• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• autumn
• /
• pp.535-538
• /
• 2002

Machine vision algorithms, which are composed of noise elimination algorithm, crack detection and mapping algorithm, and path planning algorithm, are required for sealing crack networks effectively and automation of crack sealing.. Noise elimination algorithm is the first step so that computer take cognizance of cracks effectively. Noises should be removed because common road includes a lot of noises(mark of oil, tire, traffic lane, and sealed crack) that make it difficult the computer to acknowledge cracks accurately. The objective of this paper is to propose noise elimination algorithm, prove the efficiency of the algorithm through coding. The result of the coding is represented in this paper as well.

• Journal of the Korean Society of Radiology
• /
• v.7 no.3
• /
• pp.181-189
• /
• 2013

The optimum exposure parameters are found when examined using the automatic mode in FFDM. improve the image quality by applying denoising algorithm and propose methods to reduce AGD(Average Grandular Dose) a patient can receive. For the experiment, Nuclear Associates Model 18-222 phantom was the used, and the entrance dose and AGD were measured. And then, Signal, Noise, SNR and FOM(Figure of Merit) were measured, compared and analyzed image denoising before and after. As the experiment result, first, SNR was the highest at Mo/Mo 23kVp and W/Rh 35kvp was the lowest for the average glandular dose. It showed to use 28kVp of W/Rh to be the best through the result of FOM. SNR was the highest at Mo/Mo 23kVp(image denoising), and it showed to W/Rh and 28kVp to be the best in the FOM result which AGD was considered at the same time. By the image denoising, it is possible to reduce noise while maintain important information in the image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.137-139
• /
• 2011

영상에서의 경계선 추출은 원 영상의 노이즈에 의해 크게 영향을 받는다. 따라서 먼저 그 노이즈들을 제거할만한 어떤 방법들이 필요하다. Mean Shift 알고리즘은 이러한 목적에 부합되는 유연한 함수로서, 별로 중요하지 않은 정보와 민감한 노이즈 부분을 점점 제거하는데 알맞다. 여기서는 Canny 알고리즘을 사용하여 중점으로 하는 영상에서의 윤곽선을 찾아낸다. 그리고 테스트 하고 이전의 유일한 Canny 알고리즘 보다 결과가 좋음을 알아낸다.

• Journal of the Korean Society of Radiology
• /
• v.13 no.3
• /
• pp.453-458
• /
• 2019

In this study we analyzed the tendency of the image characteristic by changing filtering factor for the proposed fast non local means (FNLM) noise reduction algorithm with designed Male Adult mesh (MASH) phantom through Geant4 application for tomographic emission (GATE) simulation program. To accomplish this purpose, MASH phantom for human copy was designed through the GATE simulation program. In addition, we acquired degraded image by adding Gaussian noise with a value of 0.005 using the MATALB program in MASH phantom. Moreover, in degraded image, the FNLM noise reduction algorithm was applied by changing the filtering factors, which set to 0.005, 0.01, 0.05, 0.1, 0.5, and 1.0 value, respectively. To quantitatively evaluate, the coefficient of variation (COV), signal to noise ratio (SNR), and contrast to noise ratio (CNR) were calculated in reconstructed images. Results of the COV, SNR and CNR were most improved in image with a filtering factor of 0.05 value. Especially, the COV was decreased with increasing filtering factor, and showed nearly constant values after 0.05 value of the filtering factor. In addition, SNR and CNR were showed that improvement with increasing filtering factor, and deterioration after 0.05 value of the filtering factor. In conclusion, we demonstrated the significance of setting the filtering factor when applying the FNLM noise reduction algorithm in degraded image.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.35 no.2
• /
• pp.175-181
• /
• 2011

We propose a novel algorithm for measuring the radius of tubes. This proposed algorithm is capable of effectively removing added noise and measuring the radius of tubes within allowable precision. The noise is removed by using a candidate true center that minimizes the standard deviation with respect to the radius. Further, the disconnection in data points resulting from noise removal is solved by using a connection algorithm. The final step of the process is repeated until the value of the standard deviation decreases to a small predefined value. Experiments were performed using circle geometries with added noise and a real tube with complex noise and that is used in the braking units of automobiles. It was concluded that the measurement carried out using the algorithm was accurate within 1.4%, even with 15% added noise.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.6
• /
• pp.59-64
• /
• 2009

Edge detection always influenced by the noise of original image, therefore need some methods to eliminate them in advance, and the Mean Shift algorithm has the smooth function which suit for this purpose, so adopt it to fade out the unimportant information and the sensitive noise portions. Above all, we use the Canny algorithm to pick up the contour of the objects we focus on. And, take tests and get better result than the former sole Canny algorithm. This combination method of Mean Shift algorithm and Canny algorithm is suitable for the edge detection processing.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.23 no.5
• /
• pp.455-463
• /
• 2003

Adaptive noise cancellation techniques are ideally suitable for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation. Grain noises have an un-correlation property, while flaw echoes are correlated. Thus, adaptive filtering algorithms use the correlation properties of signals to enhance the signal-to-noise ratio (SNR) of the output signal. In this paper, a multi-stage adaptive noise cancellation (MANC) method using adaptive least mean square error (LMSE) filter for enhancing flaw detection in ultrasonic signals is proposed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.510-512
• /
• 2018

The proposed method enhances the resolution of images by removing noise using wavelet transform to remove noise from images generated by ultrasound diagnosis. We propose an algorithm to reduce the speckle noise and enhance the edge of the ultrasound image. The proposed algorithm can enhance edges of various sizes by using wavelet transform which can use both frequency and spatial information. Experimental results show that the performance of the algorithm for noise reduction of ultrasound images is about 0.45ms for $520{\times}440$ images.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.23 no.11
• /
• pp.1343-1350
• /
• 2019

Converting boundary images to time-series makes it feasible to perform boundary matching even on a very large image database, which is very important for interactive and fast matching. In recent research, there has been an attempt to perform fast matching considering partial denoising by converting the boundary image into time series. In this paper, to improve performance, we propose an index-building method considering all possible arbitrary denoising parameters for removing arbitrary partial noises. This is a challenging problem since the partial denoising boundary matching must be considered for all possible denoising parameters. We propose an efficient single index-building algorithm by constructing a minimum bounding rectangle(MBR) according to all possible denoising parameters. The results of extensive experiments conducted show that our index-based matching method improves the search performance up to 46.6 ~ 4023.6 times.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.3213-3215
• /
• 2000

뇌자도 신호의 측정은 뇌에서 발생하는 자장 성분을 정밀하게 측정할 수 있으나, 신호의 크기가 매우 작기 때문에 노이즈에 매우 민감하게 동작하며 이러한 노이즈 성분의 발생원인은 외부 환경에 의하여 발생하거나 시스템 내부에서 발생하는 두가지로 나눌 수 있다. 따라서 뇌자도 신호를 측정하는데 있어서 가장 중요한 작업은 신호에 존재하는 노이즈 성분을 제거하는 것이다. 특히 뇌자도 측정 시스템에서는 외부 노이즈 성분을 제거하기 위하여 레퍼런스 채널이 존재한다. 따라서 본 논문에서는 청각 자극 신호에 의한 뇌자도 신호를 측정하고 측정한 데이터를 사용하여 레퍼런스 채널과 입력신호에 대하여 LMS 알고리즘을 이용한 적응 필터를 모델링 하였다. 그리고, 구현한 적응 필터를 이용하여 뇌자도 신호의 평균값, 표준편차의 통계적 결과를 비교하여 모델링한 적응 필터 방법의 유용성을 확인하였다.