Showing papers on "Canny edge detector published in 2023"
TL;DR: In this paper , an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible.
Abstract: Photographing images is used as a common detection tool during the process of bridge maintenance. The edges in an image can provide a lot of valuable information, but the detection and extraction of edge details are often affected by the image noise. This study proposes an algorithm for wavelet transform to denoise the image before edge detection, which can improve the signal-to-noise ratio of the image and retain as much edge information as possible. In this study, four wavelet functions and four decomposition levels are used to decompose the image, filter the coefficients and reconstruct the image. The PSNR and MSE of the denoised images were compared, and the results showed that the sym5 wavelet function with three-level decomposition has the best overall denoising performance, in which the PSNR and MSE of the denoised images were 23.48 dB and 299.49, respectively. In this study, the canny algorithm was used to detect the edges of the images, and the detection results visually demonstrate the difference between before and after denoising. In order to further evaluate the denoising performance, this study also performed edge detection on images processed by both wavelet transform and the current widely used Gaussian filter, and it calculated the Pratt quality factor of the edge detection results, which were 0.53 and 0.47, respectively. This indicates that the use of wavelet transform to remove noise is more beneficial to the improvement of the subsequent edge detection results.
9 citations
TL;DR: In this article , a cascaded and high-resolution network named (CHRNet) is proposed for refined edge detection using a cascade of backbones and attention modules, where sub-blocks are connected at every stage with the output of the previous layer and after each layer, a batch normalization layer is used as an erosion operation for the homogeneous region in the image.
4 citations
TL;DR: In this article , a YOLOv4 deep learning model was used to train a model for identifying cracks; the model was then employed in object detection, and the results indicated that the model had an accuracy of 92%, with width measurements as precise as 0.22 mm.
Abstract: Bridges are often at risk due to the effects of natural disasters, such as earthquakes and typhoons. Bridge inspection assessments normally focus on cracks. However, numerous concrete structures with cracked surfaces are highly elevated or over water, and is not easily accessible to a bridge inspector. Furthermore, poor lighting under bridges and a complex visual background can hinder inspectors in their identification and measurement of cracks. In this study, cracks on bridge surfaces were photographed using a UAV-mounted camera. A YOLOv4 deep learning model was used to train a model for identifying cracks; the model was then employed in object detection. To perform the quantitative crack test, the images with identified cracks were first converted to grayscale images and then to binary images the using local thresholding method. Next, the two edge detection methods, Canny and morphological edge detectors were applied to the binary images to extract the edges of the cracks and obtain two types of crack edge images. Then, two scale methods, the planar marker method, and the total station measurement method, were used to calculate the actual size of the crack edge image. The results indicated that the model had an accuracy of 92%, with width measurements as precise as 0.22 mm. The proposed approach can thus enable bridge inspections and obtain objective and quantitative data.
4 citations
TL;DR: In this paper , a dual-scale morphological filtering (DSMF) method combined with Canny operator is proposed for edge detection of delamination defects in carbon fiber-reinforced polymer (CFRP) composite materials.
4 citations
TL;DR: In this paper , fracture detection and classification are performed using various machine learning techniques using of a dataset containing various bones (normal and fractured) using X-ray images obtained are subjected to image preprocessing stages and are prepared for the feature extraction stage.
Abstract: One of the most important problems in orthopedics is undiagnosed or misdiagnosed bone fractures. This can lead to patients receiving an incorrect diagnosis or treatment, which can result in a longer treatment period. In this study, fracture detection and classification are performed using various machine learning techniques using of a dataset containing various bones (normal and fractured). Firstly, the X‐ray images obtained are subjected to image preprocessing stages and are prepared for the feature extraction stage. Then, in addition to the Canny and Sobel edge detection methods used in the image processing stage, feature extraction of X‐ray images is performed with the help of Houhg line detection and Harris corner detector. The data obtained by performing feature extraction is given to 12 different machine learning classifiers and the results are presented. Setting hyperparameters for classifiers is done by the grid search method, and the study is tested for 10‐fold cross‐validation. Classifier results are presented comparatively as accuracy, training time, and testing time, and linear discriminant analysis (LDA) reaches the highest accuracy rate with 88.67% and 0.89 AUC. The proposed computer‐aided diagnosis system (CAD) will reduce the burden on physicians by identifying fractures with high accuracy.
