Image Local Features Description Through Polynomial Approximation

This work introduces a novel local patch descriptor that remains invariant under varying conditions of orientation, viewpoint, scale, and illumination. The proposed descriptor incorporate polynomials of various degrees to approximate the local patch within the image. Before feature detection and app...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.183692-183705
Main Authors: Fawad, Rahman, Muhibur, Khan, Muhammad Jamil, Adeel Asghar, Muhammad, Amin, Yasar, Badnava, Salman, Mirjavadi, Seyed Sajad
Format: Article
Language:English
Subjects:
Algorithms
Approximation
Correlation coefficients
Covariant
Datasets
descriptor
Detectors
Equalization
Euclidean geometry
Feature extraction
handcrafted feature
Histograms
Illumination
Image coding
Image edge detection
Image filters
Lighting
Mathematical analysis
Microtexture
patch
Polynomials
Shape
textures
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Summary:This work introduces a novel local patch descriptor that remains invariant under varying conditions of orientation, viewpoint, scale, and illumination. The proposed descriptor incorporate polynomials of various degrees to approximate the local patch within the image. Before feature detection and approximation, the image micro-texture is eliminated through a guided image filter with the potential to preserve the edges of the objects. The rotation invariance is achieved by aligning the local patch around the Harris corner through the dominant orientation shift algorithm. Weighted threshold histogram equalization (WTHE) is employed to make the descriptor in-sensitive to illumination changes. The correlation coefficient is used instead of Euclidean distance to improve the matching accuracy. The proposed descriptor has been extensively evaluated on the Oxford's affine covariant regions dataset, and absolute and transition tilt dataset. The experimental results show that our proposed descriptor can categorize the feature with more distinctiveness in comparison to state-of-the-art descriptors.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2959326