Robust Keypoint Detection against Affine Transformation Using Moment Invariants on Intrinsic Mode Function


Abstract: Scale Invariant Feature Transform (SIFT) is a method to detect and match invariant feature points on images, and is robust against contrast, rotation, and scale changes. However, SIFT cannot find many correct matching points between affine transformed images because this method employs Gaussian function for scale parameter which specifies a circle area on image planes. In this paper, we propose a method using Bi-dimensional Empirical Mode Decomposition (BEMD) for keypoint detection, where a given image is decomposed into Intrinsic Mode Functions (IMFs). Our method also employs Affine Moment Invariants (AMIs) instead of SIFT’s feature values. As a result, the proposed method detects more matching points than SIFT in a steep affine
transformed image.
Keywords: Empirical Mode Decomposition, Affine Moment Invariants.

Satoru Motomatsu, Kosuke Takenaka, and Yoshimitsu Kuroki

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Volume 1 Issue 1

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59 - 65

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