• The Journal of the Korea Contents Association
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• v.13 no.3
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• pp.204-210
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• 2013

The patients who had been operated total mastectomy are different from general women in their breasts thickness due to breast surgery. As a result, digital chest image from total mastectomy patients will be different attenuation. The main objective for this study is to show that a proper Ion chamber standard combination measuring MTF which is objective basis for Digital image, when be x-ray for total mastectomy patient. We have designed the unique number that shown Left is 1, Right is 2, Center is 3 and have put the edge phantom on detector ion chamber. Lastly, we have obtained experiment images. The evaluations of all image quality have measured by 50% MTF of spatial resolution and absorption dose using Matlab(R2007a). The result showed that average exposure condition, MTF value, absorption dose for 1+3 and 2+3 combinations were 2.745 mAs, 1.925 lp/mm, 0.688 mGy. Consequently, that showed high MTF, DQE and low dose than other combinations. Therefore, a proper changes of ion chambers are able to improve image quality and to reduce radiation exposure when be X-ray for total mastectomy patients. Also, it will be possible to standard for application chamber combination and utilization on clinical detection.

• Journal of KIISE:Software and Applications
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• v.37 no.1
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• pp.29-38
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• 2010

We propose an interactive projection technique to display details of a large image in a high resolution and brightness by tracking a portable projector. A closed-loop based tracking method is presented to update the projected image while a user changes the position of the detail area by moving the portable projector. A marker is embedded in the large image to indicate the position to be occupied by the detail image projected by the portable projector. The marker is extracted in sequential images acquired by a camera attached to the portable projector. The marker position in the large display image is updated under a constraint that the center positions of marker and camera frame coincide in every camera frame. The image and projective transformation for warping are calculated using the marker position and shape in the camera frame. The marker's four corner points are determined by a four-step segmentation process which consists of camera image preprocessing based on HSI, edge extraction by Hough transformation, quadrangle test, and cross-ratio test. The interactive projection system implemented by the proposed method performs at about 24fps. In the user study, the overall feedback about the system usability was very high.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.12-20
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• 2018

Efforts have been made to reconstruct low-resolution underwater images to high-resolution ones by using the image SR (Super-Resolution) method, all to improve efficiency when acquiring side-scan sonar images. As side-scan sonar images are similar with the optical images with respect to exploiting 2-dimensional signals, conventional image restoration methods for optical images can be considered as a solution. One of the most typical super-resolution methods for optical image is a sparse coding and there are studies for verifying applicability of sparse coding method for underwater images by analyzing sparsity of underwater images. Sparse coding is a method that obtains recovered signal from input signal by linear combination of dictionary and sparse coefficients. However, it requires huge computational load to accurately estimate sparse coefficients. In this study, a sparse coding based underwater image super-resolution method is applied while a selective reconstruction method for object region is suggested to reduce the processing time. For this method, this paper proposes an edge detection and object and non object region classification method for underwater images and combine it with sparse coding based image super-resolution method. Effectiveness of the proposed method is verified by reducing the processing time for image reconstruction over 32 % while preserving same level of PSNR (Peak Signal-to-Noise Ratio) compared with conventional method.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.438-456
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• 2010

Iris recognition uses a unique iris pattern of user to identify person. In order to enhance the performance of iris recognition, it is reported that the diameter of iris region should be greater than 200 pixels in the captured iris image. So, the previous iris system used zoom lens camera, which can increase the size and cost of system. To overcome these problems, we propose a new method of enhancing the accuracy of iris recognition on low-resolution iris images which are captured without a zoom lens. This research is novel in the following two ways compared to previous works. First, this research is the first one to analyze the performance degradation of iris recognition according to the decrease of the image resolution by excluding other factors such as image blurring and the occlusion of eyelid and eyelash. Second, in order to restore a high-resolution iris image from single low-resolution one, we propose a new method based on multiple multi-layered perceptrons (MLPs) which are trained according to the edge direction of iris patterns. From that, the accuracy of iris recognition with the restored images was much enhanced. Experimental results showed that when the iris images down-sampled by 6% compared to the original image were restored into the high resolution ones by using the proposed method, the EER of iris recognition was reduced as much as 0.133% (1.485% - 1.352%) in comparison with that by using bi-linear interpolation

• Journal of IKEEE
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• v.21 no.4
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• pp.412-415
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• 2017

