• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.429-431
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• 2019

Noise in image processing has a direct effect on the quality of the image, and adversely affects the processing of the system including algorithms such as image segmentation, edge detection, and image recognition. Therefore, noise reduction plays an important role in the preprocessing process. In this paper, we propose an efficient algorithm to remove noise in high density of Salt and Pepper noise. The proposed algorithm removes noise by gradually expanding the filtering mask according to the density of the noise, and shows excellent noise cancellation performance even in a high density region. In order to evaluate the performance of the proposed algorithm, we compared and analyzed the existing method and the proposed algorithm through simulation.

• Journal of the Korean Institute of Landscape Architecture
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• 제50권6호
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• pp.15-29
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• 2022

The Byungsan Neo-Confucian Academy, a 17th-century World Heritage Site in Korea, is being praised as a manifestation of naturalness or non-artificiality of the traditional Korean borrowed scenery technique (借景, chagyeong). This study, however, aims to reinterpret the chagyeong of the Byungsan Academy (hereafter the Academy) as a device of illusion evoking an idealized vision of nature. In the process of interpretation, 'picture and frame'-a widely accepted expression that represents the chagyeong of the Academy-will be foregrounded as the pivotal concept mediating the change of perspectives from naturalistic to ideological. This study consists of the following three parts. First, it shows that 'picture and frame' represent a modern way of seeing the Academy as an architectural heritage in harmony with nature; it denotes pristine nature and the empty architectural frame that safely circumscribes the innate beauty of the natural landscape. Second, departing from the naturalistic perspective, this study argues that the architectural framework of the Academy composes scenography enticing the viewer to imagine the idealized, Confucian image of nature that compares to the landscape imagery found in the landscape poetry and paintings that were produced and appreciated by the 17th-century Confucian literati. Lastly, based on the above interpretation, this study stresses that the 'picture' one encountered at the Academy in the 17th century was not the framed scene of a natural landscape but the illusion it caused; the architectural 'frame' worked not as a symbol of naturalness but as an institutional apparatus of vision manipulating the way one sees-and therefore imagines-the landscape.

• Journal of IKEEE
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• 제23권4호
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• pp.1423-1429
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• 2019

Recently, a system that supports efficient interaction, a human machine interface (HMI), has become a hot topic. In this paper, we propose a new real time low-cost hand gesture control system as one of vehicle interaction methods. In order to reduce computation time, depth information was acquired using a time-of-flight (TOF) camera because it requires a large amount of computation when detecting hand regions using an RGB camera. In addition, fourier descriptor were used to reduce the learning model. Since the Fourier descriptor uses only a small number of points in the whole image, it is possible to miniaturize the learning model. In order to evaluate the performance of the proposed technique, we compared the speeds of desktop and raspberry pi2. Experimental results show that performance difference between small embedded and desktop is not significant. In the gesture recognition experiment, the recognition rate of 95.16% is confirmed.

• Journal of KIISE:Software and Applications
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• 제34권1호
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• pp.52-58
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• 2007

In this paper we propose a new robust method to extract accurate human silhouettes indoors with active stereo camera. A prime application is for gesture recognition of mobile robots. The segmentation of distant moving objects includes many problems such as low resolution, shadows, poor stereo matching information and instabilities of the object and background color distributions. There are many object segmentation methods based on color or stereo information but they alone are prone to failure. Here efficient color, stereo and image segmentation methods are fused to infer object and background areas of high confidence. Then the inferred areas are incorporated in graph cut to make human silhouette extraction robust and accurate. Some experimental results are presented with image sequences taken using pan-tilt stereo camera. Our proposed algorithms are evaluated with respect to ground truth data and proved to outperform some methods based on either color/stereo or color/contrast alone.

• Convergence Security Journal
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• 제22권2호
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• pp.21-26
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• 2022

Wearing face masks is an effective measure to prevent COVID-19 infection. Infrared thermal image based temperature measurement and identity recognition system has been widely used in many large enterprises and universities in China, so it is totally necessary to research the face mask detection of thermal infrared imaging. Recently introduced MTCNN (Multi-task Cascaded Convolutional Networks)presents a conceptually simple, flexible, general framework for instance segmentation of objects. In this paper, we propose an algorithm for efficiently searching objects of images, while creating a segmentation of heat generation part for an instance which is a heating element in a heat sensed image acquired from a thermal infrared camera. This method called a mask MTCNN is an algorithm that extends MTCNN by adding a branch for predicting an object mask in parallel with an existing branch for recognition of a bounding box. It is easy to generalize the R-CNN to other tasks. In this paper, we proposed an infrared image detection algorithm based on R-CNN and detect heating elements which can not be distinguished by RGB images.

