Wave atoms based compression method for fingerprint images

Haddad, Zehira; Beghdadi, Azeddine; Serir, Amina; Mokraoui, Anissa

doi:10.1016/j.patcog.2013.02.004

Cited by 19 publications

(6 citation statements)

References 36 publications

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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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Section: Proposed Methodsmentioning

confidence: 99%

Multifocus image fusion based on waveatom transform

Manchanda¹,

Gambhir

2019

Sādhanā

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“…Background noise, smudging, and partial nature of these types of fingerprints, usually obtained from crime scenes, hinder a good fit to precomputed models of ridge flows or patterns. Fingerprint quality metrics are also important for effective compression techniques [57]. Finally, quality assessment of 3D fingerprints that are obtained either from a 3D sensor or reconstructed from multiple 2D views, is an open research problem.…”

Section: Fingerprint Quality Assessmentmentioning

confidence: 99%

Biometric quality: a review of fingerprint, iris, and face

2014

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Biometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample. It is imperative to first analyze the data and incorporate this understanding within the recognition system, making assessment of biometric quality an important aspect of biometrics. Though several interpretations and definitions of quality exist, sometimes of a conflicting nature, a holistic definition of quality is indistinct. This paper presents a survey of different concepts and interpretations of biometric quality so that a clear picture of the current state and future directions can be presented. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in fingerprint, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images, to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems. The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.

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“…In recent years, new multi-scale transform called wave atoms has been emerged, it 2151 has been included in many articles in the field of image processing [11]- [14], this transform is well suited for representing the images, data, due to its directionality and sparsity compared with the discrete wavelet transform. Sparsity is the important criterion that can be considered in speech compression, in contrast of directionality, which is considered for only 2D or higher dimensional signal [15].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Speech Compression Based on Discrete Wave Atoms Transform

Bousselmi

Nouredine²,

Chérif

2016

IJECE

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This paper proposes a new adaptive speech compression system based on discrete wave atoms transform. First, the signal is decomposed on wave atoms, then wave atom coefficients are truncated using a new adaptive thresholding which depends on the SNR estimation. The thresholded coefficients are quantized using Max Lloyd scalar quantizer. Besides, they are encoded using zero run length encoding followed by Huffman coding. Numerous simulations are performed to prove the robustness of our approach. The results of current work are compared with wavelet based compression by using objective criteria, namely CR, SNR, PSNR and NRMSE. This study shows that the wave atoms transform is more appropriate than wavelets transform since it offers a higher compression ratio and a better speech quality.

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Wave atoms based compression method for fingerprint images

Cited by 19 publications

References 36 publications

Multifocus image fusion based on waveatom transform

Multifocus image fusion based on waveatom transform

Biometric quality: a review of fingerprint, iris, and face

Adaptive Speech Compression Based on Discrete Wave Atoms Transform

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