Laplacian Edge Detection Research Articles

Laplacian edge detection method based on a new approximation of the second-order fractional derivative in the Caputo-Fabrizio sense

Edge detection is an important issue in image processing and computer vision problems. It includes all mathematical methods that aim to identify the discontinuous points in the image. This process leads to construct curves that indicate the boundaries of an object and therefore extract feature information. In recent years, this task has received great attention from researchers and scientists, as we find in the literature many methods with their different algorithms. Up to now, there are two methods, the gradient and Laplacian methods. The first depends on the first derivative, where pixels with a high gradient are considered edges, whereas the second depends on the second derivative, such as the pixel whose second derivative is zero, it is marked as an edge point. Although these methods have achieved remarkable success, they are sensitive to noise and sometimes produce false edges. For this reason, in this paper, we propose an efficient edge detection method based on the fractional Laplacian operator in the Caputo-Fabrizio sense. In this method we develop a new approximation formula to the second-order with the Caputo-Fabrizio fractional derivative. Then, we construct a new fractional mask to compute the Laplacian convolution. To check the effectiveness of our proposed method, we have provided some test images that are affected by a Gaussian noise. The numerical results and visual observation show that our proposed edge detection method gives better performance compared to the classical Laplacian method in terms of the peak signal to noise ratio (psnr) and fine details extraction.

AN EFFICIENT VLSI IMPLEMENTATION OF EDGE DETECTION OF IMAGES

Edge detection is a fundamental process in image processing with significant implications across multiple domains. Within the realm of object detection, the accurate delineation of image perimeters forms the bedrock for subsequent analysis. In this study, we present an optimized Laplacian Edge Detection algorithm tailored for efficient implementation in hardware.Leveraging approximation methodologies and strategic pipelining, we streamline complex operations, thereby reducing latency and resource consumption. Our implementation, realized through the Verilog Hardware Description Language (VHDL), stands as a testament to the efficacy of our approach. Through rigorous optimization and meticulous comparison with conventional Sobel edge detection, we demonstrate superior performance metrics, including enhanced Area utilization, reduced Time Delay, and improved Power Efficiency. This work underscores the importance of methodological refinement and technological innovation in advancing edge detection techniques for practical applications. Keywords: Laplacian, Sobel, Kernel, Pixel range, Area, Power

Modeling Digital Image Segmentation with Canny Method

In general, the segmentation process is divided into three parts. classification, by edge, and by area. The digital image segmentation process divides an object whose surface or background maintains the RGB value of all pixels of a digital image so that the object can be processed for other purposes. This system aims to execute drawing objects using intelligent methods. In the Canny method, the process begins with taking digital images and continues with the grayscale process. In addition, technique selection starts with performing a complex operator or Laplacian edge detection and finally unlocks it. Successful segmentation results using edge detection with intelligent operators to separate objects. This system uses the Matlab 2017 application.

Automatic vehicle number plate recognition system

In simple terms, Automatic Vehicle Number Plate Recognition System (AVNPRS) can be described as a computer vision image processing technology. This technology captures images of vehicles and recognizes their number plates. As the world is becoming more sophisticated on each new day, security as well as management of transportation system have become a vital aspect in controlled places. Such places include modern campuses, shopping malls and other institutions. With today’s rate at which motor vehicles are increasing in numbers, there is need for an effective, affordable and efficient AVNPRS. Thus, this project proposes, outlines and discusses an efficient AVNPRS. The system under consideration in this project can be installed at main entrances of modern institutions. This is because the AVNPRS ensures that only authorized vehicles can automatically have access to such institutions. The AVNPRS ensures that it captures the image of the number plate once the input sensor detects the vehicle. The Sobel edge detection and Laplacian edge detection techniques are used at this critical stage. Thereafter, the Bounding box technique is used to find the number plate leading to character segmentation. After capturing, an image undergoes extraction and character investigation via the Optical Character Recognition (OCR). In addition, in achieving character recognition, matching between the computer template and segmented image is done via the OCR method. It is vital to mention that this system is sustainable as it successfully detects, recognizes and processes vehicle number plates on real images. The AVNPRS can be used for both traffic control and security. Therefore, the main aim of this piece of writing is to develop from a theoretical perspective an AVNPR system that can detect and capture vehicle number plate images.

