基于二进小波变换的多尺度边缘细化检测

对采用小波变换多尺度边缘检测得到的图像进行细化,并与传统的5种边缘检测算法的检测结果进行比较。选取不同小波变换的尺度,将图像信噪比作为评价指标对边缘检测算法进行度量。实验结果表明:基于二进小波变换的多尺度边缘细化检测算法在存在噪声干扰时可取得满意的边缘提取效果,在边缘信噪比方面优于传统边缘检测算法。     

基于形态学的图像分割算法研究

本设计论述了基于数学形态学的图像边缘检测算法的研究。利用形态学算法,对图像进行分割,以此提高算法的运行效率。     

基于机械零件边缘检测分析研究

针对机械零件在合格检测过程中不能直接接触测量几何参数的问题,提出通过利用实际物体的图像信息,提取目标图像的边缘轮廓,从而判断该机械零件是否符合要求。首先,建立双目立体视觉系统的硬件平台,通过分析双目视觉系统的数学模型,并采取一定措施将外界环境和系统平台的之间的误差和干扰降到最低,有效地提高边缘检测的精度;然后对相机存在的畸变进行标定,获得摄像机的畸变和内、外参数,为下一步的边缘检测打下基础;最后,在边缘检测方面,对经典边缘检测算法中的Sobel算法、Log算法、Laplacian算法、Canny算法进行了分析研究,并通过实验对不同的算法进行比较,根据分析结果提出了一种基于Laplacian算法改进的新算法。研究结果表明,改进的新算法相对于其他算法而言,检测目标物体的边缘轮廓更加清晰且不存在伪边缘,验证了新算法在边缘检测方面精度更高,从而有效地提高了机械零件几何尺寸的测量精度。     

最小二乘法与灰度重心结合实现圆度检测

产品检测中若采集到的图像是模糊的,其轮廓边缘均不清晰,常规的边缘检测算法中,二值化处理会导致图像边缘信息丢失,降低图像边缘像素位置的定位精度,最终会影响产品圆度误差计算的精度。针对这一问题,在提取图像的边缘后通过灰度重心法进行坐标区域的重新定位,然后对得到的坐标点进行最小二乘法拟合。最后,通过对激光光斑的圆度检测实验,分析拟合结果的圆度误差,证明了该算法的优越性。     

轮胎散斑干涉图像边缘检测

基于小波的同态滤波器优化传统Canny边缘检测算法,提出八邻域双线性插值非极大值抑制法改进传统Canny算法中的八邻域像素的梯度值算法,并将改进算法应用于轮胎散斑干涉图像边缘检测.结果表明,改进Canny算法可更加准确地定位散斑干涉条纹图像的条纹边缘.     

基于FPGA的Sobel边缘检测算法研究与实现

针对某中小河流水文监测系统对水位实时监测的需求,提出利用现场可编程门阵列(FPGA)外接监控摄像头采集图像,结合Sobel边缘检测算法,完成系统设计并在FPGA上实现。实验结果表明:利用FPGA硬件系统较好地实现了Sobel边缘检测算法,呈现出的图像边缘信息也较为理想,能够满足监测系统的需求。     

融合加窗和自适应加权引导滤波改进的Canny算法

针对Canny算法滤波过程中图像细节部分缺失和无法有效去除噪声,并且无法动态识别图像边缘的问题,提出一种融合加窗和自适应加权引导滤波的Canny算法。该算法对Canny算法做出两点改进：一是使用加窗滤波器对图像进行处理,有效滤除噪声,然后对图像进行数学形态学滤波处理,有效增强图像边缘;二是引入分段权值模型对引导滤波算法的正则化参数自适应修正,使其能够根据梯度信息区分边缘和非边缘,从而达到动态识别图像边缘的效果。相比传统Canny算法,改进算法处理后的信噪比提高了1.05～5.22 dB,品质因数提高了0.16～0.35。实验结果表明：改进算法能有效滤除椒盐噪声,识别出比较清晰和完整的边缘。     

一种基于双阈值的运动目标边缘检测方法

文章针对运动目标检测中的实时性和准确性需求,结合视频图像的处理算法,设计了一种运动目标边缘的检测算法,该算法具有多角度、多层次的融合运动特征和边缘特征;采用了一种双阈值融合方法,最终快速、精确地定位出运动目标的边缘。实验结果表明了该方法的优越性。     

塑料手套次品检测系统图像滤波方法

为了提高机器视觉塑料手套检测系统的检测精度和效率,有效滤除检测系统中的图像噪声,提出了一种自适应模糊神经网络的图像滤波算法。在模糊神经网络结构中引入一个鲁棒性较强的隶属函数,并通过梯度下降法对模糊神经网络中的参数进行优化训练,利用优化后的网络结构对被噪声污染的图像进行滤波处理。仿真结果表明,该算法能够在保留较完整的图像边缘和重要细节的前提下,有效滤除机器视觉检测系统中的图像噪声。该滤波算法有效提高了塑料手套图像的清晰度,对于后期塑料手套的图像分割以及边缘化处理具有重要意义。     

基于机器视觉的图像缺陷检测的研究

对基于机器视觉的黑色O形橡胶圈缺陷检测系统展开了研究,对采集到的橡胶圈图像进行相关处理以便检测出缺陷特征,通过改进后的中值滤波滤除噪声,建立图像直方图了解缺陷特征的灰度分布情况,比较了几种边缘检测算子的检测效果,并通过实验仿真验证了算法的可行性。     

Comparison of bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.     

