• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.55-63
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• 2011

Having a wide angle of view, a fisheye lens is useful for obtaining images of the inside wall of a tunnel. A circular fisheye tunnel image can be transformed into a familiar rectangular image by applying the concept of cylindrical projection. This projection transformation causes several types of distortions in the projected image. This paper discusses the distortion on the boundary lines between smoothly curved wall and flat ground. We analyzed the cause of this boundary distortion, developed transformation model, and derived a correction formular. A distortion correction software programmed in Visual C++ applied to projected image. Consequently, boundary-corrected image could be obtained. Research into other distortions of projected image will helpful in obtaining tunnel image that resembles real tunnel from fisheye tunnel image.

• Journal of the Optical Society of Korea
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• v.14 no.2
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• pp.109-120
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• 2010

We have developed mathematically precise image-processing algorithms for extracting rectilinear images from fisheye images as well as digital pan/tilt/zoom technology. Using this technology, vertical lines always appear as vertical lines in the panned and/or tilted images. Furthermore, polygonal panoramic images composed of multiple rectilinear images have been obtained using the developed digital pan/tilt technology.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.963-964
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• 2008

Recently, omni-directional cameras are broadly used due to their wide field of view. Fisheye camera is one of them. This paper proposes the system which uses a fisheye camera for face recognition and analyzes its advantages. Since face images taken with a fisheye camera are affected by perspective distortion and radial distortion, we suggest a two-step method for removing those distortions from the face images.

• Journal of Sensor Science and Technology
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• v.22 no.6
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• pp.393-399
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• 2013

We propose a method of sensing the rotation and distance of a camera by using a fisheye lens system as a vision sensor. We estimate the rotation angle of a camera with a modified correlation method by clipping similar regions to avoid symmetry problems and suppressing highlight areas. In order to eliminate the rectification process of the distorted points of a fisheye lens image, we introduce an offline process using the normalized focal length, which does not require the image sensor size. We also formulate an equation for calculating the distance of a camera movement by matching the feature points of the test image with those of the reference image.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.395-401
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• 2020

In this paper, a motion-based ROI extraction algorithm from a high resolution fisheye image is proposed for multi-view monitoring systems. Lately fisheye cameras are widely used because of the wide angle-of-view and they basically provide a lens correction functionality as well as various viewing modes. However, since the distortion-free angle of conventional algorithms is quite narrow due to the severe distortion ratio, there are lots of unintentional dead areas and they require much computation time in finding undistorted coordinates. Thus, the proposed algorithm adopts an image decimation and a motion detection methods, that can extract the undistorted ROI image with a standard angle-of-view for the fast and intelligent surveillance system. In addition, a mesh-type ROI is presented to reduce the lens correction time, so that this independent ROI scheme can parallelize and maximize the processor's utilization.

• Journal of Korea Multimedia Society
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• v.20 no.5
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• pp.769-781
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• 2017

Deep learning networks like Convolutional Neural Networks (CNNs) show successful performances in many computer vision applications such as image classification, object detection, and so on. For implementation of deep learning networks in embedded system with limited processing power and memory, deep learning network may need to be simplified. However, simplified deep learning network cannot learn every possible scene. One realistic strategy for embedded deep learning network is to construct a simplified deep learning network model optimized for the scene images of the installation place. Then, automatic training will be necessitated for commercialization. In this paper, as an intermediate step toward automatic training under fisheye camera environments, we study more precise human localization in fisheye images, and propose an accurate human localization method, Automatic Ground-Truth Labelling Method (AGTLM). AGTLM first localizes candidate human object bounding boxes by utilizing GoogLeNet-LSTM approach, and after reassurance process by GoogLeNet-based CNN network, finally refines them more correctly and precisely(tightly) by applying saliency object detection technique. The performance improvement of the proposed human localization method, AGTLM with respect to accuracy and tightness is shown through several experiments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.4
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• pp.32-40
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• 2008

Since vision-based surveillance system uses a conventional camera which has a narrow field of view, it is difficult to apply it into the environment whose the ceiling is low and the monitoring area is wide. To overcome this problem, the method of increasing the number of camera causes the increase of the cost and the difficulties of camera set-up For these problems, we propose a new surveillance system based on multiple fisheye cameras which have 180 degree field of view. The proposed method handles occlusions using the homography relation between the multiple fisheye cameras. In the experiment, four fisheye cameras were set up within the area of $17{\times}14m$ at height of 2.5 m and five people wandered and crossed with one another within this area. The detection rates of the proposed system was 83.0% while that of a single camera was 46.1%.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.84-91
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• 2016

Most recently, the technology of around view monitoring(AVM) system or the security systems could provide users with images by using a fisheye lens. The filmed images through fisheye lens have an advantage of providing a wider range of scenes. On the other hand, filming through fisheye lens also has disadvantages of distorting images. Especially, it causes the sharpness of images to degrade because the edge of images is out of focus. The influence of a blur still remains at the end of the range when the super-resolution techniques is applied in order to enhance the sharpness. It degrades the clarity of high resolution images and occurs artifacts, which leads to deterioration in the performance of super-resolution algorithm. Therefore, in this paper we propose self-similarity-based pre-processing method to improve the sharpness at the edge. Additionally, we implement the acceleration in the GPU environment of entire algorithm and verify the acceleration.

• Archives of design research
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• v.18 no.3 s.61
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• pp.191-198
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• 2005

Menu is the most common interaction tool to select and execute a specific menu item from multiple menu options. With the very rapid increasing amount of information, various new menu designs have been developed. In this research, the pull-down menu, fisheye menu and grid menu were tested to compare the performance time, error rate, simplicity, usefulness, user friendliness, and overall user preference of each menu type. The grid menu was more efficient in selection speed than the pull-down and fisheye menus when the number of menu-items was 50 and 100. The time needed to choose a menu-item with a grid menu was less affected by the size of menu. The pull-down and the grid menus were considered to be more satisfactory, simple, user friendly, and useful than the fisheye menu. 42.3 percent of subjects indicated that the grid menu was their preferred selection tool among the menus. The grid menu is an efficient and robust alternative menu choice for small and middle size menu list. Further study is required to examine the possibility of grid menu on mobile devices.

• Journal of Conservation Science
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• v.37 no.4
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• pp.312-321
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• 2021

Underwater archaeology relies heavily on photography and video image recording during surveillances and excavations like ordinary archaeological studies on land. All underwater images suffer poor image quality and distortions due to poor visibility, low contrast and blur, caused by differences in refractive indices of water and air, properties of selected lenses and shapes of viewports. In the Yellow Sea (between mainland China and the Korean peninsula), the visibility underwater is far less than 1 m, typically in the range of 30 cm to 50 cm, on even a clear day, due to very high turbidity. For photographing 1 m x 1 m grids underwater, a very wide view angle (180°) fisheye lens with an 8 mm focal length is intentionally used despite unwanted severe barrel-shaped image distortion, even with a dome port camera housing. It is very difficult to map wide underwater archaeological excavation sites by combining severely distorted images. Development of practical compensation methods for distorted underwater images acquired through the fisheye lens is strongly desired. In this study, the source of image distortion in underwater photography is investigated. We have identified the source of image distortion as the mismatching, in optical axis and focal points, between dome port housing and fisheye lens. A practical image distortion compensation method, using customized image processing software, was explored and verified using archived underwater excavation images for effectiveness in underwater archaeological applications. To minimize unusable area due to severe distortion after distortion compensation, practical underwater photography guidelines are suggested.