Combination of Sobel+Prewitt Edge Detection Method with Roberts+Canny on Passion Flower Image Identification

Autor:	Anjar Wanto, Henry Aspan, Syafrika Deni Rizki, Surmayanti Surmayanti, Ni Luh Wiwik Sri Rahayu Ginantra, Silfia Andini
Rok vydání:	2021
Předmět:	History Computer science business.industry media_common.quotation_subject Sobel operator Passion Edge detection Computer Science Applications Education Prewitt operator Image identification Computer vision Artificial intelligence business media_common
Zdroj:	Journal of Physics: Conference Series. 1933:012037
ISSN:	1742-6596 1742-6588
Popis:	Edge detection is at the forefront of image processing for object detection, so a good understanding of edge detection algorithms is essential. This paper aims to analyze the ability of combined edge detection methods to identify images, through a comparison of two different edge detection methods, namely the combination of Sobel and Prewitt (Sobel+Prewitt) with Roberts and Canny (Roberts+Canny). The analysis process uses a dataset of Passion Flower Images obtained from the United States Department of Agriculture (USDA) Plant Database, Natural Resources Conservation Service (NRCS). The Image dataset was obtained using a Nikon Coolpix 995 camera, JPG format with a resolution of 128×192 pixels. Based on the analysis and testing, the results of the research using the combined edge detection technique of Roberts and Canny resulted in better image identification accuracy compared to Sobel and Prewitt. The average accuracy was 92.84% versus 68.75%.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8de7261ec5474ca39d4de0a6f82cba67 https://doi.org/10.1088/1742-6596/1933/1/012037 Zobrazit plný text záznamu