• Title/Summary/Keyword: Smoothing algorithm

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Adaptive Noise Reduction Algorithm for an Image Based on a Bayesian Method

  • Kim, Yeong-Hwa;Nam, Ji-Ho
    • Communications for Statistical Applications and Methods
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    • v.19 no.4
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    • pp.619-628
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    • 2012
  • Noise reduction is an important issue in the field of image processing because image noise lowers the quality of the original pure image. The basic difficulty is that the noise and the signal are not easily distinguished. Simple smoothing is the most basic and important procedure to effectively remove the noise; however, the weakness is that the feature area is simultaneously blurred. In this research, we use ways to measure the degree of noise with respect to the degree of image features and propose a Bayesian noise reduction method based on MAP (maximum a posteriori). Simulation results show that the proposed adaptive noise reduction algorithm using Bayesian MAP provides good performance regardless of the level of noise variance.

A MIXED NORM RESTORATION FOR MULTICHANNEL IMAGES

  • Hong, Min-Cheol;Cha, Hyung-Tae;Hahn, Hyun-Soo
    • Proceedings of the IEEK Conference
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    • 2000.09a
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    • pp.399-402
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    • 2000
  • In this paper, we present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both within- and between- channel deterministic information is considered. For each channel a functional which combines the least mean squares (LMS), the least mean fourth(LMF), and a smoothing functional is proposed, We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters mentioned above are adjusted based on the partially restored image.

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A Study on the Transformation of CAD Data Using the Image Data Processing (화상처리를 이용한 CAD 데이터의 생성에 관한 연구)

  • Koo, Bon-Kwon;Roh, Woo-Joon
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.7 no.6
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    • pp.72-79
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    • 1998
  • In this paper, image processing algorithm is studied to enhance the preciseness of the geometry while converting captured images to CAD data. A program is developed as a result. The code, in the image processing, utilizes outline trace, point data smoothing algorithm. It is capable of automatically generating design data by converting input image data to the CAD data. The output can be made in DXF, IGES formats. The current research can be utilized as a base data for the development of factory automation or flexible manufacturing system which adopt image processing based automatic inspection and measuring system.

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Individual Tooth Image Segmentation with Correcting of Specular Reflections (치아 영상의 반사 제거 및 치아 영역 자동 분할)

  • Lee, Seong-Taek;Kim, Kyeong-Seop;Yoon, Tae-Ho;Lee, Jeong-Whan;Kim, Kee-Deog;Park, Won-Se
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.59 no.6
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    • pp.1136-1142
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    • 2010
  • In this study, an efficient removal algorithm for specular reflections in a tooth color image is proposed to minimize the artefact interrupting color image segmentation. The pixel values of RGB color channels are initially reversed to emphasize the features in reflective regions, and then those regions are automatically detected by utilizing perceptron artificial neural network model and those prominent intensities are corrected by applying a smoothing spatial filter. After correcting specular reflection regions, multiple seeds in the tooth candidates are selected to find the regional minima and MCWA(Marker-Controlled Watershed Algorithm) is applied to delineate the individual tooth region in a CCD tooth color image. Therefore, the accuracy in segmentation for separating tooth regions can be drastically improved with removing specular reflections due to the illumination effect.

VHDL Implementation of an LPC Analysis Algorithm (LPC 분석 알고리즘의 VHDL 구현)

  • 선우명훈;조위덕
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.32B no.1
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    • pp.96-102
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    • 1995
  • This paper presents the VHSIC Hardware Description Language(VHDL) implementation of the Fixed Point Covariance Lattice(FLAT) algorithm for an Linear Predictive Coding(LPC) analysis and its related algorithms, such as the forth order high pass Infinite Impulse Response(IIR) filter, covariance matrix calculation, and Spectral Smoothing Technique(SST) in the Vector Sum Exited Linear Predictive(VSELP) speech coder that has been Selected as the standard speech coder for the North America and Japanese digital cellular. Existing Digital Signal Processor(DSP) chips used in digital cellular phones are derived from general purpose DSP chips, and thus, these DSP chips may not be optimal and effective architectures are to be designed for the above mentioned algorithms. Then we implemented the VHDL code based on the C code, Finally, we verified that VHDL results are the same as C code results for real speech data. The implemented VHDL code can be used for performing logic synthesis and for designing an LPC Application Specific Integrated Circuit(ASOC) chip and DsP chips. We first developed the C language code to investigate the correctness of algorithms and to compare C code results with VHDL code results block by block.

