• Journal of IKEEE
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• v.25 no.1
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• pp.10-14
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• 2021

This paper presents a hardware that improves the complexity of the CIE1931 color coordinate algorithm operation. The conventional algorithm has disadvantage of growing hardware due to 4-Split Multiply operations used to calculate large bits in the computation process. But the proposed algorithm pre-calculates the defined R2X, X2R Matrix operations of the conventional algorithm and makes them a matrix. By applying the matrix to the images and improving the color, it is possible to reduce the amount of computation and hardware size. By comparing the results of Xilinx synthesis of hardware designed with Verilog, we can check the performance for real-time processing in 4K environments with reduced hardware resources. Furthermore, this paper validates the hardware mount behavior by presenting the execution results of the FPGA board.

• (The) Research of the performance art and culture
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• no.41
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• pp.157-186
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• 2020

It illuminates the representation of Korean dance in the sense of color. The unique color and light of Korean dance reflects the essence of Korean art and the consciousness of Koreans. Therefore, analyzing Korean art, colors and meanings can provide the principle of aesthetic interpretation to re-examine Korean colors. This means that it is necessary to pay attention to the possibility of developing original contents as a humanistic basis, asking the origin of Korean art. The Korean thought and philosophy in which color and life become cultures remain the roots for another re-creating vision of Korean art. Therefore, it is time to establish a system of Korean identity as an art with the expansion of various interpretations of various aesthetic attitudes that recognize Korean dance.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.733-739
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• 2012

In this paper, a smoke detection algorithm robust to brightness and color variations depending on time and weather is proposed. The proposed smoke detection algorithm specifies the candidate region using difference images of input and background images, determines smoke by comparing feature coefficients of Gaussian mixture model of difference images. Thresholds for specifying candidate region is divided by four levels according to average brightness and chrominance of input images. Clusters of Gaussian mixture models of difference images are aligned according to average brightness. Smoke is determined by comparing distance of Gaussian mixture model parameters. The proposed algorithm is implemented by media dedicated DSP. As results of experiments, it is shown that the proposed algorithm is effective to detect smoke with camera installed outdoor.

• The Research Journal of the Costume Culture
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• v.19 no.3
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• pp.460-472
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• 2011

This paper developed motifs through the use of images of masks with which the Korean innate consciousness of aesthetics is expressed. The motifs were applied to fashion items. This paper investigated the concept, origin and types of traditional masks through the existing literature. Using the computer graphic programs of Illustrator and Photoshop CS2, three basic motifs were set from the images of the nobleman, Bune of Hahoi Tal and Byeongsan Tal. Each motif was extended into two motifs by changing shape and color. For the basic motif design, this study sought to express contemporary images, suitably for each fashion item, while maintaining the basic shape of the masks and their traditional images. In addition, this study set the concept of the design so that could be accepted by various age groups. For the design of handkerchiefs, a rotating array, involving enlargement, reduction, repetition, and the gradation of motifs, as well as a diagonal symmetric array, and all-over patterns were developed. The T-shirt design created here was divided into a half-sleeve box type, a round neckline sleeveless type, a V-neckline close-fitting sleeveless type, and a V-neckline close-fitting cap sleeve type. For the design of necklaces, OLED or LED, which are considered as a future display type, was used. Additionally, the production of an entertainment styled new atmosphere is proposed.

• Journal of Broadcast Engineering
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• v.11 no.4 s.33
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• pp.507-520
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• 2006

In this paper, we propose an application of OpenGL functions for novel view synthesis from multi-view images and depth maps. While image based rendering has been meant to generate synthetic images by processing the camera view with a graphic engine, little has been known about how to apply the given images and depth information to the graphic engine and render the scene. This paper presents an efficient way of constructing a 3D space with camera parameters, reconstructing the 3D scene with color and depth images, and synthesizing virtual views in real-time as well as their depth images.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.145-156
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• 2003

Most of the content-based image retrieval systems focuses on similarity-based retrieval of natural picture images by utilizing color. shape, and texture features. For the neuroscience image databases, we found that retrieving similar images based on global average features is meaningless to pathological researchers. To realize the practical content-based retrieval on images in neuroscience databases, it is essential to represent internal contents or semantics of images in detail. In this paper, we present how to represent image contents and their related concepts to support more useful retrieval on such images. We also describe the operational semantics to support these advanced retrievals by using object-oriented message path expressions. Our schemes are flexible and extensible, enabling users to incrementally add more semantics on image contents for more enhanced content searching.

