• Title/Summary/Keyword: laplacian of Gaussian filter

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An Evaluation and Combination of Noise Reduction Filtering and Edge Detection Filtering for the Feature Element Selection in Stereo Matching (스테레오 정합 특징 요소 선택을 위한 잡음 감소 필터링과 에지 검출 필터링의 성능 평가와 결합)

  • Moon, Chang-Gi;Ye, Chul-Soo
    • Korean Journal of Remote Sensing
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    • v.23 no.4
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    • pp.273-285
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    • 2007
  • Most stereo matching methods use intensity values in small image patches to measure the correspondence between two points. If the noisy pixels are used in computing the corresponding point, the matching performance becomes low. For this reason, the noise plays a critical role in determining the matching performance. In this paper, we propose a method for combining intensity and edge filters robust to the noise in order to improve the performance of stereo matching using high resolution satellite imagery. We used intensity filters such as Mean, Median, Midpoint and Gaussian filter and edge filters such as Gradient, Roberts, Prewitt, Sobel and Laplacian filter. To evaluate the performance of intensity and edge filters, experiments were carried out on both synthetic images and satellite images with uniform or gaussian noise. Then each filter was ranked based on its performance. Among the intensity and edge filters, Median and Sobel filter showed best performance while Midpoint and Laplacian filter showed worst result. We used Ikonos satellite stereo imagery in the experiments and the matching method using Median and Sobel filter showed better matching results than other filter combinations.

An Enhanced Algorithm for an Optimal High-Frequency Emphasis Filter Based on Fuzzy Logic for Chest X-Ray Images

  • Shin, Choong-Ho;Lee, Jung-Jai;Jung, Chai-Yeoung
    • Journal of information and communication convergence engineering
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    • v.13 no.4
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    • pp.264-269
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    • 2015
  • The chest X-ray image cannot be focused in the same manner that optical lenses are and the resultant image generally tends to be slightly blurred. Therefore, the methods to improve the quality of chest X-ray image have been studied. In this paper, the inherent noises of the input images are suppressed by adding the Laplacian image to the original. First, the chest X-ray image using an Gaussian high pass filter and an optimal high frequency emphasis filter has shown improvements in the edges and contrast of flat areas. Second, using fuzzy logic_histogram equalization, each pixel of the chest X-ray image shows the normal distribution of intensities that are not overexposed. As a result, the proposed method has shown the enhanced edge and contrast of the images with the noise canceling effect.

Automated Visual Inspection System of Double Gear using Inspection System (더블기어 자동 시각 검사 시스템 실계 및 구현)

  • Lee, Young Kyo;Kim, Young Po
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.7 no.4
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    • pp.81-88
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    • 2011
  • Mini Double Gears Frame is critical part of PDP and also produces couple hundred thousand every month. In the process of mass production, product inspection is very important process. Double Gear, one of the part of machine, was inspected by human eyes which caused mistakes and slow progress. To achieve the speed and accuracy the system was compensated by vision system which is inspecting automatically. The focus value is measured based on the fact that high contrast images have much high frequency edge information. High frequency term of the image is extracted using the high-pass filter and the sum of the high frequency term is used as the focus value. We used a Gaussian smoothing filter to reduce the noise and then measures the focus value using the modified Laplacian filter called a Sum modified Laplacian Focus values for the various lens positions are calculated and the position with the maximum focus value is decided as the focused position. The focus values calculated in various lens position showed the Gaussian distribution. We proposed a method to estimate the best focus position using the Gaussian curve fitting. Focus values of the uniform interval lens positions are calculated and the values are used to estimate the Gaussian distribution parameters to find the best focus position.

Edge Detection based on Contrast Analysis in Low Light Level Environment (저조도 환경에서 명암도 분석 기반의 에지 검출)

  • Park, Hwa-Jung;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.437-440
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    • 2022
  • In modern society, the use of the image processing field is increasing rapidly due to the 4th industrial revolution and the development of IoT technology. In particular, edge detection is widely used in various fields as an essential preprocessing process in image processing applications such as image classification and object detection. Conventional methods for detecting an edge include a Sobel edge detection filter, a Roberts edge detection filter, a Prewitt edge detection filter, Laplacian of Gaussian (LoG), and the like. However, existing methods have the disadvantage of showing somewhat insufficient performance of edge detection characteristics in a low-light level environment with low contrast. Therefore, this paper proposes an edge detection algorithm based on contrast analysis to increase edge detection characteristics even in low-light level environments.

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FUSESHARP: A MULTI-IMAGE FOCUS FUSION METHOD USING DISCRETE WAVELET TRANSFORM AND UNSHARP MASKING

  • GARGI TRIVEDI;RAJESH SANGHAVI
    • Journal of applied mathematics & informatics
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    • v.41 no.5
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    • pp.1115-1128
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    • 2023
  • In this paper, a novel hybrid method for multi-focus image fusion is proposed. The method combines the advantages of wavelet transform-based methods and focus-measure-based methods to achieve an improved fusion result. The input images are first decomposed into different frequency sub-bands using the discrete wavelet transform (DWT). The focus measure of each sub-band is then calculated using the Laplacian of Gaussian (LoG) operator, and the sub-band with the highest focus measure is selected as the focused sub-band. The focused sub-band is sharpened using an unsharp masking filter to preserve the details in the focused part of the image.Finally, the sharpened focused sub-bands from all input images are fused using the maximum intensity fusion method to preserve the important information from all focus images. The proposed method has been evaluated using standard multi focus image fusion datasets and has shown promising results compared to existing methods.

