基于卷积神经网络的颜色图像分割算法卷积神经网络能够从图像中提取出深度特征,这些特征能够有效地用于颜色图像分割。
基于卷积神经网络的颜色图像分割算法通常采用全卷积神经网络实现像素级别的分割。
在训练过程中,首先采用交叉熵误差作为损失函数,然后通过反向传播算法对神经网络进行优化。
一种基于K-L变换和Otsu阈值选择的彩色图像分割算法
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超像素分割算法研究综述
超像素分割算法研究综述超像素分割是计算机视觉领域的一个重要研究方向,它的目标是将图像分割成一组紧密连接的区域,每个区域都具有相似的颜色和纹理特征。
超像素分割可以在许多计算机视觉任务中发挥重要作用,如图像分割、目标检测和图像语义分割等。
本综述将介绍一些常见的超像素分割算法及其应用。
1. SLIC (Simple Linear Iterative Clustering):SLIC是一种基于k-means聚类的超像素分割算法。
它首先在图像中均匀采样一组初始超像素中心,并通过迭代的方式将每个像素分配给最近的超像素中心。
SLIC算法结合了颜色和空间信息,具有简单高效的特点,适用于实时应用。
2.QuickShift:QuickShift是一种基于密度峰值的超像素分割算法。
它通过利用图片的颜色相似性和空间相似性来计算每个像素的相似度,并通过移动像素之间的边界来形成超像素。
QuickShift算法不依赖于预定义的超像素数量,适用于不同大小和形状的图像。
3. CPMC (Constrained Parametric Min-Cuts):CPMC是一种基于图割的超像素分割算法。
该算法通过求解最小割问题来获得具有边界连通性的超像素分割结果。
CPMC算法能够生成形状规则的超像素,适用于对形状准确性要求较高的应用。
4. LSC (Linear Spectral Clustering):LSC是一种基于线性谱聚类的超像素分割算法。
它通过构建图像的颜色和空间邻接图,并对其进行谱分解来获取超像素分割结果。
LSC算法具有良好的分割结果和计算效率,适用于大规模图像数据的处理。
5. SEEDS (Superpixels Extracted via Energy-Driven Sampling):SEEDS是一种基于随机采样的超像素分割算法。
它通过迭代的方式将像素相似度转化为能量函数,并通过最小化能量函数来生成超像素。
SEEDS算法能够快速生成具有边界连通性的超像素,并适用于实时应用。
dipa21tat4标准
dipa21tat4标准
Dipa21TAT4标准是一种用于描述数字图像处理和分析的国际标准。
它是由国际数字图像处理协会(Digital Image Processing Society)制定的,用
于规范数字图像处理和分析领域的术语、定义、符号和缩略语。
Dipa21TAT4标准包含以下内容:
1. 数字图像处理的基本概念和术语,如像素、分辨率、灰度级、颜色空间等。
2. 数字图像处理和分析的方法和技术,如滤波、变换、分割、识别等。
3. 数字图像处理和分析的应用领域,如医学影像、遥感、计算机视觉等。
4. 数字图像处理和分析的算法和实现,如滤波器设计、特征提取、图像匹配等。
5. 数字图像处理和分析的工具和软件,如MATLAB、OpenCV、Python等。
Dipa21TAT4标准是数字图像处理和分析领域的重要参考,有助于规范该领域的术语和定义,促进学术交流和技术发展。
基于S-CIELAB空间的彩色图像分割
基于S-CIELAB空间的彩色图像分割李光;王朝英;侯志强【摘要】提出一种基于S-CIELAB颜色空间的彩色图像分割算法.在人类视觉彩色传递模型的基础上,将原始的RGB图像转换到S-CIELAB空间,运用均值漂移算法对图像进行分割.实验结果表明,该算法能模拟人类视觉模糊特性,得到与人类视觉非常接近的分割结果.对于被高斯噪声严重污染的彩色图像,该算法也能有效地进行分割.【期刊名称】《计算机工程》【年(卷),期】2010(036)004【总页数】2页(P198-199)【关键词】彩色图像分割;模式-彩色分离模型;颜色空间;均值漂移算法【作者】李光;王朝英;侯志强【作者单位】空军工程大学电讯工程学院,西安,710077;空军工程大学电讯工程学院,西安,710077;空军工程大学电讯工程学院,西安,710077【正文语种】中文【中图分类】TP3911 概述图像分割是图像分析与理解过程中一个最基本的处理步骤,其目的是将图像划分为多个互不重叠的区域,每个区域内的像素具有相似或一致的性质,并且相邻区域不具有类似性质。
由于图像分割实现了对图像中人们感兴趣区域的分离,使得目标特征和参数的提取成为可能,因此多年来它一直是人们高度重视的研究领域[1]。
然而,现有算法还存在一些重要的问题有待解决[2]。
传统分割算法很少考虑人眼的视觉模糊机制,当处理有空间频率变化的图像时,如mosaic或半色调图像,传统算法的效果就很差。
而且,分割算法的性能在很大程度上依赖于颜色空间的选择,在大量的颜色模型中,如RGB, YUV, HIS, Lab, Luv,哪个空间最适合用来进行图像分割还没有一个明确的标准。
人类视觉系统具有优良的彩色图像分割性能,在实际应用中对彩色图像分割的最终结果也是由人眼主观判定的。
文献[3]用颜色的非对称匹配方法研究人眼的彩色传递特性,在一系列生理实验的基础上,提出了模式-彩色分离(patterncolor separable)模型,该模型是目前较为完整的人眼彩色视觉模型。
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附录2:外文翻译Robust Analysis of Feature Spaces: Color ImageSegmentationAbstractA general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors, a preprocessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.Keywords: robust pattern analysis, low-level vision, content-based indexing1 IntroductionFeature space analysis is a widely used tool for solving low-level image understanding tasks. Given an image, feature vectors are extracted from local neighborhoods and mapped into the space spanned by their components. Significant features in the image then correspond to high density regions in this space. Feature space analysis is the procedure of recovering the centers of the high density regions, i.e., the representations of the significant image features. Histogram based techniques, Hough transform are examples of the approach.When the number of distinct feature vectors is large, the size of the feature space is reduced by grouping nearby vectors into a single cell. A discretized feature space is called an accumulator. Whenever the size of the accumulator cell is not adequate for the data, serious artifacts can appear. The problem was extensively studied in the context of the Hough transform, e.g.. Thus, for satisfactory results a feature space should have continuous coordinate system. The content of a continuous feature space can be modeled as a sample from a multivariate, multimodal probability distribution. Note that for real images the number of modes can be very large, of the order of tens.The highest density regions correspond to clusters centered on the modes of the underlying probability distribution. Traditional clustering techniques, can be used for feature space analysis but they are reliable only if the number of clusters is small and known a priori. Estimating the number of clusters from the data is computationally expensive and not guaranteed to produce