• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.451-459
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• 2016

While an object detection algorithm plays a key role in many computer vision applications, it requires extensive computation to show robustness under varying lightning and geometrical distortions. Recently, some approaches formulate the problem in a classification framework and show improved performances in object recognition. Among them, random fern algorithm drew a lot of attention because of its simple structure and high recognition rates. However, it reveals performance degradation under the illumination changes and noise addition, since it computes patch features based only on pixel intensities. In this paper, we propose a new structure of combined random fern which incorporates depth information into the conventional random fern reflecting 3D structure of the patch. In addition, a new structure of object tracker which exploits the combined random fern is also introduced. Experiments show that the proposed method provides superior performance of object detection under illumination change and noisy condition compared to the conventional methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.97-99
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• 2022

Recently, the amount of data is increasing with the development of industries and technologies. Research on the processing and transmission of large amounts of data is attracting attention. Therefore, in this paper, compressed sensing was used to reduce the amount of data and its effect on Mask R-CNN algorithm was analyzed. We confirmed that as the compressed sensing rate increases, the amount of data in the image and the resolution decreases. However, it was confirmed that there was no significant degradation in the performance of object detection.

• Cross-Cultural Studies
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• v.19
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• pp.101-126
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• 2010

Narcissism is a pathological phenomenon and narcissistic subject always needs to put itself on the top and has interest in nothing but its own determination. The protagonist of Adversary by ?mmanuel Carr?re does pay careful attention to what others are thinking of him while he does not distinguish difference between object of love and himself. So he can be allegedly narcissistic subject. And it can be said that the behaviors of Mary in the movie Under the sand by Ozon are included in narcissism in that narcissistic subject has the characteristics that idealizes the object of libido. However, in this study, the heroine is examined based on such a point that the object of love and extreme idealization incline toward others. We call this case reverse-narcissism. In Part 2, it is investigated what relation narcissism as an unconscious psychological tool has with Oedipus complex, which plays an important role in forming human psyche. For instance, disappointment caused by prohibition at the oedipal stage is too severe, which creates superego and its idealization to protect in such a way that narcissistic regression can not be done. Cases of extremely big gap between ego and ideal type are perversion, impostor, mania, paranoia, etc, where narcissistic and oedipal elements are combined to affect.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.419-428
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• 2021

Shadow removal plays a key role for the pre-processing of image processing techniques such as object tracking and detection. With the advances of image recognition based on deep convolution neural networks, researches for shadow removal have been actively conducted. In this paper, we propose a novel method for shadow removal, which utilizes self attention distillation to extract semantic features. The proposed method gradually refines results of shadow detection, which are extracted from each layer of the proposed network, via top-down distillation. Specifically, the training procedure can be efficiently performed by learning the contextual information for shadow removal without shadow masks. Experimental results on various datasets show the effectiveness of the proposed method for shadow removal under real world environments.

• Electronics and Telecommunications Trends
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• v.34 no.6
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• pp.133-144
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• 2019

Although deep learning-based visual image recognition technology has evolved rapidly, most of the commonly used methods focus solely on recognition accuracy. However, the demand for low latency and low power consuming image recognition with an acceptable accuracy is rising for practical applications in edge devices. For example, most Internet of Things (IoT) devices have a low computing power requiring more pragmatic use of these technologies; in addition, drones or smartphones have limited battery capacity again requiring practical applications that take this into consideration. Furthermore, some people do not prefer that central servers process their private images, as is required by high performance serverbased recognition technologies. To address these demands, the object and scene recognition technologies for mobile/embedded devices that enable optimized neural networks to operate in mobile and embedded environments are gaining attention. In this report, we briefly summarize the recent trends and issues of object and scene recognition technologies for mobile and embedded devices.

• Spatial Information Research
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• v.15 no.1
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• pp.35-52
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• 2007

Three-dimensional GIS, which provides spatial information through expression techniques of virtual reality close to the real world and the web, is one of the fields that attract a new attention. In particular, Open GIS Consortium(OGC) announced a topological relationship specification of spatial object which supports interoperability while interest in interoperability of spatial data is increasing. However, this specification is limited to two-dimensional spatial object. So this research established a topological relationship of three-dimensional spatial object in order to improve urban landscape and provide a foundation to use GIS. Based on this, this study proposes ways to visualize landscape information which is appropriate for new town's circumstances. It can be concluded that this research has a bigger meaning since it established a base of sharing information about realistic urban landscape that can be accessed regardless of place and time.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.128-132
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• 2023

Recently, with the development of deep learning technology, research on pedestrian attribute recognition technology using deep neural networks has been actively conducted. Existing pedestrian attribute recognition techniques can be obtained in such a way as global-based, regional-area-based, visual attention-based, sequential prediction-based, and newly designed loss function-based, depending on how pedestrian attributes are detected. It is known that the performance of these pedestrian attribute recognition technologies varies greatly depending on the type of backbone network that constitutes the deep neural networks model. Therefore, in this paper, several backbone networks are applied to the baseline pedestrian attribute recognition model and the performance changes of the model are analyzed. In this paper, the analysis is conducted using Resnet34, Resnet50, Resnet101, Swin-tiny, and Swinv2-tiny, which are representative backbone networks used in the fields of image classification, object detection, etc. Furthermore, this paper analyzes the change in time complexity when inferencing each backbone network using a CPU and a GPU.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.

• The Journal of Information Systems
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• v.18 no.3
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• pp.71-88
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• 2009

Recently, RFID(Radio Frequency IDentification) has emerged as the main technology in the logistic services. When the existing recognition technology based on bar codes brings about lots of problem due its own limits. RFID becomes the center of attention to solve them. However, RFID is not without any obstacles : companies have their own operating systems. while RFID is developed regardless of each campany's special features. RFID middleware system based on web service is expected to remove these obstacles. This paper shows how to operate the middleware based on web service and to lay in the DB the tag informations taken from reader system Middle assures that companies adopting RFID system for their logistic service are given athptabwebty to any systems whatsoever, avaweable by way of defining logistic information, tag information and reader information. For this purse, we implement as the basic web service a middleware system that turns all data into XML(eXtensatle Markupmsngunfo) of SOAP(Simple Object Access Protocol), the standard data.

• Journal of Digital Convergence
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• v.5 no.2
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• pp.1-13
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• 2007

Recently, RFID has emerged as the main technology in the logistic services. When the existing recognition technology based on bar codes brings about lots of problem due to its own limits. RFID becomes the center of attention to solve them. However, RFID is not without any obstacles: companies have their own operating systems, while RFID is developed regardless of each company's special features. RFID middleware system based on web service is expected to remove these obstacles. This paper shows how to operate the middleware based on web service and to lay in the DB the tag informations taken from reader system. Middle assures that companies adopting RFID system for their logistic service are given adaptability to any systems whatsoever, available by way of defining logistic information, tag information and reader information. For this purpose, we implement as the basic web service a middleware system that turns all data into XML(eXtensible Markup Language) of SOAP (Simple Object Access Protocol), the standard data.