Fractional Fourier-Radial Transform for Digital Image Recognition

López-Ávila, Luis Felipe; Álvarez-Borrego, Josué; Solorza-Calderón, Selene

doi:10.1007/s11265-020-01543-0

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…A non-linear filter was used because several images were combined to generate the control image. Also, it can be optimized to achieve lower sensitivity to scale, rotation, and lighting of all images used (Guerrero-Moreno & Álvarez-Borrego, 2009). The resultant screen (Fig.…”

Section: Methodsmentioning

confidence: 99%

Differentiation of Military Macaws (Ara militaris) in captivity using photo-identification

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Differentiation of Military Macaws (Ara militaris) in captivity using photo-identification

et al. 2021

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“…Sci. 2023, 13, x FOR PEER REVIEW 3 of 21 90%) [22]. In this paper, we implemented the Hilbert mask on the module of the Fourier-Mellin Transform to obtain a unique signature of the skin lesion called the radial Fourier-Mellin signature, which is invariant to rotation, scale, and translation.…”

Section: Image Datasetmentioning

confidence: 99%

“…Now, using the Hilbert transform, we also achieve the skin lesion in the image being invariant to rotation. The Hilbert transform of the image is given by [22,[26][27][28][29]:…”

Section: Radial Fourier-mellin Signatures Through Hilbert Transformmentioning

confidence: 99%

“…Therefore, this angle is determined by θ = acos u/ √ u 2 + v 2 . Then, using the Euler's formula, we calculated the binary ring masks of the RGB channels and gray-scale skin lesion digital image, using both the real (H R ) and imaginary (H I ) parts of the radial Hilbert transform of the image as follows (Figure 2), [22,[26][27][28][29].…”

Section: Radial Fourier-mellin Signatures Through Hilbert Transformmentioning

confidence: 99%

“…Although the SVM, k-NN, and ensemble classifiers have been successfully implemented to classify skin lesions, digital images under rotation, scaling, or translation were not considered. The application of the fractional radial Fourier transform in pattern recognition for digital images is invariant to translation, scale, and rotation, and the results obtained to classify phytoplankton species showed a high level of confidence (greater than 90%) [22]. In this paper, we implemented the Hilbert mask on the module of the Fourier-Mellin Transform to obtain a unique signature of the skin lesion called the radial Fourier-Mellin signature, which is invariant to rotation, scale, and translation.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures

Guerra-Rosas,

López-Ávila,

Garza-Flores

et al. 2023

Applied Sciences

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This manuscript proposes the possibility of concatenated signatures (instead of images) obtained from different integral transforms, such as Fourier, Mellin, and Hilbert, to classify skin lesions. Eight lesions were analyzed using some algorithms of artificial intelligence: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), melanoma (MEL), actinic keratosis (AK), benign keratosis (BKL), dermatofibromas (DF), melanocytic nevi (NV), and vascular lesions (VASCs). Eleven artificial intelligence models were applied so that eight skin lesions could be classified by analyzing the signatures of each lesion. The database was randomly divided into 80% and 20% for the training and test dataset images, respectively. The metrics that are reported are accuracy, sensitivity, specificity, and precision. Each process was repeated 30 times to avoid bias, according to the central limit theorem in this work, and the averages and ± standard deviations were reported for each metric. Although all the results were very satisfactory, the highest average score for the eight lesions analyzed was obtained using the subspace k-NN model, where the test metrics were 99.98% accuracy, 99.96% sensitivity, 99.99% specificity, and 99.95% precision.

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Shape feature representation by the multiscale model of CRF orientation selectivity

Duan¹,

Li²,

Shen³

et al. 2022

Sci. Sin.-Tech.

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Fractional Fourier-Radial Transform for Digital Image Recognition

Cited by 4 publications

References 35 publications

Differentiation of Military Macaws (Ara militaris) in captivity using photo-identification

Differentiation of Military Macaws (Ara militaris) in captivity using photo-identification

Classification of Skin Lesion Images Using Artificial Intelligence Methodologies through Radial Fourier–Mellin and Hilbert Transform Signatures

Shape feature representation by the multiscale model of CRF orientation selectivity

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