Image Enhancement Technique at Different Distance for Iris Recognition

Hassan, Rohayanti; Kasim, Shahreen; Jafery, Wan Ain Zubaidah Wan Chek; Shah, Zuraini Ali

doi:10.18517/ijaseit.7.4-2.3392

Cited by 8 publications

(9 citation statements)

References 21 publications

(23 reference statements)

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“…The CLAHE method involves applying equalization based on dividing the image into several regions of almost equal sizes 19 . Applying the CLAHE method has been found to improve the image quality compared to other histogram equalization methods, and it has been widely used in deep learning model studies based on medical images 20 – 23 . The experiments using the original images and CLAHE images were implemented in Windows 10 with the TensorFlow 1.16.0 framework on an NVIDIA GPU (TITAN RTX).…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

Jeon

Kim

et al. 2021

Sci Rep

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This study aimed to develop an artificial intelligence model that can detect mesiodens on panoramic radiographs of various dentition groups. Panoramic radiographs of 612 patients were used for training. A convolutional neural network (CNN) model based on YOLOv3 for detecting mesiodens was developed. The model performance according to three dentition groups (primary, mixed, and permanent dentition) was evaluated, both internally (130 images) and externally (118 images), using a multi-center dataset. To investigate the effect of image preprocessing, contrast-limited histogram equalization (CLAHE) was applied to the original images. The accuracy of the internal test dataset was 96.2% and that of the external test dataset was 89.8% in the original images. For the primary, mixed, and permanent dentition, the accuracy of the internal test dataset was 96.7%, 97.5%, and 93.3%, respectively, and the accuracy of the external test dataset was 86.7%, 95.3%, and 86.7%, respectively. The CLAHE images yielded less accurate results than the original images in both test datasets. The proposed model showed good performance in the internal and external test datasets and had the potential for clinical use to detect mesiodens on panoramic radiographs of all dentition types. The CLAHE preprocessing had a negligible effect on model performance.

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Section: Methodsmentioning

confidence: 99%

Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

Jeon

Kim

et al. 2021

Sci Rep

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“…Overall, the CLAHE + GLCM + ELM method recorded the lowest classification accuracy performance in both datasets tested. This is due to the drawbacks of the CLAHE approach that sometimes may produce unwanted gray level artifact and creates an equal density in all the histogram bins during the image enhancement process [62]. In contrast, the obvious accuracy performance difference can be observed from the SPM method between NDDA and NDDAW dataset.…”

Section: Classmentioning

confidence: 95%

“…Though there are many image enhancement techniques, in apple classification, it is challenging to enhance the low-quality region while at the same time reduce the uneven illumination effect on images with less computational time and cost [61]. Some of the image enhancement technique such as Adaptive Histogram Equalization (AHE) and CLAHE are unsuitable to be used for real-time application due to high computational time [62,63]. It is also difficult to enhance the low-quality region using traditional image enhancement technique such as frequency-domain.…”

Section: Related Workmentioning

confidence: 99%

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

et al. 2019

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Smart manufacturing optimizes productivity with the integration of computer control and various high level adaptability technologies including the big data evolution. The evolution of big data offers optimization through data analytics as a predictive solution in future planning decision making. However, this requires accurate and reliable informative data as input for analytics. Therefore, in this paper, the fusion features for apple classification is investigated to classify between defective and non-defective apple for automatic inspection, sorting and further predictive analytics. The fusion features with Decision Tree classifier called Curvelet Wavelet-Gray Level Co-occurrence Matrix (CW-GLCM) is designed based on symmetrical pattern. The CW-GLCM is tested on two apple datasets namely NDDA and NDDAW with a total of 1110 apple images. Each dataset consists of a binary class of apple which are defective and non-defective. The NDDAW consists more low-quality region images. Experimental results show that CW-GLCM successfully classify 98.15% of NDDA dataset and 89.11% of NDDAW dataset. A lower classification accuracy is observed in other five existing image recognition methods especially on NDDAW dataset. Finally, the results show that CW-GLCM is more accurate among all the methods with the difference of more than 10.54% of classification accuracy.

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“…histogram equalization or adaptive histogram equalization for iris recognition in Hassan et al study [14]. OpenCV CLAHE function has two main parameters, the clipLimit, that represents the threshold from which the histogram is clipped and redistributed, and the tileGridSize, related to the tile size that the input will be sliced for the algorithm application.…”

Section: Data Processingmentioning

confidence: 99%

Towards the Development and Validation of a Smartphone-Based Pupillometer for Neuro-Ophthalmological Diseases Screening

Sousa

Marques-Neves

Pinto

et al. 2021

Biomedical Engineering Systems and Technologies

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Pupillometry allows a quantitative measurement of PLR and has been mainly used to assess patient's consciousness and vision function. The analysis of pupil light reflex (PLR) has been showing a renewed interest since the discovery of intrinsically photosensitive retinal ganglion cells (ipRGCs), that are sensitive to the blue light, as they have an important role in pupil response to a stimulus. Some researches have studied pupillometry, particularly chromatic pupillometry that uses blue and red stimuli, to be a screening tool for neuro-ophthalmological diseases. Automated pupillometers have been widely used, however they are either not portable or expensive, reason why this technique has been mainly used in academic research. A smartphone-based pupillometer could be a promising equipment to overcome these limitations and to be a widespread screening tool, due to its low price, portability and accessibility. This work shows our latest advances towards the development and validation of an Android system for pupillometry measurements. Pupillometric data was collected with the smartphone application in a group of five healthy individuals and used to test our proposed data processing algorithms. These tests showed that the data processing methods that we are proposing, although promising, did not behave as expected, indicating that new approaches, better validations and corrections should be made in the future to get a stable software for pupil detection. Nevertheless, preliminary pupillometric data indicate that this system has the potential to work as an inexpensive, easy-to-use and portable pupillometer.

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Image Enhancement Technique at Different Distance for Iris Recognition

Cited by 8 publications

References 21 publications

Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

Automatic detection of mesiodens on panoramic radiographs using artificial intelligence

Investigation of Fusion Features for Apple Classification in Smart Manufacturing

Towards the Development and Validation of a Smartphone-Based Pupillometer for Neuro-Ophthalmological Diseases Screening

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