Image Compression with Artificial Neural Networks

Kouamo, Stephane; Tangha, Claude

doi:10.1007/978-3-642-33018-6_53

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Recognition therefore consists in comparing the input matrix to all the M i . matrices of the database for the case of identification, or to a single specific M matrix in the case of authentication [13] [14] [15].…”

Section: Classic Methodsmentioning

confidence: 99%

Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

Kouamo¹,

Tangha²,

Kouamo³

2020

JSEA

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Faultless authentication of individuals by fingerprints results in high false rejections rate for rigorously built systems. Indeed, the authors prefer that the system erroneously reject a pattern when it does not meet a number of predetermined correspondence criteria. In this work, after discussing existing techniques, we propose a new algorithm to reduce the false rejection rate during the authentication-using fingerprint. This algorithm extracts the minutiae of the fingerprint with their relative orientations and classifies them according to the different classes already established; then, make the correspondence between two templates by simple probabilities calculations from a deep neural network. The merging of these operations provides very promising results both on the NIST4 international data reference and on the SOCFing database.

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Section: Classic Methodsmentioning

confidence: 99%

Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

Kouamo¹,

Tangha²,

Kouamo³

2020

JSEA

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“…The neural network used here is a multilayer perceptron with an input layer, a hidden layer which itself contains three layers (two classification layers and a correspondence layer) and finally an output layer (see Figure 1). The principle is the same like a usual recognition method based on the use of neural network and is based on two main steps: training and recognition stage [19]. Before applying the training stage, we classify the entire sample of data into four main classes (arch, whorl, left and right loop) according to the Henry's classification and we determine two prototypes for each class of data.…”

Section: A Two Hidden Layers Perceptron For Fingerprint Recognitionmentioning

confidence: 99%

Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning

Kouamo¹,

Tangha²

2016

JILSA

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Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.

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“…In [18] A.Younus et al have proposed a hybrid medical image compression technique based on Discrete Cosines Transform (DCT) and Lapped Biorthogonal Transform (LBT). In [7] S.kuamo et al present an experimental study of some image compression methods and propose a new hybrid method based on a neural network. This paper is organized as follows, in the second section, we define neural network and give its different features.…”

Section: Introduction Dicom (Digital Imaging and Communications Inmentioning

confidence: 99%

An Enhencment Medical Image Compression Algorithm Based on Neural Network

Dridi¹,

Hajjaji²,

Bouallègue³

et al. 2016

ijacsa

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Abstract-The main objective of medical image compression is to attain the best possible fidelity for an available communication and storage [6], in order to preserve the information contained in the image and does not have an error when they are processing it. In this work, we propose a medical image compression algorithm based on Artificial Neural Network (ANN). It is a simple algorithm which preserves all the image data. Experimental results performed at 8 bits/pixels and 12bits/pixels medical images show the performances and the efficiency of the proposed method. To determine the 'acceptability' of image compression we have used different criteria such as maximum absolute error (MAE), universal image quality (UIQ), correlation and peak signal to noise ratio (PSNR).

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Image Compression with Artificial Neural Networks

Cited by 5 publications

References 9 publications

Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

Reduction of False Rejection in an Authentication System by Fingerprint with Deep Neural Networks

Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning

An Enhencment Medical Image Compression Algorithm Based on Neural Network

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