Recently a method based on feature-level fusion of fingerprint and finger-vein has been proposed [3].
Prices increase based on features and usage levels.
Most published work in this field is based on features developed from the pixel gray level co-occurrence matrix (GLCM) (13) and from the gray level run length matrix (GLRLM) (14), and on fractal texture methods (9, 15).
These algorithms can detect microscopic pathological changes based on features derived from gene expression levels and metabolic profiles.
These variants and algorithms we have developed, tend to detect microscopic pathological changes based on features derived from gene expression levels and metabolic profiles.
Algorithms based on features usually extract significant and stable features.
The most popular bimodal fusion methods are based on the feature-level fusion [13] or decision-level fusion [11].
The relational measure is based on a feature-level convolution operation defined in this paper.
The first level is based on low level features and then a stochastic context-free grammar is used to parse candidate event sequences by exploiting a priori knowledge of the domain.
However, most of the existing high-level semantic feature mining methods are based on pixel-level features.
