Canny Filter Research Articles

Информационно-аналитическая система детектирования движения объектов на пешеходном переходе

To manage pedestrian and vehicle flows at intersections, systems are implemented that use adaptive traffic light models, adjusting time intervals based on the volume of pedestrians and vehicles. These systems include video cameras that monitor road user movements, enhancing real-time traffic control. This paper introduces an information and analytical system for managing transport and pedestrian flows using the YOLO neural model, which enables object recognition. The system performs several operations: converting the original image to gray scale, applying Gaussian blurring, detecting object boundaries through the Canny filter and fuzzy logic for edge detection, and contour processing, where each identified contour is assigned a unique number. The neural network then compares detected contours to the training sample data, determining whether the object is a person or a vehicle. The paper presents experimental results for these algorithms in object recognition. Modified software and images of intersections with pedestrian crossings captured from street video cameras in Kursk were used in the experiments. The recognition accuracy rate from the experiments was 72.4%.

An Image Processing-Based Method to Analyze Driver Visual Behavior Using Eye-Tracker Data

This paper presents a practical method for analyzing drivers’ eye movements, providing a valuable tool for understanding their behavior during driving simulations. The method, which utilizes an image processing technique, addresses the challenges when the driver’s attention is on points without information about the image depth. The screen image changes or moves with the simulation. It allows us to identify the gaze position relative to the road, determining whether the glance is inside or outside. This is achieved by transforming RGB images (frames) collected by the eye-tracker video camera into a b/w image using the Canny filter. This filter can identify objects’ contours by evaluating the change in color of their surfaces. A window is then applied to these new images to extract information about the gaze position in the real world. Four drivers were used as a sample for the method’s testing. The findings demonstrate various driver variations and a disparity between driving in curved and rectilinear segments. The gaze is typically inside the road in curved sections, whereas in rectilinear sections, the gaze is frequently outside.

Development of Image Processing and AI Model for Drone Based Environmental Monitoring System

Data from environmental monitoring can be used to identify possible risks or adjustments to ecological patterns. Early detection reduces risks and lessens the effects on the environment and public health by allowing for prompt responses to ecological imbalances, pollution incidents, and natural disasters. Decision-making and analysis can be done in real time when Artificial Intelligence (AI) is integrated with Unmanned Aerial Vehicles (UAV) technology. With the help of these technologies, environmental monitoring is made possible with a more complete and effective set of tools for assessment, analysis, and reaction to changing environmental conditions. Multiple studies have shown that forest fires in India have been happening more often recently. Lightning, extremely hot weather, and dry conditions are the three main elements that might spontaneously ignite a forest fire. Both natural and man-made ecosystems are affected by forest fires. Forest fire photos are pre-processed using the Sobel and Canny filter. A Convolutional Neural Network (CNN)–based Forest Fire Image Classification Network (DFNet) using the publicly accessible Kaggle dataset is proposed in this study. The suggested DFNet classifier's hyperparameters are fine-tuned with the help of Spotted Hyena Optimizer (SHO). With a performance level of 99.4 percent, the suggested DFNet model outperformed the state-of-the-art models, providing substantial backing for environmental monitoring.

Development of a Medical Image Segmentation Algorithm based on Fuzzy C-Means Clustering

Breast mass segmentation in mammography plays a very important role in computer-aided diagnosis (CAD) systems. In this article, we propose a mammography image segmentation method based on a combined approach. The fuzzy clustering method and thresholding segmentation. Subsequently, we use the wavelet transform and the Canny filter for edge detection.

Practical study for comparing edge detection filters in digital image processing

Edge detection is a fundamental and important tool in image processing and computer vision. The research topic discusses edge extraction of a digital image using various digital image processing techniques. Digital image edge identification using variety of digital image processing methods. The most popular technique for identifying discontinuities in intensity levels is edge detection. The actual image contains noise that could affect the digital image's quality. Some edge detection filters were used such as Canny, Sobel, and Prewitt. Laplace of Gaussian (LOG) edge recognition, Robert boundary detection, zero edge recognition and analysis. In light of the results of the comparison of edge filters the best edge filter had been obtained. Also, a hybrid method that merges between Canny filter and morphology operations were produced for edge detection. The research aims to reach an appropriate image purification by using different types of filters and to reduce distortions at the edges of the image and to determine which type of filter is the best. As well as, a comparison operation is applied between the traditional method and the hybrid method. Following the results explained by the filters used and displaying the resulting images. The results evaluated from hybrid filter show that this method is optimal than the traditional filters based on the Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) with a value equals 43.43. A smaller score indicates better perceptual quality. Also, the edged images quality consequences from the traditional filters were measured by this metric. The results produced from the traditional filters prove that the best filter for edge image detection is Canny filter based on Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) that reaches 46.01. In addition, the correlation coefficient was estimated to find which of the resulting images are better and closer to the original image. While for correlation coefficient the better filter was Prewitt filter with 0.3133 value. This study program was applied under the MATLAB R2020b system.

