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Contour Extraction and Compression Scheme Utilizing Both the Transform and Spatial Image Domains

  • Remigiusz BaranEmail author
  • Andrzej Dziech
  • Jakob Wassermann
Conference paper
  • 346 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 785)

Abstract

Two new simple but fast and pretty efficient approaches for contour data detection, extraction and approximation are presented in this paper. The High-Pass Filter (HPF) method, designed to detect and extract contours from greyscale images is the first presented method. It operates in spectral domains either of the Periodic Haar Piecewise-Linear (PHL) transform or the Haar Wavelet one. The other presented method, known as the Segments Distances Ratios (SDR) approach, is used, in turn, to approximate the contour lines given by the HPF method. Its spatial approximation accuracy is carefully investigated and reported as well as referred to the universally recognized Ramer algorithm. Efficiency of both presented methods as well as their performance aspects are finally discussed and concluded.

Keywords

Contour detection and extraction Contour compression Haar wavelet PHL transform Bit-plane slicing Spatial approximation 

Notes

Acknowledgements

This work was partially supported by The Horizon 2020 project SCISSOR - Security In trusted SCADA and smart-grids (Grant agreement no: 644425) and also by The Polish National Centre for Research and Development (NCBR), as a part of the Project no. DZP/RID-I-68/14/NCBIR/2016 (RID - InPreDo).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Remigiusz Baran
    • 1
    Email author
  • Andrzej Dziech
    • 2
  • Jakob Wassermann
    • 3
  1. 1.Department of Computer Science, Electronics and Electrical EngineeringKielce University of TechnologyKielcePoland
  2. 2.Department of TelecommunicationsAGH University of Science and TechnologyKrakówPoland
  3. 3.Department of Electronics and TelecommunicationsUniversity of Applied SciencesViennaAustria

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