• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.07a
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• pp.9-12
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• 2015

In this paper, we propose a new feature matching algorithm by modifying and combining the FAST(Features from Accelerated Segment Test) feature detector and SURF feature descriptor which is robust to the distortion of the given image. Scale space is generated to consider the variation of the scale and determine the candidate of features in the image robust to the noise. The original FAST algorithm results in many feature points along edges. To solve this problem, we apply the principal curvatures for refining it. We also use SURF descriptor to make it robust against the variations in the image by rotation. Through the experiments, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load. Especially, it shows a strength for noisy images.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.1
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• pp.65-70
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• 2013

Recently, the field of computer vision has been actively researched through digital image which can be easily generated as the development and expansion of digital camera technology. Especially, research that extracts and utilizes the feature in image has been actively carried out. The image stitching is a method that creates the high resolution image using features extract and match. Image stitching can be widely used in military and medical purposes as well as in variety fields of real life. In this paper, we have proposed efficient image stitching method using fast feature descriptor extraction and matching based on SURF algorithm. It can be accurately, and quickly found matching point by reduction of dimension of feature descriptor. The feature descriptor is generated by classifying of unnecessary minutiae in extracted features. To reduce the computational time and efficient match feature, we have reduced dimension of the descriptor and expanded orientation window. In our results, the processing time of feature matching and image stitching are faster than previous algorithms, and also that method can make natural-looking stitched image.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.129-135
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• 2015

The Ubiquitous City (U-City) spatial information technology aimed to provide services freely anytime and anywhere by converging high-tech information & communication technology in urban infrastructure has been available in diverse patterns. In particular, there have been studies on the development of 3D spatial information after selecting and matching key points with stereo images from the many Closed Circuit TV (CCTV) in the U-City. However, the data mostly used in extracting matching points haven't considered external environmental impacts such as illuminance. This study tested how much the matching points needed to construct 3D spatial information with the CCTV whose image quality is dependent upon changes in illuminance fluctuate under the same hardware performances. According to analysis on the number of matching points by illuminance, the number of matching points increased up to 3,000Lux in proportion to the illuminance when IRIS, shutter speed and ISO were fixed. In addition, a border between an object and background became more distinctive. When there was too much light, however, the page became brighter, and noise occurred. Furthermore, it was difficult to name key points because of the collapse of an inter-object border. It appears that if filmed with the study results, the number of matching points would increase.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.1
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• pp.55-62
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• 2004

A semi-automatic registration process of determining specified points is presented, which is required to register brain MR images based on Talairach atlas. Generally, ten specified points that define Talairach coordinates are anterior commissure(AC), posterior commissure (PC), anterior feint (AP), posterior point (PP), superior point (SP), inferior point (IP), left point (LP), right point (RP) and two points for the midline of the brain. The suggested method reduces user interaction for S points, and finds the necessary points for registration in a more stable manner by finding AC and PC using two-level shape matching of the corpus callosum (CC) in an edge-enhanced brain M image. Remaining points are found using the intensity information of cutview.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.275-278
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• 2004

지문 인식 방식은 기존의 영상 처리와는 달리 여러 가지 문제점을 포함하고 있다. 지문에는 기준 좌표축이 존재하지 않으므로 회전되어 채취된 지문에 대한 처리가 어려우며, 신체의 일부로서 유연성을 가지고 있어 채취될 때마다 모양이 달라 보이고, 지문이 손상될 수 있어 저 품질의 지문이 빈번히 발생할 수 있다 본 논문에서는 방향성의 흐름 패턴을 이용한 특이점 추출에 초점이 맞춰져 있으며 추출된 특이점 정보는 현재 구현되어진 특징점 추출 정보와 연계해 정합을 위한 기준점으로 활용한다. 기준점을 축으로 생성되어진 직교좌표는 지문 영상의 회전변위에 대한 영향을 최소화 하여 지문의 정합도를 높여준다.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.191-194
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• 2000

일반 적인 고속 블록 정합 알고리즘들은 현재 프레임의 탐색 블록과 참조 프레임의 탐색영역 내의 블록간 MAD(Mean Absolute Distance)를 구하여 그 값을 탐색 점으로 사용하므로 탐색 점 수만큼 MAD를 구해야 하는 단점이 있다. 이와 같은 고속 블록 정합 알고리즘들의 단점을 해결하기 위해 탐색 점 예측에 의한 고속 블록 정합 알고리즘을 제안한다. 본 논문에서는 "이웃 한 화소는 서로 간에 거의 같은 값을 지니고 있다"라는 성질을 이용하여, 이웃 한 탐색 점 두개의 MAD 평균값을 계산하여 그 값을 새로운 탐색 점으로 사용하여 탐객 하기 때문에 탐색 점 수는 DS(Diamond Search)알고리즘과 비교하여 비슷하지만, 최소 오차가 center일 때의 탐색 점을 예측에 의해 산출 하므로 총 연산량은 2Ep$N_2$만큼 크게 줄어든다. Ep는 예측 탐색 점 수를 나타내며, N은 블록의 크기를 나타낸다.

• Journal of the Korea Society of Computer and Information
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• v.18 no.10
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• pp.39-46
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• 2013

Recently, the mobile industries grow up rapidly and their performances are improved. So the usage of mobile devices is increasing in our life. Also mobile devices equipped with a high-performance camera, so the image stitching can carry out on the mobile devices instead of the desktop. However the mobile devices have limited hardware to perform the image stitching which has a lot of computational complexity. In this paper, we have proposed improved feature descriptor extraction and matching method for efficient image stitching on mobile environment. Our method can reduce computational complexity using extension of orientation window and reduction of dimension feature descriptor when feature descriptor is generated. In addition, the computational complexity of image stitching is reduced through the classification of matching points. In our results, our method makes to improve the computational time of image stitching than the previous method. Therefore our method is suitable for the mobile environment and also that method can make natural-looking stitched image.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.547-559
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• 2010

Delaunay triangulation is suitable for fingerprint matching because of its robustness to rotation and translation. However, missing and spurious minutiae degrade the performance and computational efficiency. In this paper, we propose a method of combining local quality assessment and 4-category minutiae classification to improve accuracy and decrease computational complexity in matching process. Experimental results suggest that removing low quality areas from matching candidate areas and classifying minutiae improve computational efficiency without degrading performance. The results proved that the proposed algorithm outperforms the matching algorithm (BOZORTH3) provided by NIST.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.12
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• pp.2363-2371
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• 2015

In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.308-310
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• 2020

본 논문에서는 포인트 클라우드 정합 시스템 자동화를 위한 재정합 프로세스에서 정합의 실패 유무를 판단하는 기존의 정합 평가 방법을 개선한 방법을 제안한다. 포인트 클라우드 정합 자동화를 위해 정합의 실패를 판단하여 다시 정합하는 재정합 프로세스는 자동화 시스템에서 필수적인 요소이다. 기존의 정합 평가 방법은 정합하고자하는 두 포인트 클라우드의 점의 간격이나 데이터의 양이 다를 경우 계산된 정합 오차가 정성적인 결과와는 다르게 측정되는 문제가 발생하는데, 이는 재정합 프로세스에서 치명적인 오류를 초래한다. 제안하는 방법은 참조 포인트 클라우드에서 가장 인접한 목표 포인트 클라우드의 세 점이 이루는 평면과의 수직 거리를 계산하고, 일정 거리 임계치를 만족하는 점들의 개수를 측정해 계산된 오차를 검증하여 정합 오판단율을 효과적으로 감소시켰다.