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Spatially Adaptive High-Resolution Denoising Based on Nonstationary Correlation Assumption (비정적 상관관계를 고려한 공간적응적 잡음제거 알고리즘)

  • 김창원;박성철;강문기
    • Proceedings of the IEEK Conference
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    • 2003.07e
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    • pp.1711-1714
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    • 2003
  • The noise in an image degrades image quality and deteriorates coding efficiency of compression. Recently, various edge-preserving noise filtering methods based on the nonstationary image model have been proposed to overcome this problem. In most conventional nonstationary image models, however, pixels are assumed to be uncorrelated to each other In order not to increase the computational burden too much. As a result, some detailed information is lost in the filtered results. In this paper, we propose a computationally feasible adaptive noise smoothing algorithm which considers the nonstationary correlation characteristics of images. We assume that an image has a nonstationary mean and can be segmented into subimages which have individually different stationary correlations. Taking advantage of the special structure of the covariance matrix that results from the proposed image model, we derive a computationally efficient FFT-based adaptive linear minimum mean square error filter. The justification for the proposed image model is presented and the effectiveness of the proposed algorithm is demonstrated experimentally.

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Development of an Automatic Two-Dimensional Mesh Generator using an Inward Offset Boundary Technique

  • Choi, Jin-Woo;Kim, Yohng-Jo
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.2 no.4
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    • pp.61-66
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    • 2003
  • An excellent mesh construction is of Importance in yielding good results of finite element analysis. The new mesh generation algorithm, which offsets boundaries inward, was developed on the basis of a looping method. An user interface technique and automatic splitting lines which both divide a given domain into subdomains manually or automatically, were used. In addition, the separation method has advantages to prevent the large scale of element size and to control numbers of nodes and elements. This new mesh generation algorithm was proved in practice.

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English Character Recognition and Design of Preprocessing Neural Chip (영문자 인식 및 전처리용 신경칩의 설계)

  • 남호원;정호선
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.15 no.6
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    • pp.455-466
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    • 1990
  • Enalish character recognition with the neural networl algorithm has been performed. Character recognition technition techniques which are processed by software, have the limit of the recognition speed. To overcome this limit, we realize this system to hardware by using the neural network algorithm. We have designed preprocessing chip using the neural nework model, that is single layer perceptorn, in the noise elimination, smoothing, thinning and feature point extraction. These chips are implemented as a CMOS double metal 2um design rule.

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Color Image Quantization Using Local Region Block in RGB Space (RGB 공간상의 국부 영역 블럭을 이용한 칼라 영상 양자화)

  • 박양우;이응주;김기석;정인갑;하영호
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • 1995.06a
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    • pp.83-86
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    • 1995
  • Many image display devices allow only a limited number of colors to be simultaneously displayed. In displaying of natural color image using color palette, it is necessary to construct an optimal color palette and map each pixel of the original image to a color palette with fast. In this paper, we proposed the clustering algorithm using local region block centered one color cluster in the prequantized 3-D histogram. Cluster pairs which have the least distortion error are merged by considering distortion measure. The clustering process is continued until to obtain the desired number of colors. Same as the clustering process, original color image is mapped to palette color via a local region block centering around prequantized original color value. The proposed algorithm incorporated with a spatial activity weighting value which is smoothing region. The method produces high quality display images and considerably reduces computation time.

Gradual Scene Change Detection Using Variance of Edge Image (에지 영상의 분산을 이용한 비디오의 점진적 장면전환 검출)

  • Ryoo, Han-Jin;Yoo, Hun-Woo;Jang, Dong-Sik;Kim, Mun-Hwa
    • Journal of Institute of Control, Robotics and Systems
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    • v.8 no.3
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    • pp.275-280
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    • 2002
  • A new algorithm for gradual scene change detection in MPEG based frame sequences is proposed in this paper. The proposed algorithm is based on the fact that most of gradual curves can be characterized by variance distributions of edge information in the frame sequences. Average edge frame sequences are obtained by performing "sober" edge detection. Features are extracted by comparing variances with those of local blocks in the average edge frames. Those features are further processed by the opening operation to obtain smoothing variance curves. The lowest variance in the local frame sequences is chosen as a gradual detection point. Experimental results show that the proposed method provides 85% precision and 86% recall rate fur gradual scene changes.