Latent fingerprint wavelet transform image enhancement technique for optical coherence tomography

Makinana, Sisanda; Khanyile, Portia N.; Khutlang, Rethabile

doi:10.1109/icaipr.2016.7585203

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Databases can be divided by the type of data they contain, i.e. those that contain all ten fingerprints for a person whose identity has been indisputably established, palms, latents of unidentified persons from unresolved cases containing fingerprints, unresolved fingerprints containing palm prints and a database with personal information about the person [13][14][15].…”

Section: Databases and Types Of Datamentioning

confidence: 99%

Automated Fingerprint Identification System: with and without the Possibility of Correction of a Digitalised Image

2022

Teh. vjesn.

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According to the fact that systems for automatic processing of biometric data are constantly advancing in terms of speed and reliability, as well as in terms of adding new processing capabilities, the question of choosing the appropriate system becomes more important. In this paper the idea is to present the technical and technological solutions of the Automated Fingerprint Identification System with different operating principles, with and without the possibility of correction or coding of a digitized image. Comparisons of different systems were performed in test and production environments. The test database with 10 000 records and about half a million records of dactyloscoped persons in the production of database for testing the performance of search was used. The results have shown that there exists a statistically significant difference (p < 0.001) between examined systems in face fingerprint search according to latent fingerprint databases (which means indirect verification). In the production environment, it was found that there exists statistically significant difference (p < 0.001) in the direct and indirect verification showing advantages and disadvantages of the compared systems.

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Section: Databases and Types Of Datamentioning

confidence: 99%

Automated Fingerprint Identification System: with and without the Possibility of Correction of a Digitalised Image

2022

Teh. vjesn.

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“…The third type is an image enhancement method based on transform domain. Such methods perform multi-scale decomposition of images by existing multi-scale transforms, such as wavelet transform (Tao et al, 2015;Makinana et al, 2016;Witwit et al, 2017) and Curvelet transform (Bhutada et al, 2011;Hashemahmed et al, 2015), and then stretch the transform coefficients, finally inverse transform to obtain enhanced images. This multi-scale decomposition can effectively extract the feature information of 10 the image, such as curves and textures.…”

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confidence: 99%

Seismic section image detail enhancement method based on wavelet transform

Jia

Dongye

2019

Preprint

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Abstract. The seismic section image contains a wealth of texture detail information, which is important for the interpretation of the formation profile information. In order to enhance the texture detail of the image while keeping the structural information of the image intact, a multi-scale enhancement method based on wavelet transform is proposed. First, the image is wavelet decomposed to obtain a low frequency structural component and a series of high frequency texture detail components; Secondly, bilateral texture filtering is performed on the low-frequency structural components to filter out high-frequency noise while maintaining the edges of the image; adaptive enhancement is performed on the high-frequency detail components to filter out low-frequency noise while enhancing detail; Finally, the processed high and low frequency components are reconstructed by wavelet can obtained the seismic section image with enhanced detail. The method of this paper enhances the texture detail information in the image while preserving the edge of the image.

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