Gabor filter-based edge detection

It is common practice to utilize evidence from biological and psychological vision experiments to develop computational models for low-level feature extraction. The receptive profiles of simple cells in mammalian visual systems have been found to closely resemble Gabor filters. Daugman proved that Gabor filters achieve joint minimal joint uncertainty. These results led researchers to develop computational models based on Gabor filters for several low-level vision applications such as edge detection, texture classification, optical flow estimation and data compression. In this paper, the performance of a Gabor odd filter-based edge detector is investigated using the measures proposed by Canny. Based on this performance analysis a design criterion for one-dimensional (1D) Gabor filter-based edge detector is derived. It is shown that this design criterion also holds good for a two-dimensional (2D) Gabor filter-based edge detector. Experimental results are presented to demonstrate the performance of the Gabor filter-based edge detector.     

One-pixel-wide edge detection

This paper describes a new edge detection technique for obtaining spatially accurate single-pixel-wide edges in a gray scale image. The edge detection first detects multi-pixel-wide edges. Then, it reduces the edge width by a thinning edge operation. A heuristic search (i.e. minimum cost search) is then used to obtain optimal one-pixel-wide edges. Finally the detected one-pixel-wide edges are encoded for efficient edge data manipulation, storage, and retrieval. Experimental images are illustrated for showing the effectiveness of the edge detection.     

On candidates selection for hysteresis thresholds in edge detection

Manual determination of hysteresis thresholds is time-consuming. Several methods approach the problem of unsupervised determination of edge detector parameters, but they require human intervention to establish the initial range of values in which to detect the best parameter value and the result depends on the range of values initially used.In this paper, a method is proposed to determine candidates to hysteresis thresholds in an unsupervised manner. The method provides a criterion to reduce in a significant way the number of initial values to be considered as threshold candidates. The proposed method can be applied to any feature image provided by an edge detector upon which hysteresis must be implemented.     

A Deformable Object Tracking Algorithm Based on the Boundary Element Method that is Robust to Occlusions and Spurious Edges

The manipulation of deformable objects is an important problem in robotics and arises in many applications including biomanipulation, microassembly, and robotic surgery. For some applications, the robotic manipulator itself may be deformable. Vision-based deformable object tracking can provide feedback for these applications. Computer vision is a logical sensing choice for tracking deformable objects because the large amount of data that is collected by a vision system allows many points within the deformable object to be tracked simultaneously. This article introduces a template based deformable object tracking algorithm, based on the boundary element method, that is able to track a wide range of deformable objects. The robustness of this algorithm to occlusions and to spurious edges in the source image is also demonstrated. A robust error measure is used to handle the problem of occlusion and an improved edge detector based on the Canny edge operator is used to suppress spurious edges. This article concludes by quantifying the performance increase provided by the robust error measure and the robust edge detector. The performance of the algorithm is also demonstrated through the tracking of a sequence of cardiac MRI images.     

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.     

On edge gradient approximations

The effect of imposing some desirable properties on the masks of differential-type operators is studied. It is shown that in a 3×3 window there are only two free parameters to specify. It is also shown that under the assumption of these properties a widely used differential-type edge detection method is equivalent to an appropriate template-matching method and vice-versa.     

Sub-pixel edge detection based on an improved moment

A novel moment-based method for sub-pixel edge location is proposed. Based on coarse edge-location by SOBEL operator, the geometric information of the target is used to reduce the number of moment-template to only one, which can largely save the time. Experimental results demonstrate that the proposed method is effective with more robustness, higher precision and speed.     

A class of robust edge detectors based on latin squares

The theory of Latin Square experimental designs is extended to edge detection of multi-grey level pictorial data. Latin Square designs are realized using mask operations either as a square or in linear forms using ANOVA to estimate the model parameters. The test statistics are based upon the robust F-test and the thresholds are selected by an empirical interactive process. A post hoc comparison method is used to confine the edge element ambiguities to 2-pixel layer thickness in masks greater than 2 × 2 × k. Computer simulations are shown to verify the theory.     

Digital Twin-driven online anomaly detection for an automation system based on edge intelligence

Accurate anomaly detection is critical to the early detection of potential failures of industrial systems and proactive maintenance schedule management. There are some existing challenges to achieve efficient and reliable anomaly detection of an automation system: (1) transmitting large amounts of data collected from the system to data processing components; (2) applying both historical data and real-time data for anomaly detection. This paper proposes a novel Digital Twin-driven anomaly detection framework that enables real-time health monitoring of industrial systems and anomaly prediction. Our framework, adopting the visionary edge AI or edge intelligence (EI) philosophy, provides a feasible approach to ensuring high-performance anomaly detection via implementing Digital Twin technologies in a dynamic industrial edge/cloud network. Edge-based Digital Twin allows efficient data processing by providing computing and storage capabilities on edge devices. A proof-of-concept prototype is developed on a LiBr absorption chiller to demonstrate the framework and technologies' feasibility. The case study shows that the proposed method can detect anomalies at an early stage.     

