Gabor filter-based edge detection: A note

Mehrotra proposed a classical method for edge detection based on Gabor filter in 1992. He investigated the algorithm using the measures proposed by Canny and designed a criterion for one-dimensional (1-D) and two-dimensional (2-D) Gabor filter-based edge detector. The derivation was too complicated in time domain and it is not strict enough for 2-D step edge. We explain the Gabor filter-based edge detector by a more precise but much simpler mathematical derivation in frequency domain.     

An improved edge detection algorithm for depth map inpainting

Three-dimensional (3D) measurement technology has been widely used in many scientific and engineering areas. The emergence of Kinect sensor makes 3D measurement much easier. However the depth map captured by Kinect sensor has some invalid regions, especially at object boundaries. These missing regions should be filled firstly. This paper proposes a depth-assisted edge detection algorithm and improves existing depth map inpainting algorithm using extracted edges. In the proposed algorithm, both color image and raw depth data are used to extract initial edges. Then the edges are optimized and are utilized to assist depth map inpainting. Comparative experiments demonstrate that the proposed edge detection algorithm can extract object boundaries and inhibit non-boundary edges caused by textures on object surfaces. The proposed depth inpainting algorithm can predict missing depth values successfully and has better performance than existing algorithm around object boundaries.     

一种基于Mumford-Shah模型的红外图像边缘检测方法

 Chan-Vese模型是一种优秀的简化Mumford-Shah模型。然而Chan-Vese模型是以两个同质区域为基础建立的,这并不符合红外图像的特点,导致直接应用该模型处理红外图像时可能失败。针对这一问题,提出了一种适用于红外图像边缘检测的改进Mumford-Shah模型,并对该模型中目标边缘的保持、停止准则的建立及算法速度的提高作了详细讨论。实验表明,改进Mumford-Shah模型能够克服Chan-Vese模型在对红外图像边缘检测时不能跨越过渡区域的缺点,有效地检测出目标边缘。     

Defects’ geometric feature recognition based on infrared image edge detection

Edge detection is an important technology in image segmentation, feature extraction and other digital image processing areas. Boundary contains a wealth of information in the image, so to extract defects’ edges in infrared images effectively enables the identification of defects’ geometric features. This paper analyzed the detection effect of classic edge detection operators, and proposed fuzzy C-means (FCM) clustering-Canny operator algorithm to achieve defects’ edges in the infrared images. Results show that the proposed algorithm has better effect than the classic edge detection operators, which can identify the defects’ geometric feature much more completely and clearly. The defects’ diameters have been calculated based on the image edge detection results.     

A recursive color image edge detection method using Green's function approach

In this paper, an extended version of image edge detector using Green's function approach is proposed for detection of edges in the color vector space field. In the proposed method, the relationship between the Red, Green and Blue components is considered to design a differential operator for detection of edges in color images. By using the proposed operator, partial derivatives of all components of color image can simultaneously affect on the edge detection process. Therefore the proposed method can preserve the vector nature of color images during the edge processing stages. Also, the proposed method is compared both quantitatively and qualitatively with other color edge detectors. Experimental results show that the proposed method can efficiently preserve the edges even when the color images corrupted with different levels of noise.     

各向异性扩散滤波远探测声波测井图像降噪方法*

声波远探测测井技术近年来在复杂油气藏的构造识别和储层评价中发挥着重要作用。该技术利用来自井外的反射波对井旁地质构造进行准确成像,但由于反射波具有幅度低、受幅度强的井孔直达波干扰等特点,实际数据提取到的反射波信噪比往往比较低,需要对反射波进行降噪处理。非线性各向异性扩散滤波能够在滤除图像噪声的同时保留图像边缘及细节等信息,在地震数据处理和医学图像去噪中都有广泛应用。该文从各向异性扩散滤波的基本原理入手,将提取到的井外反射波信号当作图像,采用不同扩散张量进行处理,通过含噪声的模拟数据处理验证了该方法的处理效果并建立起适合于远探测测井的数据处理流程,实际远探测数据处理结果进一步表明其具有较好的应用前景。     

一种基于边缘能量图的物体检测方法

物体的变形、边缘中断等因素通常对物体检测的结果影响很大。为了减小这些因素的影响,提出一种新的基于边缘能量图的物体检测方法。边缘能量图在一定程度上反映了物体的边缘出现的概率,便于计算并具有较好的鲁棒性。计算出图像的边缘图像,在此基础上分别计算出模型和测试图像的边缘能量图。基于多分辨率的滑动窗口,在图像中找出测试图像边缘能量图与模型边缘能量图最相似的区域。基于边缘能量图快速得到目标物体的轮廓。实验结果显示,该方法使用较快的计算速度得到了相对较高的检测率。     

