Traditional Edge Detection Operators Research Articles

An edge detection method of colony image based on mediocrity ant colony algorithm

The colony is one of the important research objects in microbial technology, which can realize the evaluation of food safety level, environmental pollution degree, therapeutic effect of medical drugs, and characteristics of agricultural fungicides. Traditional colony image research requires human visual observation and statistics, which will result in low work efficiency and high work intensity. Colony image edge detection is an important basis for colony image research. Traditional edge detection operators cannot meet the accuracy requirements of the detection results. This paper proposes a Mediocrity Ant Colony Algorithm (MACA) to achieve edge detection of colony images. MACA combines the mediocrity rule, uses empirical functions to establish a pheromone database that can be used as a pheromone update reference table, adopts the Chebyshev distance as a weight that affects pheromone update, and combines heuristic information acquisition with maximum variance classification method and local path weights. The method that jointly affects the ant transition probability incorporates feedback rules for obtaining path weights to improve the edge detection effect. By performing edge detection simulation experiments on six colonies of three types of bacteria, and comparing with the classic edge detection operators and two classic ant colony edge detection algorithms, the detection performance, detection results and running time are proposed. The stability and accuracy of MACA algorithm is better than other methods, and the ideal results of the colony image edge detection by the ant colony algorithm are obtained.

Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection

Memristor with memory properties can be applied to connection points (synapses) between cells in a cellular neural network (CNN). This paper highlights memristor crossbar-based multilayer CNN (MCM-CNN) and its application to edge detection. An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors. MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation. Figure of merit (FOM) is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results. Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.

2D image edge detection enhancement using convolutional neural network

Traditional edge detection operators are usually applied on edge detection in 2D image processing. However, the edge detection system equipped with simple operators has many disadvantages, such as high sensitivity to noise and neglect of significant edge details. This work proposed a method to enhance edge detection with convolutional neural net-work. To overcome the shortcomings of the system using simple edge detection operators in 2D image processing, an edge detection system using convolutional neural network was developed with Python language. In the convolutional neural network, two convolutional layers were designed to extract 2D image features that were relative to edge information. Then a normalization layer was applied to normalize the convoluted output. After that, pre-processing was utilized to denoise and smooth the image input. The final step was edge detection using traditional operators. Experiments were also implemented to verify the improvement of the plugin of the three-layer convolutional neural network in the de-signed edge detection system. Relative frequencies were utilized to quantify the edge de-tection performance. Results showed that the involvement of convolutional neural net-work could strengthen edge detection operators performance obviously in computer vi-sion.

An Image Edge Detection Method Based on Fractional-Order Grey System Model

The detection of edges in images is a pressing issue in the field of image processing. This technique has found widespread application in image pattern recognition, machine vision, and a variety of other areas. The feasibility and effectiveness of grey theory in image engineering applications have prompted researchers to continuously explore it. The grey model (GM (1,1)) with the first-order differentiation of one variable is the grey prediction model that is most frequently used. It is a typical trend analysis model and can be used for image edge detection. The traditional integer-order differential image edge detection operator has problems such as blurred and discontinuous edges, incomplete image details, and high influence by noise. We present a novel grey model for detecting image edges based on a fractional-order discrete operator in this paper. To improve the features of the original image, our model first preprocesses it before calculating the prediction of the original image using our fractional-order cumulative greyscale model. We obtain the edge information of the image by first subtracting a preprocessed image from the predicted image and then eliminating isolated noise points using the median filtering method. Based on the discrete wavelet transform, image edges are finally extracted. The comparison experiments with a traditional edge detection operator show that our algorithm can accurately locate the image edges, the image edges are clear and complete, and this model has better anti-noise performance.

Structures Guided Dynamic Scene Deblurring Method

In this paper, we integrate image gradient priors into a generative adversarial networks (GANs) to deal with the dynamic scene deblurring task. Even though image deblurring has progressed significantly, the deep learning-based methods rarely take advantage of image gradients priors. Image gradient priors regularize the image recovery process and serve as a quantitative evaluation metric for evaluating the quality of deblurred images. In contrast to previous methods, the proposed model utilizes a data-driven way to learn image gradients. Under the guidance of image gradient priors, we permeate it throughout the design of network structures and target loss functions. For the network architecture, we develop a GradientNet to compute image gradients via horizontal and vertical directions in parallel rather than adopt traditional edge detection operators. For the loss functions, we propose target loss functions to constrain the network training. The proposed image deblurring strategy discards the tedious steps of solving optimization equations and taking further advantage of learning massive data features through deep learning. Extensive experiments on synthetic datasets and real-world images demonstrate that our model outperforms state-of-the-art (SOAT) methods.

