• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.6
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• pp.609-617
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• 2020

Non-destructive inspection using X-rays is used as a method to check the inside of products. In order to accurately inspect, a X-ray image requires a higher spatial resolution. However, the reduction in pixel size of the X-ray detector, which determines the spatial resolution, is time-consuming and expensive. In this regard, a DMM has been proposed to obtain an improved spatial resolution using the same X-ray detector. However, this has a limitation that the motion blur phenomenon, which is a decrease in spatial resolution. In this paper, motion blur was removed by applying Non-Blind Deconvolution to the DMM image, and the increase in spatial resolution was confirmed. DMM and Non-Blind Deconvolution were sequentially applied to X-ray images, confirming 62 % MTF value by an additional 29 % over 33 % of DMM only. In addition, SSIM and PSNR were compared to confirm the similarity to the 1/2 pixel detector image through 0.68 and 33.21 dB, respectively.

• Journal of Digital Convergence
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• v.14 no.11
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• pp.319-324
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• 2016

In the case of robot and Argument Reality applications using a camera in environments, a technology to estimate planes is a very important technology. A RGB-D camera can get a three-dimensional measurement data even in a flat which has no information of the texture of the plane;, however, there is an enormous amount of computation in order to process the point-cloud data of the image. Furthermore, it could not know the number of planes that are currently observed as an advance, also, there is an additional operation required to estimate a three dimensional plane. In this paper, we proposed the real-time method that decides the number of planes automatically and estimates the three dimensional plane by using the continuous data of an RGB-D camera. As experimental results, the proposed method showed an improvement of approximately 22 times faster speed compared to processing the entire data.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.568-574
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• 2020

In this paper, we propose a method to reduce the square root computation having high computation complexity in Canny edge detection algorithm using image processing. The proposed method is to reduce the number of operation calculating gradient magnitude using pixel's continuity using make a specific pattern instead of square root computation in gradient magnitude calculating operation. Using various test images and changing number of hole pixels, we can check for calculate match rate about 97% for one hole, and 94%, 90%, 88% when the number of hole is increased and measure decreasing computation time about 0.2ms for one hole, and 0.398ms, 0.6ms, 0.8ms when the number of hole is increased. Through this method, we expect to implement low power embedded vision system through high accuracy and a reduced operation number using two-hole pixels.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.2 s.5
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• pp.19-28
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• 2004

This paper shows how to compensate the skew from the traffic sign included in the natural image and extract the text. The research deals with the Process related to the array image. Ail the process comprises four steps. In the first fart we Perform the preprocessing and Canny edge extraction for the edge in the natural image. In the second pan we perform preprocessing and postprocessing for Hough Transform in order to extract the skewed angle. In the third part we remove the noise images and the complex lines, and then extract the candidate region using the features of the text. In the last part after performing the local binarization in the extracted candidate region, we demonstrate the text extraction by using the differences of the features which appeared between the tett and the non-text in order to select the unnecessary non-text. After carrying out an experiment with the natural image of 100 Pieces that includes the traffic sign. The research indicates a 82.54 percent extraction of the text and a 79.69 percent accuracy of the extraction, and this improved more accurate text extraction in comparison with the existing works such as the method using RLS(Run Length Smoothing) or Fourier Transform. Also this research shows a 94.5 percent extraction in respect of the extraction on the skewed angle. That improved a 26 percent, compared with the way used only Hough Transform. The research is applied to giving the information of the location regarding the walking aid system for the blind or the operation of a driverless vehicle

• Journal of Biomedical Engineering Research
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• v.16 no.3
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• pp.289-300
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• 1995

The conventionally used method requires centerline of vessels to estimate the vessel diameter. Two methods of estimating the centerline of vessels are reported : One is manually observer-defined method. This potentially contributes to inter-and intra-observer variability. And the other is to auto- matically detect the centerline of vessels. But this is very complicated method. In this paper, we propose a new method of estimating vessel diameter using direction codes and position informs:ion without detecting centerline. Since this method detects the vessel boundary and direction code at d same time, it simplifies the procedure and reduces execution time in estimating the vessel diameter. Compared to a method that automatically estimates the vessel diAmeter uslng centerline, our method provides improved accuracy in image with poor contrast, branching or obstructed vessels. Also, this provides a good compression of boundary description, because each direction code element can be coded with 3 bits only, instead of the 4 bytes required for the storage of the coordinates of each border pixel. Our experiments demonstrate the usefulness of the technique using direction code for quantitative analysis of coronary angiography Experimental results Justify the validity of the proposed method.

