• Proceedings of the Zoological Society Korea Conference
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• 1997.10b
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• pp.285.1-285
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• 1997

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
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• 1997.10b
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• pp.268.1-268
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• 1997

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
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• 1997.10b
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• pp.270.1-270
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• 1997

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
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• 1997.10b
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• pp.168.2-168
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• 1997

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
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• 2003.08a
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• pp.120.3-120
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• 2003

No Abstract, See Full Text

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.19-28
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• 2004

This paper shows how to compensate the skew from the traffic sign included in the natural image and extract the text. The research deals with the Process related to the array image. Ail the process comprises four steps. In the first fart we Perform the preprocessing and Canny edge extraction for the edge in the natural image. In the second pan we perform preprocessing and postprocessing for Hough Transform in order to extract the skewed angle. In the third part we remove the noise images and the complex lines, and then extract the candidate region using the features of the text. In the last part after performing the local binarization in the extracted candidate region, we demonstrate the text extraction by using the differences of the features which appeared between the tett and the non-text in order to select the unnecessary non-text. After carrying out an experiment with the natural image of 100 Pieces that includes the traffic sign. The research indicates a 82.54 percent extraction of the text and a 79.69 percent accuracy of the extraction, and this improved more accurate text extraction in comparison with the existing works such as the method using RLS(Run Length Smoothing) or Fourier Transform. Also this research shows a 94.5 percent extraction in respect of the extraction on the skewed angle. That improved a 26 percent, compared with the way used only Hough Transform. The research is applied to giving the information of the location regarding the walking aid system for the blind or the operation of a driverless vehicle

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.192-198
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• 2010

With the popularization of the mobile phone with a built-in camera, there are a lot of effort to provide useful information to users by detecting and recognizing the text in the video which is captured by the camera in mobile phone, and there is a need to detect the text regions in such mobile phone video. In this paper, we propose a method to detect the text regions in the mobile phone video. We employ morphological operation as a preprocessing and obtain binarized image using modified k-means clustering. After that, candidate text regions are obtained by applying connected component analysis and general text characteristic analysis. In addition, we increase the precision of the text detection by examining the frequency of the candidate regions. Experimental results show that the proposed method detects the text regions in the mobile phone video with high precision and recall.

• Journal of the Korean Society of Food Culture
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• v.35 no.1
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• pp.65-78
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• 2020

The objective of this study was to review and evaluate the growing subject of food tourism research, and thus identify the trend of food tourism research. Using a Text mining technique, this paper discovered the trends of the literature on food tourism that was published from 2004 to 2018. The study reviewed 201 articles that include the words 'food' and 'tourism' in their abstracts in the KCI database. The Wordscloud analysis results presented that the research subjects were predominantly 'Festival', 'Region', 'Culture', 'Tourist', but there was a slight difference in frequency according to the time period. Based on the main path analysis, we extracted the meaningful paths between the cited references published domestically, resulting in a total of 12 networks from 2004 to 2018. The Text network analysis indicated that the words with high centrality showed similarities and differences in the food tourism literature according to the time period, displaying them in a sociogram, a visualization tool. This study has implications that it offers a new perspective of comprehending the overall flow of relevant research.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.767-774
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• 2002

Conventional research on image restoration has focused on restoring degraded images resulting from image formation, storage and communication, mainly in the signal processing field. Related research on recovering original image information of caption regions includes a method using BMA(block matching algorithm). The method has problem with frequent incorrect matching and propagating the errors by incorrect matching. Moreover, it is impossible to recover the frames between two scene changes when scene changes occur more than twice. In this paper, we propose a method for recovering original images using EBMA(Extended Block Matching Algorithm) and a region compensation method. To use it in original image recovery, the method extracts a priori knowledge such as information about scene changes, camera motion and caption regions. The method decides the direction of recovery using the extracted caption information(the start and end frames of a caption) and scene change information. According to the direction of recovery, the recovery is performed in units of character components using EBMA and the region compensation method. Experimental results show that EBMA results in good recovery regardless of the speed of moving object and complexity of background in video. The region compensation method recovered original images successfully, when there is no information about the original image to refer to.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.29-34
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• 2022

We propose a novel network architecture and build dataset for recognizing clickable objects on mobile device screens. The data was collected based on clickable objects on the mobile device screen that have numerous resolution, and a total of 24,937 annotation data were subdivided into seven categories: text, edit text, image, button, region, status bar, and navigation bar. We use the Deconvolution Single Shot Detector as a baseline, the backbone network with Squeeze-and-Excitation blocks, the Single Shot Detector layer structure to derive inference results and the Feature pyramid networks structure. Also we efficiently extract features by changing the input resolution of the existing 1:1 ratio of the network to a 1:2 ratio similar to the mobile device screen. As a result of experimenting with the dataset we have built, the mean average precision was improved by up to 101% compared to baseline.