3 citations
TL;DR: In this paper , the shadow of the bottom of the cable under different illumination was used to measure the frequency of a cable by locating the shadows of the cables under different lighting conditions.
2 citations
TL;DR: In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method, which provides better security services and it is computationally less expensive.
Abstract: With the advancement in information technology, digital data stealing and duplication have become easier. Over a trillion bytes of data are generated and shared on social media through the internet in a single day, and the authenticity of digital data is currently a major problem. Cryptography and image watermarking are domains that provide multiple security services, such as authenticity, integrity, and privacy. In this paper, a digital image watermarking technique is proposed that employs the least significant bit (LSB) and canny edge detection method. The proposed method provides better security services and it is computationally less expensive, which is the demand of today’s world. The major contribution of this method is to find suitable places for watermarking embedding and provides additional watermark security by scrambling the watermark image. A digital image is divided into non-overlapping blocks, and the gradient is calculated for each block. Then convolution masks are applied to find the gradient direction and magnitude, and non-maximum suppression is applied. Finally, LSB is used to embed the watermark in the hysteresis step. Furthermore, additional security is provided by scrambling the watermark signal using our chaotic substitution box. The proposed technique is more secure because of LSB’s high payload and watermark embedding feature after a canny edge detection filter. The canny edge gradient direction and magnitude find how many bits will be embedded. To test the performance of the proposed technique, several image processing, and geometrical attacks are performed. The proposed method shows high robustness to image processing and geometrical attacks.
2 citations
TL;DR: In this article , an improved Canny algorithm is proposed, which uses the Sobel operator and approximation methods to calculate the gradient magnitude and direction for replacing complex operations with reduced hardware costs.
Abstract: Canny edge detection is one of the most widely used edge detection algorithms due to its superior performance. However, it is a complex, time-consuming process and has a high hardware cost. To overcome these issues, an improved Canny algorithm is proposed in this paper. It uses the Sobel operator and approximation methods to calculate the gradient magnitude and direction for replacing complex operations with reduced hardware costs. Otsu’s algorithm is introduced to adaptively determine the image threshold. However, Otsu’s algorithm has division operations, and the division operation is complex and has low efficiency and slow speed. We introduce a logarithmic unit to turn the division into a subtraction operation that is easy to implement by hardware but does not affect the selection of the threshold. Experimental results show that the system can detect the edge of the image well without adjusting the threshold value when the external environment changes and requires only 1.231 ms to detect the edges of the 512 × 512 image when clocked at 50 MHz. Compared with existing FPGA implementations, our implementation uses the least amount of logical resources. Thus, it is more suitable for platforms that have limited logical resources.
2 citations
TL;DR: A novel adaptive threshold Sobel edge detection algorithm based on the improved genetic algorithm is proposed to detect edges in this paper , which has a better detection effect and applicability than the traditional Sobel algorithm.
Abstract: In this paper, a novel adaptive threshold Sobel edge detection algorithm based on the improved genetic algorithm is proposed to detect edges. Because of the influence of external factors in actual detection process, the result of detection is often not accurate enough when the configured threshold of the target image is far away from the real threshold. Different thresholds of images are calculated by improved genetic algorithm for different images. The calculated threshold is used in edge detection. The experimental results show that the image processed by the improved algorithm has stronger edge continuity. It is shown that proposed algorithm has a better detection effect and applicability than the traditional Sobel algorithm. Keywords—Genetic algorithm; Sobel operator; edge detection; adaptive threshold
1 citations
TL;DR: In this paper , an Otsu threshold and Canny-edge-detection-based fast Hough transform (FHT) approach was proposed to improve the accuracy of lane detection for autonomous vehicle driving.
Abstract: An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems.
1 citations
TL;DR: In this paper , a novel shape descriptor for object recognition is proposed, which is composed of deviations from average range and average angle, which are used as a feature extractor output of which is fed to linear classifier.
Abstract: In this study a novel shape descriptor for object recognition is proposed. As a preprocessing stage, Canny edge detection [4] is applied to input images. Output of Canny edge detector, namely edge image, is sampled and various number of points are selected. Chosen points are input to the new shape descriptor. Proposed shape descriptor is composed of deviations from average range and average angle. Shape descriptor is used as a feature extractor output of which is fed to linear classifier. Linear classifier is trained using pseudo-inverse and gradient descent techniques. Full MNIST dataset is used to test the system and results are reported.
