  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.