satisfactory result.A much too often used assumption is that the individual clusters obey multivariate normal distributions, i.e., the feature space can be modeled as a mixture of Gaussians. The parameters of the mixture are then estimated by minimizing an error criterion. For example, a large class of thresholding algorithms are based on the Gaussian mixture model of the histogram, e.g.. However, there is no theoretical evidence that an extracted normal cluster necessarily corresponds to a significant image feature. On the contrary, a strong artifact cluster may appear when several features are mapped into partially overlapping regions.Nonparametric density estimation avoids the use of the normality assumption. The two families of methods, Parzen window, and k-nearest neighbors, both require additional input information (type of the kernel, number of neighbors). Thisinformation must be provided by the user, and for multimodal distributions it is difficult to guess the optimal setting.Nevertheless, a reliable general technique for feature space analysis can be developed using a simple nonparametric density estimation algorithm. In this paper we propose such a technique whose robust behavior is superior to methods employing robust estimators from statistics.2 Requirements for RobustnessEstimation of a cluster center is called in statistics the multivariate location problem. To be robust, an estimator must tolerate a percentage of outliers, i.e., data points not obeying the underlying distribution of the cluster. Numerous robust techniques were proposed, and in computer vision the most widely used is the minimum volume ellipsoid (MVE) estimator proposed by Rousseeuw.The MVE estimator is affine equivariant (an affine transformation of the input is passed on to the estimate) and has high breakdown point (tolerates up to half the data being outliers). The estimator finds the center of the highest density region by searching for the minimal volume ellipsoid containing at least h data points. The multivariate location estimate is the center of this ellipsoid. To avoid combinatorial explosion a probabilistic search is employed. Let the dimension of the data be p. A small number of (p+1) tuple of points are randomly chosen. For each (p+1) tuple the mean vector and covariance matrix are computed, defining an ellipsoid. The ellipsoid is inated to include h points, and the one having the minimum volume provides the MVE estimate.Based on MVE, a robust clustering technique with applications in computer vision was proposed in. The data is analyzed under several \resolutions" by applying the MVE estimator repeatedly with h values representing fixed percentages of the data points. The best cluster then corresponds to the h value yielding the highest density inside the minimum volume ellipsoid. The cluster is removed from the feature space, and the whole procedure is repeated till the space is not empty. The robustness of MVE should ensure that each cluster is associated with only one mode of the underlying distribution. The number of significant clusters is not needed a priori.The robust clustering method was successfully employed for the analysis of a large variety of feature spaces, but was found to become less reliable once the number of modes exceeded ten. This is mainly due to the normality assumption embeddedinto the method. The ellipsoid defining a cluster can be also viewed as the high confidence region of a multivariate normal distribution. Arbitrary feature spaces are not mixtures of Gaussians and constraining the shape of the removed clusters to be elliptical can introduce serious artifacts. The effect of these artifacts propagates as more and more clusters are removed. Furthermore, the estimated covariance matrices are not reliable since are based on only p + 1 points. Subsequent post processing based on all the points declared inliers cannot fully compensate for an initial error.To be able to correctly recover a large number of significant features, the problem of feature space analysis must be solved in context. In image understanding tasks the data to be analyzed originates in the image domain. That is, the feature vectors satisfy additional, spatial constraints. While these constraints are indeed used in the current techniques, their