• The Journal of Korea Robotics Society
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• v.10 no.4
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• pp.223-229
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• 2015

This paper proposes a novel method for detection of hand raising poses from images acquired from a single camera attached to a mobile robot that navigates unknown dynamic environments. Due to unconstrained illumination, a high level of variance in human appearances and unpredictable backgrounds, detecting hand raising gestures from an image acquired from a camera attached to a mobile robot is very challenging. The proposed method first detects faces to determine the region of interest (ROI), and in this ROI, we detect hands by using a HOG-based hand detector. By using the color distribution of the face region, we evaluate each candidate in the detected hand region. To deal with cases of failure in face detection, we also use a HOG-based hand raising pose detector. Unlike other hand raising pose detector systems, we evaluate our algorithm with images acquired from the camera and images obtained from the Internet that contain unknown backgrounds and unconstrained illumination. The level of variance in hand raising poses in these images is very high. Our experiment results show that the proposed method robustly detects hand raising poses in complex backgrounds and unknown lighting conditions.

• Journal of the Korea Society of Computer and Information
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• v.8 no.2
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• pp.37-43
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• 2003

The purpose of this thesis is to detect multiple vehicles using sequence image analysis at process that detect forward vehicles and lane from sequential color images. Detection of vehicles candidate area uses shadow characteristic and edge information in one frame. And, method to detect multiple vehicles area analyzes Estimation of Vehicle(EOV) and Accumulated Similarity Function(ASF) of vehicles candidate areas that exist in sequential images and examine possibility to be vehicles. Most researches detected a forward vehicles in road images but this research presented method to detect several vehicles and apply enough in havy traffic. To verify the effects of the proposed method, we capture the road images with notebook and CCD camera for PC and present the results such as processing time, accuracy and vehicles detection in the images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2287-2312
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• 2018

Extracting key visual information from images containing natural scene is a challenging task and an important step for the visually impaired to recognize information based on tactile graphics. In this study, a novel method is proposed for extracting salient regions based on global contrast enhancement and saliency cuts in order to improve the process of recognizing images for the visually impaired. To accomplish this, an image enhancement technique is applied to natural scene images, and a saliency map is acquired to measure the color contrast of homogeneous regions against other areas of the image. The saliency maps also help automatic salient region extraction, referred to as saliency cuts, and assist in obtaining a binary mask of high quality. Finally, outer boundaries and inner edges are detected in images with natural scene to identify edges that are visually significant. Experimental results indicate that the method we propose in this paper extracts salient objects effectively and achieves remarkable performance compared to conventional methods. Our method offers benefits in extracting salient objects and generating simple but important edges from images containing natural scene and for providing information to the visually impaired.

• Journal of Korea Multimedia Society
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• v.25 no.7
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• pp.895-902
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• 2022

Building segmentation using satellite imagery such as EO (Electro-Optical) and SAR (Synthetic-Aperture Radar) images are widely used due to their various uses. EO images have the advantage of having color information, and they are noise-free. In contrast, SAR images can identify the physical characteristics and geometrical information that the EO image cannot capture. This paper proposes a learning framework for efficient building segmentation that consists of a teacher-student-based privileged knowledge distillation and deformable convolution block. The teacher network utilizes EO and SAR images simultaneously to produce richer features and provide them to the student network, while the student network only uses EO images. To do this, we present objective functions that consist of Kullback-Leibler divergence loss and knowledge distillation loss. Furthermore, we introduce deformable convolution to avoid pixel-level noise and efficiently capture hard samples such as small and thin buildings at the global level. Experimental result shows that our method outperforms other methods and efficiently captures complex samples such as a small or narrow building. Moreover, Since our method can be applied to various methods.