The Biometrics System Based on Iris Image Processing: A Review

2019 ◽  
Author(s):  
Alfi Zuhriya Khoirunnisaa ◽  
Lutfi Hakim ◽  
Adhi Dharma Wibawa
Keyword(s):  
Image Processing ◽  
Iris Image

scholarly journals Pengenalan Gangguan Ginjal Melalui Iridologi Menggunakan Hidden Markov Model (HMM)

2019 ◽  
Vol 9 (2) ◽  
pp. 19-26
Author(s):  
Reza Satria Rinaldi ◽  
Wagiasih Wagiasih ◽  
Ika Novia Anggraini
Keyword(s):  
Image Processing ◽  
Singular Value Decomposition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Singular Value ◽  
Parameter Determination ◽  
Iris Image ◽  
Determination System ◽  
Value Decomposition

ABSTRACTIridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512. Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).

scholarly journals Image processing for early detection of Alzheimer’s disease using Iridology

2020 ◽  
Vol 11 (4) ◽  
pp. 5555-5559
Author(s):  
Asuntha A ◽  
Sai Kalyan Reddy R ◽  
Vamshikrishna K ◽  
Premsagar N
Keyword(s):  
Alzheimer’S Disease ◽  
Image Processing ◽  
Alzheimer's Disease ◽  
Memory Loss ◽  
Region Of Interest ◽  
Colour Pattern ◽  
Training Methods ◽  
Iris Image ◽  
Alternative Research ◽  
The Brain

Alzheimer's disease is caused by genetics, personal lifestyle and other environmental factors. It is an irreversible disease that slowly destroys the brain memory cells. There are no specific methods for the detection of Alzheimer's disease. The primary symptoms of Alzheimer's disease are memory loss, difficulty in thinking, a problem in writing and speaking and others. Iridology is alternative research that has gained more popularity in recent years, which studies the alterations of the iris in correspondence with the organs of the human body. The combination of digital image processing with Iridology gives an excellent opportunity to explore and learn about different neuronal diseases, specifically Alzheimer's disease. In this work, MATLAB software is applied to determine the colour, pattern and other factors that show the existence of Alzheimer's disease. The noise in the iris image is removed by the Gaussian filter, followed by histogram analyses and cropping. The Hough circle transform is used to identify the region of interest and to convert the circular iris image into rectangle form. In the training methods, the SVM and CNN classifiers are used to classify whether the person has Alzheimer's disease. Finally, the results are compared with the real-time images.

Image Processing of Lesion in Iris Image Using OpenCV

2018 ◽  
Vol 19 (11) ◽  
pp. 2035-2040 ◽  
Author(s):  
Jin-Pil Kim ◽  
Young-Bok Cho
Keyword(s):  
Image Processing ◽  
Iris Image

scholarly journals The Expert System of Cholesterol Detection Based on Iris Using the Gabor Filter

SinkrOn ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 13 ◽  
Author(s):  
Louis Paulus Doli Simangunsong ◽  
Indri Natalia Napitupulu ◽  
Rina E Lumbantoruan ◽  
Situlus Zalukhu ◽  
Mariana Zebua
Keyword(s):  
Image Processing ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Gabor Filter ◽  
Systemic Disease ◽  
Filter Method ◽  
Image Processing Techniques ◽  
Iris Image ◽  
Cholesterol Detection ◽  
Processing Techniques

Abstract     Cholesterol is a systemic disease. Many complications of its could affect other organs due to uncontrolled cholesterol / fat in the blood. One of them is coronary heart disease. One way to recognize someone having cholesterol is through the eyes. By using the Iridology method, cholesterol disease in a person's body can be detected or seen through the iris of the eye. Checking cholesterol-related conditions is usually done in a hospital or pharmacy. But the problem is that people are still lazy to check their cholesterol. Therefore, we need a software that can make it easier for people to do cholesterol checks. This device will detect cholesterol by using image processing techniques through the iris image accompanied by the Gabor Filter method. From 15 tested data, 13 iris image images were successfully identified, so that the percentage of success of this program was 86%.with 35 trained data.

Experiments on Iris Image Processing

2012 ◽  
pp. 85-95
Author(s):  
Christian Rathgeb ◽  
Andreas Uhl ◽  
Peter Wild
Keyword(s):  
Image Processing ◽  
Iris Image

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

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