A new approach to morphological color image processing

This paper presents a new approach to the generalization of the concepts of grayscale morphology to color images. A new vector ordering scheme is proposed, infimum and supremum operators are defined, and the fundamental vector morphological operations are extracted. The basic properties of the presented vector morphology are described and its similarities to grayscale morphological operators are pointed out. The main advantages of the proposed methodology are that is vector preserving and provides improved results in many morphological applications. Furthermore, experimental results demonstrate the applicability of the proposed technique in a number of image processing and analysis problems, such as noise removal, edge detection and skeleton extraction.     

Comparative evaluation of noise insensitivity of linear edge detection techniques

This work presents a comparative estimate of white-noise insensitivity of the most requested linear edge detection techniques dealing with grayscale images. We consider detectors based on the first and second derivatives of the image brightness. The comparative evaluation was based on statistical analysis. The Kirsh and Frei-Chen operators were found to be less sensitive to noise.     

基于改进高斯-拉普拉斯算子的噪声图像边缘检测方法

针对现有梯度算子在图像边缘检测中存在的对噪声比较敏感的问题,提出了一种基于改进高斯-拉普拉斯算子的图像边缘检测方法。噪声图像中的边缘检测是一项关键任务,然而目前常用的几种梯度算子,包括已经提出的高斯-拉普拉斯算子都没能取得理想效果。提出的方法对传统的拉普拉斯边缘检测算子做了改进,并与高斯滤波器相结合。首先,应用高斯滤波器来平滑图像,抑制噪声。然后基于拉普拉斯梯度边缘检测器进行边缘检测。最后在标准图像上进行评估,评估结果显示,提出的边缘检测方法所获得的峰值信噪比(PSNR)和均方误差(MSE)均优于其他几种对比方法。     

Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement

The paper presents a fuzzy neural network system for edge detection and enhancement. The system can both: (a) obtain edges and (b) enhance edges by recovering missing edges and eliminate false edges caused by noise. The research is comprised of three stages, namely, adaptive fuzzification which is employed to fuzzify the input patterns, edge detection by a three-layer feedforward fuzzy neural network, and edge enhancement by a modified Hopfield neural network. The typical sample patterns are first fuzzified. Then they are used to train the proposed fuzzy neural network. After that, the trained network is able to determine the edge elements with eight orientations. Pixels having high edge membership are traced for further processing. Based on constraint satisfaction and the competitive mechanism, interconnections among neurons are determined in the Hopfield neural network. A criterion is provided to find the final stable result that contains the enhanced edge measurement. The proposed neural networks are simulated on a SUN Sparc station. One hundred and twenty-three training samples are well chosen to cover all the edge and non-edge cases and the performance of the system will not be improved by adding more training samples. Test images are degraded by random noise up to 30% of the original images. Compared with standard edge detection operators and enhancement techniques, the proposed system based on the neuro-fuzzy synergism obtains very good results.     

灰阶边缘的细化

本文提出了一种改进了的灰阶图象Sobel边缘细化新方法,通过对灰阶边缘图多次Sobel边缘细化结果的叠加使边缘点的数值拔高、变陡,再通过灰阶边缘图中各3x3邻域内取数值最大的三个点作边缘,得到单点宽的边缘-其中后者也适用于其它边缘检测算子.此方法处理简单、效果明显,为从图象中提取模糊边缘和微弱边缘、提高定位精度,提供了一种有效的手段.     

Edge detection in multispectral images using the self-organizing map

In this paper, two new methods for edge detection in multispectral images are presented. They are based on the use of the self-organizing map (SOM) and a grayscale edge detector. With the 2-dimensional SOM the ordering of pixel vectors is obtained by applying the Peano scan, whereas this can be omitted using the 1-dimensional SOM. It is shown that using the R-ordering based methods some parts of the edges may be missed. However, they can be found using the proposed methods. Using them it is also possible to find edges in images which consist of metameric colors. Finally, it is shown that the proposed methods find the edges properly from real multispectral airplane images. The size of the SOM determines the amount of found edges. If the SOM is taught using a large color vector database, the same SOM can be utilized for numerous images.     

