边缘检测器性能的客观定量评价

本文提出了一种新的边缘检测器的客观评价方法。该方法既能反映边缘检测器对图像轮廓的检测性能,又能对图像细节处的边缘检测能力进行正确的评价。由于该方法将整个测试图区分成三个不同性质的区域,不同的区域用不同的参数来反映边缘检测器的不同性能。因此,能准确反映噪声对边缘检测器的影响。同时,它将整个边缘检测器的性能分解成边缘提取能力和噪声抑制能力两部分,便于我们全面、准确和客观地评价不同特性的边缘检测器,以适应不同应用场合的需要。     

Edge detection in noisy images by neuro-fuzzy processing

A novel neuro-fuzzy (NF) operator for edge detection in digital images corrupted by impulse noise is presented. The proposed operator is constructed by combining a desired number of NF subdetectors with a postprocessor. Each NF subdetector in the structure evaluates a different pixel neighborhood relation. Hence, the number of NF subdetectors in the structure may be varied to obtain the desired edge detection performance. Internal parameters of the NF subdetectors are adaptively optimized by training by using simple artificial training images. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature. Simulation results indicate that the proposed NF operator outperforms competing edge detectors and offers superior performance in edge detection in digital images corrupted by impulse noise.     

基于对数螺旋线边缘拟合的SAR 图像漩涡信息提取方法

合成孔径雷达(SAR)为漩涡研究提供了大量数据,如何有效提取SAR 图像中漩涡的信息十分重要。该文提出了一种基于对数螺旋线边缘拟合的SAR 图像漩涡信息提取方法,用于提取中心位置、直径、边缘尺寸等漩涡信息。基于此方法,该文利用ENVISAT ASAR 和ERS-2 获得的时序SAR 图像进行了漩涡信息提取实验,得到了漩涡的信息及其变化趋势,并与伪彩色合成结果进行对比,验证了该方法的有效性。      

基于ROC融合准则的SAR边缘检测算法

根据ROC(receiver operating characteristics)技术能评估分类器在所有可能工作阈值下总体性能的特点,建立包含边缘像素点相关分析与ROC分类决策的ROC融合准则。依据该准则组合多种SAR边缘检测算子,并得到合成孔径雷达(SAR)影像的"理想"边缘检测结果。实验结果表明,本文方法能融合多种边缘检测算子的优点,有较强的开放性与目标适应性,并且不需要手工设置阈值,自动化程度高,有很强的工程实用性。     

Histogram-based morphological edge detector

Presents a new edge detector for automatic extraction of oceanographic (mesoscale) features present in infrared (IR) images obtained from the Advanced Very High Resolution Radiometer (AVHRR). Conventional edge detectors are very sensitive to edge fine structure, which makes it difficult to distinguish the weak gradients that are useful in this application from noise. Mathematical morphology has been used in the past to develop efficient and statistically robust edge detectors. Image analysis techniques use the histogram for operations such as thresholding and edge extraction in a local neighborhood in the image. An efficient computational framework is discussed for extraction of mesoscale features present in IR images. The technique presented in the present article, called the Histogram-Based Morphological Edge detector (HMED), extracts all the weak gradients, yet retains the edge sharpness in the image. A new morphological operation defined in the domain of the histogram of an image is also presented. An interesting experimental result was found by applying the HMED technique to oceanographic data in which certain features are known to have edge gradients of varying strength     

Color edge detection using vector order statistics

A method is proposed whereby a color image is treated as a vector field and the edge information carried directly by the vectors is exploited. A class of color edge detectors is defined as the minimum over the magnitudes of linear combinations of the sorted vector samples. From this class, a specific edge detector is obtained and its performance characteristics studied. Results of a quantitative evaluation and comparison to other color edge detectors, using Pratt's (1991) figure of merit and an artificially generated test image, are presented. Edge detection results obtained for real color images demonstrate the efficiency of the detector.     

Nonlinear processing and analysis of angular signals

Physical quantities referring to angles, like vector direction, color hue, etc., exhibit an inherently periodic nature. Due to this periodicity, digital filters and edge operators proposed for data on the line cannot be applied on such data. We introduce filters for angular signals (circular mean, circular median, circular a-trimmed mean, circular modified trimmed mean). Particular emphasis is given to the circular median filter, for which some interesting properties are derived. We also use estimators of circular dispersion to introduce edge detectors for angular signals. Three variations for the extension of quasirange to circular data are proposed, and expressions for their output PDF are derived. These “circular” quasiranges have good and user-controlled properties as edge detectors in noisy angular signals. The performance of the proposed edge operators is evaluated on angular edges, using certain quantitative criteria. Finally, a series of experiments featuring one-dimensional (1-D) angular signals and hue images is used to illustrate the operation of the new filters and edge detectors     

An ideal image edge detection scheme

This paper introduces a scale-invariant and contrast-invariant multi-scale differential edge detector. The method is a direct consequence of two key discoveries: (1) a precise scale normalization method and (2) a formula to verify scale-invariant detectors. The new scale normalization method provides differential operators with respect to scale, among them the scale-invariant edge detectors. To investigate these differential detectors quantitatively, mathematical functions were used to represent the edges and to solve for the parameters, including position, width, contrast, offset, and orientation, in closed form. Noise is filtered as a low-contrast feature. The method has been tested with various kinds of synthesized edge functions and can extract edge features accurately. It is suitable for real-world images of several kinds of degradation.     

