• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.06a
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• pp.313-316
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• 2018

최근 미디어의 생성 및 소비 기술의 발전으로 몰입도 있는 콘텐츠에 대한 수요가 증가하고 있다. View Interpolation 기술은 두 개의 좌/우 영상을 기반으로 하여 두 영상의 중간 시점에 해당하는 영상을 생성해내는 기술이다. 먼저 Depth Hole Filling Module을 이용하여 좌/우 영상 및 그에 대응하는 깊이 지도를 입력으로 받아 깊이 지도에 존재하는 오류를 검출하고, 보정한다. 깊이 지도의 오류 보정이 완료되면, 해당 데이터를 각각 Feature Matching Module 및 Layer Dividing Module로 전달한다. Feature Matching Module은 실사 영상 내의 특징점들을 검출하고, 두 영상 내 특징점을 매칭하는 역할을 수행하며, Layer Dividing Module은 깊이 값을 기반으로 영상의 Layer를 분할한다. Feature Matching Module에서 특징점의 매칭이 완료되면, 특징점의 영상 내 좌표 및 해당 좌표에서의 깊이 값을 Distance Estimating Module로 전달한다. Distance Estimating Module은 전달받은 특징점의 좌표 및 해당 좌표에서의 깊이 값을 기반으로 전체 깊이 값에서의 이동도를 계산한다. 이와 같이 이동도의 계산 및 Layer 분할이 완료되면, 각 Layer를 이동도에 기반하여 이동시키고, 이동된 Layer들을 포개어 배치함으로써 View interpolation을 완성한다.

• Convergence Security Journal
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• v.13 no.3
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• pp.9-15
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• 2013

Calculating of matching cost is an important for efficient stereo matching. To investigate the performance of matching process, the concepts of the existing methods are introduced. Also we analyze the performance and merits of them. The simplest matching costs assume constant intensities at matching image locations. We consider matching cost functions which can be distinguished between pixel-based and window-based approaches. The Pixel-based approach includes absolute differences (AD) and sampling-intensitive absolute differences (BT). The window-based approach includes the sum of the absolute differences, the sum of squared differences, the normalized cross-correlation, zero-mean normalized cross-correlation, census transform, and the absolute differences census transform (AD-Census). We evaluate matching cost functions in terms of accuracy and time complexity. In terms of the accuracy, AD-Census method shows the lowest matching error ratio (the best solution). The ZNCC method shows the lowest matching error ratio in non-occlusion and all evaluation part. But it performs high matching error ratio at the discontinuities evaluation part due to blurring effect in the boundary. The pixel-based AD method shows a low complexity in terms of time complexity.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1257-1271
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• 2022

Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.

• Proceedings of the Korea Multimedia Society Conference
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• 2003.11a
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• pp.81-85
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• 2003

본 논문에서는 기존 지문 인식 시스템에서 사용하는 특이점 추출 알고리즘과 매칭 알고리즘을 개선한 알고리즘들을 설계하고 구현함 으로서 새로운 지문 인식 시스템 개발에 목표를 두었다. 현재, 이진화된 지문 이미지를 표현하는 자료구조인 Union and Division을 고안하였고, Union and Division에 기반한 특이점 추출 알고리즘을 설계 및 구현하였다. 또한 특이점 추출에서의 과다한 추출 단계로 인하여 발생되는 문제점들을 줄이기 위하여 기존의 6 단계인 특이점 추출 과정을 개선하여 3단계로 줄이면서도 정확성을 높이는 특이점 추출 알고리즘을 적용하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.389-390
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• 2018