墙地砖边缘检测中的小波去噪

在墙地砖的边缘检测过程中,噪声的引入是不可避免的,有效地消除噪声而不影响检测结果是检测过程中需要解决的关键问题。本文采用了小波去噪方法—对图像进行去噪后再进行边缘检测的方法,其检测结果较好地满足了检测要求。     

边缘检测算子在古陶瓷器型研究中的应用

本文对古陶瓷数字图像进行边缘检测,实际上就是对古陶瓷器型轮廓进行轮廓识别、轮廓形状提取等一系列的数字处理过程。它是古陶瓷器型研究领域中尝试的一种新方法。本文选用5种常用的边缘检测算子对古陶瓷器型进行边缘提取,对比了它们各自的优缺点。实验表明,在实际应用中要根据具体情况和要求选择合适的边缘提取算子。     

边角检测技术在光伏玻璃深加工中的应用

本文阐述了边角检测设备系统结构及工作原理,介绍了光伏玻璃深加工中的几种边角检测技术方案,并结合实际应用经验提出一些待改进的问题和未来技术发展方向。     

Colour metrics for image edge detection

Image edge detection based on low-level feature is usually performed on gray-scale images. Some methods have been developed for edge detection on colour images based on low-level feature, but they are not consistent with human colour perception. This research provides a new algorithm for edge detection based on the “HyAB” large-colour-difference formula. This algorithm uses Sobel operators for gradient-magnitude calculations and Canny methods for localizing edge points. The performance of the new algorithm is qualitatively compared with Sobal and Canny methods using some challenging colour images. The results indicate that gradient magnitudes are best calculated using the HyAB colour-difference formula, and that CIELAB and CIEDE2000 differences are not suitable for this purpose. Definition of gradient magnitudes according human perception is essential in applications such as quality control of fabric printing, calculation of disruptive colouration, and so on. The new algorithm is successful in accuracy and fine edge detection in comparison with the Sobel and Canny methods. The new method is quantitatively compared with state-of-the-art methods using three datasets including BSDS500, MBDD, and BIPED. The correctness and accuracy of annotations of images in datasets have an important effect on results. The new method does not reach scores better than deep-learning-based methods, but it is simple and does not need training. It could probably have better results with improving noise-suppression.     

基于自适应谷底检测的浮选泡沫形态特征提取

针对矿物浮选泡沫大小形状不一、光照不均等特点,提出一种基于适应度反馈微粒群优化算法自适应优选谷底检测阚值的泡沫形态特征提取方法.将微粒群算法的惯性权重和加速因子设置为全局最优点适应度的函数,优选谷底检测阚值,采用局部灰度极小选择边界检测模板,根据多角度逻辑规则比较,获取气泡边界,分割结果的统计分析表明泡沫形态特征服从g...     

A CLUSTERED CLASS DISTRIBUTION APPROACH FOR PROCESS MONITORING AND FAULT DETECTION

Clustering algorithms have been useful for fault detection of process systems. Among many algorithms, ART2 neural networks have provided good results for fault detection and identification. In this article, a new algorithm for fault detection is proposed that is based on clustered classes from ART2 neural networks. An index for detecting changes of process state is defined, to represent differences between recent class distribution and historical class distribution. The proposed algorithm was applied to detection of state change of an industrial wastewater treatment process and was shown to be effective in detecting a change in the process.     

医学图像分割方法研究

分析了医学图像处理的基本流程,对医学图像的分割进行了数学描述。探讨了医学图像分析中的三类主要方法,包括基于区域的图像分割,基于边缘的图像分割,基于特定理论的图像分割。详细阐述了阈值法,边缘检测法及分水岭算法的主要思想。     

A CLUSTERED CLASS DISTRIBUTION APPROACH FOR PROCESS MONITORING AND FAULT DETECTION

Clustering algorithms have been useful for fault detection of process systems. Among many algorithms, ART2 neural networks have provided good results for fault detection and identification. In this article, a new algorithm for fault detection is proposed that is based on clustered classes from ART2 neural networks. An index for detecting changes of process state is defined, to represent differences between recent class distribution and historical class distribution. The proposed algorithm was applied to detection of state change of an industrial wastewater treatment process and was shown to be effective in detecting a change in the process.