Canny Method Research Articles

Comparative Study on the Performance of the Bagging Algorithm in the Breast Cancer Dataset

Breast cancer remains a predominant health concern globally. Early detection, powered by advancements in medical imaging and computational methods, plays a vital role in enhancing survival rates. This research delved into the application and performance of the Bagging algorithm on a Breast Cancer dataset that underwent image segmentation using the Canny method and feature extraction through Hu-Moments. The Bagging algorithm demonstrated moderately consistent performance across a 5-fold cross-validation, with average metrics of 56.9% accuracy, 58.3% precision, 57.7% recall, and 56.6% F-measure. While the results showcased the potential of the Bagging algorithm in classifying breast cancer data, there remains an avenue for further optimization and exploration of other ensemble or deep learning techniques. The findings contribute to the broader domain of machine learning in medical imaging and offer insights for future research directions and clinical diagnostic tool development.

Application of the K-Nearest Neighbors (KNN) Algorithm on the Brain Tumor Dataset

Brain tumors pose significant challenges in the medical domain, necessitating advanced diagnostic techniques for early and accurate detection. This research paper presents a comprehensive study on the application of the K-Nearest Neighbors (KNN) algorithm to a dataset comprising brain tumor images. The methodology involved segmenting the images using the Canny method, extracting relevant features via Hu Moments, and subsequently employing the KNN algorithm for classification. Using a 5-fold cross-validation, the system consistently achieved an average accuracy of approximately 62%. These findings highlight the potential of traditional machine learning algorithms in medical imaging, providing valuable insights for both researchers and practitioners. While the results are promising, the study also underscores the importance of integrating such algorithms with other diagnostic methods for optimal results

Effectiveness Evaluation of the RandomForest Algorithm in Classifying CancerLips Data

Lip cancer, though less commonly discussed, remains a significant concern in the realm of oncology. Early detection and diagnosis are paramount for improved patient outcomes. This research evaluated the effectiveness of the RandomForest algorithm in classifying the CancerLips dataset, a collection of lip images processed using the Canny segmentation method and described using Hu moments. Using a 5-fold cross-validation approach, the algorithm achieved an average accuracy of approximately 70.96%. The results highlight the potential of machine learning techniques, specifically RandomForest, in aiding lip cancer detection. However, the choice of preprocessing methods and feature extraction plays a crucial role in determining the outcome. The study underscores the need for further research, focusing on algorithm optimization and comparisons with other datasets or feature extraction methods, to enhance diagnostic precision in medical imaging.

Classification Optimization of Skin Cancer Using the Adaboost Algorithm

Early detection of melanoma skin cancer is crucial in improving prognosis and saving lives. This research aimed to optimize the classification of melanoma images using the Adaboost algorithm. Employing a dataset of 10,000 melanoma images, the study combined the Canny method for image segmentation, Hu Moments for feature extraction, and the Adaboost algorithm for classification. The 5-fold cross-validation results revealed an average accuracy of 61.52%. While the precision consistently surpassed recall, indicating the model's conservative nature in predicting positive cases. The outcomes align with previous research, emphasizing the challenges in melanoma classification. This study contributes to the domain by showcasing the potential and areas of improvement for machine learning in early melanoma detection. Future research is recommended to explore hybrid models and diversify data sources for enhanced robustness and generalizability.

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.

A Robust Method With Adaptive Inpainting for Edge Detection of Single Silicon Crystal

The improvement of the edge detection accuracy and integrity of the single silicon crystal and bright halo is the key to measuring the crystal diameter. This paper proposes an edge detection method with adaptive image inpainting. Firstly, according to the image characteristics, an evaluation function is designed to obtain the optimal order of fractional differential operators for the Canny method. Then, the initial edges of the single silicon crystal and bright halo are obtained with the Canny method based on these operators, and many details of the edges can be obtained. Meanwhile, an improved curvature driven diffusions (CDD) model is proposed for repairing the initial edge, which introduces the gradient information of edge into the CDD model to repair the edge breaks in the initial edges and enhance the edge integrity. Finally, a series of experiments are conducted to validate the effectiveness and performance of the proposed method on the images of practical engineering applications. The experimental results show that the proposed method performs significantly better than the existing methods in the edge detection accuracy and integrity of the single silicon crystal and highlight halo.