본 논문에서는 3차원 복원을 위한 특징점 추출 및 매칭에 대한 보다 정확한 방법을 제안한다. 이 방법은 컴퓨터 비전의 기본이 되는 분야로 복원뿐 만 아니라 SLAM과 같은 지도 작성 및 자율 운행에도 필요한 방법이다. 본 연구는 3차원 물체 복원을 위해서 사용하는 방법 중 하나인 Column space fitting(CSF)을 이용하여 turntable-image data에 적용하여 성능을 평가하여 정확성을 검증을 한다. 오늘날 3D scanner를 이용하여 물체를 3차원 모델을 획득하고 3D프린터를 이용하여 다양한 분야에 적용한다. 그러나 고가의 장비이기 때문에 접근성이 떨어진다. 본 연구는 영상들만을 가지고 기하학적 계산을 통해 3차원 모델을 획득한다. 본 연구결과는 기존의 방법인 KLT 알고리즘과 비교하여 RMSE의 값을 약 5배를 줄이는 성능 향상을 보인다.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.1
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• pp.9-18
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• 2018

Recently, the use of image stitching technology has been increasing as the number of contents based on virtual reality increases. Image Stitching is a method for matching multiple images to produce a high resolution image and a wide field of view image. The image stitching is used in various fields beyond the limitation of images generated from one camera. Image Stitching detects feature points and corresponding points to match multiple images, and calculates the homography among images using the RANSAC algorithm. Generally, corresponding points are needed for calculating conversion relation. However, the corresponding points include various types of noise that can be caused by false assumptions or errors about the conversion relationship. This noise is an obstacle to accurately predict the conversion relation. Therefore, RANSAC algorithm is used to construct an accurate conversion relationship from the outliers that interfere with the prediction of the model parameters because matching methods can usually occur incorrect correspondence points. In this paper, we propose an algorithm that extracts more accurate inliers and computes accurate transformation relations by using correspondence point relation information used in RANSAC algorithm. The correspondence point relation information uses distance ratio between corresponding points used in image matching. This paper aims to reduce the processing time while maintaining the same performance as RANSAC.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.37-46
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• 2010

In this paper we have presented a two dimensional model based tracking system using improved chamfer matching. Conventional chamfer matching could not calculate similarity well between the object and image when there is very cluttered background. Then we have improved chamfer matching to calculate similarity well even in very cluttered background with edge and corner feature points. Improved chamfer matching is used as likelihood function of particle filter which tracks the geometric object. Geometric model which uses edge and corner feature points, is a discriminant descriptor in color changes. Particle Filter is more non-linear tracking system than Kalman Filter. Then the presented method uses geometric model, particle filter and improved chamfer matching for tracking object in complex environment. In experimental result, the robustness of our system is proved by comparing other methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.2
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• pp.113-121
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• 2011

In this paper, we proposed the method automatically to match areal feature based on similarity using spatial information. For this, we extracted candidate matching pairs intersected between two different spatial datasets, and then measured a shape similarity, which is calculated by an weight sum method of each matching criterion automatically derived from CRITIC method. In this time, matching pairs were selected when similarity is more than a threshold determined by outliers detection of adjusted boxplot from training data. After applying this method to two distinct spatial datasets: a digital topographic map and street-name address base map, we conformed that buildings were matched, that shape is similar and a large area is overlaid in visual evaluation, and F-Measure is highly 0.932 in statistical evaluation.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.175-182
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• 2020

In this study, we introduced the technique of matching classical music using deep learning to design soundscape that can help the viewer appreciate painting and proposed an evaluation index to evaluate how well matching painting and music. The evaluation index was conducted with suitability evaluation through the Likeard 5-point scale and evaluation in a multimodal aspect. The suitability evaluation score of the 13 test participants for the deep learning based best match between painting and music was 3.74/5.0 and band the average cosine similarity of the multimodal evaluation of 13 participants was 0.79. We expect multimodal evaluation to be an evaluation index that can measure a new user experience. In addition, this study aims to improve the experience of multisensory artworks by proposing the interaction between visual and auditory. The proposed matching of painting and music method can be used in multisensory artwork exhibition and furthermore it will increase the accessibility of visually impaired people to appreciate artworks.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.26-38
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• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.