The EBB algorithm is presented as follows: //EBB(G), Edge Betweenness Based method.
First of all, we propose the K-Nearest Neighborhood method and the Edge Betweenness Based method to decompose a social network into multiple sub-graphs.
Many image segmentation methods have been proposed, which includes histogram based methods, edge detection based methods, region based methods, active contour model based methods, etc. Active contour model based methods have drawn a lot of attention in the past decade because of their significant advantages.
The experimental results, on the public FVC2002 and FVC2004 databases, show that the proposed mask exhibits an average increase in the correct core point detection per fingerprint by 17.35%, with a reduction in the false detection by 51.23%, compared to a fast edge-map based method.
The proposed method is superior to the existed edge-map based method from the experimental comparison.
Therefore, it was experimentally verified that the edge-map based method was extremely limited to acquire the rotational difference between images.
Then, test results of image pairs in Figure 10 by the edge-map based method [ 15] were tabulated in Table 2.
Therefore, the rotational differences between such two image sets detected by the edge-map based method were very accurate as shown in the Table 2. Specifically, their phase correlation of the polar presentation of Fourier spectrums had distinctive peaks in Figures 11(a), 11(d), and 11(g), which corresponded to correct estimation of rotational differences.
In the implementation of the edge-map based method, note that phase correlation [ 26] was used to determine the rotation between images rather than using the cross correlation recommended in the original paper due to the fact that phase correlation can generate the same results as cross correlation but is more robust than cross correlation with respect to partial occlusion [ 27].
His approach integrates cutting edge template-based methods and empirical score based methods using various machine learning techniques including Bayesian network methods to analyze large collections of biological data and elucidate interaction specificities among the molecules.
