Dataset Enhancement in Hair Follicle Detection: ESENSEI Challenge
Journal Title: Annals of Computer Science and Information Systems - Year 2018, Vol 16, Issue
Abstract
In this paper, a solution to ESENSEI data mining challenge concerning the analysis of microscopic hair images is described. The task of the challenge was to detect locations of hair follicles in closeup images of a human scalp. The proposed solution is based on a convolutional neural network architecture. To improve generalization performance, we enhance training and test datasets using image transformations applied to both input and output. The chosen transformations are two axis symmetries and switching axes, all of which are possible to apply regardless of resolution without producing interpolation artifacts. Since these can be combined, 2^3 = 8 possible views of each image can be created to expand both training and test data. We demonstrate the effects of dataset enhancement in both training and classifying on results achievable on the competition dataset. The solution placed 2nd in the final challenge evaluation.
Authors and Affiliations
Jan Jakubik
Keywords
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- EP ID EP567978
- DOI 10.15439/2018F388
- Views 19
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