Automatic Extraction Method of Weld Weak Defect Features for Ultra-High Voltage Equipment

To solve the problems of low precision of weak feature extraction, heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage (UHV) equipment key parts, an automatic feature extraction algorithm is proposed. Firstly, the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method. Then, binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation. Finally, the automatic identification of weld defect area is realized based on the sequential traversal of binary tree. Several characteristic analysis dimensions are established for weld defects of UHV key parts, including defect area, perimeter, slenderness ratio, duty cycle, etc. The experiment using the weld detection image of the actual production site shows that the proposed method can effectively extract the weak feature information of weld defects and further provide reference for decision-making.

Resource optimized Region Based Image Encryption Using Chaotic Maps

Securing medical images becomes a major concern to avoid leaking confidential data. This problem motivated the development of many low computational complexity methods to encrypt these medical images. In this research work, block cipher-based region of interest medical image encryption with multiple maps is proposed. Primarily, region of interest (ROI) regions and region of background (ROB) are extracted with the help of Laplacian edge detection operator. Further, important ROI regions are permuted in a circular fashion with the help of Arnold cat map and angle value. Then permuted ROI part is encrypted using the duffling system and unimportant regions are unchanged. The advantage of the work is that it encrypts only selected/important parts and that will achieve fast execution speed and reduction in computation complexity. The approach presented here enables the storage and transmission of medical image data within an open network. These results show that the security in the proposed method is much better than many chaotic encryption algorithms proposed in the recent times.

Motion Velocity Detection of Circuit Breaker Operating Mechanism

The movement speed of the operating mechanism is one of the main factors affecting the breaking performance of the circuit breaker. To accurately detect the movement speed, this paper proposes an image processing method based on LabVIEW virtual instrument and designs a set of software and hardware systems for real-time movement speed detection of the operating mechanism. On the basis of the movement images of the operating mechanism collected by the high-speed camera, it uses the image processing module in LabVIEW to pre-process the images. The paper adopts the Laplacian edge detection operators to detect the edges and extract the edge feature information for straight line fitting and data processing. The experimental results show that the detection system can effectively detect the motion state of the operating mechanism, realize the real-time detection of the motion state of the operating mechanism, and provide technical support for the detection of the motion characteristics of the circuit breaker.

Resource Optimized Selective Image Encryption of Medical Images Using Multiple Chaotic Systems

Medical images typically contain personal and diagnostic information about patients and are typically shared among hospitals, doctors, and patients via public networks. As a result, they must be kept secure while being stored and transported to protect the patient's privacy. However, due to the unique qualities of digital image data, such as significant redundancy and correlation between pixels and their huge size, traditional cryptographic techniques are ineffective for providing proper security while encrypting them. The necessity to develop enhanced and specialized image encryption algorithms arise as traditional techniques cease to be a trustworthy option. This problem motivated them to develop many low computational complexity methods to encrypt these medical images. In this research work, Block Cipher based Region of interest medical image encryption with multiple maps is proposed. Primarily, Region of Interest (ROI) regions are extracted with the help of a Laplacian edge detection operator, and then based on the edges in the blocks, the image is classified into wanted (ROI) and unwanted (RONI or ROB) parts. Next, the important ROI regions are permuted circularly with the help of Arnold's cat map and angle value. Then permuted ROI part is encrypted using the duffling system and unimportant regions are unaltered. Finally, the ROI encrypted image is obtained by concating the ROI encrypted part and the Region of Background (ROB) unaltered part. The encryption algorithm proposed has better security performance which is demonstrated in the experiments such as attacks on data loss, differential attacks, statistical attacks, and brute force attacks. These results show that the security in the proposed method is much better than many chaotic encryption algorithms proposed in recent times.

