• Journal of Korea Multimedia Society
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• v.3 no.1
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• pp.34-40
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• 2000

'[n this paper, we propose an efficient multi-view image coding algorithm which finds the optimal texture from a restricted number of multi-view image. The X-Y plane of the normalized object space is divided into the triangular patches. The depth of each node is determined by appling a block based disparity compensation method. Thereafter the texture of each patch is extracted by appling an affine transformation based disparity compensation method to the multi-view images. We reduced the number of images needed to determine the texture compared to traditional methods which use all the multi-view image in the texture extraction. The experimental results show that the SNR of images encoded by the proposed algorithm is better than that of images encoded by the traditional method by the approximately 0.2dB for the test sets of multi -view image called dragon, santa, city and kid. Image data recovered after encoding by the proposed method show a better visual results than after using traditional method.

• Current Optics and Photonics
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• v.8 no.3
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• pp.215-224
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• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.26-38
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• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.

• Journal of Magnetics
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• v.8 no.1
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• pp.13-17
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• 2003

The longitudinal magnetooptical Kerr effect was used to analyse magnetic domains in soft magnetic ${(Fe_{97}A1_3)}_{85}N_{15}$/$Al_{2}O_{3}$ multilayers in order to get microscopic understanding of interlayer exchange coupling. The measuring system consists of a Kerr microscope, a CCIR camera (with an 8-bit framegrabber), 16 bit digital camera and computer system for real-time image processing and to control external magnetic field and cameras. The strength of ferromagnetic (EM) coupling as a function of the spacer thickness of $Al_2O_3$ was investigated. It was found that strong FM-coupling, strong uniaxial anisotropy and coherent rotation of the magnetization have been observed for the spacer thickness in the range of 0.2 nm $\leq$ t $\leq$ 1 m, however, weak FM-coupling, patch domains and $360^{\circ}$-walls occur for the spacer thickness of t = 2.5 nm. At a spacer thickness of t $\geq$ 5 nm transition takes place from weak FM-coupling to the decoupled state where complex interlayer interactions and different types of the domain walls were observed.

• Journal of the Korean Society of Costume
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• v.65 no.5
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• pp.88-102
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• 2015

The purpose of this study is to investigate the examples of using illustration as a strategic factor of composing brand cultures and as a factor for brand identity design. Through analyzing the external characteristics and the internal characteristics of illustration, this study will give suggestions on ways to apply the examples to real design. Illustration in external characteristics is investigated as a case of applying it directly to fashion design and to fashion marketing. Most of the fashion items were printed or weaved and most of the bags, shoes, and accessories were printed on the cover, attached as a patch, and expressed three-dimensionally. Illustration in internal characteristics is investigated as fixing and expansion of brand image, improving artistic and emotional value of brand, vitalization of masstige items, and cultural support and expression of social responsibility. The three themes used to develop the illustrations of "Hello ZIBI", which was used in this study, were "Graphic", "Forest" and "Flower", and these were based on modified brand symbol. Casual brands grafted fashion item designs onto T-shirts, bags, hats, and scarves. Marketing items were designed as shopping bags that could reflect brand image, as well as other items, such as key holders, mug cups, and tumblers, with designs that targeted specific age groups.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.510-512
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• 2023

In order to enhance a model's capability for detecting facial expressions, this research suggests a pipeline that makes use of the GradCAM component. The patching module and the pseudo-labeling module make up the pipeline. The patching component takes the original face image and divides it into four equal parts. These parts are then each input into a 2Dconvolutional layer to produce a feature vector. Each picture segment is assigned a weight token using GradCAM in the pseudo-labeling module, and this token is then merged with the feature vector using principal component analysis. A convolutional neural network based on transfer learning technique is then utilized to extract the deep features. This technique applied on a public dataset MMI and achieved a validation accuracy of 96.06% which is showing the effectiveness of our method.

• Journal of Korea Multimedia Society
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• v.11 no.2
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• pp.153-162
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• 2008

We propose a novel image inpainting method composed of two parts: band matching and seamless cloning. In band matching, a band enclosing the boundary of a missing region is compared to those from the other parts of the image. The inner area of the minimum difference band is then copied to the missing region. Even though this band matching results in successful inpainting in many practical applications, brightness discontinuity (a seam) may appear between the filled missing region and its neighborhood. We apply seamless cloning to remove such discontinuity between the two regions. However, since this basic method using one patch may not deal with cases where there are abrupt changes of color or brightness along the boundary, we furthermore devise one more step: target sub-division. The target area is subdivided into small sub-areas, and the band matching and seamless cloning are applied to each of them. The multiple results from the sub-division are then ordered according to inpainting quality, which is measured based on the edge map or discontinuity map along the boundary band.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.546-555
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• 2009

There is increasingly interested in the measurement of antenna's characteristics for one's manufacture according to one's various application. Due to this, the antenna measurement system need be made with more and more great precision. On measuring of the antenna's characteristic, the conventional system handled by human generates the error due to controlling the position of the system by user. Therefore there need be introduced the automatic measurement system of antenna's characteristic. In this paper, we propose antenna extraction algorithm for the Antenna automatic measurement system of antenna's characteristic. The proposed algorithm gets the antenna image from antenna measurement system, extracts an antenna object from the image, and extracts the parameters for antenna's slant and antenna's location. The extracted parameters is used to correct location and distortions of the antenna and automatic measurement. The proposed algorithm is applied to the patch antenna. The results show that antenna's object is efficiently extracted and the angle for correcting the error is calculated well.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.1
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• pp.21-30
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• 2011

In this paper, an effective method to detect the laser pointer on the screen using the neural network algorithm for implementing the human-computer interaction system. The proposed neural network algorithm is used to train the patches without a laser pointer from the input camera images, the trained neural network then generates output values for an input patch from a camera image. If a small variation is perceived in the input camera image, amplify the small variations and detect the laser pointer spot in the camera image. The proposed system consists of a laser pointer, low-price web-camera and image processing program and has a detection capability of laser spot even if the background of computer monitor has a similar color with the laser pointer spot. Therefore, the proposed technique will be contributed to improve the performance of human-computer interaction system.

• Fashion & Textile Research Journal
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• v.16 no.4
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• pp.645-652
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• 2014

This study identifies consumer needs and a pattern sensory evaluation of jacquard fabrics for raincoats using quick-drying-absorbing polyester. We investigate the consumer's consciousness and raincoat improvements. Twelve kinds of jacquard fabrics were developed for use in this study. Developed jacquard fabrics were assessed subjectively by 152 university students using a 7-point scale of 26 consumer needs and 31 pattern image sensory descriptors. Data were analyzed by SPSS. The major results were: There was a need for consumers to improve the front fastener type, cuff fastener, mesh patch position, and raincoat pocket position. The most important parameter to choose raincoat fabric was waterproof and the other parameters were vapor-porous/water repellent, design, color, fashionability, air-permeability and easy-put on/off. The pattern image sensibility of jacquard fabrics was explained by seven factors: gorgeous, simple, cute, futuristic, ethnic, feminine, and cool. A higher pattern preference was found in the jacquard fabrics of unique, sporty, natural, luxurious, and trendy images. The pattern preference was predicted at 45.3% with gorgeous, simple, pure, cute, futuristic factors. The correlation coefficient between the pattern image sensibility factor 1 (gorgeous) and pattern preference was 0.674 and with factor 3 (cute) was 0.416, and with factor 6 (cool) was 0.209. The 4 factors (gorgeous, simple, cute, futuristic) were selected as a significant pattern image sensibility that influenced preference.