Deep Image Segmentation Using a Morphological Edge Operator

Background: Segmentation of deep images is a difficult, persistent problem in the computer vision field. This paper aimed to address the defects of traditional segmentation methods with deep images, presenting a deep image segmentation algorithm based on a morphological edge operator. Methods: Deep image edge features were first extracted using three traditional edge operators; the edge and tip type jump edges were then extracted via a morphological edge operator, which was used to make the boundary connection; finally, to obtain more accurate segmentation results, skeletonizing was used to refine the image. Results: Compared with traditional segmentation algorithms, the improved algorithm obtained smooth and continuous boundaries, protected edge information from blurring, and was slightly more efficient. When Mickey Mouse depth images were used as experimental subjects, the computational time was reduced by 12.62 seconds; when rabbit depth images were used, computational time was reduced by 17.53 seconds. Conclusion: Morphological edge operator algorithm proposed in this paper is much more effective than traditional edge detection operators algorithms for deep image segmentation; it can clearly divide Mickey Mouse's ears, eyes, pupils, nose, and mouth.

Vector co‐occurrence morphological edge detection for colour image

Morphological edge detection is a principal component in pattern recognition and machine vision. Traditional edge detection operators only take pixel mutual into consideration. However, the edges are influenced not only by pixel mutual but also by the boundary characteristics. Here, the vector co-occurrence morphological edge detection operator is proposed, which takes the pixel and boundary information both into consideration. The vector co-occurrence algorithm is exploited to resist the influence of the noise points and detect the edges from the colour image rather than the grey image. And, we lead to define a precise definition of the manner of sorting high-dimensional data for the colour image. The experiment results always illustrate the advancement and practicability of our methods against the baseline method. In terms of experiments, the BSDS500 dataset is introduced to compare and analyse with other algorithms. Based on the standard benchmark index evaluation in the BSDS500 dataset, the ODS and AP of various algorithms are compared and analysed.

Adaptive Edge Detection Technique Implemented on FPGA

This article proposes an adaptive model that improves the edge detection operation in digital images. One of the disadvantages of traditional edge detection operators such as Prewitt, Sobel and Canny is using fixed-size masks which limit the edge detection operation in images with different degrees of the gray levels. In the proposed model, the image histogram is used as a criterion for selecting a suitable mask according to image specifications. In this model, the mask size of the edge detection operator is determined by measuring the contrast of the image to improve the quality of edge detection. For this purpose, a mask with a small size is used for images that have high contrast to increase processing speed, and for the images that do not have high contrast larger masks are used to increase the accuracy of edge detection. The proposed model is implemented utilizing real-time processing and parallelism capabilities of the Xilinx-Virtex6 FPGA. And, in order to reduce the effect of parallelism on increasing the area size of implemented hardware resources, existing resources are reused appropriately. The results show that with the use of the proposed method the edge detection is done in just 2.2 ms and a trade-off between the quality of detected edge and the hardware resources is established.

Research on improved edge extraction algorithm of rectangular piece

Traditional edge detection operators such as Prewitt operator, LOG operator and Canny operator, etc. cannot meet the requirements of the modern industrial measurement. This paper proposes a kind of image edge detection algorithm based on improved morphological gradient. It can be detect the image using structural elements, which deals with the characteristic information of the image directly. Choosing different shapes and sizes of structural elements to use together, the ideal image edge information can be detected. The experimental result shows that the algorithm can well extract image edge with noise, which is clearer, and has more detailed edges compared with the previous edge detection algorithm.

Edge Detection in UAV Remote Sensing Images Using the Method Integrating Zernike Moments with Clustering Algorithms

Due to the unmanned aerial vehicle remote sensing images (UAVRSI) within rich texture details of ground objects and obvious phenomenon, the same objects with different spectra, it is difficult to effectively acquire the edge information using traditional edge detection operator. To solve this problem, an edge detection method of UAVRSI by combining Zernike moments with clustering algorithms is proposed in this study. To begin with, two typical clustering algorithms, namely, fuzzy c-means (FCM) and K-means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. Then, Zernike moments are applied to carry out edge detection on the remote sensing images clustered. Finally, visual comparison and sensitivity methods are adopted to evaluate the accuracy of the edge information detected. Afterwards, two groups of experimental data are selected to verify the proposed method. Results show that the proposed method effectively improves the accuracy of edge information extracted from remote sensing images.