3D DENSITY-GRADIENT BASED EDGE DETECTION ON NEURAL RADIANCE FIELDS (NERFS) FOR GEOMETRIC RECONSTRUCTION

Abstract. Generating geometric 3D reconstructions from Neural Radiance Fields (NeRFs) is of great interest. However, accurate and complete reconstructions based on the density values are challenging. The network output depends on input data, NeRF network configuration and hyperparameter. As a result, the direct usage of density values, e.g. via filtering with global density thresholds, usually requires empirical investigations. Under the assumption that the density increases from non-object to object area, the utilization of density gradients from relative values is evident. As the density represents a position-dependent parameter it can be handled anisotropically, therefore processing of the voxelized 3D density field is justified. In this regard, we address geometric 3D reconstructions based on density gradients, whereas the gradients result from 3D edge detection filters of the first and second derivatives, namely Sobel, Canny and Laplacian of Gaussian. The gradients rely on relative neighboring density values in all directions, thus are independent from absolute magnitudes. Consequently, gradient filters are able to extract edges along a wide density range, almost independent from assumptions and empirical investigations. Our approach demonstrates the capability to achieve geometric 3D reconstructions with high geometric accuracy on object surfaces and remarkable object completeness. Notably, Canny filter effectively eliminates gaps, delivers a uniform point density, and strikes a favorable balance between correctness and completeness across the scenes.

MSCF: Multi-Scale Canny Filter to Recognize Cells in Microscopic Images

Fish fecundity is one of the most relevant parameters for the estimation of the reproductive potential of fish stocks, used to assess the stock status to guarantee sustainable fisheries management. Fecundity is the number of matured eggs that each female fish can spawn each year. The stereological method is the most accurate technique to estimate fecundity using histological images of fish ovaries, in which matured oocytes must be measured and counted. A new segmentation technique, named the multi-scale Canny filter (MSCF), is proposed to recognize the boundaries of cells (oocytes), based on the Canny edge detector. Our results show the superior performance of MSCF on five fish species compared to five other state-of-the-art segmentation methods. It provides the highest F1 score in four out of five fish species, with values between 70% and 80%, and the highest percentage of correctly recognized cells, between 52% and 64%. This type of research aids in the promotion of sustainable fisheries management and conservation efforts, decreases research’s environmental impact and gives important insights into the health of fish populations and marine ecosystems.

An Efficient Preprocessing Technique for Multimodality Breast Cancer Images

On average, one in every eight women is diagnosed with breast cancer during their lifetime, and accounts for 14% of cancers in women. Since early diagnosis could improve treatment outcomes and longer survival times for patients, it is absolutely necessary to develop techniques to classify lesions within breast cancer mammograms and ultrasound images. The main goal is to determine the class of tumor present within the image, which is pivotal in diagnosing breast cancer patients. In this paper, we propose an Sobel-Canny-Gabor(SCG) model, which is a hybrid model that implements three different edge detection filters; Sobel filter, Gabor filter, and Canny filter. This model is used to enhance the appearance of the mammogram and ultrasound images, which is then fed into a classification model. Through classification, there could be a potential improvement in the results of the overall classification. Post-classification, the model is then evaluated using the metric Peak Signal-to-Noise Ratio (PSNR), which measures the quality between the original image and the compressed image.On average, one in every eight women is diagnosed with breast cancer during their lifetime, and accounts for 14% of cancers in women. Since early diagnosis could improve treatment outcomes and longer survival times for patients, it is absolutely necessary to develop techniques to classify lesions within breast cancer mammograms and ultrasound images. The main goal is to determine the class of tumor present within the image, which is pivotal in diagnosing breast cancer patients. In this paper, we propose an Sobel-Canny-Gabor(SCG) model, which is a hybrid model that implements three different edge detection filters; Sobel filter, Gabor filter, and Canny filter. This model is used to enhance the appearance of the mammogram and ultrasound images, which is then fed into a classification model. Through classification, there could be a potential improvement in the results of the overall classification. Post-classification, the model is then evaluated using the metric Peak Signal-to-Noise Ratio (PSNR), which measures the quality between the original image and the compressed image.

