Open AccessCrop Images Segmentation using Adaptive Morphologic Descriptors
Author(s) -
Miguel Rios,
Juan López-Hernández,
Dolores Juárez Ramírez,
Maria del Carmen Ruiz Robledo,
Laura Paulina Badillo Canchola,
Ariana Aranda López
Publication year - 2020
Publication title -
nova scientia
Language(s) - English
Resource type - Journals
ISSN - 2007-0705
DOI - 10.21640/ns.v12i24.2152
Subject(s) - segmentation , artificial intelligence , computer science , ground truth , pattern recognition (psychology) , range (aeronautics) , operator (biology) , image segmentation , measure (data warehouse) , process (computing) , computer vision , mathematics , data mining , materials science , biochemistry , chemistry , repressor , transcription factor , composite material , gene , operating system
This research is focused on the segmentation improvement of crop images by using adaptive morphologic descriptors instead of classic algorithms like K-means and the top-hat operator using predefined shapes like disk or diamond. Obtained results shows that using an adaptive morphologic descriptor improves the segmentation performance against the classic shapes like disc and diamond. In order to measure the process a set of 60 crop images was used including their respective ground-truth images. The images were segmented using the K-Means algorithm and the top-hat operator with the disk and diamond shapes at different sizes into a range to validate their performance. In order to generate the adaptive morphologic descriptor, the Univariated Marginal Distribution Algorithm was used with no constraints by exploring a range of different sizes. Also, performance metrics like receiver operating characteristic and accuracy rate were applied to the generated data in order to assess the results.