• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1464-1485
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• 2021

Emotional style of multimedia art works are abstract content information. This study aims to explore emotional style transfer method and find the possible way of matching music with appropriate images in respect to emotional style. DCNNs (Deep Convolutional Neural Networks) can capture style and provide emotional style transfer iterative solution for affective image generation. Here, we learn the image emotion features via DCNNs and map the affective style on the other images. We set image emotion feature as the style target in this style transfer problem, and held experiments to handle affective image generation of eight emotion categories, including dignified, dreaming, sad, vigorous, soothing, exciting, joyous, and graceful. A user study was conducted to test the synesthesia emotional image style transfer result with ground truth user perception triggered by the music-image pairs' stimuli. The transferred affective image result for music-image emotional synesthesia perception was proved effective according to user study result.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.11
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• pp.1076-1079
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• 2009

This article deals with an optical image stabilizer which moves an image sensor in the direction of cancelling the vibration caused by hand shaking to prevent a photographed image from blurring. The ball-guide way method adopted as a transfer device of the image sensor is easy to be manufactured because of its simple structure and is suitable to minimize the friction between mechanisms, but has weakness of a chance of physical defect such as groove and rising. In case that the movement of the transfer device equipped with the image sensor is blunted because a ball is stuck in defects of guide way, the performance of the image stabilizer falls down drastically. We propose a method to prevent the transfer device from blunting by applying artificial vibration. At this time, the artificial vibration should be designed under consideration of dynamic characteristics and specifications of the system to be discriminated from the vibration caused by hand shaking.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.31-38
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• 2023

Style transfer is one of deep learning-based image processing techniques that has been actively researched recently. These research efforts have led to significant improvements in the quality of result images. Style transfer is a technology that takes a content image and a style image as inputs and generates a transformed result image by applying the characteristics of the style image to the content image. It is becoming increasingly important in exploiting the diversity of digital content. To improve the usability of style transfer technology, ensuring stable performance is crucial. Recently, in the field of natural language processing, the concept of Transformers has been actively utilized. Attention maps, which forms the basis of Transformers, is also being actively applied and researched in the development of style transfer techniques. In this paper, we analyze the representative techniques SANet and AdaAttN and propose a novel attention map-based structure which can generate improved style transfer results. The results demonstrate that the proposed technique effectively preserves the structure of the content image while applying the characteristics of the style image.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.127-134
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• 2023

Existing studies on this topic have focused on the effect of online content quality on consumer attitudes and behavior, with very few illustrating the effect of influencer image on consumer attitudes and behavior. The purpose of this study intents to reveal how influencer image affect consumer behavior. We have developed an image transfer theory-based research model to reveal how influencers transfer their image to endorsed products to influence consumer behavior. The results show that influencer image positively affects satisfaction, which in turn affects the product's cognitive and affective images in the vlog. Moreover, it was found that a product's cognitive image and affective image influence consumer behavior intention. Furthermore, purchase experience exhibits significant differences in its path. Based on these results, the social media-related research theoretical implication will be offered, and managerial implications will be provided for foreign brand promotion strategies

• Design & Manufacturing
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• v.15 no.3
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• pp.1-6
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• 2021

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

• 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.

• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.25-28
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• 2001

In this paper we implement moving picture transfer system using bluetooth Development Kit (DK). To reduce the size of the image data, we use M-JPEG compression. We use bluetooth Synchronous Connection-Oriented (SCO) link to transfer voice data. Server receive image data from camera and compress the image data in M-JPEG format, and then transmit the image data to client using bluetooth Asynchronous connection-less (ACL) link. Client receive image data from bluetooth ACL link and decode the compressed image and then display the image to screen. Sever and Client can transmit and receive voice data simultaneously using bluetooth SCO link. In this paper bluetooth HCI commands and events generated by host controller to return the results of HCI commands are explained and the flow of bluetooth connection procedure is presented.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.674-677
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• 2020

The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.

• ETRI Journal
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• v.39 no.1
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• pp.76-86
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• 2017

In this study, we propose an automatic contrast enhancement method based on transfer function modification (TFM) by histogram equalization. Previous histogram-based global contrast enhancement techniques employ histogram modification, whereas we propose a direct TFM technique that considers the mean brightness of an image during contrast enhancement. The mean point shifting method using a transfer function is proposed to preserve the mean brightness of an image. In addition, the linearization of transfer function technique, which has a histogram flattening effect, is designed to reduce visual artifacts. An attenuation factor is automatically determined using the maximum value of the probability density function in an image to control its rate of contrast. A new quantitative measurement method called sparsity of a histogram is proposed to obtain a better objective comparison relative to previous global contrast enhancement methods. According to our experimental results, we demonstrated the performance of our proposed method based on generalized measures and the newly proposed measurement.

• Journal of KIISE:Information Networking
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• v.35 no.3
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• pp.207-214
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• 2008

There are lots of studies on application systems using wireless sensor networks. As the application systems are adapted to industrial field, the reliability of these systems becomes new key feature. The lack of reliability is an obstacle to extension of wireless sensor networks. In this paper, we propose the monitoring system framework that can offer the reliability of wireless sensor networks using a micro camera module and wireless sensor network nodes. And also we propose the efficient transfer method for image information over low rate wireless networks. Using these system framework and transfer method, we implement WiSN(Wireless image Sensor Network) based fire monitoring system.