TL;DR: In this article , the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests was compared, and the study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability.
Abstract: Digital image-correlation (DIC) algorithms rely heavily on the accuracy of the initial values provided by whole-pixel search algorithms for structural displacement monitoring. When the measured displacement is too large or exceeds the search domain, the calculation time and memory consumption of the DIC algorithm will increase greatly, and even fail to obtain the correct result. The paper introduced two edge-detection algorithms, Canny and Zernike moments in digital image-processing (DIP) technology, to perform geometric fitting and sub-pixel positioning on the specific pattern target pasted on the measurement position, and to obtain the structural displacement according to the change of the target position before and after deformation. This paper compared the difference between edge detection and DIC in accuracy and calculation speed through numerical simulation, laboratory, and field tests. The study demonstrated that the structural displacement test based on edge detection is slightly inferior to the DIC algorithm in terms of accuracy and stability. As the search domain of the DIC algorithm becomes larger, its calculation speed decreases sharply, and is obviously slower than the Canny and Zernike moment algorithms.
TL;DR: In this article , the authors proposed smart edge detection techniques in x-ray images for improving PSNR using canny edge detection algorithm and compared with laplacian algorithm and found that the Canny edge detection has insignificantly greater PSNR when compared to laplACian algorithm.
Abstract: Aim: The aim of this study is to propose smart edge detection techniques in x-ray images for improving PSNR using canny edge detection algorithm and compared with laplacian algorithm. Materials and Methods: Using the design of edge detection technique and to improve PSNR, canny edge detection algorithm is used along with gaussian filter and it is compared with laplacian algorithm. Canny edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc. com by keeping g-power at 80%, confidence interval at 95% and threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the canny edge detection algorithm has a higher mean PSNR value of 28.98db than the laplacian algorithm 27.08 db. It is observed that the canny edge detection algorithm performed better than the laplacian algorithm (p>0.05) by performing an independent sample t-test. Conclusion: Canny edge detection has insignificantly greater PSNR when compared to laplacian algorithm.
TL;DR: In this article , the authors proposed a novel approach of Filtered Canny Misalignment Analysis (FCMA) to capture and quantify in-plane and out-of-plane fibre waviness.
01 Jan 2023
TL;DR: In this article , the authors presented a smart device with various safety features, which can be mounted in any four wheeler vehicle and can make an ordinary vehicle to semi-smart vehicle.
Abstract: Travelling by roads is the most common and oldest way to reach the destination. The fast speed vehicles are growing each day. Many automobile industries are working on development of fast speed vehicle with various security features. Security features include safety air bags, safety breaks, and many electronic supports. But still due to various reasons vehicle crashes and road accidents happen every day. Road accidents cause loss of infrastructure and monitory. These results a lot of injuries and deaths every year in every city. Government has lot of road safety protocols, rules, and regulations to prevent road accidents. Every year government is spending a lot for road safety. This paper is presenting a smart device with various safety features. The device can be mounted in any four wheeler vehicle and can make an ordinary vehicle to semi-smart vehicle. The device is featured with monitoring of engine, tires, and other peripherals. This paper is focused on lane marking and obstacle detection on the roads. For the lane marking detection improved Hough transform, Canny edge detector has been used. And for the obstacle detection, improved CNN has been used. The algorithms are implemented on hardware and tested on real objects by mounting the device on vehicle. The developed algorithms are efficient, and results are promising.
29 Jan 2023
TL;DR: Wang et al. as mentioned in this paper made a comparative study on the application of yolov5 and fast RCNN in railway foreign object intrusion detection, which showed that YOLOv5 has better comprehensive performance in detection rate and detection speed, and Faster RCNN model cannot meet the requirements of real-time detection of track foreign objects intrusion.
Abstract: Due to the lack of track foreign body intrusion dataset, classical target detection models are rarely used in the field of foreign body intrusion on railway tracks, and model comparison experiments are also insufficient. Aiming at these problems, this paper makes a comparative study on the application of yolov5 and fast RCNN in railway foreign object intrusion detection. First, the train and test dataset was established by image preprocessing, data cleaning and data labeling of UAV aerial images. Second, the canny edge detection algorithm combined with Hough transform was used to extract the track features for delineating the detection area. Finally, Yolov5 and fast RCNN, two widely used models, were used to train and test respectively based on our dataset for comparative studies. Experiment results show that YOLOv5 has better comprehensive performance in detection rate and detection speed, and Faster RCNN model cannot meet the requirements of real-time detection of track foreign objects intrusion.