Identification of Breast Tumors With Image Processing Using Canny Edge Detection

Breast tumor is one of the leading causes of death in women worldwide. The term tumor is often used for all lumps found in the human body. The increase in the number of cases of breast tumors that occur each year is due to the absence of prevention or early detection. The research was carried out by utilizing the development of information technology to create a breast tumor detection system with digital image processing. The application system will process mammogram images to detect edges with the canny method and will be classified using the SVM method. The data used is 176 data obtained from the Kaggle dataset. The test results revealed that 64 patients were classified as having malignant breast tumors (M), and 113 did not have breast tumors (B), with a classification accuracy rate of 95%. From the results obtained, the application system is very good for the early identification of breast tumors.

An Investigation of Heterogeneous Embedded CPU and GPU Architectures

Edge detection using the Canny method is popular. The Canny method’s calculation is too complex for traditional embedded systems, preventing real-time edge recognition. High-end embedded CPUs with GPGPUs can process more data (General Purpose Graphics Processing Units). This research proposes a distributed computing-friendly parallel canny technique.

Edge Detection Method Driven by Knowledge-Based Neighborhood Rules

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).

Experimental Curves Segmentation Using Variable Resolution

A new segmentation method of signals distorted by noise is proposed. Unlike other known methods, for example, the Canny method, a priori data on interference and / or a signal (image) is not used. Segmentation of signals and halftone images distorted by interference is one of the oldest problems in computer vision. But human vision solves this task almost independently of our consciousness. It was discovered for visual neurons, that sizes of receptive fields' excitatory zones change during visual act, which eventually mean dynamical changes in visual system's resolution i.e. coarse-tofine phenomenon in living organism. We assumed that „coarse-to-fine“ phenomenon, i.e. several different resolutions, is used in human vision to segment images. A „coarse-to-fine“ algorithm for segmentation of experimental graphs was developed. The main difference of algorithm mentioned above from others is that decision is made taking into account all partial solutions for all resolutions being used. This ensures stability of final global solution. The algorithm verification results are presented. It is expected that the method can naturally be expanded to segmentation of halftone images.

Horizon detection and tracking in sea-ice conditions using machine vision

An automated process is proposed for horizon detection and tracking using machine vision cameras and in polar, sea-ice conditions. These conditions present unique challenges for machine vision applications, such as a large amount of clutter (e.g. icebergs) and secondary edge lines from broken ice pieces. The process is divided in two parts: a more computationally expensive, yet robust detection algorithm in the first stage, based on Convolutional Neural Networks, and used to detect the horizon line in an arbitrary sea-ice image; followed by a tracking algorithm, responsible of efficiently detecting the horizon line in the subsequent images of a sequence. We propose two tracking algorithms, one based on the traditional Canny and Hough line detection methods; and a second novel approach using entropy as a measure of randomness, to segment between sea-ice and sky. Our automated process was compared to manually obtained ground-truth data and the results indicate good agreement, especially for the texture-based tracking algorithm.

Canny and Morphological Approaches to Calculating Area and Perimeter of Two-Dimensional Geometry

Calculating area and perimeter in real-world conditions has its challenges. The actual conditions include applications in the medical field to measure the presence of tumors or the condition of human organs and applications in geography to measure specific areas on a map; applications in architecture often calculate the area and perimeter of buildings, interior design, exterior design, and other uses. Technology can make it easier with automatic calculations. Mathematical methods and computer vision techniques are required to create automated systems. The Canny method is usually used, which is good enough for detecting edges but not sufficient for measuring irregular geometric shapes. This paper aims to calculate the area and perimeter of a geometric shape using the Canny method and geometry. Data samples in various forms are used in this study. Calculating area and perimeter using the Canny method involves obtaining the length (X,Y) of the RGB image converted to HSV. Edge detection values are used to calculate the area and perimeter of objects. The morphological method uses binary image input as input data. Then proceed to the convolution process with structuring and calculating the area and circumference of objects. Based on the research results, calculating the area and circumference of objects is more effective using morphological methods. However, the level of accuracy is affected by the selection of structuring elements (strels) which must be optimal and global.