Iban plaited mat motif classification with adaptive smoothing

<span lang="EN-US">Decorative mats plaited by the Iban communities in Borneo contains motifs that reflect their traditional beliefs. Each motif has its own special meaning and taboos. A typical mat motif contains multiple smaller patterns that surround the main motif hence is likely to cause misclassification. We introduce a classification framework with adaptive sampling to remove smaller features whilst retaining larger (and discriminative) image structures. Canny filter and probabilistic hough transform are gradually applied to a clean greyscale image until a threshold value pertaining to the image’s structural information is reached. Morphological dilation is then applied to improve the appearance of the retained edges. The resulting image is described using binary robust invariant scalable keypoints (BRISK) features with random sample consensus (RANSAC). We reported the classification accuracy against six common image deformations at incremental degrees: scale+rotation, viewpoint, image blur, joint photographic experts group (JPEG) compression, scale and illumination. From our sensitivity analysis, we found the optimal threshold for adaptive smoothing to be 75.0%. The optimal scheme obtained 100.0% accuracy for JPEG compression, illumination, and viewpoint set. Using adaptive smoothing, we achieved an average increase in accuracy of 11.0% compared to the baseline.</span>

Detection of Violence Behavior using Deep Learning Technique

Deep learning-based violence detection approaches from video streams are a rapidly expanding subject of study, this is due to the necessity to develop appropriate and automated violence detection techniques based on visual data obtained from security cameras mounted in various locations. In this research, a modified pre-trained deep learning technique named Convolutional Neural Network-Visual Geometry Group16 (CNN-VGG16) are employed to implement a low complex and uncomplicated model for the detection of violence. The transfer learning technique is applied to take advantage of the pre-knowledge VGG16 in detecting shapes and edges. The final layers of the default VGG16 structure are replaced to accommodate the purpose of the research. The efficiency of this approach is evaluated using two datasets (Automatic Violence Detection Dataset (AvdDS) and Surveillance fight dataset (SfDS)). The experimental outcomes prove the efficiency of the proposed model against alternative methods. In experiment accuracy results for Dataset 1 94% and 91% after applying Canny filter, as for dataset 2, the accuracy is 99% and 92% using canny filter. Also In this paper, the effect of applying edges detector on classification accuracy results and processing time for training the model is observed.

Detection of Deep Fake in Face Images Based Machine Learning

Fake face images is a recent critical issue of artificial intelligent due to it has directly impacts on the social lives, and may be made to imply threats against privacy, fraud, and other issues. Currently, creating fake images has become relatively simple due to the powerful yet user-friendly mobile applications that navigate in the social media world and with the invention of the Generative Adversarial Network (GAN) that provides a good quality images that might be difficult for humans to differentiate with their eyes and makes image and video manipulation simple to do, quickly spread and hard to detect, Therefor, image processing and artificial intelligence are crucial in solving such problems. That is why scientists must create technologies or algorithms to control and to avoid these various negative impacts by different detection approaches can be applied. The proposed approach is more robust than current methods when propose a model based on support vector machine as a classifiers to detect fake human faces created by machines. The first stages includes a preprocessing that start with changing images from RGB to YCbCr and then applying the gamma correction. finalize the results show that the extracted edges using Canny filter were useful for detecting fakes in face images. After that, applying two distinct methods of detection by utilizing "Support Vector Machine" with "Principal Component Analysis" and "Support Vector Machine" without "Principal Component Analysis" as a classifiers. The findings show that the highest accuracy gained is 96.8% when using the SVM with PCA while the accuracy obtained is 72.2% when using the SVM alone.

License Plate Recognition System Based on Artificial Intelligence with Different Approach

An license plate recognition system (LPRS) generally provides control and security. These systems are created using methods such as artificial intelligence, machine learning, artificial neural networks (ANN), deep learning, fuzzy logic, expert systems, and image processing. This study aims to create an LPRS using artificial intelligence and image processing techniques. The prepared system is for rectangular-sized plates. An LPRS consists of 3 main stages. The first stage is to detect the plate region. At this stage, converting to grayscale, bilateral filtering, canny filtering, and contour were applied to vehicle images. The second stage is to crop the plate region. In the second stage, the masking method was employed. The pytesseract algorithm was used to recognize license plate characters in the last stage. To create the system, Raspberry Pi 4 Single-Board Computer (SBC) was used for hardware; python programming language was utilized for software. The results showed that the system worked successfully at the rate of 100% in the first two stages and at the rate of 91.82% in the last stage. The results suggest that the system works successfully.