01 May 2023
TL;DR: In this article , a fuzzy logic-based approach for image edge detection is proposed, which uses an open-loop fuzzy logic controller which comprises a series of steps instead of a simple thresholding technique whose values are emperically determined.
Abstract: Edge detection finds a greater significance in image processing and computer vision, as many machine learning models require images as input data. Edge detection can be used to extract important features to simplify the visual data. With the increased use of AI, latency can be reduced by processing the data locally which enhances the performance capabilities of the model. This paper reviews the effectiveness of the Fuzzy Inference System over traditional gradient-based approaches such as the Canny edge detection technique and presents a fuzzy logic-based approach for image edge detection. The fuzzy-based approach uses an open-loop fuzzy logic controller which comprises a series of steps instead of a simple thresholding techniques whose values are emperically determined. The performance is analysed for implementation in Python and MATLAB Platforms, with some variations in logic for algorithms in each software. The proposed model is applied to MRI images inorder to detect abnormalities such as tumours.
TL;DR: Canny-Net as mentioned in this paper is a modification of the 2D Canny edge detector into a pre-weighted neural network, which reduces maximum error bounds for edge detection in CT scans.
Abstract:
We present Canny-Net, a modification of the 2D Canny edge detector into a pre-weighted neural network. CT scans of objects that contain metal components are characterized by artifacts like beam hardening, total absorption and scatter. Edge detection with classical methods, e.g. the Canny edge detector, is therefore prone to error. Using known operator learning, Canny-Net reduces maximum error bounds for edge detection in such CT scans. We show that Canny-Net is a light-weight neural network with a small number of trainable parameters. After training, we observe an increase of 11% in F1 score on 2304 test images when compared to the Canny edge detector. Due to its adaptability and low computational cost, Canny-Net can be considered for a wide range of different applications.
29 Jan 2023
TL;DR: In this article , the hollowing out internal point algorithm was used to achieve the contour extraction of the sugarcane image, and five classic edge detection operators were used to realize the edge detection of the image.
Abstract: Using image processing technology to study sugarcane image contour extraction and edge detection methods has important research value and practical application significance for realizing pre-seed sugarcane planter to complete the seeding task stably, quickly, accurately and efficiently. In this regard, with the help of the MATLAB platform, this paper uses the hollowing out internal point algorithm to achieve the contour extraction of the sugarcane image, and uses five more classic edge detection operators for the same original sugarcane image to realize the edge detection of the sugarcane image. Analyze the results and draw the conclusion that Canny operator is the most suitable and effective edge detection operator for sugarcane image edge detection. This also provides an important reference for subsequent monitoring of the seeding status and efficiency of the precut sugarcane planter.
TL;DR: Canny et al. as discussed by the authors proposed a framework to improve the quality of the data collected by the data collection system by using the information gathered from the data acquisition system of the database.
Abstract: 红外人脸图像的边缘轮廓特征对于红外人脸检测、识别等相关应用具有重要价值。针对红外人脸图像边缘轮廓提取时存在伪边缘的问题，提出了一种改进Canny算法的红外人脸图像边缘轮廓提取方法。首先通过对引导滤波算法引入“动态阈值约束因子”替换原始算法中的高斯滤波，解决了原始算法滤波处理不均匀和造成红外人脸图像弱边缘特征丢失的弊端；接着对原始算法的非极大值抑制进行了改进，在原始计算梯度方向的基础上又增加了4个梯度方向，使得非极大值抑制的插值较原始算法更加精细；最后改进OTSU（大津）算法，构造灰度-梯度映射函数确定最佳阈值，解决了原始算法人为经验确定阈值的局限性。实验结果表明：提出的改进Canny算法的红外人脸轮廓提取方法滤波后的图像，相较于原始Canny算法滤波处理，信噪比性能提升了34.40%，结构相似度性能提升了21.66%；最终的红外人脸边缘轮廓提取实验的优质系数值高于对比实验的其他方法，证明改进后的算法对于红外人脸图像边缘轮廓提取具有优越性。
TL;DR: In this paper , the results of edge detection of images that have additive gaussian noise or salt and pepper noise by using various techniques of noise removal such as filtering, morphology and deep learning based ones are compared.
