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  • 标题:Segmentation of Single and Overlapping Leaves by Extracting Appropriate Contours
  • 本地全文:下载
  • 作者:Rafflesia Khan ; Rameswar Debnath
  • 期刊名称:Computer Science & Information Technology
  • 电子版ISSN:2231-5403
  • 出版年度:2019
  • 卷号:9
  • 期号:13
  • 页码:287-300
  • DOI:10.5121/csit.2019.91323
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:Leaf detection and segmentation is a complex image segmentation problem as leaves are most often found in groups with natural background. Edges of leaves cannot be clearly defined from image because of their color similarities.Also,separating every single as well as overlapping leaf individually is even more challenging as leaves share almost same color, texture and shape. In this paper, we propose a new automatic approach for leaf segmentation from image. Our leaf segmentation process uses efficient techniques for processing an image to obtain contours of every individual objects. Then, it selects the best appropriate connected contours that represent region of every leaves appearing in an image. Our model archives an overall 90.46% segmentation rate where segmentation rates for single and overlapping leaves are 95.34% and 86.73%, respectively.
  • 关键词:image processing; leaf object segmentation; overlapping leaves; connected contour; object; boundary detection.
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