• Journal of Korea Multimedia Society
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• v.3 no.5
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• pp.454-460
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• 2000

To retrieve complex data such as images, the content-based retrieval method rather than keyword based method is required. In this paper, we implemented a content-based image retrieval system which retrieves object of user query effectively using invariant moments which have invariant properties about linear transformation like position transition, rotation and scaling. To extract the shape feature of objects in an image, we propose a labeling algorithm that extracts objects from an image and apply invariant moments to each object. Hashing method is also applied to reduce a retrieval time and index images effectively. The experimental results demonstrate the high retrieval efficiency i.e precision 85%, recall 23%. Consequently, our retrieval system shows better performance than the conventional system that cannot express the shale of objects exactly.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.497-502
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• 2016

Shape Contexts Recognition(SCR) is a technology recognizing shapes such as figures and objects, greatly supporting technologies such as character recognition, motion recognition, facial recognition, and situational recognition. However, generally SCR makes histograms for all contours and maps the extracted contours one to one to compare Shape A and B, which leads to slow progress speed. Thus, this paper has made simple yet more effective algorithm with optimized contour, finding the outlines according to shape figures and using the improved Douglas-Peucker algorithm and Harris corner detector. With this improved method, progress speed is recognized as faster.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.55-65
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• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Journal of Platform Technology
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• v.12 no.1
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• pp.44-54
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• 2024

The existing indoor positioning methods using beacons or tags suffer from issues such as occasional undetection or increased errors due to noise. In this paper, we propose a method for determining the indoor position of a robot using the distance and, the angle between the direction of a target object whose position is known and the direction in which the robot views the target object from the front. The method proposed in this paper utilizes a depth camera to measure distance and calculate angles. Distance is measured using depth information captured by the camera, while angles are determined using images captured by the camera to determine the orientation of the target object. The proposed method calculates coordinate displacements using distance and angle. And then the method determines the position of the mobile robot using these displacements and the coordinates of the target object. To show the applicability of the proposed method for indoor positioning, we conducted experimental implementation and compared measured displacements. The results showed errors within 50mm, but considering the size of the mobile robot, it is judged that the method proposed in this paper can be sufficiently used for indoor positioning.

• Journal of the Korean Vacuum Society
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• v.18 no.2
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• pp.108-115
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• 2009

The superlattice infrared photodetector (SLIP) with an active layer of 8/8-ML InAs/GaSb type-II strained-layer superlattice (SLS) of 150 periods was grown by MBE technique, and the proto-type discrete device was defined with an aperture of $200-{\mu}m$ diameter. The contrast profile of the transmission electron microscope (TEM) image and the satellite peak in the x-ray diffraction (XRD) rocking curve show that the SLS active layer keeps abrupt interfaces with a uniform thickness and a periodic strain. The wavelength and the bias-voltage dependences of responsivity (R) and detectivity ($D^*$) measured by a blackbody radiation source give that the cutoff wavelength is ${\sim}5{\mu}m$, and the maximum Rand $D^*$ ($\lambda=3.25{\mu}m$) are ${\sim}10^3mA/W$ (-0.6 V/13 K) and ${\sim}10^9cm.Hz^{1/2}/W$ (0 V/13 K), respectively. The activation energy of 275 meV analyzed from the temperature dependent responsivity is in good agreement with the energy difference between two SLS subblevels of conduction and valence bands (HH1-C) involving in the photoresponse process.