• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.331-338
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• 2001

In this paper, we have proposed a new computer retina model reflecting the mechanism of transient amacrine cell on the basis of a conventional computer retina model to understand mechanism of visual information processing. The conventional computer retina model contained most of mechanism for other retina models and it was verified with the physiological data. However, we found that a conventional computer retina model doesn't have the mechanism of amacrine cell that was likely to respond to moving stimulus. In proposed model, therefore, a conventional computer model that considered from photoreceptors to bipolar cells and a new computer model that considered for transient amacrine cell and ganglion cell was combined. As we compared the physiological data with the results of computer simulation of transient amacrine cell about fixed stimulus and moving stimulus, we confirmed that the proposed new computer retina model was normally operated.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.136-146
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• 2022

Some surface defects make the weak quality of steel materials. To limit these defects, we advocate a one-stage detector model RetinaNet among diverse detection algorithms in deep learning. There are several backbones in the RetinaNet model. We acknowledged two backbones, which are ResNet50 and VGG19. To validate our model, we compared and analyzed several traditional models, one-stage models like YOLO and SSD models and two-stage models like Faster-RCNN, EDDN, and Xception models, with simulations based on steel individual classes. We also performed the correlation of the time factor between one-stage and two-stage models. Comparative analysis shows that the proposed model achieves excellent results on the dataset of the Northeastern University surface defect detection dataset. We would like to work on different backbones to check the efficiency of the model for real world, increasing the datasets through augmentation and focus on improving our limitation.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.637-640
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• 2000

ERG signal represents the responses of the each layer of retina for the visual stimulus and accumulated responses according to the signal processing occurring in the retina. By investigating the reaction types of each wave of the ERG, various kinds of information for the diagnosis and the signal processing mechanisms in the retina can be obtained. In this paper, the ERG signal is generated by simulating of the volume conductor field of response of each retina layer and summing of them algebraically. The retina model used for simulation is Shah’s Computer Retina model which is one of the most reliable models recently developed. The generated ERG is compared with the typical ERG and shows a very close similarity. By changing the parameters of the retina model, the diagnostic investigation is performed with the variation of the ERG waveform.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.49-58
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• 2002

Based on electrophysiological retina mechanism, a retina model is proposed, which has similar response characteristics compared with the real primate retina. Photoreceptors, horizontal cells, and bipolar cells are modeled based on the previously studied retina models. And amacrine cells known to have relation to movements detection, and bipolar cell terminals are newly modeled using 3 NDP mechanism. The proposed model verified by analyzing the spatial response characteristics to stationary and moving stimuli, and characteristics for different speeds. Through this retina model, human vision system could be applied to computer vision systems for movement detection, and it could be the basic research for the implantable artificial retina.

• Journal of Biomedical Engineering Research
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• v.9 no.1
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• pp.17-24
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• 1988

This paper describes a neural network modelling of a vertebrate retina using a discrete-time and discrete-space approach based on neuro-anatomical data, and the computer simulations of the model which approximate the frog/amphibian negro-physiological data. It then compares them and describes how such a model can be beneficially used for confirming the hypothesis of a given neural system and further predict yet unknown experimental data.

• Journal of information and communication convergence engineering
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• v.2 no.2
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• pp.132-137
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• 2004

Most of the current computer vision theories are based on hypotheses that are difficult to apply to the real world, and they simply imitate a coarse form of the human visual system. As a result, they have not been showing satisfying results. In the human visual system, there is a mechanism that processes information due to memory degradation with time and limited storage space. Starting from research on the human visual system, this study analyzes a mechanism that processes input information when information is transferred from the retina to ganglion cells. In this study, a model for the characteristics of ganglion cells in the retina is proposed after considering the structure of the retina and the efficiency of storage space. The MNIST database of handwritten letters is used as data for this research, and ART2 and SOM as recognizers. The results of this study show that the proposed recognition model is not much different from the general recognition model in terms of recognition rate, but the efficiency of storage space can be improved by constructing a mechanism that processes input information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.519-525
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• 2022

The face detection task of detecting a person's face in an image is used as a preprocess or core process in various image processing-based applications. The neural network models, which have recently been performing well with the development of deep learning, are dependent on 2D images, so if noise occurs in the image, such as poor camera quality or pool focus of the face, the face may not be detected properly. In this paper, we propose a face detection method that uses depth information together to reduce the dependence of 2D images. The proposed model was trained after generating and preprocessing depth information in advance using face detection dataset, and as a result, it was confirmed that the FRN model was 89.16%, which was about 1.2% better than the RetinaNet model, which showed 87.95%.

• The Transactions of the Korean Institute of Power Electronics
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• v.8 no.1
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• pp.17-23
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• 2003

To show a retina shape and thickness on the computer monitor, a laser has been used in Scanning Laser Ophthalmoscope(SLO) equipment using the travelling difference. This method requires exact synchronous control of laser travelling in optic system to show a clear 3-dimensional image of retina. To obtain this image, this exact synchronism is very important for making the perfect plane scanning. In this study, a synchronous control of the galvanometer to make 3-dimensional retina image is presented. For the more, a very simple mathematical model of the galvanometer is approved by experimental result.

• Journal of Multimedia Information System
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• v.8 no.2
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.496-496
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• 2000

We propose a neuron computer for tracking motion of particles in multi-dimensional space. The neuron computer is constructed of neural networks and their connections, which is a simplified model of the brain. The neuron computer is assemblage of neural networks, it includes a control unit, and the actions of the unit are represented by instructions. We designed a neuron computer to recognize and predict motion of particles. The recognition unit is constructed of neuron-array, encoder, and control part. The neuron-array is a model of the retina, and particles crease an image on the array, where the image is binary. The encoder picks one particle from the array, and translates the particle's location to Cartesian coordinates, which is scaled in [0, 1] intervals. Next, the encoder picks another particle, and does same process. The ordering and reduction of complex processes are executed by instructions. The instructions are held in the control part. The prediction unit is constructed of a multi-layer neural network and a feedback loop, where real time learning is executed. The particles' future locations are forecasted by coordinate values. The neuron computer can chase maximum 100 particles that take evasions.