Line Edge Detection and Characterization in SEM Images Using Wavelets

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

We propose a method for edge detection in images with multiplicative noise based on Ant Colony System (ACS). To adapt the Ant Colony System algorithm to multiplicative noise, global pheromone matrix is computed by the Coefficient of Variation. We carried out a performance comparison of the edge detection Ant Colony System algorithm among several techniques, the best results were found in the gradient and the coefficient of variation.

Edge detection is regularly used as a fundamental operation for correctly identifying and measuring some required features. In a grey-level image, an area where the grey-level value moves from a high value to a low value or vice versa is considered an edge. Edges are indicative of a boundary between an object and a background or between two objects. Consequently, edge detection in earth sciences is an important tool for locating geological features and determining their shapes and sizes. Edge detection usually forms a part of the geophysical interpretation or inversion procedure. Seismic tomography is a straightforward field of applying edge detection because the tomogram can be directly considered as an image. In the tomographic reconstruction of seismic travel time data, care must be taken to keep the propagation of data errors to the model space under control. The noise - especially the outliers in the data sets - can cause appreciable distortions in the tomographic imaging. To reduce the noise sensitivity well-developed tomography algorithms can be used. On the other hand, the quality of the tomogram can further be improved by using image processing tools. This is especially important in edge detection, as it is extremely sensitive to noise. In the paper, we present two ways to find robust edge detection. At first, remaining in the framework of traditional image processing a robust Cauchy–Steiner filter is used to improve the quality of edge detection in tomographic images. In the second part of the paper Deep Learning algorithm developed for edge detection is shown and investigating its noise sensitivity the robustness of the method is demonstrated.

In order to solve the problems of limited computing resources on the fuselage and autonomous recognition of visual images during the real-time control of the flapping-wing flying robot, and to realize the intelligent navigation control of the flapping-wing flying robot based on vision, this paper proposes a visual image processing method and edge detection method for a flapping-wing flying robot based on cluster analysis. Using cluster analysis method to classify the visual image pixel data of the flapping-wing flying robot, and using the classic edge detection algorithm to perform edge detection on the flying robot image after clustering analysis, and reasonably extracting the visual target contour features and environmental edges of the flapping-wing flying robot feature. An experimental platform for a flapping-wing flying robot was built with a flapping-wing flying robot and an airborne vision camera. Flight experiments are carried out on the visual image processing method and image edge detection method of flying robot based on cluster analysis. The experimental results verify the feasibility and effectiveness of the method in this paper. The innovation of this paper is embodied in the application of cluster analysis method to the visual image processing of the flapping-wing flying robot, and the edge detection of the visual image of the flapping-wing flying robot.

Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3×3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270×290 pixels having 24 dB ‘salt and pepper’ noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.

Edge detection in range images provides rich information about the scene being observed. Many researchers have been taking advantage of this information to improve image segmentation by integrating edge detection with other segmentation techniques. This paper presents a methodology to perform edge detection in range images in order to provide a reliable and meaningful edge map, which helps to guide and improve range image segmentation by clustering techniques. The obtained edge map leads to three important improvements: (1) the definition of the ideal number of clusters to initialise the clustering algorithm; (2) the selection of suitable initial cluster centres; and (3) the successful identification of distinct regions with similar features. Experimental results that substantiate the effectiveness of this work are presented.

Notice of Violation of IEEE Publication Principles<br><br>" A Support Vector Machine Approach for Edge Detection in Noisy Images,"<br>by Jian-Min Zhang and Lei Li<br>in the Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, 12-15 July 2009<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.<br><br>This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br>"Edge detection in noisy images using the Support Vector Machines"<br>by H. Gómez Moreno, S. Maldonado Bascón, F. López Ferreras,<br>in Lecture Notes in Computer Science. Vol. 2084, pp. 685-692, 2001. © Springer-Verlag.<br><br> "A new and improved edge detector using the Support Vector Machines".<br>by H. Gómez Moreno, S. Maldonado Bascón, F. López Ferreras, P. Gil Jiménez,<br>in Advances in Systems Engineering, Signal Processing and Communications, pp. 239-243.<br>Ed: N. Mastorakis. Editorial WSES Press, USA, 2002. ISBN:960-8052-69-6<br><br> <br/> An innovative edge detection method, using the support vector machine, is presented in this paper. This method shows how to use the SVM to detect edge in an efficient way. The noisy images are processed in two ways in this paper, first reducing the noise by using the SVM regression networks filter and then performing the classification using the SVM classification. The experimental results indicate that this method is near equal to the Canny in the performance and is fast in the speed when the images are affected by impulsive noise.