role is mostly limited to compensating for feature allocation errors made during the independent analysis of the feature space. To be robust the feature space analysis must fully exploit the image domain information.As a consequence of the increased role of image domain information the burden on the feature space analysis can be reduced. First all the significant features are extracted, and only after then are the clusters containing the instances of these features recovered. The latter procedure uses image domain information and avoids the normality assumption.Significant features correspond to high density regions and to locate these regions a search window must be employed. The number of parameters defining the shape and size of the window should be minimal, and therefore whenever it is possible the feature space should be isotropic. A space is isotropic if the distance between two points is independent on the location of the point pair. The most widely used isotropic space is the Euclidean space, where a sphere, having only one parameter (its radius) can be employed as search window. The isotropy requirement determines the mapping from the image domain to the feature space. If the isotropy condition cannot be satisfied, a Mahalanobis metric should be defined from the statement of the task.We conclude that robust feature space analysis requires a reliable procedure for the detection of high density regions. Such a procedure is presented in the next section.3 Mean Shift AlgorithmA simple, nonparametric technique for estimation of the density gradient was proposed in 1975 by Fukunaga and Hostetler. The idea was recently generalized by Cheng.Assume, for the moment, that the probability density function p(x) of the p-dimensional feature vectors x is unimodal. This condition is for sake of clarity only, later will be removed. A sphere X S of radius r, centered on x contains the featurevectors y such that r x y ≤-. The expected value of the vector x y z -=, given x and X S is[]()()()()()dy S y p y p x y dy S y p x y S z E X X S X S X X ⎰⎰∈-=-==μ(1) If X S is sufficiently small we can approximate()()X S X V x p S y p =∈,where p S r c V X ⋅=(2)is the volume of the sphere. The first order approximation of p(y) is()()()()x p x y x p y p T∇-+=(3) where ()x p ∇ is the gradient of the probability density function in x. Then()()()()⎰∇--=X X S S Tdy x p x p V x y x y μ(4) since the first term vanishes. The value of the integral is()()x p x p p r ∇+=22μ(5) or[]()()x p x p p r x S x x E X ∇+=-∈22(6) Thus, the mean shift vector, the vector of difference between the local mean and the center of the window, is proportional to the gradient of the probability density at x. The proportionality factor is reciprocal to p(x). This is beneficial when the highest density region of the probability density function is sought. Such region corresponds to large p(x) and small ()x p ∇, i.e., to small mean shifts. On the other hand, low density regions correspond to large mean shifts (amplified also by small p(x) values).The shifts are always in the direction of the probability density maximum, the mode. At the mode the mean shift is close to zero. This property can be exploited in a simple, adaptive steepest ascent algorithm.Mean Shift Algorithm1. Choose the radius r of the search window.2. Choose the initial location of the window.3. Compute the mean shift vector and translate the search window by that amount.4. Repeat till convergence.To illustrate the ability of the mean shift algorithm, 200 data points were generated from two normal distributions, both having unit variance. The first hundred points belonged to a zero-mean distribution, the second hundred to a distribution having mean 3.5. The data is shown as a histogram in Figure 1. It should be emphasized that the feature space is processed as an ordered one-dimensional sequence of points, i.e., it is continuous. The mean shift algorithm starts from the location of the mode detected by the one-dimensional MVE mode detector, i.e., the center of the shortest rectangular window containing half the data points. Since the data is bimodal with nearby modes, the mode estimator fails and returns a location in the trough. The starting point is marked by the cross at the top of Figure 1.Figure 1: An example of the mean