An Analysis of Chest X-ray by Laplacian Gaussian Filtering and Linear Opacity Judgment

  • Kim, Jin-Woo
    • Journal of information and communication convergence engineering
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    • v.6 no.4
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    • pp.425-429
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    • 2008
  • We investigated algorithm to detect and characterize interstitial lung abnormalities seen at chest radiographs. This method includes a process of 4 directional Laplaction-Gaussian filtering, and a process of linear opacity judgment. Two regions of interest (ROIs) were selected in each right lung of patients, and these ROIs were processed by our computer-analyzing system. For quantitative analysis of interstitial opacities, the radiographic index, which is the percentage of opacity areas in a ROI, was obtained and evaluated in the images. From or result, abnormal lungs were well differentiated from normal lungs. In our algorithm, the processing results were not only given as the numeric data named "radiographic index" but also confirmed with radiologists observation on CRT. The approach, by which the interstitial abnormalities themselves are extracted, is good enough because the results can be confirmed by the observations of radiologists. In conclusion, our system is useful for the detection and characterization of interstitial lung abnormalities.

Hybrid Filter Design for a Nonlinear System with Glint Noise (글린트잡음을 갖는 비선형 시스템에 대한 하이브리드 필터 설계)

  • Kwak, Ki-Seok;Yoon, Tae-Sung;Park, Ji-Bae;Shin, Jong-Gun
    • Proceedings of the KIEE Conference
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    • 2001.11c
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    • pp.26-29
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    • 2001
  • In a target tracking problem the radar glint noise has non-Gaussian heavy-tailed distribution and will seriously affect the target tracking performance. In most nonlinear situations an Extended Robust Kalman Filter(ERKF) can yield acceptable performance as long as the noises are white Gaussian. However, an Extended Robust $H_{\infty}$ Filter (ERHF) can yield acceptable performance when the noises are Laplacian. In this paper, we use the Interacting Multiple Model(IMM) estimator for the problem of target tracking with glint noise. In the IMM method, two filters(ERKF and ERHF) are used in parallel to estimate the state. Computer simulations of a real target tracking shows that hybrid filter used the IMM algorithm has superior performance than a single type filter.

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A Study on Edge Detection Considering Center Pixels of Mask (마스크의 중심 화소를 고려한 에지 검출에 관한 연구)

  • Park, Hwa-Jung;Jung, Hwae-Sung;Kim, Nam-Ho
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.136-138
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    • 2022
  • Edge detection includes information such as the shape, position, size, and material of an object with respect to an image, and is a very important factor in analyzing the characteristics of the image. Existing edge detection methods include Sobel edge detection filter, Roberts edge detection filter, Prewitt edge detection filter, and LoG (Lapacian of Gaussian) using secondary differentials. However, these methods have a disadvantage in that the edge detection results are somewhat insufficient because a fixed weight mask is applied to the entire image area. Therefore, in this paper, we propose an edge detection algorithm that increases edge detection characteristics by considering the center pixel in the mask. In addition, in order to confirm the proposed edge detection performance, it was compared through simulation result images.

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An Image Denoising Algorithm for the Mobile Phone Cameras (스마트폰 카메라를 위한 영상 잡음 제거 알고리즘)

  • Kim, Sung-Un
    • The Journal of the Korea institute of electronic communication sciences
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    • v.9 no.5
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    • pp.601-608
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    • 2014
  • In this study we propose an image denoising algorithm appropriate for mobile smart phone equipped with limited computing ability, which has better performance and at the same time comparable quality comparing with previous studies. The proposed image denoising algorithm for mobile smart phone cameras in low level light environment reduces computational complexity and also prevents edge smoothing by extracting just Gaussian noises from the noisy input image. According to the experiment result, we verified that our algorithm has much better PSNR value than methods applying mean filter or median filter. Also the result image from our algorithm has better clear quality since it preserves edges while smoothing input image. Moreover, the suggested algorithm reduces computational complexity about 52% compared to the method applying original Laplacian mask computation, and we verified that our algorithm has good denoising quality by implementing the algorithm in Android smart phone.

An Approximate Gaussian Edge Detector (근사적 가우스에지 검출기)

  • 정호열;김회진;최태영
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.17 no.7
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    • pp.709-718
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    • 1992
  • A new edge detection operator superimposing two displaced Gaussian smoothing filters Is proposed as an approximate operator for the DroG(flrst derivative of Gaussian) known as a sub-op-timal step edge detector. The performance of the proposed edge detector Is very close to that of the DroG with the performance criteria . signal to noise ratio, locality, and multiple response. And the computational complexity can be reduced almost by a half of that of DroG, because of the use of common 2-D smoothing filter for DroG and LoG ( Laplacian of Gausslan) systems.

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