基于TOF深度图像修复的输送带煤流检测方法

传统的带式输送机煤流检测装置中,核子胶带秤存在一定安全和环保隐患,电子胶带秤检测精度易受输送带张力、刚度等因素的影响;而基于超声波、线激光条纹、双目视觉等技术的非接触式检测方法存在实时性差、测量误差较大等问题。提出了一种基于飞行时间(TOF)深度图像修复的输送带煤流检测方法。通过TOF相机获取输送带运煤图像;对TOF图像进行均衡化处理,采用帧差法和边界跟随算法去除背景噪声,获得感兴趣的煤料区域;针对TOF深度图像因边缘处存在飞行像素噪声与多径误差噪声而导致的边缘信息不准确问题,提出强度图像引导的深度图像修复算法,通过Canny边缘检测算法寻找深度图像和强度图像的相似边缘,基于强度图像的有效边缘信息对深度图像边缘处的不可靠数据进行校正,并进一步基于Navier-Stokes方程和中值滤波器得到高精度深度图像;对煤料区域进行像素级分割,并建立煤料体积计算模型,结合输送带速度得出输送带煤流。实验结果表明,该方法的检测误差不超过3.78%,标准差不超过0.491,平均处理时间为83 ms,满足实际生产要求。     

基于矢量Prewitt算子的多尺度彩色图象边缘检测方法

在矢量Ｐｒｅｗｉｔｔ算子的基础上,引入多凡度组合正则化处理,提出了一种新的彩色图象边缘检测方法,实验表明,若对检测精度,边缘完整性,抗噪性等方面进行综合评价,该方法优于标量算子和单一尺度下的矢量算子。     

基于矢量的彩色图像边缘检测

在矢量算子的基础上,利用图像边缘在一定尺度下二阶导数的特性,提出了一种新的彩色图像边缘检测方法。试验证明,若对检测精度,边缘完整性等方面进行综合评价,该方法优于一般方法。     

基于邻域灰度信息的Hausdorff距离图像匹配方法

针对可见光与红外图像由于成像机理不同引起的图像灰度值差异大、边缘轮廓不一致、传统基于灰度和基于特征的匹配方法匹配概率不高等问题,在分析了各种Hausdorff距离算法的前提下,引入可见光与红外图像的灰度信息,提出一种基于邻域灰度信息Hausdorff距离的图像匹配方法。该方法在计算图像边缘特征点相似性的基础上,增加了邻域归一化灰度方差计算,有效解决了由于边缘差异引起的Hausdorff距离算法对可见光/红外图像匹配概率不高的问题。经可见光与红外图像匹配的仿真实验表明,在各种条件下,该算法与传统Hausdorff距离算法相比,有效提高了在不同光照下图像的匹配效率以及对噪声的抗干扰性能。     

基于多方向的各向异性边缘检测算法

针对传统边缘检测算法抗噪性差的不足,提出了一种多方向的各向异性边缘检测算法。该算法构造了4个具有各向异性的5阶差分模板,对其进行归一化处理后,分别对待检测图像进行卷积处理,根据检测算法在各方向上卷积结果的幅值和方向信息得到灰度边缘图,最后采用最大类间方差法确定阈值进行边缘二值化。多组仿真实验结果表明,该方法能有效实现边缘提取,比传统方法具有更高的检测精度和更强的噪声鲁棒性。     

Retinal vessel segmentation using a probabilistic tracking method

Vessel structures such as retinal vasculature are important features for computer-aided diagnosis. In this paper, a probabilistic tracking method is proposed to detect blood vessels in retinal images. During the tracking process, vessel edge points are detected iteratively using local grey level statistics and vessel's continuity properties. At a given step, a statistic sampling scheme is adopted to select a number of vessel edge points candidates in a local studying area. Local vessel's sectional intensity profiles are estimated by a Gaussian shaped curve. A Bayesian method with the Maximum a posteriori (MAP) probability criterion is then used to identify local vessel's structure and find out the edge points from these candidates. Evaluation is performed on both simulated vascular and real retinal images. Different geometric shapes and noise levels are used for computer simulated images, whereas real retinal images from the REVIEW database are tested. Evaluation performance is done using the Segmentation Matching Factor (SMF) as a quality parameter. Our approach performed better when comparing it with Sun's and Chaudhuri's methods. ROC curves are also plotted, showing effective detection of retinal blood vessels (true positive rate) with less false detection (false positive rate) than Sun's method.     

