• Journal of Biomedical Engineering Research
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• v.23 no.5
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• pp.365-373
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• 2002

Accurate localization of target lesion is required to protect normal peripheral tissue and irradiate exactly to tumors in stereotactic radiosurgery(SRS). Digital angiography is one of the most effective diagnostic tools to detect and identify the target tumors. However, it shows pincushion distortion due to the characteristics of the image intensifier. We have implemented a simulation study for the correction of distortion using the geometric transformation. Phantom images were produced transformation. In conclusion, the geometric transformation could effectively be used for the pincushion distortion of image intensifier and there was no significant different between two methods indicating 2% correction error from the ideal image in all cases.

• The korean journal of orthodontics
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• v.34 no.5 s.106
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• pp.448-457
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• 2004

As computer Programs for cephalometric analysis were developed in diagnosis & treatment planning, digital imaging of film-based cephalograms came to be needed. When a digital camera is used, a problem encountered the image distortion produced according to the focal length, which causes errors in indentifying landmarks. In addition, changes in the image size and compression ratio will inevitably produce a low quality image, causing errors in identifying landmarks. Hence. we have found the focal length producing the least image distortion when digital imaging the film-based cephalograms and the minimal digital camera setting which helps to identify the correct landmarks using the COOLPIX4500 digital camera (Nikon, Japan). The results were as follows The image distortion was minimized at a focal length of 16.4mm (79.4mm when converted into a 35mm film camera) when digital imaging the film-based cephalograms. When wide imaging, with a focal length of under IS.4mm, barrel distortion was found and when tole imaging. with a focal length of over 15.4mm pincushion distortion was found. The minimal digital camera setting was $2272{\times}1704$ pixel at normal (1/8) compression from which we can identify the correct landmarks at the same level as tracing the film-based cephalograms manually. As a result. when digital imaging the film-based cephalograms, using a COOLPIX4500 digital camera (Nikon, Japan), the focal length should be 16.4mm the pixel image size over $2272{\times}1704$, and the compression ratio over normal (1/8).

• 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.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.43-50
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• 2016

In this paper, we present a distortion correction for wearable Augmented Reality (AR) on mobile phones. Head Mounted Display (HMD) using mobile phones, such as Samsung Gear VR or Google's cardboard, introduces lens distortion of the rendered image to user. Especially, in case of AR the distortion is more complicated due to the duplicated optical systems from mobile phone's camera and HMD's lens. Furthermore, such distortions generate mismatches of the visual cognition or perception of the user. In a natural way, we can assume that transparent wearable displays are the ultimate visual system which generates the least misperception. Therefore, the image from the mobile phone must be corrected to cancel this distortion to make transparent-like AR display with mobile phone based HMD. We developed a transparent-like display in the mobile wearable AR environment focusing on two issues: pincushion distortion and field-of view. We implemented our technique and evaluated their performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3981-4004
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• 2019

This paper proposes a novel method for locating objects in real space from a single remote image and measuring actual distances between them by automatic detection and perspective transformation. The dimensions of the real space are known in advance. First, the corner points of the interested region are detected from an image using deep learning. Then, based on the corner points, the region of interest (ROI) is extracted and made proportional to real space by applying warp-perspective transformation. Finally, the objects are detected and mapped to the real-world location. Removing distortion from the image using camera calibration improves the accuracy in most of the cases. The deep learning framework Darknet is used for detection, and necessary modifications are made to integrate perspective transformation, camera calibration, un-distortion, etc. Experiments are performed with two types of cameras, one with barrel and the other with pincushion distortions. The results show that the difference between calculated distances and measured on real space with measurement tapes are very small; approximately 1 cm on an average. Furthermore, automatic corner detection allows the system to be used with any type of camera that has a fixed pose or in motion; using more points significantly enhances the accuracy of real-world mapping even without camera calibration. Perspective transformation also increases the object detection efficiency by making unified sizes of all objects.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.855-856
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• 2008

In this paper, we studied the performance evaluation of a vehicular rear-view camera through quantifying the image quality based on several objective criteria from the ISO (International Organization for Standardization). In addition, various experimental environments are defined considering the conditions under which a rear-view camera may need to operate. The process for evaluating the performance of a rear-view camera is composed of five objective criteria: noise test, resolution test, OECF (opto-electronic conversion function) test, color characterization test, and pincushion and barrel distortion tests. The proposed image quality quantification method then expresses the results of each test as a single value, allowing easy evaluation.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.76-79
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• 2009

This paper investigates the performance of a vehicular rear-view camera through quantifying the image quality based on several objective criteria from the ISO (International Organization for Standardization). In addition, various experimental environments are defined considering the conditions under which a rear-view camera may need to operate. The process for evaluating the performance of a rear-view camera is composed of five objective criteria: noise test, resolution test, OECF (opto-electronic conversion function) test, color characterization test, and pincushion and barrel distortion tests. The proposed image quality quantification method then expresses the results of each test as a single value, allowing easy evaluation. In experiments, the performance evaluation results are analyzed and compared with those for a regular digital camera.