Scale, translation and rotation invariant Wavelet Local Feature Descriptor

Barajas-García, Carolina; Solorza-Calderón, Selene; Gutiérrez-López, Everardo

doi:10.1016/j.amc.2019.124594

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…However, many attempts have been made to preserve rotation, translation, and scaling. The study in [22] used a wavelet transform, namely, the Harr transform, to obtain different scales and keep the rotation and translation invariant. The performance of such descriptors is comparable to that of SIFT, and the computational process is less complex.…”

Section: Related Workmentioning

confidence: 99%

Triangular Regions Representations for Matching Images With Viewpoint Changes

Rube

Alsharif

2022

IEEE Access

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This paper proposes triangular region representations based on keypoints detected in images with viewpoint changes. The strongest keypoints in the reference and query images are allocated individually using a previously published contourlet-based approach to determine the keypoints. These selected keypoints serve as vertices of the triangular regions to be transformed into rectangular representations as simple numeric matrices. The suggested representation methods used to form rectangular matrices are full triangle representation (FTR) and a lighter representation called triangle medians and sides representation (TMSR). For the former, the intensity values along the lines connecting the anchor keypoint to the points between the other two triangular vertices form the rows of the representation matrix. These two triangle vertices are located in an allotted window around the anchor keypoint from the nearby keypoints. For the latter, the intensity values on the six triangular lines, medians, and sides formed the resulting rectangular matrix. The proposed representations are validated for image-matching applications using a descriptor-less matching method. Moreover, the performances of these algorithms are compared with those of traditional algorithms. The results confirmed the superiority of the proposed method over these algorithms.

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Section: Related Workmentioning

confidence: 99%

Triangular Regions Representations for Matching Images With Viewpoint Changes

Rube

Alsharif

2022

IEEE Access

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“…Many studies are trying to improve descriptor's efficiency or reduce matching errors. The paper [11] proposed invariant wavelet-based descriptors. It extracts features in three scale pyramids key points to construct such descriptors.…”

Section: Related Workmentioning

confidence: 99%

Keypoint Description Using Statistical Descriptor with Similarity-Invariant Regions

Rube¹,

Alsharif²

2022

Computer Systems Science and Engineering

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This article presents a method for the description of key points using simple statistics for regions controlled by neighboring key points to remedy the gap in existing descriptors. Usually, the existent descriptors such as speeded up robust features (SURF), Kaze, binary robust invariant scalable keypoints (BRISK), features from accelerated segment test (FAST), and oriented FAST and rotated BRIEF (ORB) can competently detect, describe, and match images in the presence of some artifacts such as blur, compression, and illumination. However, the performance and reliability of these descriptors decrease for some imaging variations such as point of view, zoom (scale), and rotation. The introduced description method improves image matching in the event of such distortions. It utilizes a contourlet-based detector to detect the strongest key points within a specified window size. The selected key points and their neighbors control the size and orientation of the surrounding regions, which are mapped on rectangular shapes using polar transformation. The resulting rectangular matrices are subjected to two-directional statistical operations that involve calculating the mean and standard deviation. Consequently, the descriptor obtained is invariant (translation, rotation, and scale) because of the two methods; the extraction of the region and the polar transformation techniques used in this paper. The description method introduced in this article is tested against well-established and wellknown descriptors, such as SURF, Kaze, BRISK, FAST, and ORB, techniques using the standard OXFORD dataset. The presented methodology demonstrated its ability to improve the match between distorted images compared to other descriptors in the literature.

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“…The two significant drawbacks of wavelet transform are lack of shift-invariant and limited directionality [23]. A recent study in [24] evaluated that the wavelet local feature descriptor (WLFD) was invariant to scale, rotation, and translation. They modified and combined the WLFD by generating wavelet pyramids, keypoint localization, and descriptors.…”

Section: A Literature Reviewmentioning

confidence: 99%

Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition

et al. 2022

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Aiming at the problems in the best basis selection, this paper presents a novel criterion based on the statistical measurement of the curvature wavelet coefficient to dynamically select the single best basis of the quad-tree wavelet packet transformation. The selected single best basis works as an extracted feature for biometric periocular recognition system. The proposed method first extracts the mean curvature of wavelet coefficients inside the quad-tree wavelet packet transform. Second, the method finds the most distinctive features based on the largest standard deviation and dynamically selects the extracted curvature wavelet coefficients as the single best basis. Third, the selected single curvature best basis works as an extracted feature, and then it is combined with the histogram of oriented gradients method. Finally, the support vector machine is employed to perform classification. Two datasets of two-dimensional periocular digital images are tested against the proposed method. To show the extended ability, we analyze the curvature best basis method against wavelet functions and characteristics and test the proposed method against the plain face and masked face recognition. The proposed method achieves the highest performance results inside periocular recognition (97.53% accuracy for UBIPr-1 and 97.77% accuracy for EYB-P1), masked face recognition (98.11% accuracy), and plain face recognition (98.26% accuracy). The proposed method is robust against glasses occlusion, artificial geometry transformations, Gaussian and salt pepper noise. Comparison with other works in a similar recognition system shows that our proposed curvature best basis method yields the highest performance results.INDEX TERMS Best basis, curvature, periocular recognition, wavelet packet transform.

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Scale, translation and rotation invariant Wavelet Local Feature Descriptor

Cited by 8 publications

References 11 publications

Triangular Regions Representations for Matching Images With Viewpoint Changes

Triangular Regions Representations for Matching Images With Viewpoint Changes

Keypoint Description Using Statistical Descriptor with Similarity-Invariant Regions

Curvature Best Basis: A Novel Criterion to Dynamically Select a Single Best Basis as the Extracted Feature for Periocular Recognition

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