Deteksi Tepi untuk Mengidentifikasi Kanker Ginjal Menggunakan Metode Sobel, Roberts dan Canny

Kidneys are organs that function to filter metabolic waste in the blood and dispose of them in the form of urine. One of kidney disease is kidney cancer. Kidney cancer occurs due to gene mutations in kidney cells that cause kidney cells to grow abnormally and uncontrollably. To take an image of kidney cancer using a Magnetic Resonance Imaging (MRI) tool. The purpose of this research is to identify and recognize the pattern object of kidney cancer in the MRI image. To identify kidney cancer images, it begins with collecting image data, image processing, image edge detection, image thinning, and identification processes. Edge detection is used to detect the boundaries of objects in the image. The method used in this research is the Canny and Sobel method. The number of images taken were 23 images of kidney cancer samples. The results of this research show that the Canny method gives better image results than the Sobel image results

Optimization Ground Glass Opacities (GGO) Detection Using Multipixel Interpolation Techniques

Ground Glass Opacities (GGO) are a picture of abnormal lung conditions characterized by white or gray areas. This picture of GGO in the lungs could previously be detected based on the results of medical examinations such as Computerized Tomography (CT scan) and Magnetic Resonance Imaging (MRI) images of patients suffering from Covid-19. However, from the results of the examination, it can be seen that the CT scan and MRI images still have a noise level that is too high, causing difficulties in describing the distribution pattern of the GGO itself. The purpose of this study was to optimize the detection of GGO on MRI images using the Multipixel Interpolation technique. The detection process adopts several stages including image preprocessing, edge detection process, and gradient morphological segmentation. Image preprocessing is done to remove noise and improve the MRI input image. The edge detection process is carried out to detect lung organs automatically using the Canny method which is optimized with the multipixel interpolation technique. The final stage of the research is the segmentation process using a gradient morphology technique to see the spread of GGO in patients with Covid-19 contained in the MRI image. The results of this study present an overview of the GGO pattern with fairly good results. The results of the GGO pattern description will also measure the level of spread to see the severity of pneumonia. Based on the results presented, this research is useful as an alternative solution in the process of diagnosis and treatment of Covid-19 patients.

Improved Edge Detection using Variable Thresholding Technique and Convolution of Gabor with Gaussian Filters

Medical Field, Robotic vision, Pattern recognition, Hurdle detection, and smart city are examples of areas that require image processing to achieve automation. Detecting an edge is an important stage in any computer vision application. The performance of the edge detecting algorithm is largely affected by the noise present in an image. An Image with a low signal-to-noise ratio (SNR), imposes a challenge to locate its edges. To improve the observable image boundaries, an adaptive filtering technique is proposed in this article. The proposed algorithm uses convolution of Gabor filter with Gaussian (GoG) operator to clean the noise before non-Maxima suppression. Furthermore, using variable hysteresis thresholding can further improve edge locating. The implementation of the algorithm was done by Python and Matlab. The obtained results were compared to a number of reviewed algorithms such as the Canny method, Laplacian of Gaussian, The Marr-Hildreth method, Sobel operator, and the Haar wavelet-based method. Three performance factors were used; PNSR, MSE, and processing time. The simulation result shows that the proposed method has higher PNSR, lower MSE, and shorter processing time when compared to the Canny detector, the Marr-Hildreth, Haar wavelet-based, Laplacian of Gaussian, and the Sobel operator methods. The higher PNSR, lower MSE, and shorter processing time mean improved edge details of the processed image.

