• IEIE Transactions on Smart Processing and Computing
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• v.1 no.1
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• pp.17-26
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• 2012

This paper presents an image processing-based validation method for unrecognizable numbers in severely distorted license plate images which have been degraded by various factors including low-resolution, low light-level, geometric distortion, and periodic noise. Existing vehicle license plate recognition (LPR) methods assume that most of the image degradation factors have been removed before performing the recognition of printed numbers and letters. If this is not the case, conventional LPR becomes impossible. The proposed method adopts a novel approach where a set of reference number images are intentionally degraded using the same factors estimated from the input image. After a series of image processing steps, including geometric transformation, super-resolution, and filtering, a comparison using cross-correlation between the intentionally degraded reference and the input images can provide a successful identification of the visually unrecognizable numbers. The proposed method makes it possible to validate numbers in a license plate image taken under low light-level conditions. In the experiment, using an extended set of test images that are unrecognizable to human vision, the proposed method provides a successful recognition rate of over 95%, whereas most existing LPR methods fail due to the severe distortion.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.55-68
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• 2012

In this paper, we propose a novel method to solve the problem of excessive segmentation out of the method of segmenting regions from an image using Homogeneity Threshold($H_T$). The algorithm of the previous image segmentation based on $H_T$ was carried out region growth by using only the center pixel of selected window. Therefore it was caused resulting in excessive segmented regions. However, before carrying region growth, the proposed method first of all finds out whether the selected window is homogeneity or not. Subsequently, if the selected window is homogeneity it carries out region growth using the total pixels of selected window. But if the selected window is not homogeneity, it carries out region growth using only the center pixel of selected window. So, the method can reduce remarkably the number of excessive segmented regions of image segmentation based on $H_T$. In order to show the validity of the proposed method, we carried out multiple experiments to compare the proposed method with previous method in same environment and conditions. As the results, the proposed method can reduce the number of segmented regions above 40% and doesn't make any difference in the quality of visual image when we compare with previous method. Especially, when we compare the image united with regions of descending order by size of segmented regions in experimentation with the previous method, even though the united image has regions more than 1,000, we can't recognize what the image means. However, in the proposed method, even though image is united by segmented regions less than 10, we can recognize what the image is. For these reason, we expect that the proposed method will be utilized in various fields, such as the extraction of objects, the retrieval of informations from the image, research for anatomy, biology, image visualization, and animation and so on.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.527-532
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• 2010

This paper is concerned with the measurement of static and dynamic displacement by image processing(IP) and study for prediction method of velocity and acceleration. To measure the displacement visually, the measurement system consists of a telephoto zoom camera, ccd image device and a computer. The specific target on the white board is used to calculate the displacement of the 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 pixel-size of image. In this paper, we developed for the displacement measurement using the image processing method. The proposed method enables us to measure the vibration measurement, velocity and acceleration directly without any contact. The current resolution of the displacement measurement is limited to 1/100 millimeter scale.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.56-62
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• 2004

This research attempts to suggest a detecting method for cutting position of an object using the neural network, which is one of intellectual methods, and the digital image processing method. The extraction method of object information using the image data obtained from the CCD camera as a replacement of traditional analog sensor thanks to the development of digital image processing. Accordingly, this research determines the threshold value in binary-coding of an input image with the help of image processing method and the neural network for the real-time gray-leveled input image in substitution for lighting; as a result, a specific position is detected from the processed binary-coded image and an actual system designed is suggested as an example.

• Journal of Information Display
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• v.9 no.4
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• pp.39-44
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• 2008

This paper investigates the effects of the existing sealing methods, such as the conventional atmospheric-pressure sealing method and vacuum sealing, on temporal bright-image sticking. To produce a residual image caused by temporal brightimage sticking, the entire region of a 42-in panel with an Xe-(11%)-He(35%) gas mixture was abruptly changed to a full-white background image after displaying a square-type image at peak luminance for about 60s. From the monitoring of the difference in the display luminance, infrared emission, color temperature, and disappearing time between the cells with and without temporal bright-image sticking, it was observed that the vacuum sealing method contributes to the reduction of temporal bright-image sticking.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.96-101
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• 2001

The development of a new experimental method is required to easily observe the growth behavior of fatigue cracks. To satisfy the requirement, an image processing technique was introduced to fatigue testing. The length of surface fatigue crack could be successfully measured by the image processing system. At first, the image data of cracks were stored into the computer while the cyclic loading was interrupted. After testing, crack length was determined using image processing software which was developed by ourselves. Block matching method was applied to the detection of surface fatigue cracks. By comparing the data measured by image processing system with the data measured by manual measurement with a microscope, the effectiveness of the image processing system was established. If the proposed method is used to monitor and observe the crack growth behavior automatically, the time and efforts for fatigue test could be dramatically reduced.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.112-119
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• 2011

This paper is concerned with the measurement of static and dynamic displacement by image processing(IP) and study for prediction method of velocity and acceleration. To measure the displacement visually, the measurement system consists of a telephoto zoom camera, CCD(charge coupled device) image device and a computer. The specific target on the white board is used to calculate the displacement of the 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 pixel-size of image. In this paper, we developed for the displacement measurement using the image processing method. The proposed method enables us to measure the vibration displacement, velocity and acceleration directly without any contact. The current resolution for the displacement measurement can be seen from the results.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.670-675
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• 2022

This study proposes an improved binarization method to improve image recognition rate. The research goal is to minimize the information loss that occurs during the binarization process, and to transform the object of the original image that cannot be determined through the transformation process into an image that can be judged. The proposed method uses a stepwise segmentation method of an image and divides blocks using prime numbers. Also, within one block, a trapezoidal type of fuzzy is applied. The fuzzy trapezoid is binarized by dividing the brightness histogram area into three parts according to the degree of membership. As a result of the experiment, information loss was minimized in general images. In addition, it was found that the converted binarized image expressed the object better than the original image in the special image in which the brightness region was tilted to one side.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.338-339
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• 2021

In this paper, we proposed a multiclass image scene classification method based on transform learning. The method using the Residual Network (ResNet) model which pre-trained on the large image dataset ImageNet for image classification. Compared with the image classification method of the CNN model, it can greatly improve the classification accuracy and efficiency

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3620-3637
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• 2019

Since the birth of Synthetic Aperture Radar (SAR), it has been widely used in the military field and so on. However, the existence of speckle noise makes a good deal inconvenience for the subsequent image processing. The continuous development of sparse representation (SR) opens a new field for the speckle suppressing of SAR image. Although the SR de-noising may be effective, the over-smooth phenomenon still has bad influence on the integrity of the image information. In this paper, one novel SAR image de-noising method based on residual image fusion and sparse representation is proposed. Firstly we can get the similar block groups by the non-local similar block matching method (NLS-BM). Then SR de-noising based on the adaptive K-means singular value decomposition (K-SVD) is adopted to obtain the initial de-noised image and residual image. The residual image is processed by Shearlet transform (ST), and the corresponding de-noising methods are applied on it. Finally, in ST domain the low-frequency and high-frequency components of the initial de-noised and residual image are fused respectively by relevant fusion rules. The final de-noised image can be recovered by inverse ST. Experimental results show the proposed method can not only suppress the speckle effectively, but also save more details and other useful information of the original SAR image, which could provide more authentic and credible records for the follow-up image processing.