In this paper, we propose a multi license plate recognition system using high resolution $360^{\circ}$ omnidirectional IP camera. The proposed system consists of a planar division part of $360^{\circ}$ circular image and a multi license plate recognition part. The planar division part of the $360^{\circ}$ circular image are divided into a planar image with enhanced image quality through processes such as circular image acquisition, circular image segmentation, conversion to plane image, pixel correction using color interpolation, color correction and edge correction in a high resolution $360^{\circ}$ omnidirectional IP Camera. Multi license plate recognition part is through the multi-plate extraction candidate region, a multi-plate candidate area normalized and restore, multiple license plate number, character recognition using a neural network in the process of recognizing a multi-planar imaging plates. In order to evaluate the multi license plate recognition system using the proposed high resolution $360^{\circ}$ omnidirectional IP camera, we experimented with a specialist in the operation of intelligent parking control system, and 97.8% of high plate recognition rate was confirmed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.295-302
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• 2018

Face recognition (FR) has been widely used in various applications, such as smart surveillance systems, immigration control in airports, user authentication in smart devices, and so on. FR in well-controlled conditions has been extensively studied and is relatively mature. However, in unconstrained conditions, FR performance could degrade due to undesired characteristics of the input face image (such as irregular facial pose variations). To overcome this problem, this paper proposes a new method for determining if an input image is suitable for FR. In the proposed method, for an input face image, reconstruction error is computed by using a predefined set of reference face images. Then, suitability can be determined by comparing the reconstruction error with a threshold value. In order to reduce the effect of illumination changes on the determination of suitability, a preprocessing algorithm is applied to the input and reference face images before the reconstruction. Experimental results show that the proposed method is able to accurately discriminate non-frontal and/or incorrectly aligned face images from correctly aligned frontal face images. In addition, only 3 ms is required to process a face image of $64{\times}64$ pixels, which further demonstrates the efficiency of the proposed method.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.107-114
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• 2022

In order to classify 3D film images as right or wrong, it is necessary to detect the pattern in a 3D film image. However, if the contrast of the pixels in the 3D film image is low, it is not easy to classify as the right and wrong 3D film images because the pattern in the image might not be clear. In this paper, we propose a method of classifying 3D film images as right or wrong by comparing the width at a specific frequency of each histogram after obtaining the histogram. Since, it is classified using the width of the histogram, the analysis process is not complicated. From the experiment, the histograms of right and wrong 3D film images were distinctly different, and the proposed algorithm reflects these features, and showed that all 3D film images were accurately classified at a specific frequency of the histogram. The performance of the proposed algorithm was verified to be the best through the comparison test with the other methods such as image subtraction, otsu thresholding, canny edge detection, morphological geodesic active contour, and support vector machines, and it was shown that excellent classification accuracy could be obtained without detecting the patterns in 3D film images.

• Journal of the Korea Computer Graphics Society
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• v.14 no.3
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• pp.1-9
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• 2008

This paper presents a novel optimization-based saliency-preserving method for converting color images to grayscale in a manner consistent with conventional approaches of black-and-white photographers. In black-and-white photography, a colored filter called a contrast filter has been commonly employed on a camera to lighten or darken selected colors. In addition, local exposure controls such as dodging and burning techniques are typically employed in the darkroom process to change the exposure of local areas within the print without affecting the overall exposure. Our method seeks a digital version of a conventional contrast filter to preserve visually-important image features. Furthermore, conventional burning and dodging techniques are addressed, together with image similarity weights, to give edge-aware local exposure control over the image space. Our method can be efficiently optimized on GPU. According to the experiments, CUDA implementation enables 1 megapixel color images to be converted to grayscale at interactive frames rates.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.1966-1972
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• 2011

Optical flow estimation based on multi constraint approaches is frequently used for recognition of moving objects. This paper proposed the method to monitor passenger boarding using image processing when a train is operated based on Automatic Train Operation(ATO). The movement of passenger can be detected to compare two images, one is a basic image and another is immediately captured by CCTV. Optical Flow helps to find the movement of passenger when two images are compared. The movement of passenger is one of important informations for ATO system because it needs to decide door status.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.53-62
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• 2013

This paper proposes a method of automatically predicting the vanishing point for the purpose of detecting the road region from natural images. The proposed method stably detects the vanishing point in the road environment by analyzing the dominant orientation of the image and predicting the vanishing point to be at the position where the feature components of the image are concentrated. For this purpose, in the first stage, the image is partitioned into sub-blocks, an edge sample is selected randomly from within the sub-block, and RANSAC is applied for line fitting in order to analyze the dominant orientation of each sub-block. Once the dominant orientation has been detected for all blocks, we proceed to the second stage and randomly select line samples and apply RANSAC to perform the fitting of the intersection point, then measure the cost of the intersection model arising from each line and we predict the vanishing point to be located at the average point, based on the intersection point model with the highest cost. Lastly, quantitative and qualitative analyses are performed to verify the performance in various situations and prove the efficiency of the proposed algorithm for detecting the vanishing point.