The results are compared with those of common techniques based on correlation filtering.
In this section, a method of adaptive parameter identification of Morlet wavelet based on correlation filtering is presented.
For this reason, we implemented three methods for visual analysis of large numbers of correlations: one is to filter correlations based on correlation strength, the second is to create a larger image using zooming functions, the third enables the user to move a molecule (node) or an experiment category (circle) around to facilitate visualization.
For tuber profiling, each sample was assayed twice in a two-colour dye experiment, allowing cross-validation of the observed variation and subsequent filtering based on correlation scores (Methods).
Filter methods based on correlation or mutual information ranking [21] are easy to implement; however, selecting the most relevant variables is usually suboptimal for building a predictor, particularly if the variables are redundant.
The approach proposed in [22] is based on correlation techniques in the time-domain, whereas in [21], a subband-based solution with two filters is presented.
Univariate statistical associations used to filter HTS data based on correlations with zebrafish angiogenic inhibition in vivo revealed 132 total significant associations, 33 of which were already captured in the pVDC signature, and 689 non-significant assay associations.
establish Morlet wavelet dictionary; find optimal Morlet wavelet using correlation filtering based on maximal correlation coefficient criterion; construct cyclic Morlet wavelet given by (10) obtained in step 2; find cyclic period using CMWCF based on maximal correlation coefficient criterion.
In this paper, an infrared target tracking method under tracking by detection framework based on a weighted correlation filter is presented.
A reliable algorithm for target tracking based on dynamically adaptive correlation filtering is presented.