A gravitational approach to edge detection based on triangular norms

We study the method of Sun et al. for edge detection based on the Law of Universal Gravity. We analyze the effect of the substitution of the product operation by other triangular norms in the calculation of the gravitational forces. We treat edges as fuzzy sets for which membership degrees are extracted from the resulting gravitational force on each pixel. We consider several prototypical triangular norms and experimentally show that their features determine the kind of edges detected. The new method is tested on the Berkeley Segmentation Dataset, showing to be competitive compared to the Canny method.     

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.     

SEDMI: Saliency based edge detection in multispectral images

Detecting edges in multispectral images is difficult because different spectral bands may contain different edges. Existing approaches calculate the edge strength of a pixel locally, based on the variation in intensity between this pixel and its neighbors. Thus, they often fail to detect the edges of objects embedded in background clutter or objects which appear in only some of the bands.We propose SEDMI, a method that aims to overcome this problem by considering the salient properties of edges in an image. Based on the observation that edges are rare events in the image, we recast the problem of edge detection into the problem of detecting events that have a small probability in a newly defined feature space. The feature space is constructed by the spatial gradient magnitude in all spectral channels. As edges are often confined to small, isolated clusters in this feature space, the edge strength of a pixel, or the confidence value that this pixel is an event with a small probability, can be calculated based on the size of the cluster to which it belongs.Experimental results on a number of multispectral data sets and a comparison with other methods demonstrate the robustness of the proposed method in detecting objects embedded in background clutter or appearing only in a few bands.     

基于Canny算法的红外小目标边缘检测方法

从红外图像的特点出发,基于Canny算法进行了目标边缘检测。首先,对源图像进行小波分解和重构,对图像进行消噪,抑制噪声对目标提取的影响。然后对消噪后的图像用Canny算法进行边缘检测。这种边缘检测方法利用了小波消噪的优点,在抑制背景噪声的同时保留了目标边缘细节。实验结果表明,这种结合方法优于单独使用Canny算法。     

十字线激光图像的快速边缘检测方法

针对工程应用中十字线激光图像存在较大干扰噪声和要求图像处理速度快的特点，提出了一个快速准确的边缘检测方法。在图像处理时先找出图像中的有用区域，采用基于局域亮度最大的方法在有用区域内找出像素级的边缘点，对这些像素级的边缘点进行亚像素处理得到更高角度的边缘点，用最小二乘法计算出两条直线，最后计算出交点坐标和水平倾角。用640×480像素摄像机（光靶分辨率为0．0627mm／像素）在4．3m摄取50幅图像进行处理，标准差X为0．0ll9mm，Y为0．0235mm。水平倾角为1．89’。方法应用于导轨直线度和扭曲度检测中，取得了很好的效果。     

一种改进的基于机器视觉的边缘检测算法

图像的边缘保留了图像最基本也是最重要的特征，边缘检测减少了数据量，在机器视觉系统中得到广泛的应用，是图形图像领域里的重要研究方向之一．本文提出了一种基于小波变换技术和数学形态学的边缘检测方法，并用智能图像传感器获得的图像教据对所提出的方法进行了验证．实验结果表明：该方法可较好地提取视觉图像的边缘信息．     

A contrast- and brightness-insensitive optoelectronic edge detector

A specialized photodetector structure for optical edge detection and its CMOS-compatible realization are presented. It can detect a single edge with a relative motion to the detector. Because of point symmetry, the edge may be detected regardless of its orientation. Influences of contrast and brightness variations at an edge can be eliminated by subtracting the photocurrents of two photodiodes arranged in a circle/ring structure. The detectors are fabricated in CMOS technology and have been successfully tested.     

Robust fault detection of uncertain linear systems via quasi-LMIs

A novel H_∞ deconvolution filter design method is discussed for fault detection of uncertain polytopic linear systems subject to unknown inputs. The enhancement of fault sensitivity is characterized in terms of an H_- index. By means of the Projection Lemma and Congruence Transformations, a quasi-convex LMI formulation of the design problem is obtained. The effectiveness of the filter is illustrated via a numerical example.     

基于OpenCV的边缘检测算法在仪表识别中的应用与改进

介绍一种Canny边缘检测算法的改进方法,采用自适应确定阈值,研究了在仪表LCD数字显示图像的边缘检测应用,Visual C 使用OpenCV类库作为基础函数库,程序经测试,边缘检测处理效果好,处理速度快。     

Color edge detection using the minimal spanning tree

In this study, the edge detection task in vector-valued images is examined as a clustering problem. Using samples within a data window, the minimal spanning tree (MST) provides the ordering of multivariate observations and facilitates the identification of similar classes. The edge detector parameters like edge strength, type and orientation are subsequently determined from the clustered data. Experiments and comparisons are performed, revealing the enhanced performance of the proposed approach.