Real-Time Foreign Object and Production Status Detection of Tobacco Cabinets Based on Deep Learning

Visual inspection plays an important role in industrial production and can detect product defects at the production stage to avoid major economic losses. Most factories mainly rely on manual inspection, resulting in low inspection efficiency, high costs, and potential safety hazards. A real-time production status and foreign object detection framework for smoke cabinets based on deep learning is proposed in this paper. Firstly, the tobacco cabinet is tested for foreign objects based on the YOLOX, and if there is a foreign object, all production activities will be immediately stopped to avoid safety and quality problems. Secondly, the production status of tobacco cabinet is judged to determine whether it is in the feeding state by the YOLOX position locating method and canny threshold method. If it is not in the feeding state, then the three states of empty, full, and material status of the tobacco cabinet conveyor belt are judged based on the ResNet-18 image classification network. Ultilizing our proposed method, the accuracy of foreign object detection, feeding state detection and the conveyor belt of tobacco cabinet state detection are 99.13%, 96.36% and 95.30%, respectively. The overall detection time was less than 1 s. The experimental results show the effectiveness of our method. It has important practical significance for the safety, well-being and efficient production of cigarette factories.

Anti-spoofing detection based on eyeblink liveness testing for iris recognition

Iris pattern is the most stable biometric trait for personal identification. It is the only traits that can’t be used after a person death. Despite its stability and difficulty to spoof, it has found that presenting a high quality image of an iris one can spoof and gain an access. Furthermore, the use of video frames of an authorized personnel and the use of 3D models can cheat the system. This study aimed at presenting a solution to this problem by testing the liveness of an eye being scanned by an access control device. The algorithm works by additional process of detecting an eyeblink and background subtraction and correlation to assume liveness. For one to gain access, first an iris is scanned and identified, secondly if this iris is in the database before providing an access, an eyeblink is also sensed. If eyeblink is sensed an access is granted otherwise access is denied. An algorithm has been developed in MATLAB adopting an adaptive Canny method for edge detection. The proposed algorithm validates the user being scanned by two stages which are; Eyeblink detection, background subtraction and correlation. Testing on standard datasets of ZJU Eyeblink, ACASIA v3 and the TalkingFace databases showed show 96.47% accuracy.

Evaluation of a panoramic dental radiography properties by using Canny method

The main goal of this paper is to analyse the panoramic dental radiography by applying the Canny technique which is customized for this type of images and to obtain an image much more clear to interpret after processing it. From the resulted image then can be extracted all the necessary information for giving a diagnostic. The method can be extended and an application can be designed in such a way that a dental specialist can use it to better examine the characteristics of a patient's intraoral configuration. The simulations on different images hard to read by the human eye were done using the Matlab work environment. Image-processing techniques represent a very important part in this process and the best method which was successful will be detailed in this paper. Some image properties can be identified automatically even before the original image has been processed, but after the processing step, all the information and also the hidden details can be observed by a specialist. As a conclusion, each step performed and described in this article will aid in the analysis of panoramic dental radiography characteristics.

IMAGE DETECTION EDGE IMAGE USING CANNY EDGE ALGORITHM

In general, edge detection is widely used in image processing to find the boundaries of objects in an image. In this study, a technique for detecting edges that is commonly used will be studied, namely the Canny method under different conditions using Visual Studio software. In the testing process that the author carried out, it was concluded that sooner or later a process of converting an object would be, that the following factors affect the conversion process, including; type of image, image resolution, image format, specifications of the camera used to take pictures as well as the specifications of a computer that has a processing speed that is expected to be able to process images (images). The use of the Canny method to detect the edges of an image is considered more accurate and effective because this method is able to provide detailed output by including a calculated execution time

Automatic Number Plate Recognition of Saudi License Car Plates

Automatic license plate recognition has become a significant tool as a result of the development of smart cities. During the experiment studied in the current paper, 50 images were used to detect Saudi car plates. After the preprocessing stage, the canny edge method to detect the car edges and different threshold techniques were used to reduce noise. Horizontal projection was applied in the segmentation process to split the plate. After that, a masking technique was utilized to locate and separate the region of interest in the image. OCR was applied to the processed images to read the characters and numbers in English and Arabic separately. Then, combining the English and Arabic text, after using the re-shaper for the Arabic letters. Finally, rendering of the results of text on images down the plate regions took place. The canny algorithm with projection technique with a proper preprocessing for images produces results with accuracy of 92.4% and 96% for Arabic and English language respectively.