Single-sample augmentation framework for training Viola-Jones classifiers
Daniil P. Matalov, Sergey A. Usilin, Vladimir V. Arlazarov · 2020
In this paper we present a single-sample augmentation framework. The key idea of the framework consists of synthesizing a positive training set from a single natural sample using relevant geometric and pixel intensity transforms. The efficiency of the proposed framework has been demonstrated solving round seal stamp detection problem using Viola-Jones approach on the public “SPODS” dataset. The mentioned image transformations make it possible to simulate different orientation of the stamps, color differences, and distortions caused by stamping process and document aging. The proposed framework can be applied to training various machine learning algorithms for solving computer vision and computed tomography problems.