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Noise injection into Freeman chain codes
Luka Lukač1, Andrej Nerat1, Damjan Strnad1
1Faculty of Electrical Engineering and Computer Science, University of Maribor, Maribor, Slovenia.
Abstract:
This article presents a novel method for direct noise injection into geometric shapes described by eight-or four-directional Freeman chain codes. Noise is applied to randomly selected segments of a chain code sequence using a set of predefined actions. The design of alterations retains topological characteristics of shapes. The method is tested on various shapes, including open, self-intersecting, and simple shapes, among which the latest two may contain holes. Fractal dimension and mean distance from original are utilised in order to analyse the amount of injected noise in sequences of chain codes. The proposed method enables efficient noise injection directly into Freeman chain codes for use in data augmentation and regularization during neural network training.
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