An efficient fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization

Mohammed, Abdul Adeel; Minhas, Rashid; Wu, Qing; Sid-Ahmed, M.A.

doi:10.3233/ica-2010-0327

Cited by 19 publications

(5 citation statements)

References 64 publications

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“…The joint question to this topic is fingerprint image compression—the common compression methods for fingerprint images use JPEG-2000 or WSQ ( wavelet scalar quantization ), whereas other possible compression scenarios exist [ 14 ]. It is unclear how the compression algorithm can influence the quality of such fingerprint image, containing a skin disease.…”

Section: Concrete Cases Of Skin Diseases On Fingerprintsmentioning

confidence: 99%

Influence of Skin Diseases on Fingerprint Recognition

Drahanský

Doležel

Urbánek

et al. 2012

Journal of Biomedicine and Biotechnology

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There are many people who suffer from some of the skin diseases. These diseases have a strong influence on the process of fingerprint recognition. People with fingerprint diseases are unable to use fingerprint scanners, which is discriminating for them, since they are not allowed to use their fingerprints for the authentication purposes. First in this paper the various diseases, which might influence functionality of the fingerprint-based systems, are introduced, mainly from the medical point of view. This overview is followed by some examples of diseased finger fingerprints, acquired both from dactyloscopic card and electronic sensors. At the end of this paper the proposed fingerprint image enhancement algorithm is described.

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Section: Concrete Cases Of Skin Diseases On Fingerprintsmentioning

confidence: 99%

Influence of Skin Diseases on Fingerprint Recognition

Drahanský

Doležel

Urbánek

et al. 2012

Journal of Biomedicine and Biotechnology

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“…Since there have been many applications such as image denoising, fingerprint identification, image compression, watermarking, etc. that explored the potential capability of waveatoms [15][16][17][18], waveatom-transform-based multifocus image fusion method has been proposed.…”

Section: Proposed Methodsmentioning

confidence: 99%

Multifocus image fusion based on waveatom transform

Manchanda¹,

Gambhir

2019

Sādhanā

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Multifocus image fusion has emerged as a challenging research area due to the availability of various image-capturing devices. The optical lenses that are widely utilized in image-capturing devices have limited 'depth-of-focus' and, therefore, only the objects that lie within a particular depth remain 'in-focus', whereas all the other objects go 'out-of-focus'. In order to obtain an image where all the objects are well focused, multifocus image fusion method based on waveatom transform is proposed. The core idea is to decompose all input images using waveatom transform and perform fusion of resultant waveatom coefficients. The waveatom coefficients with higher visibility, corresponding to sharper image intensities, are used to perform the process of image fusion. Finally, the fused image is obtained by performing inverse waveatom transform. The performance of the proposed method is demonstrated by performing fusion on different sets of multifocus images and comparing the results of the proposed method to the results of existing image fusion methods.

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“…Wavelets are frequently used in bioinformatics, biometrics and image applications [30,32] due to their ability to represent data compactly [11,19,27] and to estimate precisely details of ECG signals [18]. Furthermore, thresholding methods can be easily derived to enhance the sparsity of the data representation [3] and characterize biomedical data [2,9].…”

Section: Alternative Dictionariesmentioning

confidence: 99%

Blind analysis of atrial fibrillation electrograms: A sparsity-aware formulation

Luengo

Monzon

Trigano

et al. 2015

ICA

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The problem of blind sparse analysis of electrogram (EGM) signals under atrial fibrillation (AF) conditions is considered in this paper. A mathematical model for the observed signals that takes into account the multiple foci typically appearing inside the heart during AF is firstly introduced. Then, a reconstruction model based on a fixed dictionary is developed and several alternatives for choosing the dictionary are discussed. In order to obtain a sparse solution, which takes into account the biological restrictions of the problem at the same time, the paper proposes using a Least Absolute Shrinkage and Selection Operator (LASSO) regularization followed by a post-processing stage that removes low amplitude coefficients violating the refractory period characteristic of cardiac cells. Finally, spectral analysis is performed on the clean activation sequence obtained from the sparse learning stage in order to estimate the number of latent foci and their frequencies. Simulations on synthetic signals and applications on real data are provided to validate the proposed approach.

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An efficient fingerprint image compression technique based on wave atoms decomposition and multistage vector quantization

Cited by 19 publications

References 64 publications

Influence of Skin Diseases on Fingerprint Recognition

Influence of Skin Diseases on Fingerprint Recognition

Multifocus image fusion based on waveatom transform

Blind analysis of atrial fibrillation electrograms: A sparsity-aware formulation

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