基于改进Sobel算子的边缘检测算法的研究

针对传统Sobel边缘检测算法对噪声敏感的缺点进行了分析,提出了自适应权值的改进算法,该权值的大小取决于Sobel算子模板内像素灰度值之和。通过不同形式的噪声图像处理对比,结果表明,该算法不仅计算简单,而且具有较强的抗噪能力,对图像边缘有较高的检验精度,极大地改进了图像边缘识别的效果。     

图像边缘检测方法研究综述

图像的边缘是图像最基本也是最重要的特征之一。边缘检测一直是计算机视觉和图像处理领域的经典研究课题之一。图像分析和理解的第一步常常是边缘检测。边缘检测的目的是去发现图像中关于形状和反射或透射比的信息,是图像处理、图像分析、模式识别、计算机视觉以及人类视觉的基本步骤之一。其结果的正确性和可靠性将直接影响到机器视觉系统对客观世界的理解。对一些传统的边缘检测方法和近年来广泛收到关注的边缘检测算法进行了简单介绍。综述中只涉及到检测方面,而没有讨论滤波、边缘定位、算法的复杂程度和边缘检测器性能的评价。     

一种改进的Sobel图像边缘检测算法

边缘检测在数字图像处理和计算机视觉中有着重要的应用。对数字图像处理中具有代表性的Sobel边缘检测算法进行了分析。针对该算法存在检测出的边缘粗且对噪声极其敏感的缺点,提出了一种改进算法。该算法对实际图像中出现的边缘类型进行了数学模型描述,然后把连续型的边缘模型作为研究对象,重新构造了对图像边缘方向进行检测的模板。针对Sobel边缘检测基于一阶导数极大值或二阶导数零交叉而带来的边缘定位准确度不高的缺点,对图像梯度图进行了细化处理。仿真结果表明:该算法对图像噪声干扰有较强的抑制能力,提取的边缘定位准确、结构细腻。     

Improved multi-scale wavelet in pantograph slide edge detection

The mainstream methods of pantograph slide edge detection are based on canny operator and multi-scale wavelet. The former has good single edge response but the edge is fractured, the latter performs good edge continuity but contains excessive edge points. This paper combines the advantages of both methods and proposes as an improved multi-scale wavelet edge detection method based on canny criteria. Firstly we filtered the pantograph image with edge-preserving symmetric near neighbor filter. Secondly calculated the Gaussian wavelet modulus and arguments at all levels of scale, then suppressed the non-maxima value of modulus along the corresponding arguments. At last, we integrated the modulus drawings at all levels of scale, and connected edge with applicable dual-threshold. Experiments results show that the improved algorithm has both satisfactory performances in single edge response and edge continuity, it markedly improves the efficiency of edge detection algorithm. Peak signal to noise ratio (PSNR) analysis finds that the improved algorithm exceeds canny operator and traditional multi-scale wavelet edge detection. Moreover, it has higher positioning accuracy, clearer details and better noise performance.     

误差分散类彩色半调图的边缘检测算法

针对误差分散类彩色半调图提出一种边缘检测算法.首先应用K－L变换,将(R,G,B)空间转换为具有正交特性的彩色特征空间(I1,I2,I3),构造灰度图I使之包含所有单色图像的边缘信息,最后设计多尺度边缘检测模板以获取图像边缘.实验表明,算法较传统方法抗半调噪音的能力强,能快速、准确地提取彩色半调图像的边缘.     

Canny edge detection enhancement by general auto-regression model and bi-dimensional maximum conditional entropy

This paper proposes a novel Canny algorithm without manually setting parameters. Adaptive filter design, implementation and automatic calculation of low and high thresholds are studied in this paper. A general auto-regressive model is deduced that uses only uniform expression for both the linear and non-linear autoregressive model based on Weierstrass theory. Moreover, the bi-dimensional expression of the model is deduced by using bi-vectors instead of scalar parameters. The Generalized M-estimator is chosen for the new model. An adaptive filter is implemented based on the general auto-regression model and simulations are carried out. Gray entropy mathematical model is established according to the gray level-gradient co-occurrence matrix of image and the simulated annealing algorithm is used to solve the gray entropy model. Experiments are done on the worldwide datasets to evaluate the performance of our method. Results demonstrate the superiority of our method compared with the best parameter values method and standard Canny, especially when images are polluted by mixed noises containing Gaussian noise, Poisson noise and impulse noise.     

