• Journal of Korea Game Society
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• v.17 no.6
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• pp.153-164
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• 2017

In spherical panoramic images, SURF, a feature description method for planar patterns, does not work correctly due to heavy spherical distortion. Since a plane pattern is distorted in a spherical image, the pattern search and replacement in a spherical panoramic image should be treated differently from the case of the planar image. This paper proposes a planar texture replacement method, which transforms a spherical panoramic image into a cubemap panoramic image, searches a pattern using SURF, replaces a plane pattern, and then converts it into a spherical panoramic image.

• Journal of KIISE
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• v.43 no.4
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• pp.437-449
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• 2016

Previous image processing techniques based on plane-to-plane transformations cannot be utilized for spherical panoramic images. In this paper, we propose a new method to inpaint a spherical panoramic image using exemplar, which is deformed by the location of the patch. Our proposed method makes the deformed exemplar patch by latitude and uses it as the reference patch to restore the damaged area. The exemplar-based inpainting method is based on the planar image coordinate system and thus the classical method cannot be applied to the spherical panoramic image. The merit of our proposed method is the fact that it is not dependent on the location of the damaged area. From the experimental results, we proved that our proposed method satisfies the original purpose of the exemplar-based inpainting technique for the spherical panoramic image.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.952-955
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• 2021

사람의 자세는 구면 파노라마에서 다양한 형태로 왜곡되어 나타날 수 있다. 따라서 구면 파노라마에서의 자세 추정은 평면 이미지에서의 경우보다 정확도가 떨어진다. 본 논문에서는 인식률이 높은 얼굴 인식 기법을 도입하여 구면 파노라마 영상에서 안정적으로 사람의 자세를 추정하는 방법을 제시한다. 먼저 구면 파노라마에서 얼굴을 인식한 후에 이에 기반하여 사람의 전신 영역을 추정하고 전신 영역을 포함하는 평면 영상을 획득한다. 획득된 평면 영상에서 자세를 추정하여 스켈레톤을 얻고 이를 캐릭터 모델에 적용한다. 제안 방법을 실영상에 적용하여 실험한 결과 평면 이미지에서와 동일한 수준의 정확도를 보임을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.1080-1083
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• 2020

본 논문에서는 구면 파노라마를 기반으로 하는 SLAM 시스템을 제안한다. Vision SLAM은 촬영하는 시야각이 넓을수록 적은 프레임으로도 주변을 빠르게 파악할 수 있고, 많은 양의 주변 데이터를 이용해 더욱 안정적인 추정이 가능하다. 구면 파노라마 비디오는 가장 화각이 넓은 영상으로, 모든 방향을 활용할 수 있기 때문에 Fisheye 영상보다 더욱 빠르게 3D 맵을 확장해나갈 수 있다. 기존의 시스템 중 Fisheye 영상을 기반으로 하는 시스템은 전면 광각만을 수용할 수 있기 때문에 구면 파노라마를 입력으로 하는 경우보다 적용 범위가 줄어들게 된다. 본 논문에서는 기존에 Fisheye 비디오를 기반으로 하는 SLAM 시스템을 구면 파노라마의 영역으로 확장하는 방법을 제안한다. 제안 방법은 카메라의 투영 모델이 요구하는 파라미터를 정확히 계산하고, Dual Fisheye Model을 통해 모든 시야각을 손실 없이 활용한다.

• Journal of KIISE
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• v.42 no.10
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• pp.1185-1194
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• 2015

A spherical panoramic image does not conform to the pin-hole camera model, and, hence, it is not possible to utilize previous techniques consisting of plane-to-plane transformation. In this paper, we propose a new method to estimate the planar geometric transformation between the planar image and a spherical panoramic image. Our proposed method estimates the transformation parameters for latitude, longitude, rotation and scaling factors when the matching pairs between a spherical panoramic image and a planar image are given. A planar image is projected into a spherical panoramic image through two steps of nonlinear coordinate transformations, which makes it difficult to compute the geometric transformation. The advantage of using our method is that we can uncover each of the implicit factors as well as the overall transformation. The experiment results show that our proposed method can achieve estimation errors of around 1% and is not affected by deformation factors, such as the latitude and rotation.

• Journal of Korea Game Society
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• v.18 no.5
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• pp.5-14
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• 2018

A lot of research has been done on image recognition technique for planar images and the performance has also been improved. However, it is difficult to recognize objects in spherical panoramic images or images in special form which are given in various environments because of the spherical distortion given in different form from the planar case. In this paper, we show that the neural network recognition approach can be used for object recognition in spherical image and suggest a method of using cubemap transform in order to increase recognition accuracy in spherical image.

• The Journal of the Korea Contents Association
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• v.17 no.3
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• pp.42-52
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• 2017

Panorama image is a single image obtained by combining images taken at several viewpoints through matching of corresponding points. Existing panoramic image generation methods that find the corresponding points are extracting local invariant feature points in each image to create descriptors and using descriptor matching algorithm. In the case of video sequence, frames may be a lot, so therefore it may costs significant amount of time to generate a panoramic image by the existing method and it may has done unnecessary calculations. In this paper, we propose a method to quickly create a single panoramic image from a video sequence. By assuming that there is no significant changes between frames of the video such as in locally, we use the FAST algorithm that has good repeatability and high-speed calculation to extract feature points and the Lucas-Kanade algorithm as each feature point to track for find the corresponding points in surrounding neighborhood instead of existing descriptor matching algorithms. When homographies are calculated for all images, homography is changed around the center image of video sequence to warp images and obtain a planar panoramic image. Finally, the spherical panoramic image is obtained by performing inverse transformation of the spherical coordinate system. The proposed method was confirmed through the experiments generating panorama image efficiently and more faster than the existing methods.

• Journal of Korea Game Society
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• v.17 no.6
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• pp.139-152
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• 2017

In this paper, we propose a ground area detection technique based on region segmentation in the spherical image. We modified the Watershed planar image segmentation method to segment spherical images. After regions are segmented, the ground area is detected by comparing colors and textures of pixels of the assumed ground region with the pixels of other regions. The ground detection technique for planar images cannot be used for spherical images due to the spherical distortion. Considering the spherical distortion, we designed the ground shape detection algorithm to detect the ground area in the spherical images. Our experimental results show that the proposed technique properly detects ground areas both for the flat ground and the obstacle-filled ground environments.

• Journal of Broadcast Engineering
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• v.27 no.4
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• pp.487-498
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• 2022

Depth map is an image that contains distance information in 3D space on a 2D plane and is used in various 3D vision tasks. Many existing depth estimation studies mainly use narrow FoV images, in which a significant portion of the entire scene is lost. In this paper, we propose a technique for generating 360° omnidirectional RGBD images from a sparse set of narrow FoV images. The proposed generative adversarial network based image generation model estimates the relative FoV for the entire panoramic image from a small number of non-overlapping images and produces a 360° RGB and depth image simultaneously. In addition, it shows improved performance by configuring a network reflecting the spherical characteristics of the 360° image.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.850-853
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• 2017

In this paper, we propose an image analysis system based on omnidirectional image and object tracking image display using super wide angle camera. In order to generate spherical images, the projection process of converting from two wide-angle images to the equirectangular panoramic image was performed and the spherical image was expressed by converting rectangular to spherical coordinate system. Object tracking was performed by selecting the desired object initially, and KCF(Kernelized Correlation Filter) algorithm was used so that robust object tracking can be performed even when the object's shape is changed. In the initial dialog, the file and mode are selected, and then the result is displayed in the new dialog. If the object tracking mode is selected, the ROI is set by dragging the desired area in the new window.