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To solve this problem, we propose HDIB1M - a handwritten document image binarization dataset of 1M images.
These techniques take advantage of the knowledge learned in one domain, for which labeled data are available, to apply it to other domains for which there are no labeled data.
We also show that adjusting the threshold values of binary activation functions results in the unbalanced distribution of the binary activation, which increases the accuracy of BNN models.
Skin lesion segmentation is one of the crucial steps for an efficient non-invasive computer-aided early diagnosis of melanoma.
A number of image transformations are considered to increase the efficiency of the Hough algorithm.