• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.11-17
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• 2022

In this paper, a model that can increase the efficiency of work in arranging interior furniture by applying augmented reality technology was studied. In the existing system to which augmented reality is currently applied, there is a problem in that information is limitedly provided depending on the size and nature of the company's product when outputting the image of furniture. To solve this problem, this paper presents an AR labeling algorithm. The AR labeling algorithm extracts feature points from the captured images and builds a database including indoor location information. A method of detecting and learning the location data of furniture in an indoor space was adopted using the CNN technique. Through the learned result, it is confirmed that the error between the indoor location and the location shown by learning can be significantly reduced. In addition, a study was conducted to allow users to easily place desired furniture through augmented reality by receiving detailed information about furniture along with accurate image extraction of furniture. As a result of the study, the accuracy and loss rate of the model were found to be 99% and 0.026, indicating the significance of this study by securing reliability. The results of this study are expected to satisfy consumers' satisfaction and purchase desires by accurately arranging desired furniture indoors through the design and implementation of AR labels.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.43-50
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• 2017

In this paper, we are proposing improved vanishing points detection and segments labeling methods for inside wall frame detection from indoor image of a piece of having a colour RGB. A lot of research related to recognizing the frame of artificial structures from the image is being performed due to increase in demand for AR technology. But detect the inside wall frame in indoor images have many objects that caused the occlusion is still a difficult issue. Inner wall frame detection methods are usually segment labeling methods and detect vanishing point methods are used together. In order to improve the vanishing point detection method we proposed using inner wall orthogonality which forms the cube. Also we proposed labeling method using tree based learning and superpixel based segmentation method for labelingthe segments in indoor images. Finally, in experiments have shown improved results about inside wall frame detection according to our methods.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.119-121
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• 2022

본 논문은 실내가구 인테리어를 배치하는데 있어 증강현실 기술을 적용하여 작업의 효율성을 높일 수 있는 모델을 연구하였다. 현재 증강현실을 적용하는 프로세스에서는 가구의 이미지를 출력할 때 기업의 규모나 제품의 성격 등에 따라 정보가 제한적으로 제공되는 문제를 안고 있다. 이러한 문제점을 해결하기 위하여 본 논문에서 제시하는 알고리즘을 이용하여 AR 레이블링을 생성함으로써, 가구의 정확한 이미지 추출과 함께 가구에 대한 상세한 정보를 제공 받아 사용자가 원하는 가구들을 증강현실을 통해 쉽게 배치할 수 있도록 하는 연구를 진행하였다. 본 연구는 AR 레이블링의 설계, 구현과 3D 렌더링을 통해 원하는 가구들을 실내에 정확히 배치할 수 있어 소비자의 만족도와 구매욕구를 충족시킬 수 있을 것으로 기대된다.