Blind blur assessment for vision-based applications

In this paper, a criterion for objective defocus blur measurement is theoretically derived from one image. The essential idea is to estimate the point spread function (PSF) from the line spread function (LSF), whereas the LSF is constructed from edge information. It is proven that an edge point corresponds to the local maximal gradient in a blurred image, and therefore edges can be extracted from blurred images by conventional edge detectors. To achieve high accuracy, local Radon transform is implemented and a number of LSFs are extracted from each edge. The experimental results on a variety of synthetic and real blurred images validate the proposed method. The algorithm can be implemented for image quality evaluation in vision-based applications as no reference images are needed.     

A generation method of insulator region proposals based on edge boxes

High-quality insulator region proposals play important roles in the process of transmission line inspection images. A generation method of insulator region proposals based on edge boxes is proposed in this paper, and edge boxes are applied to the localization of insulators in inspection images creatively. We take a series of operations to generate insulator region proposals:K-means cluster is used on curvature scale space (CSS) points extracted from edge images, the most appropriate cluster number is chosen, and the circle is drawn on the insulator subclass. We consider the characteristics of insulators’ edge images, and combine these characteristics with edge boxes. As a result, more insulator region proposals are displayed. The experimental results show that our method can effectively reduce the interference area, meanwhile, has high quality of region proposals with fast calculation speed.     

SAR图像线条特征提取方法研究

雷达图像的线条特征提取算法一般分为三步，首先作图像预处理，然后采用特定的边缘检测算子提取边缘点，第三步形成各种有意义的线条特征，并且将断裂的长线连起来。常见的提取算法都是基于光学图像的，所以在确定边缘点时，假定图像中的噪声是加性高斯白噪声。这样，在合成孔径雷达（SAR）图像中使用光学图像中提取边缘点的方法就不行了，这是因为SAR图像中的相干斑噪声是服从K分布的。不过，将边缘点连接为有意义的线条特征的方法还可以沿用。所以，我们可以采用两步检测算子来检测边缘点，然后使用从相位编组思想演化而来的方向编组法形成直线条特征方法。大量的实验验证了这种方法对于SAR图像是切实可行的。     

An optimal multiedge detector for SAR image segmentation

Edge detection is a fundamental issue in image analysis. Due to the presence of speckle, which can be modeled as a strong, multiplicative noise, edge detection in synthetic aperture radar (SAR) images is extremely difficult, and edge detectors developed for optical images are inefficient. Several robust operators have been developed for the detection of isolated step edges in speckled images. The authors propose a new step-edge detector for SAR images, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model. It computes a normalized ratio of exponentially weighted averages (ROEWA) on opposite sides of the central pixel. This is done in the horizontal and vertical direction, and the magnitude of the two components yields an edge strength map. Thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme. Experimental results obtained from simulated SAR images as well as ERS-1 data are presented     

Edge-enhancement postprocessing using artificial dissipation.

We present a method to enhance, by postprocessing, the performance of gradient-based edge detectors. It improves the performance of the edge detector by adding terms which are similar to the artificial dissipation that appear in the numerical solution of hyperbolic PDEs. This term is added to the output of the edge detector. The edges that are missed or blurred by the edge detector are reconstructed through the addition of the artificial dissipation terms. Edges that are detected correctly by the edge detector are preserved. We present the theory of the artificial dissipation and its improvement of the quality of the detected edges. We demonstrate the performance of the algorithm on diverse images.     

基于图像的晶粒定位技术研究

对基于图像的晶粒定位技术进行了研究,提出了一种利用绘制等间隔网格线定位晶粒位置的方法。首先对获取的晶圆片图像进行阈值分割,对分割后的图像进行晶粒边缘提取,依据提取的晶粒边缘和"非共线三点确定圆"的原理计算出三个相邻但不在同一条直线上的晶粒圆心坐标,计算出绘制网格的偏移和斜率,在晶圆片上绘制等间隔网格线来定位晶粒形心位置和晶粒缺失位置,精确控制微小芯片贴装过程中晶粒的拾起。实验结果表明,该方法能精确地识别出图像中的晶粒形心位置和晶粒缺失位置。     

Statistical behavior of edge detectors

In this paper we present a method for estimating the statistical properties of two well-known edge detectors: the non maxima suppression and the zero crossing of the Laplacian algorithms. Assuming the data are corrupted by an additive Gaussian noise we derive the probability density function (pdf) of the detected edge. Thanks to this approach the computed pdf explicitly depends on the parameters of the edge detector. Experimental results on real images and comparisons with Monte Carlo simulations are presented in order to characterize the performance of this method.     

