MAVIfiber2d

Additionally can be offered

Fiber orientation analysis:

• Measurement of local fiber orientation in each fiber pixel without segmentation of individual fibers
• Result: area or length weighted orientation distribution, degree of anisotropy, preferred direction, export as a csv-File
• Visualization: orientation map

Cloudiness analysis:

• Cloudiness is a weighted mean over 7-8 scales. The user can chose between preset weights or specifying them, as the weight of the results on the different scales depends highly on the specific application.
• Result: cloudiness at all chosen scales, cumulated cloudiness, export as a csv file
• Visualization: cloudiness map for one scale.

Objective, Reproducible Evaluations of Nonwoven Samples

Diffusion filters have been the nucleus of image processing at ITWM and the VQC project, which measured the cloudiness of nonwovens and was one of the first industry projects in the image processing department. MAVIfiber2d combines this experience with new tools of mathematical morphology and the typical point concept of stochastic geometry to create software for objective, reproducible evaluations of nonwoven samples.

Local Analysis without Fiber Separation

In a random closed set, the typical point concept makes it possible to measure the density and distribution of fiber orientation without having to separate the fibers in the image. In unclear situations, it is not necessary to decide where each intersecting or looped fiber begins or ends. Rather a simple binarization suffices: for every image pixel, a decision is made as to where it belongs in the fiber system, i.e., the foreground or the background. Local density and orientation are defined for every foreground pixel. The result is an area-weighted distribution of density and orientation.

Cloudiness based on Normalized Grayscale Variances

Cloudiness is mathematically not that clearly defined. MAVIfiber2d builds on the VQC project findings. The input image is smoothed stepwise by approximated Gauss filters. The grayscale variances of the normalized filtered images reflect the cloudiness for the scale under consideration. The cloudiness index is calculated from the variances as a weighted average. The scales and weights are chosen such that measuring results meet as well as possible both the technical requirements and the subjective visual impression.