{"title":"Segmentation of plant leaf images in natural scenes by integrating graph diffusion compactness and global refinement","authors":"Lyasmine Adada , Idir Filali , Bouzefrane Samia","doi":"10.1016/j.ijleo.2025.172493","DOIUrl":null,"url":null,"abstract":"<div><div>Plant recognition, driven by advancement in image processing and machine learning, is crucial for ecological studies and agricultural management. However, challenges arise in complex backgrounds, requiring robust algorithms to differentiate target plants from surrounding elements. Leaf image segmentation is pivotal in accurately isolating and analyzing individual leaf structure, facilitating precise species identification. In this paper, we propose an algorithm for leaf segmentation in complex backgrounds. Our approach is notable for its low computational complexity making it well-suited for environments with restricted computational resources. First, we delimit roughly leaf areas in order to define the foreground template. Second, we rank the similarity of remaining image regions according to the foreground template through a graph based saliency process. Finally, the obtained contrast map is refined by using random forests to ensure optimal leaf/background separation. Experiments demonstrate that our algorithm outperforms several competing segmentation methods.</div></div>","PeriodicalId":19513,"journal":{"name":"Optik","volume":"338 ","pages":"Article 172493"},"PeriodicalIF":3.1000,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optik","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0030402625002815","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Plant recognition, driven by advancement in image processing and machine learning, is crucial for ecological studies and agricultural management. However, challenges arise in complex backgrounds, requiring robust algorithms to differentiate target plants from surrounding elements. Leaf image segmentation is pivotal in accurately isolating and analyzing individual leaf structure, facilitating precise species identification. In this paper, we propose an algorithm for leaf segmentation in complex backgrounds. Our approach is notable for its low computational complexity making it well-suited for environments with restricted computational resources. First, we delimit roughly leaf areas in order to define the foreground template. Second, we rank the similarity of remaining image regions according to the foreground template through a graph based saliency process. Finally, the obtained contrast map is refined by using random forests to ensure optimal leaf/background separation. Experiments demonstrate that our algorithm outperforms several competing segmentation methods.
期刊介绍:
Optik publishes articles on all subjects related to light and electron optics and offers a survey on the state of research and technical development within the following fields:
Optics:
-Optics design, geometrical and beam optics, wave optics-
Optical and micro-optical components, diffractive optics, devices and systems-
Photoelectric and optoelectronic devices-
Optical properties of materials, nonlinear optics, wave propagation and transmission in homogeneous and inhomogeneous materials-
Information optics, image formation and processing, holographic techniques, microscopes and spectrometer techniques, and image analysis-
Optical testing and measuring techniques-
Optical communication and computing-
Physiological optics-
As well as other related topics.