• Communications for Statistical Applications and Methods
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• v.9 no.2
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• pp.355-366
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• 2002

The web is one of the most plentiful sources of images. The web has an immediate need for image processing technology in Java. This paper provides a practical introduction to statistical image processing using Java on the web. The paper describes how images are represented in Java and deals with four image processing operations based on basic statistical methods: point processing, spatial filtering, edge detection and image segmentation.

• Korean Journal of Remote Sensing
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• v.36 no.4
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• pp.503-513
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• 2020

This paper presents a method to evaluate denoising filters based on edge locations in their denoised images. Image quality assessment has often been performed by using structural similarity (SSIM). However, SSIM does not provide clearly the geometric accuracy of features in denoised images. Thus, in this paper, a method to localize edge locations with subpixel accuracy based on adaptive weighting of gradients is used for obtaining the subpixel locations of edges in ground truth image, noisy images, and denoised images. Then, this paper proposes a method to evaluate the geometric accuracy of edge locations based on root mean squares error (RMSE) and jaggedness with reference to ground truth locations. Jaggedness is a measure proposed in this study to measure the stability of the distribution of edge locations. Tested denoising filters are anisotropic diffusion (AF), bilateral filter, guided filter, weighted guided filter, weighted mean of patches filter, and smoothing filter (SF). SF is a simple filter that smooths images by applying a Gaussian blurring to a noisy image. Experiments were performed with a set of simulated images and natural images. The experimental results show that AF and SF recovered edge locations more accurately than the other tested filters in terms of SSIM, RMSE, and jaggedness and that SF produced better results than AF in terms of jaggedness.

• Journal of Korea Multimedia Society
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• v.8 no.12
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• pp.1565-1571
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• 2005

Images reconstructed with Maximum-Likelihood Expectation-Maximization (MLEM) algorithm have been observed to have checkerboard effects and have noise artifacts near edges as iterations proceed. To compensate this ill-posed nature, numerous penalized maximum-likelihood methods have been proposed. We suggest a simple algorithm of applying edge detecting process to the MLEM and Bayesian Expectation-Maximization (BEM) to reduce the noise artifacts near edges and remove checkerboard effects. We have shown by simulation that this algorithm removes checkerboard effects and improves the clarity of the reconstructed image and has good properties based on root mean square error (RMS).

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.5
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• pp.726-731
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• 1986

An effective filtering algorithm which can reduce noise and preserve edges for the restoration of an image degraded by additive white Gaussian noise is presented. The algorithm proposed in this paper is an extension of Lee's algorithm modified to use local gradient information as well as local statistics. It does not require image modeling, and removes noise along the orientaiton of edges so that it does not blur the edge.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1129-1144
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• 2020

The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the high frequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposed based on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image is decomposed into low frequency subgraphs and high frequency subgraphs with different resolutions using wavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphs which represent structural information of the image. At the same time, for the high frequency subgraphs which represent textural information of the image, the extracted and repaired edge contour information is used to constrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texture synthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved method is compared with the existing three methods. It is found that the improved method is superior to them in inpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchical method combined with structural constraints has a good effect on the edge damage of thangka images.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2005.10a
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• pp.297-302
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• 2005

In this paper, an estimating damage on external surface of container using Capsize-Gaussian-Function (be called CGF) is presented. The estimation of the damage size can be get directly from two parameters of CGF, these are the depth and the flexure, also the direction of damage. The performance of the present method has been illustrated using an image of damage container, which had been taken from Hanjin Busan Port, after using image processing techniques to do preprocessing of the image, especially, the main used technique is Canny edge detecting that is widely used in computer vision to locate sharp intensity and to find object boundaries in the image, then correlation between the edge image from the preprocessing step and the CGF with three parameters (direction, depth, flexure), as a result, we get an image that perform damage information, and these parameters is an estimator directly to the damage.

• Journal of Sensor Science and Technology
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• v.30 no.5
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• pp.286-289
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• 2021

Image classification is one of the fundamental applications of computer vision. It enables a system to identify an object in an image. Recently, image classification applications have broadened their scope from computer applications to edge devices. The convolutional neural network (CNN) is the main class of deep learning neural networks that are widely used in computer tasks, and it delivers high accuracy. However, CNN algorithms use a large number of parameters and incur high computational costs, which hinder their implementation in edge hardware devices. To address this issue, this paper proposes a lightweight image classifier that provides good accuracy while using fewer parameters. The proposed image classifier diverts the input into three paths and utilizes different scales of receptive fields to extract more feature maps while using fewer parameters at the time of training. This results in the development of a model of small size. This model is tested on the CIFAR-10 dataset and achieves an accuracy of 90% using .26M parameters. This is better than the state-of-the-art models, and it can be implemented on edge devices.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.195-203
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• 2022

Noise generated during image acquisition and transmission can negatively impact the results of image processing applications, and noise removal is typically a part of image preprocessing. Denoising techniques combined with nonlocal techniques have received significant attention in recent years, owing to the development of sophisticated hardware and image processing algorithms, much attention has been paid to; however, this approach is relatively poor for edge preservation of fine image details. To address this limitation, the current study combined a steering kernel technique with adaptive masks that can adjust the size according to the noise intensity of an image. The algorithm sets the steering weight based on a similarity comparison, allowing it to respond to edge components more effectively. The proposed algorithm was compared with existing denoising algorithms using quantitative evaluation and enlarged images. The proposed algorithm exhibited good general denoising performance and better performance in edge area processing than existing non-local techniques.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.201-205
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• 2004

The digital color image can be distorted by noise for a transmission or other elements of system. It happens to vague of a boundary side in the division of a color image object, especially, boundary side of an input color image is very important because it can be determined to the division and detection element in pattern recognition. Therefore it is boundary part In this paper, it detects the optimal edge with applying this color image to WCNN algorithm, after it does level up a boundary side of a color image by using the adaptive morphology as the threshold of an input color image. Also, it is used not a conventional fixed mask edge detection method but variable mask method which is cal led a variable BBM. It is confirmed by simulation that the proposed algorithm can be got the batter result edge at the place of closing to each edges and having smoothly curved line.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.10
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• pp.1294-1304
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• 1995

In this paper, a method has been proposed for the fully automatic detection of left ventricular endocardial boundary in B-mode short axis echocardiography without manual intervention by human operator. The proposed method makes use of the weighted model that approximates to endocardium and incomplete edge information for echocardiography. Therefore, this method is more effective than boundary detection by only edge information. The implementation of this method is as follows. First, the proposed algorithms are used in order to detect the approximate boundary, then a weighted model with the approximate boundary is constructed. Finally, the cavity center of the left ventricle performing the Hough transform with the weighted model and edge image can be found automatically, and then the endocardial boundary using detected center, original image, weighted model, and edge image can be detected. validations of this method with experimental results on echo image of dog's heart and clinical echocardiography is verified.