Rotation-invariant fingerprint matching using radon and DCT

Bharkad, Sangita; Kokare, Manesh

doi:10.1007/s12046-017-0752-3

Cited by 1 publication

(1 citation statement)

References 33 publications

(48 reference statements)

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“…Rotational invariance is achieved using a model based approach in [9] and signal processing approach in [5]. Other popular methods for rotation invariance texture classification include statistical approaches like Local Binary Patterns [10], wavelets [11], Gabor filters [12], Radon transform and Discrete Cosine Transform [13].…”

Section: Related Workmentioning

confidence: 99%

Galois Field-based Approach for Rotation and Scale Invariant Texture Classification

Shivashankar¹,

Kudari²,

Hiremath³

2018

IJIGSP

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In this paper, a novel Galois Field-based approach is proposed for rotation and scale invariant texture classification. The commutative and associative properties of Galois Field addition operator are useful for accomplishing the rotation and scale invariance of texture representation. Firstly, the Galois field operator is constructed, which is applied to the input textural image. The normalized cumulative histogram is constructed for Galois Field operated image. The bin values of the histogram are considered as rotation and scale invariant texture features. The classification is performed using the K-Nearest Neighbour classifier. The experimental results of the proposed method are compared with that of Rotation Invariant Local Binary Pattern (RILBP) and Log-Polar transform methods. These results obtained using the proposed method are encouraging and show the possibility of classifying texture successfully irrespective of its rotation and scale.

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