Adaptive Fractional Differentiation Harris Corner Detection Algorithm for Vision Measurement of Surface Roughness | Zendy

Ruiyin Tang | Zendy; Zhoumo Zeng | Zendy

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Adaptive Fractional Differentiation Harris Corner Detection Algorithm for Vision Measurement of Surface Roughness

Author(s) -

Ruiyin Tang,

Zhoumo Zeng

Publication year - 2014

Publication title -

advances in mathematical physics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.283

H-Index - 23

eISSN - 1687-9139

pISSN - 1687-9120

DOI - 10.1155/2014/494237

Subject(s) - fractal , algorithm , fractal dimension , surface (topology) , dimension (graph theory) , image processing , surface roughness , surface finish , enhanced data rates for gsm evolution , image (mathematics) , mathematics , computer science , fractional calculus , fractal analysis , computer vision , artificial intelligence , geometry , mathematical analysis , physics , materials science , combinatorics , composite material , quantum mechanics

The Harris algorithm via fractional order derivative (the adaptive fractional differentiation Harris corner detection algorithm), which adaptively adjusts the fractal dimension parameter, has been investigated for an analysis of image processing relevant to surface roughness by vision measurements. The comparative experiments indicate that the algorithm allows the edge information in the high frequency areas to be enhanced, thus overcoming shortcomings. The algorithm permits real-time measurements of surface roughness to be performed with high precision, superior to the conventional Harris algorithm

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