• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.42-47
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• 2017

In the distribution of digital image, the median filtering is used for a forgery. This paper proposed the algorithm of a image forensics detection for the classification of median filtering. For the solution of this grave problem, the feature vector is composed of 42-Dim. The detected quantity 32, 64 and 128 of forgery image edges, respectively, which are processed by the Hough transform, then it extracted from the start-end point coordinates of the Hough Lines. Also, the Hough Peaks of the Angle-Distance plane are extracted. Subsequently, both of the feature vectors are composed of the proposed scheme. The defined 42-Dim. feature vector is trained in SVM (Support Vector Machine) classifier for the MF classification of the forged images. The experimental results of the proposed MF detection algorithm is compared between the 10-Dim. MFR and the 686-Dim. SPAM. It confirmed that the MF forensic classification ratio of the evaluated performance is 99% above with the whole test image types: the unaltered, the average filtering ($3{\times}3$), the JPEG (QF=90 and 70)) compression, the Gaussian filtered ($3{\times}3$ and $5{\times}5$) images, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.1
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• pp.13-18
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• 2017

With the advancement of internet technology, the importance of data protection is gaining more attention. As a possible data protection solution, we propose a novel video encryption method using complemented maximum-length cellular automata (C-MLCA). The first step for encryption is to use 90/150 CA rule to generate a transition matrix T of a C-MLCA state followed by a 2D C-MLCA basis image. Then, we divide the video into multiple frames. Once, we perform exclusive-OR operation with the split frames and the 2D basis image, the final encrypted video can be obtained. By altering values of pixel, the fundamental information in visualizing image data, the proposed method provides improved security. Moreover, we carry out some computational experiments to further evaluate our method where the results confirm its feasibility.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.797-798
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• 2005

We report a novel structure for a full-color AMOLED (Active Matrix Organic Light Emitting Diode) eliminating the patterning process of a blue emitting layer. The patterning of the three primary colors, RGB, is a key technology in the OLED fabrication process. Conventional full color AMOLED containing RGB layers includes the three opportunities of the defects to make an accurate position and fine resolution using various technologies such as fine metal mask, ink-jet printing and laser-induced transfer system. We can skip the blue patterning step by simply stacking the blue layer as a common layer to the whole active area after pixelizing two primary colors, RG, in the conventional small molecular OLED structure. The red and green pixel showed equivalent performances without any contribution of the blue emission.

• International Journal of Knowledge Content Development & Technology
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• v.7 no.1
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• pp.57-78
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• 2017

There are several statistical classification algorithms available for land use/land cover classification. However, each has a certain bias or compromise. Some methods like the parallel piped approach in supervised classification, cannot classify continuous regions within a feature. On the other hand, while unsupervised classification method takes maximum advantage of spectral variability in an image, the maximally separable clusters in spectral space may not do much for our perception of important classes in a given study area. In this research, the output of an ANN algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means on both moderate resolutions Landsat8 and a high resolution Formosat 2 images. The Formosat 2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat 8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms. When you compare the results of the two methods on a per-class-basis, ANN had a crisper output compared to PCM which yielded clusters with pixels especially on the moderate resolution Landsat 8 imagery.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.1
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• pp.74-82
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• 2008

The objective of this paper is to remove noises of image based on the heuristic noises filter and to extract a tumor region by using morphology techniques in breast ultrasound image. Similar objective studies have been conducted based on ultrasound image of high resolution. As a result, efficiency of noise removal is not fine enough for low resolution image. Moreover, when ultrasound image has multiple tumors, the extraction of ROI (Region Of Interest) is not accomplished or processed by a manual selection. In this paper, our method is done 4 kinds of process for noises removal and the extraction of ROI for solving problems of restrictive automated segmentation. First process is that pixel value is acquired as matrix type. Second process is a image preprocessing phase that is aimed to maximize a contrast of image and prevent a leak of personal information. In next process, the heuristic noise filter that is based on opinion of medical specialist is applied to remove noises. The last process is to extract a tumor region by using morphology techniques. As a result, the noise is effectively eliminated in all images and a extraction of tumor regions is possible though one ultrasound image has several tumors.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.4
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• pp.207-213
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• 2018

This paper investigates applicability of Microsoft $Kinect^{(R)}$, RGB-depth camera, to implement a 3D image and spatial information for sensing a target. The relationship between the image of the Kinect camera and the pixel coordinate system is formulated. The calibration of the camera provides the depth and RGB information of the target. The intrinsic parameters are calculated through a checker board experiment and focal length, principal point, and distortion coefficient are obtained. The extrinsic parameters regarding the relationship between the two Kinect cameras consist of rotational matrix and translational vector. The spatial images of 2D projection space are converted to a 3D images, resulting on spatial information on the basis of the depth and RGB information. The measurement is verified through comparison with the length and location of the 2D images of the target structure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.3
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• pp.221-231
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• 1990

In this paper, A new 2-D DCT algorithm is proposed to reduce the computational amount of transform operation using the distribution of the motion compensated error signal and the bit allocation table. In the this algorithm, 2-D Walsh-Hadamard transform is directly computed and then multiplied by a constant matrix. Multiplications are concentrated on the final stage in thie algorithm, thus the computational amount is reduced in proportion to the number of transform coefficients that are excluded from quatization. The computational amount in computing only the DCT coefficients allocated to the bit allocation table is calculated. As the result, the number of multiplications is less thn the algorithm known to have the fewest number of computations when less than 0.6 bits per pixel are allocated to tranform coding for the motion compensated error image in the case of the proposed algorithm. Thus, it shows that the proposed algorithm is valid in reducing the computational loads of transform coding.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.1
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• pp.84-89
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• 2019

JPEG is a most widely used standard image compression technology. This research introduces the JPEG image compression algorithm and describes each step in the compression and decompression. Image compression is the application of data compression on digital images. The DCT (discrete cosine transform) is a technique for converting a time domain to a frequency domain. First, the image is divided into 8 by 8 pixel blocks. Second, working from top to bottom left to right, the DCT is applied to each block. Third, each block is compressed through quantization. Fourth, the matrix of compressed blocks that make up the image is stored in a greatly reduced amount of space. Finally if desired, the image is reconstructed through decompression, a process using IDCT (inverse discrete cosine transform). The purpose of this research is to review all the processes of image compression / decompression using the discrete cosine transform method.

• Journal of radiological science and technology
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• v.42 no.4
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• pp.259-263
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• 2019

This study was purpose to compare image quality of Indirect digital radiography (IDR) system by using the International electro-technical commission standard(IEC 62220-1) which were applied to IEC in medical imaging. To evaluation the analysis of Modulation transfer function(MTF) measurements edge device each angle by using edge method. In this study, Aero (Konica, Japan) which is Indirect flat panel detector(FPD) was used, the size of image receptor matrix $1994{\times}2430$ which performed 12bit processing and pixel pitch is $175{\mu}m$. In IEC standard method were applied to each angle were compared. The results of shown as LSF at $2.0^{\circ}$ and $3.0^{\circ}$ angeles. Shape is constant and shows smooth shape. The amount of data seemed reasonable and 2.19 cycles/mm and 2.01 cycles/mm at a spatial frequency of $2.0^{\circ}$ and $3.0^{\circ}$ at an MTF value of 0.1. At an MTF value of 0.5, the spatial frequencies were $2.0^{\circ}$ and 1.11 cycles/mm and 0.93 cycles/mm at an angle of $3.0^{\circ}$. This study were to evaluate MTF by setting the each $2{\sim}3^{\circ}$ each angle and to suggest the quantitative methods of measuring by using IEC.