• 디지털융복합연구
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• 제10권4호
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• pp.191-197
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• 2012

본 논문에서는 단일 프레임 영상에서 초점을 이용하여 초기 깊이정보를 추출한 후 입체 영상을 생성하는 방법을 제안하였다. 단일 프레임 영상에서 깊이를 추정하기 위해 원본 영상과 가우시안 필터를 중첩 적용하여 생성된 영상의 비교를 통해 영상의 초점 값을 추출하고 추출된 값을 기반으로 초기 깊이정보를 생성하도록 하였다. 생성된 초기 깊이정보를 Normalized cut을 이용한 객체 분할 결과에 할당하고 각 객체의 깊이를 객체 내 깊이 정보의 평균값으로 보정하여 동일 객체가 같은 깊이 값을 갖도록 하였다. 객체를 제외한 배경 영역은 객체를 제외한 배경 영역의 에지 정보를 이용하여 깊이를 생성하였다. 생성된 깊이를 DIBR(Depth Image Based Rendering)을 이용하여 입체 영상으로 변환하였고 기존 알고리즘을 통해 생성된 영상과 비교 분석하였다.

• 한국정보통신학회논문지
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• 제20권11호
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• pp.2045-2053
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• 2016

2009년 3D 입체영화 아바타의 등장 이후 많은 연구기관에서 3D 입체영상에 대한 연구를 지속해 왔다. 또한 대학에서 3D 입체영상 관련 연구 및 교육이 다양하게 이루어져 왔으며, 대학 교육과정에까지 3D 입체영상 제작 교육을 다루고 있다. 그러나 대학에서 고가의 카메라 및 리그 등의 장비를 구입하여 3D 입체영상 콘텐츠 제작 교육을 하기에는 많은 어려움이 있는 것이 현실이다. 본 논문은 대학의 교육과정 중에서 소프트웨어를 이용한 3D 입체영상 콘텐츠 제작 교육과정에 대해 다룬다. 3D 입체영상 콘텐츠 교육과정에서 다루어져야 할 이론적인 내용과 이론을 바탕으로 실시한 실습 교육과정을 수행하였다. 그 결과 학생들은 3D 입체영상의 제작원리 대하여 이해하고 다양한 소프트웨어를 이용하여 3D 입체영상을 제작할 수 있었다. 이러한 제작 과정을 통하여 대학에서의 올바른 3D 입체영상 콘텐츠 제작에 대한 교육방법을 논의하고자 한다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2003년도 춘계학술발표논문집 (상)
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• pp.409-412
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• 2003

The 3-D shape use to effect of movie, animation, industrial design, medical treatment service, education, engineering etc... But it is not easy to make 3-D shape from the information of 2-D image. There are two methods in restoring 3-D video image through 2-D image; First the method of using a laser; Second, the method of acquiring 3-D image through stereo vision. Instead of doing two methods with many difficulties, I study the method of simple 3-D image in this research paper. We present here a simple and efficient method, called direct calibration, which does not require any equations at all. The direct calibration procedure builds a lookup table(LUT) linking image and 3-D coordinates by a real 3-D triangulation system. The LUT is built by measuring the image coordinates of a grid of known 3-D points, and recording both image and world coordinates for each point; the depth values of all other visible points are obtained by interpolation.

• 정보교육학회논문지
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• 제26권1호
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• pp.1-10
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• 2022

인공지능의 발달과 함께 사회는 이전에는 없었던 다른 세상으로 나아가고 있다. 그에 따라 인공지능 교육에 대한 관심도 높아지는 가운데 우리나라에서도 인공지능 교육에 대한 연구가 더욱 활발하게 진행되고 있다. 하지만 지금까지 이루어진 연구는 초등학교 고학년을 중심으로 한 연구가 많이 진행되었고, 저학년을 위한 교육과정이나 프로그램은 아직 부족한 실정이다. 따라서 본 연구에서는 초등학교 저학년을 위한 이미지 인식 이해 인공지능 프로그램 총 6차시를 개발하였다. 전문가 8명을 대상으로 전문가 타당도를 진행하여 타당성을 확보하였고, 이를 실험반에 적용하여 사전 사후 대응표본 t검정을 통해 그 효과성을 검증하였다. 그 결과 인공지능 이해도, 인공지능 태도에서 모두 통계적으로 유의미한 결과를 나타내었고, 교육 프로그램의 흥미도와 난이도 모두 저학년 학생들에게 적합한 것으로 나타났다. 본 연구의 내용을 바탕으로 향후 후속 연구를 통해 다양한 환경에서의 적용과 효과성 검토가 필요하다.

