• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.366-370
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• 2010

This paper presents an automatic Oriental ink-rendering technique that recreates the artistic style of Oriental paintings from a single image. In Oriental paintings are characterized by strokes with various thickness and disordered dispersion. In this study, a stroke drawing method was developed based on the canny edge detector and radial curvature that are suitable for lines with varied thickness even along a single stroke. A dispersion-shading method was likewise developed by applying a set of iterated dual-filtering, and intensity exaggeration methods. The dispersion-shading method is designed to increase the local shade details, to decrease the global shade. Unlike the existing watercolor-rendering and abstraction system the proposed dispersion-shading method achieves disordered shade details rather than simplification.

• Journal of IKEEE
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• v.23 no.3
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• pp.984-987
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• 2019

Recently, autonomous driving technology based on object recognition and lane recognition has attracted attention. However, in foggy weather, haze removal technology is needed because it is difficult to recognize surrounding objects. The technology of removing hazy is currently being studied in many ways, and a single image based haze removal algorithms are typical. In this paper, we design the hardware for haze removal by estimating the hazy partical map. Proposed hardware architecture is designed to have a cumulative histogram based filter that does not affect the hardware size even if the window size of filter increases. The hardware design is implemented with XILINX's xc7z045-ffg900 as the target board.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Korean Journal of Remote Sensing
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• v.10 no.1
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• pp.31-42
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• 1994

SDIAS (Single Doppler Image Analysis System), which can estimate the vertical wind profiles from single Doppler color displays, is developed here. Various single Doppler velocity signatures are simulated using horizontally uniform wind fields that vary with height. This system is very simple and efficient for interpreting the color displays. So, SDIAS can be used to get some useful informations on wind for understanding the present weather situation and forecast.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.232-239
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• 2023

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.149-156
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• 2008

In this paper, we propose a method aiming at updating the building information of the digital maps using single high resolution satellite image and digital map. Firstly we produced a digital orthoimage through the automatic co-registration of QuickBird image and 1:1,000 digital map. Secondly we extracted building height information through the template matching of digital map's building vector data and the image's edges obtained by Canny operator. Finally we refined the shape of some buildings by using the result from template matching as the seed polygon of the greedy snake algorithm. In order to evaluate the proposed method's effectiveness, we estimated accuracy of the extracted building information using LiDAR DSM and 1:1,000 digital map. The evaluation results showed the proposed method has a good potential for extraction and revision of building information.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.137-145
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• 2014

This paper suggests an LDA-based long distance face recognition algorithm for intelligent surveillance system. The existing face recognition algorithm using single distance face image as training images caused a problem that face recognition rate is decreased with increasing distance. The face recognition algorithm using face images by actual distance as training images showed good performance. However, this also causes user inconvenience as it requires the user to move one to five meters in person to acquire face images for initial user registration. In this paper, proposed method is used for training images by using single distance face image to automatically create face images by various distances. The test result showed that the proposed face recognition technique generated better performance by average 16.3% in short distance and 18.0% in long distance than the technique using the existing single distance face image as training. When it was compared with the technique that used face images by distance as training, the performance fell 4.3% on average at a close distance and remained the same at a long distance.

• Journal of Broadcast Engineering
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• v.24 no.4
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• pp.633-642
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• 2019

Recently, the convolution neural network (CNN) model at a single image super-resolution (SISR) have been very successful. The residual learning method can improve training stability and network performance in CNN. In this paper, we propose a SISR using recursive residual network architecture by introducing dense skip connections for learning nonlinear mapping from low-resolution input image to high-resolution target image. The proposed SISR method adopts a method of the recursive residual learning to mitigate the difficulty of the deep network training and remove unnecessary modules for easier to optimize in CNN layers because of the concise and compact recursive network via dense skip connection method. The proposed method not only alleviates the vanishing-gradient problem of a very deep network, but also get the outstanding performance with low complexity of neural network, which allows the neural network to perform training, thereby exhibiting improved performance of SISR method.

• Journal of the Korea Convergence Society
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• v.9 no.6
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• pp.45-52
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• 2018

In this paper, we propose a linear interpolation method based on patch information generated from a low - resolution image for generating a super resolution image in a single image. Using the regression model of the global space, which is a conventional super resolution generation method, results in poor quality in general because of lack of information to be referred to a specific region. In order to compensate for these results, we propose a method to extract meaningful information by dividing the region into patches in the process of super resolution image generation, analyze the constituents of the image matrix region extended for super resolution image generation, We propose a method of linear interpolation based on optimal patch information that is searched by correlating patch information based on the information gathered before the interpolation process. For the experiment, the original image was compared with the reconstructed image with PSNR and SSIM.

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.127-134
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• 2012

Video digital door lock(VDDL) system detects people who access to the door and acquires the human image. Design considerations is that current consumption must be minimized by applying fast human detection algorithm because of battery-based operation. Since the digital door lock takes an image through a fixed camera, detection of a person based on background image leads to high degree of reliability. This paper deals with a single human detection algorithm suitable for VDDL with fulfilling these requirements such that it detects a moving object in an image, then identifies whether the object is a person or not using image processing. The proposed image processing algorithm consists of two steps: Firstly, it detects the human image region using both background image and skin color information. Secondly, it identifies the person using polar histogram based on proportional information of human body. Proposed algorithm is implemented in VDDL and is verified the performance through experiments.