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Robust vegetation segmentation under field conditions using new adaptive weights for hybrid multichannel images based on the Chan-Vese model
Affiliation:1. Agricultural and Ecological Research Unit, Indian Statistical Institute, Kolkata, India;2. Kerala University of Digital Sciences, Innovation and Technology, Thiruvananthapuram, Kerala, India;3. Department of Statistics, Visva-Bharati, Santiniketan, Birbhum, India;1. Department of Health Science and Biostatistics, School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia;2. Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia;3. Sir Peter MacCallum Department of Oncology, The University of Melbourne, Victoria, Australia;1. Wildlife Institute of India, Dehradun 248001, India;2. Graphic Era University, Dehradun 248002, India;3. Birla Institute of Technology, Mesra, Ranchi 835215, India;1. Department of Geographical Sciences, School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;2. Hubei Key Laboratory of Critical Zone Evolution, China University of Geosciences, Wuhan 430074, China;3. Guizhou Electric Power Design Research Institute, Power Construction Corporation of China, Guiyang 550002, Guizhou, China;1. Department of Zoology, University of Calcutta, Kolkata, India;2. Department of Zoology, Shibpur Dinobundhoo Institution (College), Shibpur, Howrah, India
Abstract:
Keywords:Vegetation segmentation  Active contours  Chan-Vese model  Level sets  Adaptive weights  Fast optimization
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