The shadow removal method based on color model might not work in such situations.
Xiao et al. [13] proposed a shadow removal method based on edge information for traffic scenes which in sequence consisted of an edge extraction technique, the morphological operations on removing the edges of shadows, and the analysis of spatial properties for separating the occluded vehicles which resulted from the influences of shadows.
To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information.
Moreover, research indicates that methods based on second-order statistics might outperform methods based on higher-order statistics in the removal of ocular artifacts [ 10, 22].
Other methods based on gas stripping, liquid-liquid extraction, adsorption, and membranes are also energy intensive due to the bulk removal of water.
Table 1 lists some separation methods based on equilibria, and Table 2 indicates those methods based on rate phenomena.
Different from most existing artifact removal techniques, we propose a method based on dictionary learning framework.
This article presents a shadow removal algorithm with background difference method based on shadow position and edges attributes.
However, the results showed that the number of eliminated nodes was greater in filtering by the node elimination method compared with filtering by the edge removal method, but in the method based on the similarity, the amount of sensitivity (94%) showed the higher capability of this method to not eliminate the known complex proteins.
Similarly, the slow-fast method based on removal of the fastest evolving sites was shown to reduce phylogenetic artifacts [ 48- 54], as well as purine/pyrimidine (RY) data recoding [ 55- 58] or amino acid data recoding [ 59, 60].
