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Cluster Computing

Erschienen in:

01.12.2016

Robust detection of mosaic regions in visual image data

verfasst von: Seok-Woo Jang, Myunghee Jung

Erschienen in: Cluster Computing | Ausgabe 4/2016

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Abstract

Due to the explosive increase in the production and sharing of digital visual media such as photos, animations, films and video clips, the need to intentionally or unintentionally create mosaic blocks to effectively cover user-designated regions within images has grown. In this paper, a method using boundary characteristics to effectively detect the grid-type mosaic blocks existing within the input image is proposed. Initially, the Canny edge is detected from the input image. Then, the boundary characteristics of mosaic blocks are extracted from the detected edges, and the candidate regions which may contain mosaic blocks are detected. After this stage, geometric characteristics are used to eliminate non-mosaic blocks and select actual mosaic blocks. In the experiment performed in this paper, it was observed that the method using boundary characteristics achieved more robust detection of the grid-type mosaic blocks from various input images than other existing methods. The mosaic-detection method proposed in this paper is expected to be valuable for applications in various fields.

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Titel: Robust detection of mosaic regions in visual image data
verfasst von: Seok-Woo Jang
Myunghee Jung
Publikationsdatum: 01.12.2016
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 4/2016
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-016-0621-6

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