• Journal of Intelligence and Information Systems
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• 제29권1호
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• pp.143-174
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• 2023

With the development of deep learning technologies, Artificial Intelligence powered Optical Character Recognition (AI-OCR) has evolved to read multiple languages from various forms of images accurately. For the financial industry, where a large number of diverse documents are processed through manpower, the potential for using AI-OCR is great. In this study, we present a configuration and a design of an AI-OCR modality for use in the financial industry and discuss the platform construction with application cases. Since the use of financial domain data is prohibited under the Personal Information Protection Act, we developed a deep learning-based data generation approach and used it to train the AI-OCR models. The AI-OCR models are trained for image preprocessing, text recognition, and language processing and are configured as a microservice architected platform to process a broad variety of documents. We have demonstrated the AI-OCR platform by applying it to financial domain tasks of document sorting, document verification, and typing assistance The demonstrations confirm the increasing work efficiency and conveniences.

• The Journal of the Korea Contents Association
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• 제22권3호
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• pp.71-80
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• 2022

In the past, fish diseases were bacterial in aqua farms, but in recent years, the frequency of fish diseases has increased as they have become viral and mixed. Viral diseases in an enclosed space called a aqua farm have a high spread rate, so it is very likely to lead to mass death. Fast identification of fish diseases is important to prevent group death. However, diagnosis of fish diseases requires a high level of expertise and it is difficult to visually check the condition of fish every time. In order to prevent the spread of the disease, an automatic identification system of diseases or fish is needed. In this paper, in order to improve the performance of the disease identification system of Paralichthys olivaceus based on deep learning, the existing pre-processing method is compared and tested. Target diseases were selected from three most frequent diseases such as Scutica, Vibrio, and Lymphocystis in Paralichthys olivaceus. The RGB, HLS, HSV, LAB, LUV, XYZ, and YCRCV were used as image pre-processing methods. As a result of the experiment, HLS was able to get the best results than using general RGB. It is expected that the fish disease identification system can be advanced by improving the recognition rate of diseases in a simple way.

• Journal of the Korea Convergence Society
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• 제9권7호
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• pp.1-7
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• 2018

Feature selection, one of data preprocessing techniques, is one of major research areas in many applications dealing with large dataset. It has been used in pattern recognition, machine learning and data mining, and is now widely applied in a variety of fields such as text classification, image retrieval, intrusion detection and genome analysis. The proposed method is based on a genetic algorithm which is one of meta-heuristic algorithms. There are two methods of finding feature subsets: a filter method and a wrapper method. In this study, we use a wrapper method, which evaluates feature subsets using a real classifier, to find an optimal feature subset. The training dataset used in the experiment has a severe class imbalance and it is difficult to improve classification performance for rare classes. After preprocessing the training dataset with SMOTE, we select features and evaluate them with various machine learning algorithms.

• Journal of Korea Multimedia Society
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• 제8권9호
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• pp.1227-1238
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• 2005

Recently, with the advance of network and internet technologies, it is appeared the Problem that the digital contents such as image, voice and video are illegally pirated and distributed. To protect the copyright of the digital contents, the digital watermarking technology of inserting the provider's information into the contents has been widely used. In this paper, we propose the method of applying the digital watermarking into biometric information such as fingerprint and iris in order to prevent the problem caused by steal and misuse. For that, we propose the method of inserting watermark in frequency domain, compare the recognition performance before and aster watermark inserting. Also, we experiment the robustness of proposed method against blurring attack, which is conventionally taken on biometrics data. Experimental results show that our proposed method can be used for protecting iris and fingerprint data, efficiently.

• Journal of Advanced Navigation Technology
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• 제16권2호
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• pp.394-400
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• 2012

SURF is a robust local invariant feature descriptor and has been used in many applications such as object recognition. Even though this algorithm has similar matching accuracy compared to the SIFT, which is another popular feature extraction algorithm, it has advantage in matching time. However, these descriptors do not consider relative location information of extracted interesting points to guarantee rotation invariance. Also, since they use gray image of original color image, they do not use the color information of images, either. In this paper, we propose a method for improving matching performance of SURF descriptor using the color and relative location information of interest points. The location information is built from the angles between the line connecting the centers of interest points and the orientation line constructed for the center of each interest points. For the color information, color histogram is constructed for the region of each interest point. We show the performance of our scheme through experiments.