Local multiple orientations estimation using k-medoids

Estimation of local multiple orientations plays an important role in many image processing and computer vision tasks. It has been shown that the detection of orientations in an image patch corresponds to fitting multiple axes to its Fourier transform. In this paper, k-medoids are introduced to detect local multiple orientations in the Fourier domain. Medoids are related to a well-known matrix eigenvector problem. A hierarchical schema with eigensystem and energy distribution analysis is employed to determine the number of orientations in an image patch. The proposed approach detects two types of orientation structure (ridges and edges) without difference. Experimental results on synthetic and real images show that the proposed method can detect multiple orientations with high accuracy and is robust against noise.