Based on the shortcomings of the traditional edge detection algorithm easily lead to the brink of fracture and discontinuous, a new improved Pulse-coupled neural network (PCNN) is proposed to image segmentation and edge detection. Using Gaussian filter pre-processing, gray image is segmented into binary image. Based on this binary image, the edge detection is accomplished using improved PCNN, which can increase contrast of image and enhance the edge of image. Compared with correlative result using Canny operator and others in the existing references, simulation results show that our methods have both preferable results of gray image edge detection and fine applicability.

In this paper the problem of edge detection in images of heat-emitting objects is regarded. Especially images of molten specimens of metal are considered. The images were obtained from computerized system for high-temperature measurements of metals and alloys. The method for defining specimen profile with sub-pixel accuracy is presented. Results provided by the proposed method are presented and compared with those given by the traditional approach to edge detection.

Rotary drilling rigs are extensively used in foundational construction, with main components including the mast, drill rods, drill bits, power head, and cockpit. The mast, a critical component, must be of high quality to ensure operational reliability. To ensure the reliability of the mast during operation, it is necessary to detect defects in the most stress-prone areas. Based on finite element analysis and practical engineering experience, the welds on the large circular disc of the drill mast are identified as the most vulnerable to damage. Accordingly, this paper prepared test specimens specifically from these weld locations and conducted X-ray inspections, obtaining images for analysis. Given the inevitable presence of noise due to objective conditions, the images first underwent median filtering. For effective subsequent defect detection, edge detection was performed on the images. Among various edge detection operators, the Scharr operator was found to be highly effective. To further enhance the edge detection performance, this paper introduces an improved Scharr edge detection algorithm that incorporates dynamic thresholds and Markov Random Field (MRF) techniques. The enhanced algorithm was evaluated using signal-to-noise ratio and edge localization accuracy as metrics, and it was found to significantly improve performance.

An online visual measurement method for workpiece dimension based on deep learning

Image edge detection based tool condition monitoring with morphological component analysis

Edge detection technology of oil spills image on the sea is one of most key technologies to monitor oil spills on the sea. This paper presents a novel method to detect edge of oil spills image on the sea. The algorithm is composed of two parts: nonmaximal suppression realizes candidate edge detection of oil spills image, self-adaptive dynamic block threshold (SADBT) algorithm eliminates the noise and pseudo edge in candidate edge, and makes edge locate more accurately and continuously. SADBT is that the whole image is divided into a number of subimages, and according to the characteristics of the global edge gradient, combined with the edge gradient information of each sub-image to generate dynamic threshold adaptively. The algorithm improves the degree of automation of edge detection, suppresses the noise and the formation of pseudo-edge effectively. At last the paper gives the results of image processing by the algorithm mentioned above and other algorithms. By comparison, it's concluded that the processing results by the algorithm suggested in this paper are more legible, continuous and effective. As to the oil-infrared image with more noises, the effect of edge detection is also ideal.

This chapter focuses on the development of novel image enhancement and robust edge detection methods for practical medical image processing. It is known that the popular transformation-domain shrinkage approach for image enhancement applies a fixed mathematical basis to transform all images to be processed for noise or artifact reduction. However it is not adaptable to processed images, and then easily leads to blurring in the enhanced images. On the other hand, the techniques that are commonly used for edge detection are known as gradient and Laplacian operators (or mask), and smoothed gradient masks are typically used for edge detection in noisy images. However, these methods share a common major drawback wherein the associated masks are always fixed irrespective of the noise level in the images. In this study, we propose a novel learning-based method to adaptively deduce the transforming basis or masks from the processing data for medical image enhancement and robust edge detection. By using independent component analysis (ICA), the proposed learning-based method can extract suitable basis functions or masks for image transformation for processing data, which are adaptable to both the processed image and related noise in the image. The efficiency of the proposed learning-based method for medical image enhancement and edge detection is demonstrated experimentally using positron emission tomography (PET) and magnetic resonance imaging (MRI) medical images.

As medical images are fuzzy, edge detection based on texture characteristics is comparatively effective than intensity based techniques. A new methodology is described for texture edge detection in medical images that is applicable across modalities. We use a multi-scale filter to capture texture edge information. An experimental prototype based on the proposed methodology provides a test bed for comparison with a popular edge detection technique.