激光CCD自准直仪圆目标中心抗噪声精确定位方法

为满足高精度计量和方位瞄准跟踪系统的发展对激光CCD自准直仪测量精度的要求,提出一种基于正交傅里叶-梅林矩的激光CCD自准直仪圆目标中心抗噪声精确定位方法。首先利用正交傅里叶-梅林矩(OFMM)的幅值旋转不变性和更低径向矩阶数在充分提取图像边缘细节信息的同时抑制图像噪声的影响,通过对图像边缘旋转后垂直方向上不同阶次的正交傅里叶-梅林矩之间关系的分析将圆目标轮廓定位至亚像素级,然后采用最小二乘拟合方法实现圆目标中心的精确定位。结果表明,该方法稳定性好,定位精度高且抗干扰能力强,改进后的激光CCD自准直仪的测量分辨力提高了10倍,测量精度由2″提高到±0.18″,可有效满足在小角度测量和瞄准等领域的高精度测量需要。     

CFAR edge detector for polarimetric SAR images

Finding the edges between different regions in an image is one of the fundamental steps of image analysis, and several edge detectors suitable for the special statistics of synthetic aperture radar (SAR) intensity images have previously been developed. In this paper, a new edge detector for polarimetric SAR images is presented using a newly developed test statistic in the complex Wishart distribution to test for equality of covariance matrices. The new edge detector can be applied to a wide range of SAR data from single-channel intensity data to multifrequency and/or multitemporal polarimetric SAR data. By simply changing the parameters characterizing the test statistic according to the applied SAR data, constant false-alarm rate detection is always obtained. An adaptive filtering scheme is presented, and the distributions of the detector are verified using simulated polarimetric SAR images. Using SAR data from the Danish airborne polarimetric SAR, EMISAR, it is demonstrated that superior edge detection results are obtained using polarimetric and/or multifrequency data compared to using only intensity data.     

一种海洋涡旋SAR图像仿真方法

海洋涡旋对海洋热循环起着关键作用,是海洋科学研究中的一个重要分支。合成孔径雷达(Synthetic Aperture Radar, SAR)为海洋涡旋的观测和研究提供了大量的图像数据,但是涡旋在SAR成像时会受到各种海洋环境因素的影响,难以解译涡旋SAR图像特征。仿真SAR图像可以用于研究涡旋的特征,但是目前极少有关于涡旋SAR图像仿真方法的研究。为了更好地解译SAR图像中的涡旋特征,该文提出了一种涡旋SAR图像仿真方法。首先,基于流体力学中典型的Burgers-Rott涡旋模型,建立涡旋2维表面流场;然后,利用SAR海洋成像仿真模型,仿真给定涡旋2维流场、海面风场以及雷达系统参数下的涡旋SAR图像。该文针对气旋式涡旋与反气旋式涡旋进行了仿真实验,并建立了仿真涡旋SAR图像的相似度评价标准。实验结果表明,仿真的涡旋SAR图像与真实星载涡旋SAR图像能够较好地吻合,验证了方法的有效性。      

Quantitative design and evaluation of enhancement/thresholding edge detectors

Quantitative design and performance evaluation techniques are developed for the enhancement/thresholding class of image edge detectors. The design techniques are based on statistical detection theory and deterministic pattern-recognition classification procedures. The performance evaluation methods developed include: a)deterministic measurement of the edge gradient amplitude; b)comparison of the probabilities of correct and false edge detection; and c) figure of merit computation. The design techniques developed are used to optimally design a variety of small and large mask edge detectors. Theoretical and experimental comparisons of edge detectors are presented.     

A morphological gradient approach to color edge detection.

A new color edge detector based on vector differences is proposed. The basic technique gives as its output the maximum distance between the vectors within a mask. When applied to scalar-valued images, the method reduces to the classic morphological gradient. The technique is relatively computationally efficient and can also be readily applied to other vector-valued images. To improve the performance in the presence of noise, a novel pairwise outlier rejection scheme is employed. A quantitative evaluation using Pratt's figure of merit shows the new technique to outperform other recently proposed color edge detectors. In addition, application to real images demonstrates the approach to be highly effective despite its low complexity.