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Fig. 3 Example image of the datasets used.
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For each image of the dataset, the set of corresponding overlapping images is first determined.
Figures 5 and 6 show the output of every detector for an image of the dataset.
In this process, points of interest are identified in each image of the dataset [25].
Finally, using a new similarity function, the similarity of each test image to each retina image of the dataset is determined.
These figures show the ratio between NEp c and NEp d for each image of the dataset considering values for dEp of 3 and 2, respectively.
These figures show the ratios NS c /NS d and NS c /NS g 4 for each image of the dataset considering values of 3 and 2 for dS.
Table 1 Estimated eye sizes taking into account the distance Form theachmera.
The current single-machine implementation available in the Tapioca command of MicMac is the following: For each image of the dataset, the set of corresponding overlapping images is first determined.
The absolute value of difference between the histogram of new rotated image and histogram of each image in dataset (region with 460 × 460 pixels around the center of image) is calculated, and we sum the results of different points to obtain a value for each test image to each image of the dataset.
It can be cropped from one ultrasound image of the dataset and then smoothed by bilateral filtering.
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