A Detection of Deep Fake in Face Images Using Deep Learning

Fake images are one of the most widespread phenomena that have a significant influence on our social life, particularly in the world of politics and celeb. Nowadays, generating fake images has become very easy due to the powerful yet simple applications in mobile devices that navigate in the social media world and with the emergence of the Generative Adversarial Network (GAN) that produces images which are indistinguishable to the human eye. Which makes fake images and fake videos easy to perform, difficult to detect, and fast to spread. As a result, image processing and artificial intelligence play an important role in solving such issues. Thus, detecting fake images is a critical problem that must be controlled and to prevent these numerous harmful effects. This research proposed utilizing the most popular algorithm in deep learning is (Convolution Neural Network) to detect the fake images.
 The first steps includes a preprocessing which start with converting images from RGB to YCbCr color space, after that entering the Gamma correction. finally extract edge detection by entering the Canny filter on them. After that, utilizing two different method of detection by applying (Convolution Neural Network with Principal Component Analysis) and (Convolution Neural Network without Principal Component Analysis) as a classifiers.
 The results reveal that the use of CNN with PCA in this research results in acceptable accuracy. In contrast, using CNN only gave the highest level of accuracy in detecting manipulated images.

Evaluation of the best Edge Filters in Image Processing Based on the Color Fabric Texture

With the development and complexity of life, the need to improve images appeared, especially when used in the fields of life, including industry and its branches, which affect the life of the citizen, such as the manufacture of fabrics. Which requires precision in the production of these fabrics from the colors and pattern of the fabric. Edge identification is the first step in many digital image-processing applications. Edge identification greatly decreases the data quantity, undesirable filters or unimportant data and provides the important data into the image. There are some issues such as false edge identification, noise issues, low contrast and other edge issues. This paper presents a practical study to compare different edge detectors to determine which edge detector achieves better results, which in turn reflects the best pattern in the fabric. These detectors are Canny, Roberts, Laplace and Gabor. A database of thirty color JPG images collected from the Internet was arranged and a quality scale was used to compare filter detectors. The system MATLAB2020 was used to program the proposed work. The results enhancement was measured by the quality coefficient. This coefficient estimated as follows for Roberts filter (44.27-51.09). Gabor filter (43.46-44.48) and Laplace filter (44.71-5.40). Finally, the quality coefficient for Canny filter equals (44.46-52.05). Therefore, it turns out that the Gabor filter is the best of these filters in defining the edges that were used in defining the pattern.

A method for creating a depth map based on a three-level fuzzy model

A new fuzzy method for creating a depth map is presented in the article. It is based on a combination of Canny detector with a three-level fuzzy system and is designed to improve the accuracy of depth mapping. The first fuzzy model was developed to eliminate the errors inherent in Canny detector. The article presents examples to illustrate that Canny filter is not very sensitive to changes in the shape of the gradient. The second fuzzy model makes it possible to eliminate white color artifacts that appear due to the presence of identical large areas on a stereopair, for instance, the background. The third level of the fuzzy model refines the values of disparity obtained using the second level of the fuzzy model and the distances to the near left and right edges of the images obtained during four passes over the stereopair. It was found during the study that the following factors affect the accuracy of the resulting depth map: labels of membership functions of input variables of a three-level fuzzy system; the combination of t and s-norms in the compositional rules of fuzzy inference. The calculation of the root mean square error values made it possible to evaluate the proposed fuzzy model in relation to similar models. It has been established that value of Root Mean Square Error is minimal with the combination of MEAN-MEAN in the structure of fuzzy inference. And three-level fuzzy system for creating a depth map proposed in the article has a significant advantage over existing analogs.

