Procedure for Spaced Mesh Modeling from Digital Images: Example of the Leaf Sheath of the Coconut Palm Cocos Nucifera L. (2D)

High Resolution Image Download MS PowerPoint Slide The characterization of the two-dimensional (2D) leaf sheath of the coconut palm Cocos nucifera from digital images is part of a research project that focuses on the feasibility of using a local natural resource, the leaf sheath of the coconut palm C. nucifera (2D), for the development of a green composite material and the analysis of the influence of this type of reinforcement, which has the advantage of being naturally woven, on the properties of the biocomposites obtained. In order to characterize these properties, it is essential to extract information from the leaf sheath samples. This work consists of counting and evaluating the thicknesses and directions of all the fibers that make up the sheath. In simple cases, from sample photographs, we wish to propose a processing chain capable of automating this extraction process. We are interested here only in samples with areas of spacing between the fibers. Therefore, our proposal cannot be a solution for tightly packed fibers. The data are represented by photographs of leaf sheath samples taken with a high-resolution microscope. This results in color images of 3000 × 3000 pixels. In the following, we will call EI a space in which it is possible to locate a pixel by its spatial coordinates, and we will call EC another space in which it is possible to locate the color of a pixel by its colorimetric coordinates. In EI, the set representing the pixels denoting the fiber will be denoted EF and that representing the void areas will be denoted EV. From these starting sets, the work consisted of finding a process that allows the extraction and characterization of points of interest representing the leaf sheath.

Abstract licence: CC BY-NC-ND

39 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.