Abstract: In this paper, we show comparison results of edge detection of images that have additive gaussian noise or salt and pepper noise by using various techniques of noise removal such as filtering, morphology and deep learning based ones. In particular, this present work provides comparison results of noise removal by using gaussian filter, open and close operations of morphology and auto-encoder model followed by carrying out edge detection. Robert cross, Sobel, Prewitt and Canny detectors are used for edge detection of the images with noise removal. Experimental results show that noise removal results are different with characteristics of noise and techniques applied for noise removal. In addition, deep learning based technique, auto-encoder does not always shows superior results of noise removal, particularly in the case of existence of salt-pepper noise. In the experiments, gaussian noise or salt-pepper noise is used and peak signal noise ratio (PSNR) is used for quantitative comparison and the results of edge detection is qualitatively compared from visual perspective.
01 Jan 2023
TL;DR: In this paper , a multispectral satellite images of agriculture regions are taken for the experimental analysis in this work and the statistical results of canny edge detection and adaptive ridge edge detection algorithm are useful to detection of magnified boundaries of healthy crop area.
Abstract: In remote sensing satellite image analysis is a pivotal. Images segmentation has lot many challenges in the multispectral satellite images because of the large amount data present in it. The edges of the healthy crop regions have been extracted in this work. The multispectral satellite images of agriculture regions are taken for the experimental analysis in this work. The statistical results of canny edge detection and adaptive ridge edge detection algorithm are useful to detection of magnified boundaries of healthy crop area. Firstly, contrast enhancement of the multitemporal images has been done after converting the original images into gray images. Further, different edge detection algorithm is applied to detect the edge boundaries by taking the different threshold value. The quantitative analysis has been done by computing mean, standard deviation, sigma, and signal-to-noise ratio. The satellite image dataset has been collected from the Maharashtra State Remote Center (MRSC).
02 Feb 2023
TL;DR: In this article , a real-time object dimension detection and dimension analysis using python is proposed, which is a subset of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data.
Abstract: Deep learning is a subset of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data. Deep learning is focused on improving the AI process of having machines learning new things on its own. In this paper, proposed real time object dimension detection and dimension analysis using python. For ML and AI-based papers, python programming language offers consistency, simplicity, and access to excellent libraries and frameworks. It also offers platform freedom, flexibility, and a large developer community. Proposed work, explorations were done on some methodologies, including You Only Look Once (YOLO), for object identification that is meant for speed and real-time application use, and Region-Based Convolutional Neural Networks (R-CNNs) built for model performance and analysis. To make it better, a canny edge detection algorithm is being used. Canny edge detector is a multistage algorithm-based on edge detection operator that can identify a variety of edges in image.
10 Jan 2023
TL;DR: In this paper , a human identification technique for differentiating between samples taken before and after surgery is proposed, which operates in three stages: first, pre-, and post-surgery images are preprocessed (cropped/converted to grayscale), then fuzzy edge detection is performed.
Abstract: Abstract Many people adopt cosmetic or medical changes for aesthetic or therapeutic objectives. The paper proposes a human identification technique for differentiating between samples taken before and after surgery. The system operates in three stages. First, pre-, and post-surgery images are preprocessed (cropped/converted to grayscale), then fuzzy edge detection is performed. Next, prominent features are extracted using SURF (Speeded Up Robust Features) extractor, and finally, a KNN classifier is used to determine which pairs are genuine and which are impostor. Utilizing fuzzy edge detection as a pre-processing step for appropriate (non-redundant) feature selection is the innovation/novelty in this method (dimensionality reduction). The selected features acquired by fuzzy detection are then subjected to SURF. The purpose of SURF is to compute operators quickly by utilizing box filters, rotation invariance, and anti-blur features. Since edges are local in nature and SURF is a local extractor, we have concentrated on extracting local features since they reveal more information (high frequency components) and can represent non-linear geometrical variations brought about by medical changes. Because the image contour is not deformed and suitable edges are retained, the fuzzy detector is preferable to Sobel, Canny, Roberts, and Prewitt detectors. Surgical sample fuzzy edge detection has not yet been implemented. The anticipated scheme's evaluation measures have been documented in literature the most effectively to date.
