• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• Imaging Science in Dentistry
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• v.45 no.2
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• pp.81-87
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• 2015

Purpose: Contrast, sharpness, enhancement, and density can be changed in digital systems. The important question is to what extent the changes in these variables affect the accuracy of caries detection. Materials and Methods: Forty eight extracted human posterior teeth with healthy or proximal caries surfaces were imaged using a photostimulable phosphor (PSP) sensor. All original images were processed using a six-step method: (1) applying "Sharpening 2" and "Noise Reduction" processing options to the original images; (2) applying the "Magnification 1:3" option to the image obtained in the first step; (3) enhancing the original images by using the "Diagonal/"option; (4) reviewing the changes brought about by the third step of image processing and then, applying "Magnification 1:3"; (5) applying "Sharpening UM" to the original images; and (6) analyzing the changes brought about by the fifth step of image processing, and finally, applying "Magnification 1:3." Three observers evaluated the images. The tooth sections were evaluated histologically as the gold standard. The diagnostic accuracy of the observers was compared using a chi-squared test. Results: The accuracy levels irrespective of the image processing method ranged from weak (18.8%) to intermediate (54.2%), but the highest accuracy was achieved at the sixth image processing step. The overall diagnostic accuracy level showed a statistically significant difference (p=0.0001). Conclusion: This study shows that the application of "Sharpening UM" along with the "Magnification 1:3" processing option improved the diagnostic accuracy and the observer agreement more effectively than the other processing procedures.

• KIISE Transactions on Computing Practices
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• v.23 no.3
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• pp.188-193
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• 2017

In this paper, we propose automatic OpenCL optimization techniques that offer the best performance for image processing programs on any hardware system. Developers should seek a proper way of parallelization and an appropriate work-group size for the architecture of target compute devices to achieve the best performance. However, testing potential devices to find them is both time-consuming and costly. Our techniques automatically set up hardware-optimized parallelization and find a suitable work-group size for the target device. Furthermore, using OpenCL does not always provide better performance in image processing. Hence, we also propose a way to automatically search for a threshold image size to allow image processing programs to decide whether or not to use OpenCL. Our findings demonstrate that out techniques improve the image processing performance significantly.

• Journal of Animal Environmental Science
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• v.7 no.3
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• pp.183-190
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• 2001

The objective of this research was to design and construct an image processing system to measure easily and accurately cow's weight. The image processing system was built for a dairy cattle to be measured and estimated it's weight using camera and personal computer. The pixel numbers, which was derived from the image processing system, were counted to estimate the weight of a dairy cattle. They were utilized various was for finding the relationships between pixel numbers and it's real weight. Based on the results of this research the following conclusions were made: 1. It's weight could be estimated by using pixel numbers, which was captured from top and side cameras to measure it. The correlations with tea-view pixel numbers, side-view pixel numbers, superficial area pixel numbers and the volume pixel numbers were 0.909, 0.939, 0.944 and 0.965. 2. 50 cattle was used to execute an experiment with the image processing system, but average errors were big to make out the good relationship between cow's weight and pixel numbers. In order measure accurately a cattle weight, cattle weight, cattle groups would be divided by the age of cattle and further study should be carried out to be based on the results of this research. 3. The average time it took to perform the image processing to be measure it was 10 seconds, but it took 10 minutes for cattle to enter for measuring it's weight into the weighting system.

• Journal of IKEEE
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• v.27 no.4
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• pp.494-500
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• 2023

Image processing is currently used in various fields. Among them, autonomous vehicles, medical image processing, and robot control require fast image processing response speeds. To fulfill this requirement, hardware design for real-time processing is being actively researched. In addition to the size of the input image, the hardware processing speed is affected by the size of the inactive video periods that separate lines and frames in the image. In this paper, we design three different scaler structures based on the type of line memories, which is closely related to the inactive video periods. The structures are designed in hardware using the Verilog standard language, and synthesized into logic circuits in a field programmable gate array environment using Xilinx Vivado 2023.1. The synthesized results are used for frame rate analysis while comparing standard image sizes that can be processed in real time.

• Journal of the Korean Society of Visualization
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• v.6 no.2
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• pp.20-27
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• 2008

The color sorting technique utilizing the image processing method is very applicable tool to analyze motion of a free-falling object in many agricultural and industrial research fields. In the present study, we have developed an image processing system and algorithm to sort good quality rice grains effectively from the bad ones. The system employs a high speed rate line-scan CCD camera with 2K-pixels and worked with a high speed DSP and FPGA in-line. It can accumulate acquired line-scan image data and visualize each grain image clearly. As a result, we can easily calculate the number of pixels occupied by grain(=grain size), gray level and its correct position by visualizing grain images rapidly.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.2
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• pp.294-302
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• 2008

In this paper, we present a new image up-sampling method which registers low resolution images to the high resolution grid when Bayesian super-resolution image processing is performed. The proposed up-sampling method interpolates high-resolution pixels using high-frequency data lying in all the low resolution images, instead of up-sampling each low resolution image separately. The interpolation is based on B-spline non-uniform re-sampling, adjusted for the super-resolution image processing. The experimental results demonstrate the effects when different up-sampling methods generally used such as zero-padding or bilinear interpolation are applied to the super-resolution image reconstruction. Then, we show that the proposed hybird up-sampling method generates high-resolution images more accurately than conventional methods with quantitative and qualitative assess measures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.9
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• pp.4375-4388
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• 2018

To solve the problem of transmission errors in stereoscopic images, this paper proposes a novel error concealment (EC) method using superpixel segmentation and adaptive disparity selection (SSADS). Our algorithm consists of two steps. The first step is disparity estimation for each pixel in a reference image. In this step, the numbers of superpixel segmentation labels of stereoscopic images are used as a new constraint for disparity matching to reduce the effect of mismatching. The second step is disparity selection for a lost block. In this step, a strategy based on boundary smoothness is proposed to adaptively select the optimal disparity which is used for error concealment. Experimental results demonstrate that compared with other methods, the proposed method has significant advantages in both objective and subjective quality assessment.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.11a
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• pp.503-507
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• 2004

This paper is concerned with the measurement of low-frequency vibrations of structures using the image processing method. To measure the vibrations visually, the measurement system consists of a camera, an image grabber board, and a computer. The specific target installed on the structure is used to calculate the vibration of structure. The captured image is then converted into a pixel-based data and then analyzed numerically. The limitation of the system depends on the image capturing speed and the size of image. In this paper, we discuss the methodology for the vibration measurement using the image processing method. The method enables us to measure the displacement directly without any contact. The resolution of the vibration measurement can be refined but limited to the sub centimeter displacement.

• Ocean Science Journal
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• v.40 no.4
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• pp.213-220
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• 2005

In this paper, image processing technique that reduces video images of buoy motion to yield time series of image coordinates of buoy objects will be investigated. The buoy motion images are noisy due to time-varying brightness as well as non-uniform background illumination. The occurrence of boats, wakes, and wind-induced white caps interferes significantly in recognition of buoy objects. Thus, semi-automated procedures consisting of object recognition and image measurement aspects will be conducted. These offer more satisfactory results than a manual process. Spectral analysis shows that the image coordinates of buoy objects represent wave motion well, indicating its usefulness in the analysis of wave characteristics.