• Title, Summary, Keyword: Noise elimination algorithm

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A Study on the development of Algorithm for Removing Noise from Road Crack Image (도로면 크랙영상의 노이즈 제거 알고리즘에 관한 연구)

  • Kim Jung-Ryeol;Lee Se-Jun;Choi Hyun-Ha;Kim Young-Suk;Lee Jun-Bok;Cho Moon-Young
    • Proceedings of the Korean Institute Of Construction Engineering and Management
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    • pp.535-538
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    • 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.

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Dedicated hardware implementation for real time noise elimination of 2-valued image (실시간 처리를 위한 2차 화상의 잡음 제거 전용 하드웨어 구성)

  • Park, In-Jung;Lee, Dong-Chan
    • Proceedings of the KIEE Conference
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    • pp.243-246
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    • 1988
  • A lot of time is required in order to the process of loop repeating the preprocessing operated in the software. Specially in the preprocessing, most of the time is used for the noise elimination such a software algorithm component of a noise elimination hardware, this can operate quickly the process.

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A Study on Cascade Filter Algorithm for Random Valued Impulse Noise Elimination (랜덤 임펄스 잡음제거를 위한 캐스케이드 필터 알고리즘에 관한 연구)

  • Yinyu, Gao;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.16 no.3
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    • pp.598-604
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    • 2012
  • Image signal is corrupted by various noises in image processing, many studies are being accomplished to restore those images. In this paper, we proposed a cascade filter algorithm for removing random valued impulse noise. The algorithm consists two steps that noise detection and noise elimination. Variance of filtering mask and center pixel variance are calculated for noise detection, and the noise pixel is replaced by estimated value which first apply switching self adaptive weighted median filter and finally processed by modified weight filter. Considering the proposed algorithm only remove noise and preserve the uncorrupted information that the algorithm can not only remove noise well but also preserve edge.

Design of a Cascade Adaptive Filter for the Performance sn Detection of Segment (ST세그먼트 검출성능향상을 종속 적응필터의 세계)

  • 박광리;이경중
    • Journal of Biomedical Engineering Research
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    • v.16 no.4
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    • pp.517-524
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    • 1995
  • This paper is a study on the design of the cascade adaptive filter (CAF) for baseline wandering elimination in order to enhance the performance of the detection of ST segments in ECG. The CAF using Least Mean Square (LMS) algorithm consists of two filters. The primary adaptive filter which has the cutoff frequency of 0.3Hz eliminates the baseline wandering in raw ECG The secondary adaptive filter removes the remnant baseline wandering which is not eliminated by the primary adaptive filter. The performance of the CAF was compared with the standard filter, the recursive filter, and the adaptive impulse correlated filter (AICF). As a result, the CAF showed a lower signal distortion than the standard filter and the AICF. Also, the CAF showed a better perf'ormance in noise elimination than the standard filter and the recursive filter. In conclusion, considering the characteristics of the noise elimination and the signal distortion, the CAF shows a better performance in the removal of the baseline wandering than the other three Otters and suggests the high performance in the detection of ST segment.

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Modified Weighted Filter Algorithm for Noise Elimination In Mixed Noise Environments

  • Gao, Yinyu;Kim, Nam-Ho
    • Journal of the Institute of Convergence Signal Processing
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    • v.13 no.2
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    • pp.63-69
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    • 2012
  • Noise is regarded as an unwanted component of the image because it significantly reduces image quality. And image is often corrupted by mixed noise. In this paper an efficient modified weighted filter algorithm which combines spatial weight and intensity weight is proposed for removing mixed noise. In the proposed method, the filtering mask is separated into the four sub-windows and the parameters of the weights are confirmed by calculating local standard deviation and the mean of four sub-windows' standard deviations. Considering the spatial information and intensity information, the proposed method has good performance on not only noise elimination but also preservation of details. Simulation results demonstrate that the proposed method performs better than conventional algorithms.

The study for improve a method of Marker auto- identification (마커 자동 인식 향상 방법에 관한 연구)

  • Lee, Hyun-Seob
    • Korean Journal of Sport Biomechanics
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    • v.13 no.1
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    • pp.23-38
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    • 2003
  • The purpose of this study is to develop an improved marker auto-identification algorithm for reduce of data processing time through improve the efficiency of noise elimination and marker separation. The maker auto-identification algorithm was programming named KUMAS used Delphi language. For the study, various experiments were conducted for the verification of KUMAS. and compared two systems of established with the KUMAS. Four different motions - cycling, gait, rotation, and pendulum -, were selected and tested. Motions were filmed 30Hz frames rate per second. ${\chi}^2$ used for statistical analysis. Significant level were ${\alpha}=.05$. The test results were as follow. 1. Increased the success ratio of marker auto-identification. 2. The efficiency of marker auto-identification was remarkably improved through marker separation, noise elimination. 3. The marker auto-identification ability was improved in 2D-image plane include the 3D motion. 4. Significant different were found between KUMAS and B-SYS(established system) with non-input the artificial noise frames, input the artificial noise frames and total frames.

