[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124220-en":3,"doc-seo-124220-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124220,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","An Optimize Canny Algorithm With Traditional Machine Learning for Edge Detection Enhancement","Edge detection remains a central challenge in image processing and computer vision due to the complexity and variability of real-world imagery. Canny edge detection is effective but highly sensitive to noise, often yielding weak or unreliable edges. This work proposes an improved Canny pipeline by replacing the Gaussian filter with a bilateral filter and estimating Canny thresholds via the Flower Pollination algorithm, then integrating a machine learning model to refine edge detection decisions. Experiments on 50 Berkeley dataset images show improved performance, with AUC values of 0.81 for Random Forest and 0.75 for Logistic Regression, versus about 0.57 for the traditional Canny method.","| An optimize canny algorithm with traditional machine learning for edge |  |  |\n| --- | --- | --- |\n| detection enhancement\u003Cbr>Russel K. Lafta*, Zainab N. Sultani\u003Cbr>Computer Science Dept., College of Science, Al-Nahrain University, Jadriya, Baghdad, Iraq.\u003Cbr>*Corresponding author Email: [st.russelkareem22@ced. nahrainuniv.edu.iq](st.russelkareem22@ced. nahrainuniv.edu.iq) |  |  |\n| H I G H L I G H T S | A B S T R A C T\u003Cbr>Edge detection still represents a major challenge in image processing and computer vision because of the complexity and variability of real-world images. The canny algorithm is powerful in detecting edges. However, it is sensitive to noise, and as a result, may produce weak edges. The use of machine learning algorithms has significantly improved the performance of edge detection techniques. In this paper, an improved Canny edge detection algorithm is proposed by replacing the Gaussian filter with a bilateral filter. Also, a new approach for estimating Canny algorithm thresholds has been developed using the Flower Pollination algorithm. Subsequently, the improved Canny algorithm with a machine learning model was integrated to enhance edge detection accuracy. The performance of the improved algorithm was evaluated using 50 images from the Berkeley Computer Vision dataset. The experiment results show that the enhanced algorithm has an AUC of 0.81 for RF (Random Forest) and 0.75 for the LR (Logistic Regression) classifier, which can detect edges more accurately than the traditional Canny algorithm, which has an AUC of about 0.57. The proposed method sets a new standard for edge detection performance. |  |\n| • Canny edge detection was enhanced using FPA for optimal threshold selection\u003Cbr>• A bilateral filter was used to reduce noise while preserving fine details\u003Cbr>• The method achieved higher accuracy than traditional edge detection techniques\u003Cbr>• Precision and recall were balanced, reducing false edge detection\u003Cbr>• F1-score, precision, and recall were used for quantitative validation |  |  |\n| Keywords:\u003Cbr>Edge detection\u003Cbr>Optimized canny\u003Cbr>Flower Pollination algorithm\u003Cbr>Machine learning |  |  |\n\n1. Introduction  \nGathering the notable edge of natural images implies a difficulty for computer vision applications [1] . Because of the intricacy of images, the elements in them (plants, houses, cars), and the conflict among the objects of images, make the extraction of edges a complex task using statistical-based methods [2] . Edges in images are the curves that describe the frontier of objects. In image processing, edge detection is basically important as it can quickly identify the boundaries of objects in an image [3] . Moreover, edge is used to classify the image to reduce the amount of data to be treated. Also, computer vision technology has been advancing; edge detection is considered crucial for more challenging tasks like object detection [4], object proposal [5] and image segmentation [6] . Consequently, it is necessary to develop a proper method for edge detection.  \nClassical image edge detection Techniques have been suggested previously and advanced for an extended duration. Wherefore, classical image edge detection techniques are more grown, uncomplicated but effective. However, scholars are still working tirelessly on the road to enhancement traditional image edge detection methods, working to overcome the weaknesses of previous algorithms and enhance their performance [7] . As a result, more robust methods are needed that can more accurately identify edges in noisy and complex images. For that reason, an image edge detection algorithm based on machine learning (ML) and image pixel information is suggested to explore the problem of image edge detection [8] . In this research, an algorithm that executes a categorization at the pixel level is suggested. This way permits grouping a pixel as \"edge\" or \"non-edge,\" taking into account the pixel details associated with other local information that conta","cbCaigZDRWDdBgJv","https://ap.wps.com/l/cbCaigZDRWDdBgJv","pdf",438962,1,7,"English","en",105,"# Highlights\n## Abstract\n## Keywords\n# Introduction\n## Related work","[{\"question\":\"What limitations does the traditional Canny algorithm have in edge detection?\",\"answer\":\"The traditional Canny algorithm is sensitive to noise, which can lead to weak edges and less reliable boundary detection in complex real-world images.\"},{\"question\":\"How does the proposed method improve Canny edge detection?\",\"answer\":\"It replaces the Gaussian filter with a bilateral filter to reduce noise while preserving fine details, and it estimates Canny thresholds using the Flower Pollination algorithm.\"},{\"question\":\"How is machine learning used to further enhance edge detection accuracy?\",\"answer\":\"The improved Canny outputs are integrated with a machine learning model so the detection process is refined at a more accurate level, reducing false edge detections and improving quantitative metrics.\"}]","An Optimize Canny Algorithm With Traditional Machine Learning for Edge Detection Enhancement | 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limitations does the traditional Canny algorithm have in edge detection?","Question",{"text":75,"@type":76},"The traditional Canny algorithm is sensitive to noise, which can lead to weak edges and less reliable boundary detection in complex real-world images.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed method improve Canny edge detection?",{"text":80,"@type":76},"It replaces the Gaussian filter with a bilateral filter to reduce noise while preserving fine details, and it estimates Canny thresholds using the Flower Pollination algorithm.",{"name":82,"@type":73,"acceptedAnswer":83},"How is machine learning used to further enhance edge detection accuracy?",{"text":84,"@type":76},"The improved Canny outputs are integrated with a machine learning model so the detection process is refined at a more accurate level, reducing false edge detections and improving quantitative 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