{"title":"Edge Detection using CNN for Roof Images","authors":"Aneeqa Ahmed, Y. Byun","doi":"10.1145/3314527.3314544","DOIUrl":null,"url":null,"abstract":"In image processing detection of edges is a very significant phenomenon as it finds its utility in multiple applications from different areas of life such as security, video surveillance, detection of various diseases, text detection, vehicle detection and many more. Image processing based techniques for edge detection usually requires preprocessing of the images in order to reduce the quantity of noise before edge detection. Even after the detection of edges, most of the times these techniques have to involve various post processing steps in order to generate the fine edges to be used in concerned applications like tumor detection or critical security applications. Image processing based algorithms for edge detection are conventionally based on gradient filters and generally they are not reliable in the case of variable illumination conditions or noise deviations. As soon as there is any variation in the illumination conditions of images or change in the percentage of noise element in images, the efficiency of the traditional edge detection algorithms is affected badly because of increased mask size. This increased mask also results in additional computational load. This paper utilizes Convolutional Neural Networks (CNN) for the detection of edges in images. The dataset composes roof images of diverse types and sizes. Employing CNN for edge detection over conventional methods is more reliable and it is speedier and less complex. Also, CNN is very efficient as it can take compute all features automatically so reduces the burden of manual feature extraction. Moreover, feature detection using CNN is accurate and more precise than manual feature compilation methods. To make use of automatic feature extraction of CNN, this technique employs Visual Geometry Group (VGG) CNN network. Further, to compute the edge map, Roberts edge operator is applied on the automatically computed features. This CNN based edge detection technique results into the generation of very fine edges without the application of any post processing step. Also, the results show that this technique works equally good in the presence of noise and requires no pre-processing algorithms.","PeriodicalId":370433,"journal":{"name":"Proceedings of the 2019 Asia Pacific Information Technology Conference","volume":"4 3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 Asia Pacific Information Technology Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3314527.3314544","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In image processing detection of edges is a very significant phenomenon as it finds its utility in multiple applications from different areas of life such as security, video surveillance, detection of various diseases, text detection, vehicle detection and many more. Image processing based techniques for edge detection usually requires preprocessing of the images in order to reduce the quantity of noise before edge detection. Even after the detection of edges, most of the times these techniques have to involve various post processing steps in order to generate the fine edges to be used in concerned applications like tumor detection or critical security applications. Image processing based algorithms for edge detection are conventionally based on gradient filters and generally they are not reliable in the case of variable illumination conditions or noise deviations. As soon as there is any variation in the illumination conditions of images or change in the percentage of noise element in images, the efficiency of the traditional edge detection algorithms is affected badly because of increased mask size. This increased mask also results in additional computational load. This paper utilizes Convolutional Neural Networks (CNN) for the detection of edges in images. The dataset composes roof images of diverse types and sizes. Employing CNN for edge detection over conventional methods is more reliable and it is speedier and less complex. Also, CNN is very efficient as it can take compute all features automatically so reduces the burden of manual feature extraction. Moreover, feature detection using CNN is accurate and more precise than manual feature compilation methods. To make use of automatic feature extraction of CNN, this technique employs Visual Geometry Group (VGG) CNN network. Further, to compute the edge map, Roberts edge operator is applied on the automatically computed features. This CNN based edge detection technique results into the generation of very fine edges without the application of any post processing step. Also, the results show that this technique works equally good in the presence of noise and requires no pre-processing algorithms.