• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4255-4269
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• 2019

This paper proposes an image generation method using a Multi Discriminator Generative Adversarial Net (MDGAN) as a next generation 2D game sprite creation technique. The proposed GAN is an Autoencoder-based model that receives three areas of information-color, shape, and animation, and combines them into new images. This model consists of two encoders that extract color and shape from each image, and a decoder that takes all the values of each encoder and generates an animated image. We also suggest an image processing technique during the learning process to remove the noise of the generated images. The resulting images show that 2D sprites in games can be generated by independently learning the three image attributes of shape, color, and animation. The proposed system can increase the productivity of massive 2D image modification work during the game development process. The experimental results demonstrate that our MDGAN can be used for 2D image sprite generation and modification work with little manual cost.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.133-141
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• 2005

In this paper, we designed about OFDM/QPSK image transmission system simulator embedded watermark in wireless channel environment. Channel environment used OFDM/QPSK still image transmission system considering AWGN, Multi-path fading. And, we analyzed effect of still image and watermark information in considered channel environment. Watermark information is coded by DM-SS (Direct Matric/Spread Spectrum) image watermarking scheme, Also, we used interleaving scheme for mitigating the degradation in watermarking data under wireless channel. As a results, we could know that PSNR of watermark image improved of about 1dB by 54.2371dB then apply interleaving in same multi-path fading environment.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.16-25
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• 2020

Vegetation segmentation in a field color image is a process of distinguishing vegetation objects of interests like crops and weeds from a background of soil and/or other residues. The performance of the process is crucial in automatic precision agriculture which includes weed control and crop status monitoring. To facilitate the segmentation, color indices have predominantly been used to transform the color image into its gray-scale image. A thresholding technique like the Otsu method is then applied to distinguish vegetation parts from the background. An obvious demerit of the thresholding based segmentation will be that classification of each pixel into vegetation or background is carried out solely by using the color feature of the pixel itself without taking into account color features of its neighboring pixels. This paper presents a new pixel-based segmentation method which employs a multi-layer perceptron neural network to classify the gray-scale image into vegetation and nonvegetation pixels. The input data of the neural network for each pixel are 2-dimensional gray-level values surrounding the pixel. To generate a gray-scale image from a raw RGB color image, a well-known color index called Excess Green minus Excess Red Index was used. Experimental results using 80 field images of 4 vegetation species demonstrate the superiority of the neural network to existing threshold-based segmentation methods in terms of accuracy, precision, recall, and harmonic mean.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.263-265
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• 2003

The computation cost of image registration is affected by searching data size and space. This paper proposes an efficient image registration algorithm that uses multi-resolution wavelet decomposed image to reduce the data size search. The algorithm determines the correlation detection at low resolution on low-pass sub bands of wavelet and generate mask for higher resolution as part of a coarse to fine registration algorithm. The correlation matching is defined for coarse resolution similarity measurement, while mutual information (MI) is used at fine resolution. The results show that the new efficient mask-based algorithm improves computational efficiency and yields robust and consistent image registration results.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.27-33
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• 2022

In this paper, we propose a novel generator architecture for multi-domain style transfer method not an image to image translation, as a method of generating a styled image by transfering a style to the content image. A latent vector and Gaussian noises are added to the generator of GAN so that a high quality image is generated while considering the characteristics of various data distributions for each domain and preserving the features of the content data. With the generator architecture of the proposed GAN, networks are configured and presented so that the content image can learn the styles for each domain well, and it is applied to the domain composed of images of the four seasons to show the high resolution style transfer results.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.73-82
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• 2005

This paper proposes a multi-level thresholding method using weighted FCM(Fuzzy C-Means) algorithm in color image. FCM algerian determines a more optimal thresholding value than the existing methods and can extend to multi-level thresholding. But FCM algerian is sensitive to noise because it doesn't include spatial information. To solve the problem, we can remove noise by applying a weight based on entropy that is obtained from neighboring pixels to FCM algerian. And we determine the optimal cluster number by using within-class distance in code image based on the clustered pixels of each color component. In the experiments, we show that the proposed method is more tolerant to noise and is more superior than the existing methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.980-997
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• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.1240-1243
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• 2004

Multi-Valued Image Entropy Coding (MVIEC) is a new class of joint source channel coding, which reduces both input-width (1/4) and average current (0.36-1.3) for LCD source drivers. This paper describes the detail results on MVIEC for several image sets in order to verify the practical performance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.246-253
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• 2016

Infrared (IR) imaging has been researched for various applications such as surveillance. IR radiation has the capability to detect thermal characteristics of objects under low-light conditions. However, automatic segmentation for finding the object of interest would be challenging since the IR detector often provides the low spatial and contrast resolution image without color and texture information. Another hindrance is that the image can be degraded by noise and clutters. This paper proposes multi-level segmentation for extracting regions of interest (ROIs) and objects of interest (OOIs) in the IR scene. Each level of the multi-level segmentation is composed of a k-means clustering algorithm, an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering initializes the parameters of the Gaussian mixture model (GMM), and the EM algorithm estimates those parameters iteratively. During the multi-level segmentation, the area extracted at one level becomes the input to the next level segmentation. Thus, the segmentation is consecutively performed narrowing the area to be processed. The foreground objects are individually extracted from the final ROI windows. In the experiments, the effectiveness of the proposed method is demonstrated using several IR images, in which human subjects are captured at a long distance. The average probability of error is shown to be lower than that obtained from other conventional methods such as Gonzalez, Otsu, k-means, and EM methods.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.3 s.116
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• pp.23-31
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• 2006

Multi-sensory design results from sensory design paradigm and image centricism, which stimulates 5 sensation; visual, auditory, haptic, smell and taste sensation when we experience environment. This is helpful for designing outdoor space considering integrated sensation not only visual and auditory sensation as well as for improving visitor's satisfaction. Therefore, this study mainly intended to analyse the multi-sensory preference and the image influence of outdoor leisure space. For these purpose, this study selected 3 leisure spaces around Seoul city; Gwan-ak mountain(the type oriented natural resource), Insa-dong(the type oriented cultural resource) and Seoul land as theme park(the type oriented facility) and a survey was performed with a total of 204 visitors at the above spaces in winter and summer. The results of this study indicate that visual sensation was evaluated most high at 3 outdoor leisure spaces in both winter and summer. Visitors at Gwan-ak mountain and Seoul land prefer visual and haptic sensation, but visitors at Insa-dong prefer visual and haptic sensation in summer, visual and taste sensation in winter. Above all, this study led the designer to consider diverse sensation to access leisure space, especially haptic sense, which can be an effective design strategy to satisfy visitors.