Classical Edge Detection Operators Research Articles

A Method for Fingerprint Edge Enhancement Based on Radial Hilbert Transform

Fingerprints play a significant role in various fields due to their uniqueness. In order to effectively utilize fingerprint information, it is necessary to enhance image quality. This paper introduces a method based on Radial Hilbert transform (RHLT), which simulates the vortex filter using the point spread function (PSF) of spiral phase plate (SPP) with a topological charge l=1, for fingerprint edge enhancement. The experimental results show that the processed fingerprint image has more distinct edges, with an increase in information entropy and average gradient. Unlike classical edge detection operators, the fingerprint edge image obtained by the RHLT method exhibits a lower mean square error (MSE) and a higher peak signal-to-noise ratio (PSNR). This indicates that the RHLT method provides more accurate edge detection and demonstrates higher noise-resistance capabilities. Due to its ability to highlight edge information while preserving more original features, this method has great application potential in fingerprint image processing.

Traffic signal light detection and recognition based on canny operator

In this paper, five classical edge detection operators are compared, and then a traffic signal light detection and recognition scheme that can be used for intelligent connected vehicles is implemented. Firstly, the image to be processed is obtained by detecting the traffic signal light through the vision sensor. The image is preprocessed: the color space of the image is converted from RGB space to HSV space. Through the grayscale, histogram equalization, image binarization processing, using the morphological closure operation, the five operators are compared in noise sensitivity, positioning accuracy and signal-to-noise ratio, the Canny edge detection operator is selected for image edge detection, and the target recognition area is obtained. Finally, using the histogram drawn, the number of red, green and yellow pixel points in the histogram can be clearly counted, and the color with the largest number of pixel points can be identified as the color of the identified traffic signal light, and the identification of the traffic signal light can be completed. The actual pictures are simulated on the MATLAB, which verifies the feasibility of the proposed method of traffic signal light recognition method based on Canny operator in this paper, which can correctly identify the color of the traffic signal light.

A Reconfigurable Graphene-Based Spiking Neural Network Architecture

In the paper we propose a reconfigurable graphene-based Spiking Neural Network (SNN) architecture and a training methodology for initial synaptic weight values determination. The proposed graphene-based platform is flexible, comprising a programmable synaptic array which can be configured for different initial synaptic weights and plasticity functionalities and a spiking neuronal array, onto which various neural network structures can be mapped according to the application requirements and constraints. To demonstrate the validity of the synaptic weights training methodology and the suitability of the proposed SNN architecture for practical utilization, we consider character recognition and edge detection applications. In each case, the graphene-based platform is configured as per the application tailored SNN topology and initial state and SPICE simulated to evaluate its reaction to the applied input stimuli. For the first application, a 2-layer SNN is used to perform character recognition for 5 vowels. Our simulation indicates that the graphene-based SNN can achieve comparable recognition accuracy with the one delivered by a functionally equivalent Artificial Neural Network. Further, we reconfigure the architecture for a 3-layer SNN to perform edge detection on 2 grayscale images. SPICE simulation results indicate that the edge extraction results are close agreement with the one produced by classical edge detection operators. Our results suggest the feasibility and flexibility of the proposed approach for various application purposes. Moreover, the utilized graphene-based synapses and neurons operate at low supply voltage, consume low energy per spike, and exhibit small footprints, which are desired properties for largescale energy-efficient implementations.

Thermography Sequence Processing and Defect Edge Identification of TBC Structure Debonding Defects Detection Using Long-Pulsed Infrared Wave Non-Destructive Testing Technology

General infrared image processing and analysis methods mainly include non uniformity correction, enhancement, noise reduction, segmentation, etc. The handling objects of these algorithms are ordinarily single images, which can improve the display effect of defects in infrared image and increase the signal-to-noise ratio. The active heat source excitation is usually used for defects detection in long pulse infrared thermal wave testing technology, so the collected image sequences are dynamic images. At the same time, as in-homogeneous characteristics of thermal excitation, the detection accuracy of defects is affected. Compared with ordinary infrared images, thermal dynamic image sequence is affected by uneven thermal excitation and wave flow. So the characteristics of the dynamic sequence should be analyzed, in order to get better defect display effect. Using the SVD (singular value decomposition, SVD) method for infrared image sequence reconstruction, and the component of surface temperature signal feature information has been extracted. Compared to the original image, the contrast and signal-to-noise ratio of the reconstructed image has been improved. Canny, LOG and other classical edge detection operators were used to detect the edge of infrared images, and their detection results were compared and analyzed. On the basis of analyzing the effect of the classical edge detection operators on the defect edge recognition in infrared images, a hybrid algorithm for edge detection based on Retinex-watershed-Canny operator is proposed for image edge detection, and the thermal barrier coating structure debonding defects’ edge has been recognized. A large number of useless and untrue boundary information around has been reduced, and the boundary contour of defects is more clear and continuous, which can improve the feature extraction effect of defects. The research work of this paper lay a certain foundation for the quantitative detection of defects.

