Single-sample augmentation framework for training Viola-Jones classifiers

Daniil P. Matalov; Sergey A. Usilin; Vladimir V. Arlazarov

doi:10.1117/12.2559435

31 January 2020 Single-sample augmentation framework for training Viola-Jones classifiers

Daniil P. Matalov, Sergey A. Usilin, Vladimir V. Arlazarov

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114330I (2020) https://doi.org/10.1117/12.2559435
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

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.

Citation Download Citation

Daniil P. Matalov, Sergey A. Usilin, and Vladimir V. Arlazarov "Single-sample augmentation framework for training Viola-Jones classifiers", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114330I (31 January 2020); https://doi.org/10.1117/12.2559435

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