Hybrid modelling approach for water body change detection at Chalan Beel area in northern Bangladesh

Water is a strategic resource for both socio-economic development and human life. The current study has been carried out for spatio-temporal change detection of surface water bodies during winter period using hybrid modelling approach. The study area has fallen in the northern part of Bangladesh and is locally called Chalan Beel with 5 million of in habitants, a prominent intensive crop production, surface and groundwater irrigation, high evapotranspiration, and water scarcity. For the detection of water body changes, satellite images of 1999 and 2011 were used, and the following image fusion techniques were applied: (a) Gram-Schmidt (GS), (b) modified intensity hue saturation (IHS), (c) high-pass filter (HPF), and (d) wavelet. Landsat 7/ETM + panchromatic (PAN) band of 15 m × 15 m resolution in 1999 and Landsat 5/TM multispectral (MS) bands of 30 m × 30 m resolution in 2011 were allied each other to generate high-resolution image that contains information of two different years. The fused images were classified to extract the water bodies using four classification methods: (a) artificial neural network (ANN), (b) support vector machine (SVM) and (c) maximum likelihood (ML). To analyze the quality of the fused images, statistical calculation (quantitatively) and Laplacian edge detection (qualitatively) were used. To validate the fused image classification results, the multispectral images from 1999 and 2011 were again individually classified using principal component analysis (PCA), normalized difference water index (NDWI), and image differencing (ID) processes and compared with the previous classification. Surprisingly, the results showed that two-third of the areas dried up in 10 years.

Proposal for Automatic Vehicle Number Plate Recognition System in POLIMAS

Automatic Vehicle Number Plate Recognition (AVNPR) system is an image processing technology in computer vision which captures the image of the vehicle and recognizes its number plate. Currently, the security and management of transportation system becomes an important key in controlled place such as campus area. With an increased number of vehicles, there is a need for vehicle recognition mechanism that is effective, affordable and efficient. The objective of this paper is to propose an efficient automatic vehicle identification system by recognizing the vehicle plate number. The system will be installed at the main entrance of POLIMAS to ensure that only the authorized vehicles can automatically enter the campus area. Once the vehicle is detected by the input sensors, AVNPR system will capture the image of vehicle plate number. An image is then extracted and investigated character segmentation by using optical character recognition (OCR). The method used for detection of a plate number is by pre-processing of the image and using a combination of Sobel edge detection and Laplacian edge detection techniques. Bounding Box technique is used to find the number plate and character segmentation. For character recognition, OCR method is used by using template matching to compare the segmented image with the template. The system is sustainable as the camera will only be switched on when a car is present. The propose system successfully detects and recognizes the vehicle number plate on real images. This system can also be used for security and traffic control.

Automatic vehicle identification system using number plate recognition in POLIMAS

The security and management of transportation system becomes an important key in controlled place such as campus area. With an increased number of vehicles, there is a need for vehicle identification mechanism that is effective, affordable and efficient. This paper presents the development of automatic vehicle identification system using NP recognition in POLIMAS. Number Plate (NP) Recognition is an image processing technology in computer vision which captures the image of the vehicle and recognizes its NP. The system is installed at the main entrance to ensure that only the authorized vehicles can enter the campus area. Once the vehicle is detected by the input sensors, a system will capture the image of vehicle plate number. An image is then extracted and investigated character segmentation by using optical character recognition. The method used for detection of a plate number is by pre-processing of the image and using a combination of Sobel Edge Detection and Laplacian Edge Detection Techniques. Bounding Box technique is used to find the NP and character recognition. The accuracy of NP recognition has an average of 87%. The system is sustainable as the camera will only be switched on when a vehicle is present.

Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis.

