A New Approach of Gray Images Binarization with Threshold Methods

TL;DR

This paper introduces a novel gray level image binarization approach using temporal thresholding, addressing limitations of global methods and enhancing industrial automation vision systems.

Contribution

It proposes a new temporal thresholding method for gray image binarization tailored for industrial automation, improving over traditional global thresholding techniques.

Findings

Temporal thresholding improves binarization in moving scenes

Acquisition frequency influences thresholding effectiveness

Dynamic methods outperform static global thresholding

Abstract

The paper presents some aspects of the (gray level) image binarization methods used in artificial vision systems. It is introduced a new approach of gray level image binarization for artificial vision systems dedicated to industrial automation temporal thresholding. In the first part of the paper are extracted some limitations of using the global optimum thresholding in gray level image binarization. In the second part of this paper are presented some aspects of the dynamic optimum thresholding method for gray level image binarization. Starting from classic methods of global and dynamic optimal thresholding of the gray level images in the next section are introduced the concepts of temporal histogram and temporal thresholding. In the final section are presented some practical aspects of the temporal thresholding method in artificial vision applications form the moving scene in robotic automation class; pointing out the influence of the acquisition frequency on the methods results.