Reconstruction and Elimination of Optical Microscopic Background Using Surface Fitting Method

  • Kim Hak-Kyeong;Kim Dong-Kyu;Jeong Nam-Soo;Lee Myung-Suk;Kim Sang-Bong
    • Fisheries and aquatic sciences
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    • v.4 no.1
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    • pp.10-17
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    • 2001
  • One serious problem among the troubles to identify objects in an optical microscopic image is contour background due to non-uniform light source and various transparency of samples. To solve this problem, this paper proposed an elimination method of the contour background and compensation technique as follows. First, Otsu's optimal thresholding method extracts pixels representing background. Second, bilinear interpolation finds non-deterministic background pixels among the sampled pixels. Third, the 2D cubic fitting method composes surface function from pivoted background pixels. Fourth, reconstruction procedure makes a contour image from the surface function. Finally, elimination procedure subtracts the approximated background from the original image. To prove the effectiveness of the proposed algorithm, this algorithm is applied to the yeast Zygosaccharomyces rouxii and ammonia-oxidizing bacteria Acinetobacter sp. Labeling by this proposed method can remove some noise and is more exact than labeling by only Otsu's method. Futhermore, we show that it is more effective for the reduction of noise.

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A Fast Motion Estimation Algorithm using Adaptive Search According to Importance of Search Ranges (탐색영역의 중요도에 따라 적응적인 탐색을 이용한 고속 움직임 예측 알고리즘)

  • Kim, Tae Hwan;Kim, Jong Nam;Jeong, Shin Il
    • Journal of Korea Multimedia Society
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    • v.18 no.4
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    • pp.437-442
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    • 2015
  • Voice activity detection is very important process that voice activity separated form noisy speech signal for speech enhance. Over the past few years, many studies have been made on voice activity detection, but it has poor performance in low signal to noise ratio environment or fickle noise such as car noise. In this paper, it proposed new voice activity detection algorithm using ensemble variance based on wavelet band entropy and soft thresholding method. We conduct a survey in a lot of signal to noise ratio environment of car noise to evaluate performance of the proposed algorithm and confirmed performance of the proposed algorithm.

Development of the Noise Elimination Algorithm of Stereo-Vision Images for 3D Terrain Modeling (지반형상 3차원 모델링을 위한 스테레오 비전 영상의 노이즈 제거 알고리즘 개발)

  • Yoo, Hyun-Seok;Kim, Young-Suk;Han, Seung-Woo
    • Korean Journal of Construction Engineering and Management
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    • v.10 no.2
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    • pp.145-154
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    • 2009
  • For developing an Automation equipment in construction, it is a key issue to develop 3D modeling technology which can be used for automatically recognizing environmental objects. Recently, for the development of "Intelligent Excavating System(IES), a research developing the real-time 3D terrain modeling technology has been implemented from 2006 in Korea and a stereo vision system is selected as the optimum technology. However, as a result of performance tests implemented in various earth moving environment, the 3D images obtained by stereo vision included considerable noise. Therefore, in this study, for getting rid of the noise which is necessarily generated in stereo image matching, the noise elimination algorithm of stereo-vision images for 3D terrain modeling was developed. The consequence of this study is expected to be applicable in developing an automation equipments which are used in field environment.

A Study on Removal of Salt and Pepper Noise using Deformable Masks Depending on the Noise Density (잡음 밀도에 따라 가변 마스크를 적용한 Salt and Pepper 잡음 제거에 관한 연구)

  • Hong, Sang-Woo;Kim, Nam-Ho
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.19 no.9
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    • pp.2173-2179
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    • 2015
  • In digital era image processing has been utilized in a variety of media such as TV, camera and smart phone. Typically salt and pepper noise are generated by various causes during the analysis, identification, and processing of image data. Principal filters such as SMF, CWMF, and AMF have been used to remove these noise. But the existing filters fall short of edge preservation and noise elimination in high noise densities. Thus, a processing algorithm, on which the size of deformable mask varies depending on the noise density, is proposed to remove salt and pepper noise effectively in this study. The performance of the proposed method was evaluated compared with the existing methods using PSNR.