In this paper, the urinary bladder cancer diagnostic method which is based on Multi-Layer Perceptron and Laplacian edge detector is presented. The aim of this paper is to investigate the implementation possibility of a simpler method (Multi-Layer Perceptron) alongside commonly used methods, such as Deep Learning Convolutional Neural Networks, for the urinary bladder cancer detection. The dataset used for this research consisted of 1997 images of bladder cancer and 986 images of non-cancer tissue. The results of the conducted research showed that using Multi-Layer Perceptron trained and tested with images pre-processed with Laplacian edge detector are achieving AUC value up to 0.99. When different image sizes are compared it can be seen that the best results are achieved if 50×50 and 100×100 images were used.

A Fuzzy Approach to Recognize Face Using Contourlet Transform

Face recognition addresses identification, verification, and authentication in biometric-based security systems. This work enhances the contrast and edges in face images and recognizes the face using contourlet transform and fuzzy rules. Contourlet transformed image provides multiscale and directional information. The transformed image is divided into low-pass image (low-frequency image) and band-pass image (high-frequency image). The low-pass image is enhanced using fuzzy-based histogram specification since it deals with contrast. Band-pass image contains detailed information about the edges of the image and are enhanced using fuzzy rules and morphological gradient operators. The proposed system achieves the accuracy rate of 99.81% and 99.35% on Yale-B and JAFEE dataset, respectively, which is better than the existing curvelet and wavelet transform-based recognition. The incorporation of fuzzy rules enhances the mean intensity value of the edges to 34.19, which is better than Canny, Sobel, Prewitt, Robert and Laplacian edge detection techniques. Finally Discriminant Correlation Analysis (DCA) feature level fusion is applied to fuse enhanced edge intensities and histogram features for Support Vector Machine (SVM) classification.

Research on Coal-Rock Fracture Image Edge Detection Based on Tikhonov Regularization and Fractional Order Differential Operator

Aiming at the conventional image edge detection algorithm, the first-order differential edge detection method is easy to lose the image details and the second-order differential edge detection method is more sensitive to noise. To deal with the problem, the Tikhonov regularization method is adopted to reconstruct the input coal-rock infrared images, so as to reduce the noise interference, and then, the reconstructed image is transformed by gray level. Finally, we consider the frequency characteristics and long memory properties of fractional differential, the classical first-order Sobel and second-order Laplacian edge detection algorithms are extended to fractional order pattern, and a new pattern of fractional order differential image edge detection is constructed to realize the coal-rock fracture edge features identification. The results show that, compared with integer order differential, the error rate and omission rate of fractional order differential algorithm are smaller, the quality factor is larger, and the execution time and memory footprint are smaller. From the point of view of location criteria and location accuracy, the fractional order differential algorithm is better than the integer order. In addition, the proposed method is compared with Canny algorithm, B-spline wavelet transform, and multidirection fuzzy morphological edge detection method, can detect more coal-rock fracture infrared image edge details, and is more robust to noise.

Photo splicing detection using error level analysis and laplacian-edge detection plugin on GIMP

There are several types of photo manipulation, one of them is splicing manipulation. The problem was nowadays the image manipulation software is used by the person who has not an expert may manipulate the photo. The question was how we can be sure whether the photo is original or not. Therefore, this paper tested the photo splicing detection using two methods: error level analysis and laplacian-edge detection by the plugin of GIMP. The result was to detect photo splicing that has several variables which may enable the method to work better, i.e., the characteristic of the camera and the JPEG compression that characterized by the camera. This experiment and analysis can be used as a reference for photo forensics investigation.

