Iris segmentation using a new unsupervised neural approach

Ohmaid, Hicham; Eddarouich, Souad; Bourouhou, Abdennaser; Timouyas, Meriem

doi:10.11591/ijai.v9.i1.pp58-64

Cited by 9 publications

(8 citation statements)

References 8 publications

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“…The Hough transform for circle detection [16] is applied to image 5 ( , ). In this case a 3-dimensional accumulator was used, storing the 2 coordinates of the center and the radius.…”

Section: Circle Detection Via Hough Transformmentioning

confidence: 99%

Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

Fernández

Chávez

Kemper

2021

IJECE

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This work proposes a portable, handheld electronic device, which measures the cleanliness in fiber optic connectors via digital image processing and artificial neural networks. Its purpose is to reduce the evaluation subjectivity in visual inspection done by human experts. Although devices with this purpose already exist, they tend to be cost-prohibitive and do not take advantage of neither image processing nor artificial intelligence to improve their results. The device consists of an optical microscope for fiber optic connector analysis, a digital camera adapter, a reduced-board computer, an image processing algorithm, a neural network algorithm and an LCD screen for equipment operation and results visualization. The image processing algorithm applies grayscale histogram equalization, Gaussian filtering, Canny filtering, Hough transform, region of interest segmentation and obtaining radiometric descriptors as inputs to the neural network. Validation consisted of comparing the results by the proposed device with those obtained by agreeing human experts via visual inspection. Results yield an average Cohen's Kappa of 0.926, which implies a very satisfactory performance by the proposed device.

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“…The Hough transform for circle detection [16] is applied to image 5 ( , ). In this case a 3-dimensional accumulator was used, storing the 2 coordinates of the center and the radius.…”

Section: Circle Detection Via Hough Transformmentioning

confidence: 99%

Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

Fernández

Chávez

Kemper

2021

IJECE

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“…Scientists approximate functions by polynomials, wavelets, splines and neural networks. For the wide usage of neural networks and their ability to solve problems from different fields (see [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38]) a set of functions from Lp space is approximated by neural networks in this work. Many papers contains this topic widely, we mention some of them in the references below (see [39][40][41][42][43][44][45][46][47]).…”

Section: Construction Of Fnn With Relu Activation Functionmentioning

confidence: 99%

Best neural simultaneous approximation

Almurieb

Bhaya

2020

IJEECS

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For many years, approximation concepts has been investigated in view of neural networks for the several applications of the two topics. Researchers studied simultaneous approximation in the 2-normed space and proved essential theorems concern with existence, uniqueness and degree of best approximation. Here, we define a new 2-norm in -space, with so we call it quasi 2- normed space ( ). The set of approximations is a space of feedforward neural networks that is constructed in this paper. Existence and uniqueness of best neural approximation for a function from is proved, describing the rate of best approximation in terms of modulus of smoothness.

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“…The iris is one of the unique organs in the human body. Iris is usually used as electronic security or biometric identification system [8,9]. Nowadays, the research on the iris in the medical field is increasingly widespread and is being seriously studied by experts.…”

Section: Introductionmentioning

confidence: 99%

Identification of Coronary Heart Disease through Iris using Gray Level Co-occurrence Matrix and Support Vector Machine Classification

Gunawan¹,

Putra²,

Imansyah³

et al. 2022

IJACSA

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Now-a-days, coronary heart disease is one of the deadliest diseases in the world. An unfavorable lifestyle, lack of physical activity, and consuming tobacco are the causes of coronary heart disease aside from genetic inheritance. Sometimes the patient does not know whether he has abnormalities in heart function or not. Therefore, this study proposes a system that can detect heart abnormalities through the iris, known as the Iridology method. The system is designed automatically in the iris detection to the classification results. Feature extraction using five characteristics is applied to the Gray Level Cooccurrence Matrix (GLCM) method. The classification process uses the Support Vector Machine (SVM) with linear kernel variation, Polynomial, and Gaussian to obtain the best accuracy in the system. From the system simulation results, the use of the Gaussian kernel can be relied on in the classification of iris conditions with an accuracy rate of 91%, then the Polynomial kernel accuracy reaches 89%, and the linear kernel accuracy reaches 87%. This study has succeeded in detecting heart conditions through the iris by dividing the iris into normal iris and abnormal iris.

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Iris segmentation using a new unsupervised neural approach

Cited by 9 publications

References 8 publications

Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

Device to evaluate cleanliness of fiber optic connectors using image processing and neural networks

Best neural simultaneous approximation

Identification of Coronary Heart Disease through Iris using Gray Level Co-occurrence Matrix and Support Vector Machine Classification

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