Segmentation of Cotton Leaf Images using Parametric Deformable Model
Bhagya M Patil1, Basavaraj Amarapur2

1Bhagya M Patil*, Department of Computer Science & Application, REVA University, Bengaluru, India.
2Dr.Basavaraj Amarapur, Department of Electrical & Electronics Engineering, PDA college of Engineering, Kalaburgi, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 30, 2020. | Manuscript published on April 10, 2020. | PP: 1690-1695 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4121049620/2020©BEIESP | DOI: 10.35940/ijitee.F4121.049620
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Abstract: In this paper, the segmentation of cotton leaves from the complex background has been carried out using deformable model. In order to segment, a database of about 300 cotton leaves image was developed. The collected images were resized to 256×256 size. The resized image has been segmented using Adaptive Diffusion Flow (ADF) model. The ADF model has been obtained by replacing the smoothening energy term of gradient vector flow model with active hyper surface harmonic minimal function used to keep away from weak edges leakage. The infinite Laplace function is used to move the deformable model into narrow concave regions. Further, the developed model has been compared with the gradient vector flow and vector field convolution segmentation methods in terms of number of iterations, time taken for segmentation and various performance parameters namely precision, recall, Manhattan, Jaccard, Dice. From the results, it is concluded that the adaptive diffusion flow method is faster and performance parameters are better than the Gradient Vector Flow (GVF) and Vector Field Convolution (VFC) methods. 
Keywords: Deformable Model, Gradient Vector Flow, Vector Field Convolution, Adaptive Diffusion Flow.
Scope of the Article: Probabilistic Models and Methods