• Korean Journal of Acupuncture
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• 제25권4호
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• pp.167-174
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• 2008

Objectives : Acupuncture points and meridians have been usually depicted as a two dimensional drawing and verbal description. Recently, imaging and three-dimensional image processing technologies have been introduced into medical fields such as anatomy and virtual operation, for the purpose of enhanced efficiency in research and education. This study attempted an image modelling of the meridian and acupoint in the upper limb region. Methods : A vector image model of an arm was produced and medical information on the meridian and acupoint of the arm region was incorporated. Results : A 3D modelling of the acupuncture meridian and acupoint in the upper limb region was produced along with a user console to control the presentation of related information and to facilitate visualization of the 3D model images. Conclusions : A 3D modelling of the acupuncture meridian and acupoint will be an efficient platform for an education and research.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1437-1446
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• 2020

For the last few years, smart devices have begun to occupy an essential place in the life of children, by allowing them to access a variety of language activities and books. Various studies are being conducted on using smart devices for education. Our study extracts images and texts from kids' book with smart devices and matches the extracted images and texts to create new images that are not represented in these books. The proposed system will enable the use of smart devices as educational media for children. A deep convolutional generative adversarial network (DCGAN) is used for generating a new image. Three steps are involved in training DCGAN. Firstly, images with 11 titles and 1,164 images on ImageNet are learned. Secondly, Tesseract, an optical character recognition engine, is used to extract images and text from kids' book and classify the text using a morpheme analyzer. Thirdly, the classified word class is matched with the latent vector of the image. The learned DCGAN creates an image associated with the text.

• 통합자연과학논문집
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• 제11권3호
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• pp.131-138
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• 2018

In this paper, we have implemented speeches that utilized the emotion information of the user's speech and image matching and recommendation system. To classify the user's emotional information of speech, the emotional information of speech about the user's speech is extracted and classified using the PLP algorithm. After classification, an emotional DB of speech is constructed. Moreover, emotional color and emotional vocabulary through factor analysis are matched to one space in order to classify emotional information of image. And a standardized image recommendation system based on the matching of each keyword with the BM-GA algorithm for the data of the emotional information of speech and emotional information of image according to the more appropriate emotional information of speech of the user. As a result of the performance evaluation, recognition rate of standardized vocabulary in four stages according to speech was 80.48% on average and system user satisfaction was 82.4%. Therefore, it is expected that the classification of images according to the user's speech information will be helpful for the study of emotional exchange between the user and the computer.

• 한국간호교육학회지
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• 제19권3호
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• pp.384-395
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• 2013

Purpose: This study aimed to investigate the nurse's image and role projected in media and perceived by elementary, middle and high school students. Methods: Data were collected from 544 students in J city, Korea during December 2010 to February 2011. The nurse's image was measured by the instrument developed by Yang (1998). Nursing experiences and nurse's role in media was measured by self-administered questionnaires with 8 questions. Statistical analysis was made with t-test and ANOVA using SPSS win 18.0. Result: The score on personal image was the highest, while the score on social image was the lowest. Elementary school students reported a positive image compared with middle and high school students. Male students had a more positive image than female students. Of various media, students had more experiences related to nurses from TV rather than internet sites, books and newspaper/magazines. Especially, the nurse's role on TV 'seemed to be an assistant of a doctor' was rated the highest by the students. Conclusion: It is necessary to develop a strategy to improve the social image of the nurse through TV by providing correct information on the nurse's role. For this purpose, it is required to consistently monitor and analyze the nurse's role shown in media.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제11권1호
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• pp.1-14
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• 2004

Due to the development of computer network and mobile communications, the security in image data and other related source are very important as in saving or transferring the commercial documents, medical data, and every private picture. Nonetheless, the conventional encryption algorithms are usually focusing on the word message. These methods are too complicated or complex in the respect of image data because they have much more amounts of information to represent. In this sense, we proposed an efficient secret symmetric stream type encryption algorithm which is based on Boolean matrix operation and the characteristic of image data.

• Journal of information and communication convergence engineering
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• 제12권4호
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• pp.263-270
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• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.