shift algorithm.In this synthetic data example no a priori information is available about the analysis window. Its size was taken equal to that returned by the MVE estimator, 3.2828. Other, more adaptive strategies for setting the search window size can also be defined.Table 1: Evolution of Mean Shift AlgorithmIn Table 1 the initial values and the final location,shown with a star at the top of Figure 1, are given.The mean shift algorithm is the tool needed for feature space analysis. The unimodality condition can be relaxed by randomly choosing the initial location of the search window. The algorithm then converges to the closest high density region. The outline of a general procedure is given below.Feature Space Analysis1. Map the image domain into the feature space.2. Define an adequate number of search windows at random locations in the space.3. Find the high density region centers by applying the mean shift algorithm to each window.4. Validate the extracted centers with image domain constraints to provide the feature palette.5. Allocate, using image domain information, all the feature vectors to the feature palette.The procedure is very general and applicable to any feature space. In the next section we describe a color image segmentation technique developed based on this outline.4 Color Image SegmentationImage segmentation, partioning the image into homogeneous regions, is a challenging task. The richness of visual information makes bottom-up, solely image driven approaches always prone to errors. To be reliable, the current systems must be large and incorporate numerous ad-hoc procedures, e.g.. The paradigms of gray level image segmentation (pixel-based, area-based, edge-based) are also used for color images. In addition, the physics-based methods take into account information about the image formation processes as well. See, for example, the reviews. The proposed segmentation technique does not consider the physical processes, it uses only the given image, i.e., a set of RGB vectors. Nevertheless, can be easily extended to incorporate supplementary information about the input. As homogeneity criterioncolor similarity is used.Since perfect segmentation cannot be achieved without a top-down, knowledge driven component, a bottom-up segmentation technique should·only provide the input into the next stage where the task is accomplished using a priori knowledge about its goal; and·eliminate, as much as possible, the dependence on user set parameter values.Segmentation resolution is the most general parameter characterizing a segmentation technique. Whilethis parameter has a continuous scale, three important classes can be distinguished.Undersegmentation corresponds to the lowest resolution. Homogeneity is defined with a large tolerance margin and only the most significant colors are retained for the feature palette. The region boundaries in a correctly undersegmented image are the dominant edges in the image.Oversegmentation corresponds to intermediate resolution. The feature palette is rich enough that the image is broken into many small regions from which any sought information can be assembled under knowledge control. Oversegmentation is the recommended class when the goal of the task is object recognition.Quantization corresponds to the highest resolution.The feature palette contains all the important colors in the image. This segmentation class became important with the spread of image databases, e.g.. The full palette, possibly together with the underlying spatial structure, is essential for content-based queries.The proposed color segmentation technique operates in any of the these three classes. The user only chooses the desired class, the specific operating conditions are derived automatically by the program.Images are usually stored and displayed in the RGB space. However, to ensure the isotropy of the feature space, a uniform color space with the perceived color differences measured by Euclidean distances should be used. We have chosen the *v**L space, whose coordinates are related to the RGB values by nonlinear uD was used as reference illuminant. The transformations. The daylight standard65chromatic information is carried by *u and *v, while the lightness coordinate *L can be regarded as the relative brightness. Psychophysical