• Journal of Korean Society of Rural Planning
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• v.14 no.4
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• pp.11-19
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• 2008

The layer technique is to produce many memorable scenes by generating layers of new experiences on the existing ones as it is adding the cognitive layers on to the visually seen landscape. Its need is high for places whose landscape itself influences perception, value or expression and which determines the spatial and quality standards. The existing floor plan-based design methods have failed to be useful in generating complex visual experiences. In order to maximize the aesthetical landscape experiences in landscape planning, cognitive layers are needed which complement the input of adequate cognitive elements and the inter-element relationships. Here, layers are utilized to change the arrangement of the landscape elements and coordinate the cognitive flow so that the images could be connected and imagination could occur. A case in point is Anhyun Village in Gochang where physically distinctive layers are additionally set to make a visual experience enriching. The new landscape layers discover the fact that it provides diversity in experiencing the village landscape and forming the sense of beauty and that it is deeply immersed into the daily life of the village. Meanwhile, Guwau Village in Taebaek is an example showing the usefulness of various-layer setting in landscape planning in setting effective circulation planning. That is, the bottom line is the spacing-starting where and making it stay where for a few seconds, and the visual layers. It is also critical to encourage inducing circulation so that layers of the senses stimulating five senses could intervene. Lastly, Mulgeon-ri in Namhae is a case which directly made a parallel of the physical layers of the landscape composition and the cognitive layers of the landscape experience. Artificial landscape planning is mostly about manipulating of visual traits that people feel beautiful, but the layer technique is linked to how to make experiences enriching and renewed.

• Journal of Information Display
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• v.13 no.3
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• pp.101-106
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• 2012

This study was conducted to enhance the cubic effect by representing an image with a sense of three-dimensional (3D) depth, using chromostereopsis, among the characteristics of human visual perception. An algorithm that enhances the cubic effect, based on the theory that the cubic effect of the chromostereoptic effect and the chromostereoptic reversal effect depends on the lightness of the background, classifies the layers of the 3D image input into the foreground, middle, and background layers according to the depth of the image input. It suits the characteristics of human visual perception because it controls the color factor that was adaptively detected through experiments on each layer; and it can achieve an enhanced cubic effect that is suitable for the characteristics of the image input.

• Journal of Korean Ophthalmic Optics Society
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• v.11 no.2
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• pp.115-119
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• 2006

Electromagnetic shielding, transparent ITO coating layers have deposited on ocular lens substrate by magnetron sputtering. We investigated the effect induced by the substrate temperature on coating layer. The characteristics of the coating layers were analyzed using surface profiler, four-point probe, XRD, spectrophotometer and Auger Electron spectroscopy. As substrate temperature became higher, carrier concentration was increased and transmittance in the visible region was increased, too.

• Journal of Photoscience
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• v.2 no.2
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• pp.63-67
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• 1995

Zinc plays a key role in genetic expression, cell division, and growth and is essential for the function of more than 200 enzymes; effects of zinc deficiency induce many syndromes, including abnormal visual adaptation. The pigment epithelium (EP) contains high concentrations of zinc in humans and in animals and it participates in threshold elevation, visual sensitivity increment, and acceleration of rhodopsin regeration during visual adaptation. The origin of c-wave of electroretinogram(ERG) is not only pigment epithelium as shown in present research, but also other cell layers, perhaps the photoreceptors. We propose zinc as a candidate for an internal messenger which participates in signal amplification.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.4
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• pp.145-151
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• 2021

In this paper, we propose a novel visualization technique to explain the predictions of deep neural networks. We use knowledge distillation (KD) to identify the interior of a black-box model for which we know only inputs and outputs. The information of the black box model will be transferred to a white box model that we aim to create through the KD. The white box model will learn the representation of the black-box model. Second, the white-box model generates attention maps for each of its layers using Grad-CAM. Then we combine the attention maps of different layers using the pixel-wise summation to generate a final saliency map that contains information from all layers of the model. The experiments show that the proposed technique found important layers and explained which part of the input is important. Saliency maps generated by the proposed technique performed better than those of Grad-CAM in deletion game.

• Molecules and Cells
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• v.19 no.3
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• pp.408-417
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• 2005

Nitric oxide (NO) occurs in various types of cells in the central nervous system. We studied the distribution and morphology of neuronal nitric oxide synthase (NOS)-containing neurons in the visual cortex of mouse and rabbit with antibody immunocytochemistry. We also compared this labeling to that of calbindin D28K, calretinin, and parvalbumin. Staining for NOS was seen both in the specific layers and in selective cell types. The densest concentration of intense anti-NOS immunoreactive (IR) neurons was found in layer VI, while the weak anti-NOS-IR neurons were found in layer II/III in both animals. The NOS-IR neurons varied in morphology. The large majority of NOS-IR neurons were round or oval cells with many dendrites coursing in all directions. Two-color immunofluorescence revealed that only 16.7% of the NOS-IR cells were double-labeled with calbindin D28K in the mouse visual cortex, while more than half (51.7%) of the NOS-IR cells were double-labeled with calretinin and 25.0% of the NOS-IR cells were double-labeled with parvalbumin in mouse. By contrast, 92.4% of the NOS-IR neurons expressed calbindin D28K while only 2.5% of the NOS-IR neurons expressed calretinin in the rabbit visual cortex. In contrast with the mouse, none of the NOS-IR cells in the rabbit visual cortex were double-labeled with parvalbumin. The results indicate that neurons in the visual cortex of both animals express NOS in specific layers and cell types, which do not correlate with the expression of calbindin D28K, calretinin or parvalbumin between the two animals.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2253-2272
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• 2018

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.111-120
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• 2020

Generative adversarial networks (GANs) achieved impressive performance on image generation and visual classification applications. However, adversarial networks meet difficulties in combining the generative model and unstable training process. To overcome the problem, we combined the deep residual network with upsampling convolutional layers to construct the generative network. Moreover, the study shows that image generation and classification performance become more prominent when the residual layers include on the generator. The proposed network empirically shows that the ability to generate images with higher visual accuracy provided certain amounts of additional complexity using proper regularization techniques. Experimental evaluation shows that the proposed method is superior to image generation and classification tasks.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.6
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• pp.71-82
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• 2002

The purpose of this study is to develop a 3 dimensional spatial planning model (3DSPLAM) for facilities allocation and visual evaluation in improvement planning of rural village. For the model development, this study developed both planning layers and a modelling process for spatial planning of rural villages. The 3DSPLAM generates road networks and village facilities location automatically from built area plan map and digital elevation model generated by geographic information system. The model also simulates 3-dimensional villagescape for visual presentation of the planned results. The 3DSPLAM could be conveniently used for automatic allocation of roads, easy partition of land lots and reasonable locating of facilities. The planned results could be also presented in the stereoscopic models with varied viewing positions and angles.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.29-32
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• 2020

Due to the lack of improper image acquisition process, noise induction is an inevitable step. As a result, objective image quality assessment (IQA) plays an important role in estimating the visual quality of noisy image. Plenty of IQA methods have been proposed including traditional signal processing based methods as well as current deep learning based methods where the later one shows promising performance due to their complex representation ability. The deep learning based methods consists of several convolution layers and down sampling layers for feature extraction and fully connected layers for regression. Usually, the down sampling is performed by using max-pooling layer after each convolutional block. We reveal that this max-pooling causes information loss despite of knowing their importance. Consequently, we propose a better IQA method that replaces the max-pooling layers with strided convolutions to down sample the feature space and since the strided convolution layers have learnable parameters, they preserve optimal features and discard redundant information, thereby improve the prediction accuracy. The experimental results verify the effectiveness of the proposed method.