• Journal of the Optical Society of Korea
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• v.14 no.3
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• pp.245-259
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• 2010

We have developed mathematically precise image-processing algorithms for extracting panoramic images from fisheye images. Furthermore, we have successfully built a DSP-based panoramic camera employing single fisheye lens.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.10
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• pp.22-29
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• 2006

This paper considers the lens distortions such as a fisheye distortion and a perspective distortion. While a fisheye lens has a wide field-of-view, it causes a large distortion to the images. Regardless of a fisheye lens or a rectilinear lens, a lens generates perspective distortion in a vertical direction when the lens views in an upward direction or downward direction. These distortions deform images differently from human visual functions. Therefore, this paper presents a method to correct the distortions, and whereby, the research in this paper enlarges choices of images to image processing algorithm that may select the distorted images and the corrected images depending on applications. An infinite polynomial model is employed in the fisheye radial distortion correction, and the vertical perspective distortion correction is done by using a vanishing point. The methods introduced in this paper are implemented on the images captured by a rear-view camera installed on a vehicle and showed their robustness of the correction.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.271-274
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• 2021

Fisheye 카메라로 촬영된 영상은 일반 영상보다 넓은 시야각을 갖는 장점으로 여러 분야에서 활용되고 있다. 그러나 fisheye 카메라로 촬영된 영상은 어안렌즈의 곡률로 인하여 영상의 중앙 부분은 팽창되고 외곽 부분은 축소되는 방사 왜곡이 발생하기 때문에 영상을 활용함에 있어서 어려움이 있다. 이러한 방사 왜곡을 보정하기 위하여 기존 영상처리 분야에서는 렌즈의 곡률을 수학적으로 계산하여 보정하기도 하지만 이는 각각의 렌즈마다 왜곡 파라미터를 추정해야 하기 때문에, 개별적인 GT (Ground Truth) 영상이 필요하다는 제한 사항이 있다. 이에 본 논문에서는 렌즈의 종류마다 GT 영상을 필요로 하는 기존 기술의 제한 사항을 극복하기 위하여, fisheye 영상만을 입력으로 하여 왜곡계수를 계산하는 딥러닝 네트워크를 제안하고자 한다. 또한, 단일 왜곡계수를 왜곡모델로 활용함으로써 layer 수를 크게 줄일 수 있는 경량화 네트워크를 제안한다.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.339-344
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• 2013

This paper presents an automatic focal length estimation method to correct the fisheye lens distortion in a spatially adaptive manner. The proposed method estimates the focal length of the fisheye lens by generating two reference focal lengths. The distorted fisheye lens image is finally corrected using the orthographic projection model. The experimental results showed that the proposed focal length estimation method is more accurate than existing methods in terms of the loss rate.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1406-1407
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• 2015

본 논문에서는 도로 위 숫자 및 기호인식에 적합한 Calibration Model에 대하여 연구하였다. 기존에 제시된 Geometric Transform, Fisheye Projection, Caltech Toolbox 기반 방법으로 얻은 Calibration Model의 성능을 비교하였다. Geometric Transform은 Fisheye Distortion Correction에 부적합한 결과를 얻었고, Fisheye Projection은 성능은 좋으나 시스템에 사용할 Camera Lens의 Specification을 모르기 때문에 이를 예측해야 하는 단점이 있다. 마지막으로 Caltech Tool box 기반 방법은 Calibration을 위한 Keypoint를 수동으로 지정하다 보니까 이로 인한 오차가 존재하게 된다. Calibration을 시도 할 때마다 결과에 차이가 있었으며, Calibration 결과의 측면에서 Fisheye Projection이 가시적으로 가장 좋은 결과를 나타냈다.

• 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 the Korea Academia-Industrial cooperation Society
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• v.10 no.1
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• pp.117-121
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• 2009

The fisheye image acquired with a fisheye camera has wider field of view than a general use camera. But large distortion of the object in the image requires conversion of the fisheye image to the perspective image because of user's difficult perception. The existing Ishii's method[1] has the problem that the object can has sire and geometrical distortion in the transformed image because it uses equidistance projection. This paper presented a conversion technique of the fisheye image to the perspective image using sealing function. In the experiments, it was shown that our method reduced size and geometrical distortion by applying the scaling function.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.16-22
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• 2012

In this paper, we present spiral pattern which suits for optical simulator to calibrate fisheye lens and compensate geometric distortion. Using spiral pattern, we present calibration without mathematical modeling in advance. Proposed spiral pattern used to input image of optical simulator. Using fisheye lens image, we calibrate a fisheye lens by matching geometrically moved dots to corresponding original dots which leads not to need mathematical modeling. Proposed algorithm calibrates using dot matching which matches spiral pattern image dot to distorted image dot. And this algorithm does not need modeling in advance so it is effective. Proposed algorithm is enabled at processing of pattern recognition which has to get the exact information using fisheye lens for digital zooming. And this makes possible at compensation of geometric distortion and calibration of fisheye lens image applying in various image processing.

• Journal of Broadcast Engineering
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• v.27 no.1
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• pp.20-30
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• 2022

Recently, as VR (Virtual Reality) technology has been in the spotlight, 360° panoramic images that can view lively VR contents are attracting a lot of attention. Image stitching technology is a major technology for producing 360° panorama images, and many studies are being actively conducted. Typical stitching algorithms are based on feature point-based image stitching. However, conventional feature point-based image stitching methods have a problem that stitching results are intensely affected by feature points. To solve this problem, deep learning-based image stitching technologies have recently been studied, but there are still many problems when there are few overlapping areas between images or large parallax. In addition, there is a limit to complete supervised learning because labeled ground-truth panorama images cannot be obtained in a real environment. Therefore, we produced three fisheye images with different camera centers and corresponding ground truth image through carla simulator that is widely used in the autonomous driving field. We propose image stitching model that creates a 360° panorama image with the produced fisheye image. The final experimental results are virtual datasets configured similar to the actual environment, verifying stitching results that are strong against various environments and large parallax.

• Proceedings of the KWS Conference
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• 1995.10a
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• pp.212-213
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• 1995