• Journal of the Korean Society of Costume
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• v.53 no.7
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• pp.125-137
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• 2003

This study is to explore the Post-modern fashion image appeared in the costumes of the movie $\ulcorner$Matrix2-Reloaded$\lrcorner$, which is believed to adapt successfully the Postmodernism ideas on screen. From this study, we can find that the movie costumes express certain formative characteristics considerably affected by the contemporary fashion trends such as Minimalism, Formalism, Futurism, Ethno-eclecticism, Asexualism. These are mostly common tendencies aroused in the complex and pluralistic circumstances of Postmodernism where many diverse values are equally regarded. Finally, the 5 trends lead to represent the following symbolic meanings with entanglement in multiplex ways, that is Virtual Reality, Mechanic Artificiality, Human Dignity, Mysticism, Multi-Culturism, and Nonsexuality to create a strong unified imagery and style in that Post-modern movie.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07a
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• pp.368-370
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• 2005

High energy-efficiency TFT-LCD supporting a good image quality is developed with Data Rendering Innovation Matrix Technology. The innovative matrix consists of octal sub-pixels and sub-pixel rendering technology enhanced the light efficiency; up to 30%, and reduces the number of column drivers for the same resolution by a third.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.10 no.2
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• pp.93-103
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• 1985

The digital signal processing technique by bandwidth compression has been grown up ragidly owing to integrated circuit developments. In this project, we have proposed the Lee Weighted Hadamard (LWH) transform which retains the main properties of Hadamard matirx. The LWH matrix was weighted in the center of the spatial domain. The human visual of the mid spatial are emphasized more than the low and high spatial frequencies. The fast algorithms of the LWH transform has been studied for hardware realization. The result of this project are availabel to airplane photograph, X-Ray, CATV and the artificial satellite of the digital image processing.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.472-472
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• 2010

The tomography has played a key role in tokamak plasma diagnostics for image reconstruction. The Phillips-Tikhonov (P-T) regularization method was attempted in this work to reconstruct cross-sectional phantom images of the plasma by minimizing the gradient between adjacent pixel data. Recent studies about the comparison of the several tomographic reconstruction methods showed that the P-T method produced more accurate results. We have studied existing Laplacian matrix used in Phillips-Tikhonov regularization method and developed modified Laplacian matrix (Modified L). The comparison of the reconstruction result by the modified L and existing L showed that modified L produced more accurate result. The difference was significantly pronounced when a portion of plasma was reconstructed. These results can be utilized in the Edge Plasma diagnostics; especially in divertor diagnostics on tokamak a large impact is expected. In addition, accurate reconstruction results from received data in only one direction were confirmed through phantom test by using P-T method with modified L. These results can be applied to the tangentially viewing pin-hole camera diagnostics on tokamak.

• The Transactions of the Korea Information Processing Society
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• v.2 no.3
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• pp.390-396
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• 1995

One of the advantages of computer graphics is that it enables to view an object on different viewpoints and different angles. Therefore, a computer graphics system should be able to rotate an arbitrary object by an arbitrary angle about an arbitrary axis. This is usually done by rotating vertices that represent an object and connecting them. Hence an image may have many vertices, it is important to be able to rotate each of them quickly. Therefore, this paper is interested in a rotation matrix computation method that consists of the smallest number of computational steps. This pater proposes an algorithm that computes rotation matrix to rotate a 3 dimensional image about an arbitrary axis quickly.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3C
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• pp.264-270
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• 2010

Non-planar screens such as cylinder and sphere shaped screens are widely used for high-resolution immersive visualization environments. An existing method employs quadric matrix that maps an image onto a curved screen. However if the shape of the screen changes or moves, the quadric matrix will not be valid. In this paper, we assume that the screen is a quadric shape and the screen movement or change are relatively small. Then we propose to use a piecewise planar approximations for the screen to compensate for the geometric distortion on a non-planar screen. We demonstrate the effectiveness and efficiency of the proposed method through experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2098-2114
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• 2021

Light intensity variation is one of the key factors which affect the accuracy of vehicle face re-identification, so in order to improve the robustness of vehicle face features to light intensity variation, a Nonnegative Matrix Factorization model with the constraint of image acquisition time difference is proposed. First, the original features vectors of all pairs of positive samples which are used for training are placed in two original feature matrices respectively, where the same columns of the two matrices represent the same vehicle; Then, the new features obtained after decomposition are divided into stable and variable features proportionally, where the constraints of intra-class similarity and inter-class difference are imposed on the stable feature, and the constraint of image acquisition time difference is imposed on the variable feature; At last, vehicle face matching is achieved through calculating the cosine distance of stable features. Experimental results show that the average False Reject Rate and the average False Accept Rate of the proposed algorithm can be reduced to 0.14 and 0.11 respectively on five different datasets, and even sometimes under the large difference of light intensities, the vehicle face image can be still recognized accurately, which verifies that the extracted features have good robustness to light variation.

• Journal of Bio-Environment Control
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• v.33 no.1
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• pp.1-11
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• 2024

In this study, five artificial intelligence (AI) models: Inception v3, SqueezeNet (local), VGG-16, Painters, and DeepLoc were used to classify tea leaf diseases. Eight image categories were used: healthy, algal leaf spot, anthracnose, bird's eye spot, brown blight, gray blight, red leaf spot, and white spot. Software used in this study was Orange 3 which functions as a Python library for visual programming, that operates through an interface that generates workflows to visually manipulate and analyze the data. The precision of each AI model was recorded to select the ideal AI model. All models were trained using the Adam solver, rectified linear unit activation function, 100 neurons in the hidden layers, 200 maximum number of iterations in the neural network, and 0.0001 regularizations. To extend the functionality of Orange 3, new add-ons can be installed and, this study image analytics add-on was newly added which is required for image analysis. For the training model, the import image, image embedding, neural network, test and score, and confusion matrix widgets were used, whereas the import images, image embedding, predictions, and image viewer widgets were used for the prediction. Precisions of the neural networks of the five AI models (Inception v3, SqueezeNet (local), VGG-16, Painters, and DeepLoc) were 0.807, 0.901, 0.780, 0.800, and 0.771, respectively. Finally, the SqueezeNet (local) model was selected as the optimal AI model for the detection of tea diseases using tea leaf images owing to its high precision and good performance throughout the confusion matrix.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.940-947
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• 2015

This paper presents the region of interest pattern image extraction method on a display printed matrix pattern. Proposed method can not use conventional method such as laser, ultrasonic waves and touch sensor. It searches feature point and rotation angle using luminance and pattern reliable feature points of input image, and then it extracts region of interest. In order to extract region of interest, we simulate proposed method using pattern image written various angles on display panel. The proposed method makes progress using the OpenCV and the window program, and was designed using Verilog-HDL and was verified through the FPGA Board(xc6vlx760) of Xilinx.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.590-598
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• 2017

Recently, there has been an increase in the number of hazardous events, such as fire accidents. Monitoring systems that rely on human resources depend on people; hence, the performance of the system can be degraded when human operators are fatigued or tensed. It is easy to use fire alarm boxes; however, these are frequently activated by external factors such as temperature and humidity. We propose an approach to fire detection using an image processing technique. In this paper, we propose a fire detection method using multichannel information and gray level co-occurrence matrix (GLCM) image features. Multi-channels consist of RGB, YCbCr, and HSV color spaces. The flame color and smoke texture information are used to detect the flames and smoke, respectively. The experimental results show that the proposed method performs better than the previous method in terms of accuracy of fire detection.