experiments show that *v**L space may not be perfectly isotropic, however, it was found satisfactory for uimage understanding applications. The image capture/display operations alsointroduce deviations which are most often neglected.The steps of color image segmentation are presented below. The acronyms ID and FS stand for image domain and feature space respectively. All feature space computations are performed in the ***v u L space.1. [FS] Definition of the segmentation parameters.The user only indicates the desired class of segmentation. The class definition is translated into three parameters·the radius of the search window, r;·the smallest number of elements required for a significant color, min N ;·the smallest number of contiguous pixels required for a significant image region, con N .The size of the search window determines the resolution of the segmentation, smaller values corresponding to higher resolutions. The subjective (perceptual) definition of a homogeneous region seem s to depend on the “visual activity” in the image. Within the same segmentation class an image containing large homogeneous regions should be analyzed at higher resolution than an image with many textured areas. The simplest measure of the “visual activity” can be derived from the global covariance matrix. The square root of its trace,σ, is related to the power of the signal(image). The radius r is taken proportional to σ. The rules defining the three segmentation class parameters are given in Table 2. These rules were used in the segmentation of a large variety images, ranging from simple blood cells to complex indoor and outdoor scenes.When the goal of the task is well defined and/or all the images are of the same type, the parameters can be fine tuned.Table 2: Segmentation Class Parameters2. [ID+FS] Definition of the search window.The initial location of the search window in the feature space is randomly chosen. To ensure that the search starts close to a high density region several locationcandidates are examined. The random sampling is performed in the image domain and a few, M = 25, pixels are chosen. For each pixel, the mean of its 3 3 neighborhood is computed and mapped into the feature space. If the neighborhood belongs to a larger homogeneous region, with high probability the location of the search window will be as wanted. To further increase this probability, the window containing the highest density of feature vectors is selected from the M candidates.3. [FS] Mean shift algorithm.To locate the closest mode the mean shift algorithm is applied to the selected search window. Convergence is declared when the magnitude of the shift becomes less than 0.1.4. [ID+FS] Removal of the detected feature.The pixels yielding feature vectors inside the search window at its final location are discarded from both domains. Additionally, their 8-connected neighbors in the image domain are also removed independent of the feature vector value. These nei ghbors can have “strange” colors due to the image formation process and their removal cleans the background of the feature space. Since all pixels are reallocated in Step 7, possible errors will be corrected.5. [ID+FS] Iterations.Repeat Steps 2 to 4, till the number of feature vectors in the selected searchN.window no longer exceedsmin6. [ID] Determining the initial feature palette.N vectors.In the feature space a significant color must be based on minimumminN pixels Similarly, to declare a color significant in the image domain more thanminof that color should belong to a connected component. From the extracted colors only those are retained for the initial feature palette which yield at least one connectedN. The neighbors removed at Step 4 component in the image of size larger thanminare also considered when defining the connected components Note that the threshold N which is used only at the post processing stage.is notcon7. [ID+FS] Determining the final feature palette.The initial feature palette provides the colors allowed when segmenting the image. If the palette is not rich enough the segmentation resolution was not chosen correctly and should be increased to the next class. All the pixel are reallocated basedon this palette. First, the pixels yielding feature vectors inside the search windows at