彩色图像SUSAN边缘检测方法

传统的彩色图像边缘检测方法主要是基于灰度图像的,先将彩色图像转化为灰度图像,然后用灰度图像边缘检测方法检测边缘。这些方法利用彩色图像的亮度信息进行边缘检测,没有考虑其色度信息。因此部分边缘不能被检测出来。提出了一种基于CIELAB空间的SUSAN彩色图像边缘检测方法。该方法首先将彩色图像从RGB空间转换到CIELAB空间,然后用基于色差的SUSAN算子检测边缘。实验结果表明：此方法能有效地检测出彩色图像的边缘。在保留图像边缘方面,性能优于基于灰度图像的边缘检测方法。     

基于双向轮廓跟踪的面部轮廓线生成算法

为了提高医学图像中面部轮廓线的分割效果,提出一种新的面部轮廓线生成算法(bidirectional contour tracking,BCT).该算法首先利用边缘检测算法对CT图像进行检测,然后利用头部CT图像中各组织的灰度特征和图像的对称性,对检测后的图像进行双向轮廓跟踪,并获得面部的轮廓线.另外,根据面部轮廓线的特点,使用中点法可生成辅助轮廓线.以一组颅脑CT图像为例来验证算法的效果,实验结果表明,新的生成算法提高了轮廓线的生成速度,并有很好的分割效果.     

灰度梯度感知人工蚁群的数字图像边缘检测

基于D.R.Chialvoetal.提出的原始模型和V.Ramosetal.的扩展模型,提出了一种用于数字图像边缘检测的基于灰度梯度感知的人工蚁群模型(ACGGP)。模型利用灰度梯度启发信息和信息素轨迹信息共同来指导蚂蚁的行为,这种策略能够有效地减小群体规模进而提高算法的时间性能。此外,还对模型体现出类似于某些人类视觉感知的机制,包括群体短暂记忆、图像主要特征敏感特别是能够良好抑制噪声的自发侧抑制机制等特征进行了阐述。模型在若干幅8bit灰度图像上进行了测试,并和Ramos模型以及经典数字图像边缘检测算法(canny算法)进行了客观比较。     

An edge detection algorithm using multi-state adalines

An edge detection algorithm using multi-state adaptive linear neurons (ADALINES) is presented. Although the tri-state ADALINE is only considered in this work, general multi-state input vectors with extreme values are shown to be linearly separable from the rest of the vectors with the same dimension. The input state of each ADALINE is defined using the local mean in a predefined mask. In addition to the binary input states ± 1, the 0 input state is introduced for controlling the noise effect. If the input pattern matches one of the predefined edge patterns, the corresponding pixel is detected as an edge pixel. Experimental results are shown where the proposed detector is compared with both the Canny and LOG edge detectors.     

An optimal linear operator for step edge detection

Step edge detection is an important subject in image processing and computer vision and many methods, including some optimal filters, have been proposed. In this paper, we propose an optimal linear operator of an infinite window size for step edge detection. This operator is at first derived from the well-known mono-step edge model by use of a signal/noise ratio adapted to edge detection. Because of the infinite window size of the operator, we propose then a statistic multiedge model and analyze the optimal operator by spectral analysis. It is shown that the Infinite Symmetric Exponential Filter (ISEF) is optimal for both mono- and multiedge detection. Recursive realization of ISEF and the derivatives is presented and generalized to multidimensional cases also. The performance of ISEF is analyzed and compared with that of Gaussian and Canny filters, and it is shown that ISEF has a better performance in precision of edge localization, insensibility to noise, and computational complexity. Edge detection based on the optimal filter ISEF is thus presented and the essential difference between ISEF and some other optimal edge detectors is shown. The experimental results for computer-generated and real images, which confirm our theoretical analysis, are reported.     

Vector order statistics operators as color edge detectors

Color edge detection is approached in this paper using vector order statistics. Based on the R-ordering method, a class of color edge detectors is defined. These detectors function as vector operators as opposed to component-wise operators. Specific edge detectors can be obtained as special cases of this class. Various such detectors are defined and analyzed. Experimental results show the noise robustness of the vector order statistics operators. A quantitative evaluation and comparison to other color edge detectors favors our approach. Edge detection results obtained from real color images demonstrate the effectiveness of the proposed approach in real applications.     

一种新的边缘检测计算模型和算法

该文分析了普通线性移不变边缘检测算子与人的视觉系统感知光强度变化时性能的不一 致性,并根据视知觉原理提出了新的边缘检测模型和算法,由此所获得的边缘检测器不再仅是 局部性的而且是兼备全局性的自适应特征提取系统.对一组含有小目标的自然场景图象的实 验结果证实,与局部性算子相比,该方法具有优良的从低反差图象中提取边缘特征的性能.