Image Edge Detection Based on Solitary Wave Algorithm

Edge detection is the most basic arithmetic of the local information of image change detection, image edge detection can help people better describe or identify image, is abasic technique in image processing. Edge detection is mainly to find out the important goal of the salient points on the boundary of the image,because the traditional edge detection operators have the same direction, so easily missed in the detection process.In this paper, the solitary wave algorithm for image edge detection, this algorithm can provide both the direction and size of the image edge, a certain extent, can also solve the missing edges, it is possible to extract more accurate image information.

ADAPTIVE EDGE DETECTION USING ADJUSTED ANT COLONY OPTIMIZATION

Abstract. Edges contain important information in image and edge detection can be considered a low level process in image processing. Among different methods developed for this purpose traditional methods are simple and rather efficient. In Swarm Intelligent methods developed in last decade, ACO is more capable in this process. This paper uses traditional edge detection operators such as Sobel and Canny as input to ACO and turns overall process adaptive to application. Magnitude matrix or edge image can be used for initial pheromone and ant distribution. Image size reduction is proposed as an efficient smoothing method. A few parameters such as area and diameter of travelled path by ants are converted into rules in pheromone update process. All rules are normalized and final value is acquired by averaging.

Research of Sandbox Sand Body CT Image Processing Methods Based on Fuzzy Similarity

Based on analyzing and discussing the sand body CT fault image features in tectonic physical simulation experimental sand box, some technical schemes and methods of sand body CT image processing are proposed. Aiming at the characteristics of sand body CT sequence images, such as blurs, poor quality and obscure boundary between the layers, based on the correlation between pixels, fuzzy logic is applied to the edge detection of gray images, and an edge detection algorithm based on fuzzy similarity model is proposed. The experimental results show that the proposed algorithm has better performance than traditional edge detection operators.

Fractional differential algorithms for rock fracture images

It is a new research topic that fractional differential theory is used into image processing. This paper presents a new type of algorithms to improve the fractional differential Tiansi operator, which can significantly enhance the edge detection result. The studied algorithms are based on the enhancement ability of fractional differential to image details, and they can be used to analyse the properties of fractional differential. The general procedure of the algorithms is as follows: firstly, Tiansi template is divided into eight sub-templates with different directions around the detecting pixel, and then the eight weight sum values for the eight sub-templates are obtained. Furthermore, those eight weights are classified into different groups. In this way, the three improved algorithms with different enhancing ranges are obtained. Finally, the experiments of edge detection show that the improved algorithms can obtain edge information more effectively and can show much more detailed information than traditional edge detection operators especially for the images of fine edges such as complicated rock fracture images.

General method for edge detection based on the shear transform

A method for detecting edges is proposed in this study. First, it combines a traditional edge detection operator with the shear transform utilising the fact that the shear filters provide a more favourable treatment of directions, the shear transform makes the directional edges easy to detect by traditional operators; then the edge information in different directions are fused to complement each other, and a correspondence threshold, which is acquired according to receiver operating characteristic curves, is used to refine the edges; finally, the optimal result is obtained. Moreover, the authors can consider the proposed method as a general method since it could be applicable to other edge detection operators. The experimental results have indicated that our method has advantage over the traditional ones of the effectiveness of edge detection and the ability of noise rejection.

Straight road edge detection from high-resolution remote sensing images based on the ridgelet transform with the revised parallel-beam Radon transform

Roads are important basic geographical phenomena and the automatic recognition and extraction of road features from remote sensing images has many applications. However, automated road extraction from high-resolution remote sensing imagery is problematic. In recent years, many approaches have been explored for automatic road extraction, particularly involving road edge detection. Traditional edge detection operators such as the Canny or the Sobel operator are used frequently but there are serious problems of over- or underdetection, and time-consuming and complicated post-processing work is often required. In this paper, a new revised parallel-beam Radon transform (RPRT) approach is proposed. The traditional PRT can have problems with step values, resulting in false edge detection. To overcome these problems we introduced the RPRT, using the harmonic average of the pixel value in every strip of the Radon slice. An algorithm suitable for straight edge detection of roads in high-resolution remote sensing imagery was designed based on the ridgelet transform with the RPRT. The experimental results show that our algorithm can detect straight road edges efficiently and accurately, and avoid cumbersome and complicated post-processing work.