A Novel Flame Edge Detection Algorithm via A Novel Active Contour Model

Flame edge detection from color images is a challenging research area recently. In this paper, an extension of active contour model is proposed by adding the two information types to both internal and external energy terms. Therefore, the combination of these two forces allows for flexible initialization of the contours. This energy is then incorporated into a level set formulation with a level set regularization term that is necessary for accurate computation in the corresponding level set method. Edge extraction of different flame images using C-V model and the classical edge detection operators are compared and analyzed. Experimental results show that the existing methods do not emphasize the continuity and clarity of the flame and fire edges while the proposed method identifies the continuous and clear edges of the flame fire.

A Novel Approach of Edge Detection Based on Gray Correlation Degree and Kirsch Operator

Image edge detection plays an important role in the process of image processing. This article classified the effect of various edge detection algorithms of digital images, mainly through the analysis of characteristics of the commonly used several kinds of classical edge detection operator in the time domain. For the actual image, the edge of the gray scale is sometimes not very clear, and the image also contains noise. A new algorithm of edge detection based on gray correlation degree and Kirsch operator is discussed in the paper. Kirsch operator is the reference sequence. Firstly, calculate the gray correlation degree of each pixel in the image and its neighbors formed eight action sequences and reference sequences, then determine the detection of edges based on gray correlation degree. The results show that this method can more accurately detect the useful information of the edge, and has a certain noise immunity. It is an effective and feasible new edge detection method.

Approach of Car License Plate Location based on Improved Sobel Operator with the Combination of Mathematical Morphology

Plate license locating is the key process in vehicle license plate recognition for automated transport system. It will affect the accuracy of recognition directly. License plate area contains rich edge and texture information. In this paper, improved Sobel operator is used to detecting the edge of the car image. This paper presents a new approach of extracting car license plate from car images based on morphology. Based on the information of width and height of car license plate, and space between characters of the car license plate, we design a new structural elements for morphological operations used to the edge image of car license plate, which can obtain several candidate areas. Finally, the criterions basing on the ratio of plate area, ratio of height to width and projections are used in comprehensive analysis,and the real license plate area can be located accurately. The improved Sobel operator method is helpful to reveal distinct characteristics for some edge features, efficiently with high speed processing. Compared with the classical edge detection operators edge detection algorithm, the improved algorithm is more advantage, the quality and speed of the picture edge extraction significantly improved. Experimental results show that this method is simple, accurate.It can satisfy the real time need of license location demand, and can make the preparation for the following character identification.

Research on the Edge Detection Algorithm of Smokescreen Based on Mathematical Morphology

To solve the harmony problem of accuration, real-time with anti-noise capability on edge detection of smokescreen, the edge detection algorithm of smokescreen based on multi-scale mathematical morphological is designed, and the algorithm can effectively reduce the noise of the smokescreen image. Compared with the results of classical edge detection operator: Sobel, Roberts, Prowitt and Canny etc, it is concluded that the algorithm designed has obvious advantages in continuity, smoothness, image recognition, practical complexity, operation time and other related parameters.

Edge Detection of Inner Crack Defects Based on Improved Sobel Operator and Clustering Algorithm

The classical Sobel edge detection operator has the shortcomings of low edge positioning accuracy and coarse edge, image edge detection based on improved Sobel operator and clustering algorithm was proposed. Four Sobel-like edge operators are used to improve the edge positioning accuracy and clustering algorithm are used to edge thinning. The experimental result demonstrates that the effect of the edge detection is greatly improved comparing with the traditional edge detection methods.

A new edge detection algorithm for image corrupted by White-Gaussian noise

Because corruption of image by White-Gaussian noise is a frequently encountered problem in acquisition, transmission and processing of image, and classical edge detection operators such as Roberts, Sobel, Prewitt and LOG operator have the deficiency of being sensitive to White-Gaussian noise, this paper proposes a new edge detection algorithm for Image corrupted by White-Gaussian noise that can reasonably consider White-Gaussian noise reduction and correct location of edge, and provides its specific arithmetic process. Finally, the comparison based on principle of new edge detection algorithm and classical edge detection operator is done, the experimental results indicate that the performance of new edge detection algorithm is better than that of classical edge detection operator.

Edge detection method of remote sensing images based on mathematical morphology of multi-structure elements

This paper puts forward an effective, specific algorithm for edge detection. Based on multi-structure elements of gray mathematics morphology, in the light of difference between noise and edge shape of RS images, the paper establishes multi-structure elements to detect edge by utilizing the grey form transformation principle. Compared with some classical edge detection operators, such as Sobel Edge Detection Operator, LOG Edge Detection Operator, and Canny Edge Detection Operator, the experiment indicates that this new algorithm possesses very good edge detection ability, which can detect edges more effectively, but its noise-resisting ability is relatively low. Because of the bigger noise of remote sensing image, the authors probe into putting forward other edge detection method based on combination of wavelet directivity checkout technology and small-scale Mathematical Morphology finally. So, position at the edge can be accurately located, the noise can be inhibited to a certain extent and the effect of edge detection is obvious.