their final location are considered. These pixels are allocated to the color of the window center without taking into account image domain information. The windowsare then inflated to double volume (their radius is multiplied with p32). The newly incorporated pixels are retained only if they have at least one neighbor which was already allocated to that color. The mean of the feature vectors mapped into the same color is the value retained for the final palette. At the end of the allocation procedure a small number of pixels can remain unclassified. These pixels are allocated to the closest color in the final feature palette.8. [ID+FS] Postprocessing.This step depends on the goal of the task. The simplest procedure is the removal from the image of all small connected components of size less thanN.Thesecon pixels are allocated to the majority color in their 3⨯3 neighborhood, or in the case of a tie to the closest color in the feature space.In Figure 2 the house image containing 9603 different colors is shown. The segmentation results for the three classes and the region boundaries are given in Figure 5a-f. Note that undersegmentation yields a good edge map, while in the quantization class the original image is closely reproduced with only 37 colors. A second example using the oversegmentation class is shown in Figure 3. Note the details on the fuselage.5 DiscussionThe simplicity of the basic computational module, the mean shift algorithm, enables the feature space analysis to be accomplished very fast. From a 512⨯512 pixels image a palette of 10-20 features can be extracted in less than 10 seconds on a Ultra SPARC 1 workstation. To achieve such a speed the implementation was optimized and whenever possible, the feature space (containing fewer distinct elements than the image domain) was used for array scanning; lookup tables were employed instead of frequently repeated computations; direct addressing instead of nested pointers; fixed point arithmetic instead of floating point calculations; partial computation of the Euclidean distances, etc.The analysis of the feature space is completely autonomous, due to the extensive use of image domain information. All the examples in this paper, and dozens more notshown here, were processed using the parameter values given in Table 2. Recently Zhu and Yuille described a segmentation technique incorporating complex global optimization methods(snakes, minimum description length) with sensitive parameters and thresholds. To segment a color image over a hundred iterations were needed. When the images used in were processed with the technique described in this paper, the same quality results were obtained unsupervised and in less than a second. The new technique can be used un modified for segmenting gray level images, which are handled as color images with only the *L coordinates. In Figure 6 an example is shown.The result of segmentation can be further refined by local processing in the image domain. For example, robust analysis of the pixels in a large connected component yields the inlier/outlier dichotomy which then can be used to recover discarded fine details.In conclusion, we have presented a general technique for feature space analysis with applications in many low-level vision tasks like thresholding, edge detection, segmentation. The nature of the feature space is not restricted, currently we are working on applying the technique to range image segmentation, Hough transform and optical flow decomposition.255⨯pixels, 9603 colors.Figure 2: The house image, 192(a)(b)Figure 3: Color image segmentation example.512⨯pixels, 77041 colors. (b)Oversegmentation: 21/21(a)Original image, 512colors.(a)(b)Figure 4: Performance comparison.116⨯pixels, 200 colors. (b) Undersegmentation: 5/4 colors.(a) Original image, 261Region boundaries.(a)(b)(c)(d)(e)(f)Figure 5: The three segmentation classes for the house image. The right columnshows the region boundaries.(a)(b) Undersegmentation. Number of colors extracted initially and in the featurepalette: 8/8.(c)(d) Oversegmentation: 24/19 colors. (e)(f) Quantization: 49/37 colors.(a)(b)(c)256 Figure 6: Gray level image segmentation example. (a)Original image, 256pixels.(b) Undersegmenta-tion: 5 gray levels. (c) Region boundaries.特征空间稳健性分析:彩色图像分割摘要本文提出了一种恢复显著图像特征的普遍技术。