EIT 40‐Electrodes Breast Cancer Detection and Screening

In this paper, we presented a new electrical impedance tomography (EIT) method for the early detection of breast cancer. We designed a breast phantom comprising three tumors of different sizes. We tested the reconstruction of the image of this breast using 16, 24, 32 and 40 electrodes. The electrodes were placed according to a static, adapted and dynamic mode. The quality of the reconstruction was carried out by the root mean square error (RMSE). The best result was obtained with 40‐electrodes system and dynamic mode. The image reconstruction was improved by a segmentation, an edge detection by Canny filter and a morphological closure filter. This system allowed the detection of a T1 tumor. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Detection of communicable and non-communicable diseases using hyperparameter optimization with Bi-LSTM model in pathology images

PurposeThe present research work is carried out for determining haemoprotozoan diseases in cattle and breast cancer diseases in humans at early stage. The combination of LeNet and bidirectional long short-term memory (Bi-LSTM) model is used for the classification of heamoprotazoan samples into three classes such as theileriosis, babesiosis and anaplasmosis. Also, BreaKHis dataset image samples are classified into two major classes as malignant and benign. The hyperparameter optimization is used for selecting the prominent features. The main objective of this approach is to overcome the manual identification and classification of samples into different haemoprotozoan diseases in cattle. The traditional laboratory approach of identification is time-consuming and requires human expertise. The proposed methodology will help to identify and classify the heamoprotozoan disease in early stage without much of human involvement.Design/methodology/approachLeNet-based Bi-LSTM model is used for the classification of pathology images into babesiosis, anaplasmosis, theileriosis and breast images classified into malignant or benign. An optimization-based super pixel clustering algorithm is used for segmentation once the normalization of histopathology images is conducted. The edge information in the normalized images is considered for identifying the irregular shape regions of images, which are structurally meaningful. Also, it is compared with another segmentation approach circular Hough Transform (CHT). The CHT is used to separate the nuclei from non-nuclei. The Canny edge detection and gaussian filter is used for extracting the edges before sending to CHT.FindingsThe existing methods such as artificial neural network (ANN), convolution neural network (CNN), recurrent neural network (RNN), LSTM and Bi-LSTM model have been compared with the proposed hyperparameter optimization approach with LeNET and Bi-LSTM. The results obtained by the proposed hyperparameter optimization-Bi-LSTM model showed the accuracy of 98.99% when compared to existing models like Ensemble of Deep Learning Models of 95.29% and Modified ReliefF Algorithm of 95.94%.Originality/valueIn contrast to earlier research done using Modified ReliefF, the suggested LeNet with Bi-LSTM model, there is an improvement in accuracy, precision and F-score significantly. The real time data set is used for the heamoprotozoan disease samples. Also, for anaplasmosis and babesiosis, the second set of datasets were used which are coloured datasets obtained by adding a chemical acetone and stain.

DETECTING THE TEXTURE FOR MOBILE IMAGE BY USING PROPOSED TECHNIQUE

In this proposed search, a new technique was applied as an attempt to detect texture and the edges of a test image for mobile, lines drawn on a draft paper. Then was applied traditional spatial filters such as Sobel and Canny, comparison between them, and proposed method to detect the line edge and texture for the same image were applied. The results were that the detection method using the Canny filter showed more visual information and better accuracy than the Sobel spatial filter method, and when using the proposed technique, it gaves more information about the texture of the paper and more accurate results than the Canny filter, which was unable to detect the texture of the image.

Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

This work proposes a portable, handheld electronic device, which measures the cleanliness in fiber optic connectors via digital image processing and artificial neural networks. Its purpose is to reduce the evaluation subjectivity in visual inspection done by human experts. Although devices with this purpose already exist, they tend to be cost-prohibitive and do not take advantage of neither image processing nor artificial intelligence to improve their results. The device consists of an optical microscope for fiber optic connector analysis, a digital camera adapter, a reduced-board computer, an image processing algorithm, a neural network algorithm and an LCD screen for equipment operation and results visualization. The image processing algorithm applies grayscale histogram equalization, Gaussian filtering, Canny filtering, Hough transform, region of interest segmentation and obtaining radiometric descriptors as inputs to the neural network. Validation consisted of comparing the results by the proposed device with those obtained by agreeing human experts via visual inspection. Results yield an average Cohen's Kappa of 0.926, which implies a very satisfactory performance by the proposed device.

UnCanny: Exploiting Reversed Edge Detection as a Basis for Object Tracking in Video.

Few object detection methods exist which can resolve small objects (<20 pixels) from complex static backgrounds without significant computational expense. A framework capable of meeting these needs which reverses the steps in classic edge detection methods using the Canny filter for edge detection is presented here. Sample images taken from sequential frames of video footage were processed by subtraction, thresholding, Sobel edge detection, Gaussian blurring, and Zhang–Suen edge thinning to identify objects which have moved between the two frames. The results of this method show distinct contours applicable to object tracking algorithms with minimal “false positive” noise. This framework may be used with other edge detection methods to produce robust, low-overhead object tracking methods.