An improved computing method for the image edge detection

The framework of detecting the image edge based on the sub-pixel multi-fractal measure (SPMM) is presented. The measure is defined, which gives the sub-pixel local distribution of the image gradient. The more precise singularity exponent of every pixel can be obtained by performing the SPMM analysis on the image. Using the singularity exponents and the multi-fractal spectrum of the image, the image can be segmented into a series of sets with different singularity exponents, thus the image edge can be detected automatically and easily. The simulation results show that the SPMM has higher quality factor in the image edge detection.     

基于直方图的感兴趣区图像边缘检测

当图像中的某些区域具有比其它部分更高的重要性时,基于感兴趣区域（ROI）的边缘检测功能就尤为重要。但是当前大部分算法均针对图像整体检测,这在一定程度上会影响对ROI区域的检测效果。针对该问题,提出一种基于ROI的边缘检测算法。新算法首先利用修正后的图像直方图特征选取ROI区域的分割阈值,然后根据分割阈值从背景中分离出ROI区域,最后选择最优边缘检测算子,完成基于ROI的边缘检测。实验结果表明：新算法能够更好的支持对ROI区域的边缘抽取。     

自适应多方向灰度形态学图像边缘检测算法

针对传统的多方向灰度形态学边缘检测算法存在计算量大、效率低的缺点,提出了一种基于自适应噪声抑制的多方向灰度形态学图像边缘检测算法。根据图像所含噪声的种类,采用不同尺度的结构元素对图像进行分类滤波,再根据像素点间灰度值的变化确定边缘方向,由相应方向的结构元素进行边缘检测。实验结果表明,与传统的多方向灰度形态学边缘检测算法相比,检测到的边缘重构相似度和边缘置信度更高,边缘连续性更强,且计算量低,运行效率高。     

改进的Zernike矩工业CT图像边缘检测

为提高工业计算机断层扫描(CT)图像亚像素边缘检测的精度和速度,研究了一种改进的Zernike矩边缘检测方法。该方法采用Sobel边缘算子快速检测出图像所有可能的边缘,通过Zernike矩算子对所有可能的边缘进行重新检测,最后,检测出图像的亚像素边缘并计算其精确位置。由于采用Sobel算子检测出可能的边缘使后续Zernike矩算子检测范围缩小,从而减小了运算量,提高了运算速度。对实际CT图像进行的实验结果表明:改进的Zernike矩工业CT图像边缘检测精度绝对误差<0.24 pixel,改进算法的运算速度提高了约70%。     

干涉条纹边缘检测方法的比较与改进

研究了干涉条纹边缘检测中的Sobel算子和Laplacian算子,针对二者使干涉条纹变粗和模糊的现象,提出了改进的Laplacian算子。该改进算子先对干涉条纹利用Gauss函数进行平滑,然后再作Laplacian变换。实验结果表明,改进的Laplacian算子克服经典算子的不足,获得清晰的条纹边缘,满足了干涉条纹后续处理要求。     

An extended flow-based difference-of-Gaussians method of line drawing for polyhedral image

This paper presents a non-photorealistic rendering technique for stylizing a photograph in the line drawing style. The flow-based difference-of-Gaussians (FDoG) filter constructs lines of style and quality, which usually used to extract the line drawing for some images. In this paper, we inspired by the polyhedral image feature (the line of which are mostly straight line), proposed the method based on the standard FDoG to extract the line drawing of polyhedral image. First, we detect line by fast line segment detector with a false detection control (LSD); then construct a smooth and coherent edge tangent flow (ETF) of the polyhedral images. On the basis of the ETF, the anisotropic FDOG is used to render the lines. This scheme gives clear line features for polyhedral images without much noise, which is also simple and easy to implement, and is robust. The experimental results prove that our algorithm is more efficient than the traditional FDOG approaches in render line drawing for polyhedral image.     

多信息融合的模糊边缘检测技术

提出了一种有效的模糊边缘检测算法，与传统的单纯基于图像增强技术的模糊边缘检测算法不同，此算法采用像素点的多种信息作为边缘检测的特征信息，利用模糊逻辑对这些信息进行综合，使边缘检测器输出的边缘信息更加完善且有效.实验表明，对于处理实际工作环境中的高噪图像的边缘检测问题，此算法是一种实用而有效的方法. 关键词： 边缘检测 模糊算法 融合