Abstract
We propose a local feature descriptor based on moment. Although conventional scale invariant feature transform (SIFT)-based algorithms generally use difference of Gaussian (DoG) for feature extraction, they remain sensitive to more complicated deformations. To solve this problem, we propose MIFT, an invariant feature transform algorithm based on the modified discrete Gaussian-Hermite moment (MDGHM). Taking advantage of MDGHM’s high performance to represent image information, MIFT uses an MDGHM-based pyramid for feature extraction, which can extract more distinctive extrema than the DoG, and MDGHM-based magnitude and orientation for feature description. We compared the proposed MIFT method performance with current best practice methods for six image deformation types, and confirmed that MIFT matching accuracy was superior of other SIFT-based methods.
Highlights
The scale invariant feature transform (SIFT) was proposed by Lowe [1] to extract image features invariant to changes in image scale, rotation, illumination, viewpoint, and partial occlusion
This paper proposes MIFT, an modified discrete Gaussian-Hermite moment (MDGHM) based invariant feature transform where the MDGHM
To obtain better matching accuracy, we propose MIFT, an MDGHM based invariant in a deformed
Summary
The scale invariant feature transform (SIFT) was proposed by Lowe [1] to extract image features invariant to changes in image scale, rotation, illumination, viewpoint, and partial occlusion. Kang [8] proposed a modified local discrete Gaussian-Hermite moment based. Junaid [9] proposed binarization of gradient orientation histograms to reduce storage and computational resources These SIFT based algorithms can improve feature extraction performance, they are all the different types of Gaussian based methods and, sensitive to complicated deformations, e.g., large illumination changes [10]. This paper proposes MIFT, an MDGHM based invariant feature transform where the MDGHM based pyramid was constructed using input image MDGHMs, in contrast to the Gaussian pyramid and DoG used in the first SIFT stage, and MDGHM based magnitude and orientation were used to calculate orientation and keypoint descriptors in the third and fourth SIFT stages rather than the original gradient method. This section reviews the conventional SIFT algorithm [1] and MDGHM [8]
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