Exact(3)
Note that its oriented counterpart method, ORFC, also inherits this property.
We also assume that the estimation error of the comparison counterpart method [2] is 0%, which represents the best possible estimation.
In spite of other similarity function used in counterpart method [14], the number of similar points in two sets of points did not play an important role for recognition.
Similar(57)
Most of the counterpart methods perform well when there are no pathological regions or exudates in retinal images.
Now for better comparison, the results of counterpart methods for recognition and also dataset used are presented in Table 2.
The success rate and number of images used in proposed method show the effectiveness of the proposed method in comparison to the counterpart methods.
In the proposed method, in spite of other counterpart methods [8, 13, 14, 29 32], segmentation methods that increase computational time were not used.
The success rate and number of images used in the proposed method show the effectiveness of the proposed method in comparison to the counterpart methods.
The datasets used in the proposed method are larger than datasets used in other methods and thanks to avoiding pre-processing algorithms such as segmentation, the proposed method takes less computation time in comparison to the counterpart methods.
Most of counterpart methods can be used only in identification or verification mode, but the proposed method can be used in both identification and verification mode and its accuracy rate in these two modes is 100%.
We compensated the rotation angle of head or eye movement (gaze angle) in front of the retina fundus camera using phase correlation technique which was not mentioned in the counterpart methods.
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