TL;DR: Zhang et al. as discussed by the authors proposed an effective Canny-guided refinement of human-labeled edges whose result can be used to train crisp edge detectors, which seek for a subset of over-detected Canny edges that best align human labels.
Abstract: Learning-based edge detection usually suffers from predicting thick edges. Through extensive quantitative study with a new edge crispness measure, we find that noisy human-labeled edges are the main cause of thick predictions. Based on this observation, we advocate that more attention should be paid on label quality than on model design to achieve crisp edge detection. To this end, we propose an effective Canny-guided refinement of human-labeled edges whose result can be used to train crisp edge detectors. Essentially, it seeks for a subset of over-detected Canny edges that best align human labels. We show that several existing edge detectors can be turned into a crisp edge detector through training on our refined edge maps. Experiments demonstrate that deep models trained with refined edges achieve significant performance boost of crispness from 17.4% to 30.6%. With the PiDiNet backbone, our method improves ODS and OIS by 12.2% and 12.6% on the Multicue dataset, respectively, without relying on non-maximal suppression. We further conduct experiments and show the superiority of our crisp edge detection for optical flow estimation and image segmentation.
14 Jun 2023
TL;DR: In this article , a parabola is used to model a curved wire, where the detection confidence corresponds to how well the curved pattern in the image fits to a paraboloid, and features consist of orientation angle, the fitted parabolic parameters and the fitting confidence.
Abstract: Wires do not have linear structure to aid their detection when they are not lying straight. This paper proposes some features for curved wire detection from the images constructed by the data of a ground penetrating radar. We propose the application of a parabola to model a curved wire, where the detection confidence corresponds to how well the curved pattern in the image fits to a parabola. The processing involves projecting the 3-D GPR beamformed image onto the ground plane, applying the Canny edge detector to extract the edge points, and fitting the edge points to a parabola through a voting scheme as in the generalized Hough Transform. The features consist of the orientation angle, the fitted parabolic parameters and the fitting confidence. Some examples for the detection performance are illustrated.
TL;DR: In this article , an ANN-based secure facial recognition model that will accurately and efficiently record all data and information about an individual is presented. But the results demonstrate that the system successfully allows user to login using credentials, enrols, registers, logs and save captures data and facial biometric.
Abstract: A facial recognition system is a computer program that uses a digital image or a video frame from a video source to automatically recognize or confirm a person. An amicable approach to achieving the desired result in facial By comparing certain facial traits from the image with a facial database, biometrics. This paper developed an ANN-based secure facial recognition model that will accurately and efficiently record all data and information about an individual. This system uses Haar Classifier Technique; face detection algorithms, Opencv, Visual C++, Haar like Features and the Canny Edge Detection and OPenCV. The results therefore demonstrate that the system successfully allows user to login using credentials, enrols, registers, logs and save captures data and facial biometric. The system authenticates by analysing the upper position of the two eyebrows vertically. The method searches from w/8 to mid for the left eye and from mid to w - w/8 for the right eye. Thus, w denotes the image's width, while mid designates where the two eyes are cantered. The black pixel-lines are vertical and are positioned between mid/2 and mid/4 for the left eye and mid+(w-mid)/4 and mid-+3*(w-mid)/4 for the right eye. The height of the black pixel-lines is measured from the eyebrow starting height to (h- eyebrow starting position)/4.
TL;DR: In this article , a knowledge-based edge detection method is proposed to improve the efficiency and computational complexity of edge detection by introducing a set of knowledge-base rules. But the method is not suitable for hardware implementation on field-programmable gate arrays (FPGA).
Abstract: Edge detection is a fundamental process, and therefore there are still demands to improve its efficiency and computational complexity. This study proposes a knowledge-based edge detection method to meet this requirement by introducing a set of knowledge-based rules. The methodology to derive the rules is based on the observed continuity properties and the neighborhood characteristics of the edge pixels, which are expressed as simple arithmetical operations to improve computational complexity. The results show that the method has an advantage over the gradient-based methods in terms of performance and computational load. It is appropriately four times faster than Canny method and shows superior performance compared to the gradient-based methods in general. Furthermore, the proposed method provides robustness to effectively identify edges at the corners. Due to its light computational requirement and inherent parallelization properties, the method would be also suitable for hardware implementation on field-programmable gate arrays (FPGA).