Analysis of Proposed Hybrid Approaches for Laplacian Edge Based Image Segmentation Using Morphological Image Processing

In today’s world Images play very important role to get the information about any object. The real examples are in medical industry For M.R.I and C.T Scans, in forecasting for analyzing the satellite images and in tourism industries to select the better image among the various images of place. Sometimes images are not clear so we are unable to extract information. To solve this problem there is a concept Image Processing. Images are used to extract information about any object to generate the reports and data for the further processing. The main motive to write this paper is to detect more edges with good intensity, good contrast and less noise in an image which is enforced using PYTHON and the results obtained are studied and thereby mentioned, highlighting the techniques performance. So, in this dissertation some existing techniques are used and new improved algorithm is applied on the image and results of proposed algorithm are comparing with existing method. Performed experiments showed that improved algorithm is better as compare to the normal Laplacian edge detection and existing method; we can say this by examining images with some parameters like MSE, PSNR, standard deviation and MEAN values. The algorithm is demonstrated through the segmentation of colored or grayscale images. The correctness of proposed method is estimated and a comparative analysis against techniques Laplacian and existing method [2] and improved algorithm is presented.

Analyzing edge detection techniques for feature extraction in dental radiographs

Summary Several dental problems can be detected using radiographs but the main issue with radiographs is that they are not very prominent. In this paper, two well known edge detection techniques have been implemented for a set of 20 radiographs and number of pixels in each image has been calculated. Further, Gaussian filter has been applied over the images to smoothen the images so as to highlight the defect in the tooth. If the images data are available in the form of pixels for both healthy and decayed tooth, the images can easily be compared using edge detection techniques and the diagnosis is much easier. Further, Laplacian edge detection technique is applied to sharpen the edges of the given image. The aim is to detect discontinuities in dental radiographs when compared to original healthy tooth. Future work includes the feature extraction on the images for the classification of dental problems.

지능 영상 감시를 위한 흑백 영상 데이터에서의 효과적인 이동 투영 음영 제거

In detection of moving objects from video sequences, an essential process for intelligent visual surveillance, the cast shadows accompanying moving objects are different from background so that they may be easily extracted as foreground object blobs, which causes errors in localization, segmentation, tracking and classification of objects. Most of the previous research results about moving cast shadow detection and removal usually utilize color information about objects and scenes. In this paper, we proposes a novel cast shadow removal method of moving objects in gray level video data for visual surveillance application. The proposed method utilizes observations about edge patterns in the shadow region in the current frame and the corresponding region in the background scene, and applies Laplacian edge detector to the blob regions in the current frame and the corresponding regions in the background scene. Then, the product of the outcomes of application determines moving object blob pixels from the blob pixels in the foreground mask. The minimal rectangle regions containing all blob pixles classified as moving object pixels are extracted. The proposed method is simple but turns out practically very effective for Adative Gaussian Mixture Model-based object detection of intelligent visual surveillance applications, which is verified through experiments.

Detecting Edge Using Support Vector Machine

The edge detection is used in many applications in image processing. It is currently crucial technique of image processing. There are various methods for promoting edge detection. Here, it is presented that edge detection can be achieved using Support Vector Machine (SVM). Supervised learning method is applied. Laplacian edge detector is an instructor of Support Vector Machine. In this research, it is presented that any classical method can be applied for training of SVM as edge detector.

전신주의 종류 판별을 위한 동적 PCA 알고리즘

This paper proposes a new dynamic PCA algorithm to recognize types of electric poles, which is necessary for a mobile robot moving along the neutral line for inspecting high-voltage facilities. Since the mobile robot needs to pass over the electric poles and grasp the neutral wire again for the next region inspection, the detection of the electric pole type is a critical factor for the successful passing-over the electric pole. The CCD camera installed on the mobile robot captures the image of the electric pole while it is approaching to the electric pole. Applying the dynamic PCA algorithm to the CCD image, the electric pole type has been classified to provide the stable grasping operation for the mobile robot. The new dynamic PCA algorithm replaces the reference image in real time to improve the robustness of the PCA algorithm, adjusts the brightness to get the clear images, and applies the Laplacian edge detection algorithm to increase the recognition rate of electric pole type. Through the real experiments, the effectiveness of this proposed dynamic PCA algorithm method using Laplacian edge detecting method has been demonstrated, which improves the recognition rate about 20% comparing to the conventional PCA algorithm.