• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.111-119
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• 2006

Video viewers can not understand enough entire video contents because most video is long length data of large capacity. This paper propose efficient scene change detection and video abstracting using new shot clustering to solve this problem. Scene change detection is extracted by method that was merged color histogram with $\chi2$ histogram. Clustering is performed by similarity measure using difference of local histogram and new shot merge algorithm. Furthermore, experimental result is represented by using Real TV broadcast program.

• KSCI Review
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• v.14 no.1
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• pp.255-264
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• 2006

Video viewers can not understand enough entire video contents because most video is long length data of large capacity. This paper propose efficient scene change detection and video abstracting using new shot clustering to solve this problem. Scene change detection is extracted by method that was merged color histogram with ${\chi}^2$ histogram. Clustering is performed by similarity measure using difference of local histogram and new shot merge algorithm. Furthermore, experimental result is represented by using Real TV broadcast program.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.174-184
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• 2008

Complementary color filter array (CCFA) is widely used in consumer-level digital video cameras, since it not only has high sensitivity and good signal-to-noise ratio in low-light condition but also is compatible with the interlaced scanning used in broadcast systems. However, the full-color images obtained from CCFA suffer from the color artifacts such as false color and zipper effects. These artifacts can be removed with edge-adaptive demosaicking (ECD) approaches which are generally used in rrimary color filter array (PCFA). Unfortunately, the unique array pattern of CCFA makes it difficult that CCFA adopts ECD approaches. Therefore, to apply ECD approaches suitable for CCFA to demosaicking is one of the major issues to reconstruct the full-color images. In this paper, we propose a new ECD algorithm for CCFA. To estimate an edge direction precisely and enhance the quality of the reconstructed image, a function of spatial variances is used as a weight, and new color conversion matrices are presented for considering various edge directions. Experimental results indicate that the proposed algorithm outperforms the conventional method with respect to both objective and subjective criteria.

• 한국HCI학회:학술대회논문집
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• 2008.02b
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• pp.298-303
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• 2008

Watching a program on IPTV among the numerous choices from the internet requires a burden of searching and browsing for a favorite one. This paper introduces a new concept called Mosaic Map and presents how it provides preview information of image map links to other programs. In Mosaic Map the pixels in the still image are used both as shading the background and as thumbnails which can link up with other programs. This kind of contextualized preview of choices can help IPTV users to associate the image with related programs without making visual saccades between watching IPTV and browsing many choices. The experiments showed that the Mosaic Map reduces the time to complete search and browsing, comparing to the legacy menu and web search.

• Journal of Navigation and Port Research
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• v.27 no.4
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• pp.359-366
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• 2003

The paper describes on the construction and analysis of marine casualty numerical D/B (N-D/B) to implement Korean MArine Casualty Forecasting System (K-MACFOS). The main target of K-MACFOS is to broadcast the prediction number and risk level of marine casualties as like daily weather forecasting. The data relating to a total of 724 ship casualties in the west-southern sea area (33oN∼35oN, 124oE∼127oE) of Korean peninsula for 11 years (1990∼2000) have been compiled and converted into quantitative data with 14 numeric conversion scales. Through the statistical analysis using contour-map visualization, the usability of N-D/B and the casualty features of the target sea areas are discussed. In addition, the optimum year-band selection method is also proposed to provide correct N-D/B analysis and precise prediction of the number of marine casualties.

• Journal of the Korean Society of Floral Art and Design
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• no.40
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• pp.45-61
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• 2019

This study attempts to examine the recent trend of 'Maison & Objet' exhibition which shows everything that forms a space, and seek cases where such 'green amenity' is applied. In terms of morphology, a minimal space was filled with a curved shape, and gradually a design that reproduces nature was produced. As the maximalism gradually emerged, decorative elements were added to the design, and a lot of craft products appeared. In terms of materials, the emotion of naturalism was the most common, and natural wood materials were mainly used. These materials combine with various heterogeneous materials to complete a new design, and natural elements were shaped in space. In terms of colors, the theme in 2016 was 'Wild', and it was possible to see a space where wild nature can be experienced. It showed various colors of nature centered on brown and green of trees. 'Silence' in 2017 is distinguished and characterized by its pink color. Also, pieces of warm reddish brown furniture were made. In the past, brass or rose gold would be trendy, but in 2017, gold or silver colors showed a greater popularity. In 2018, 'Show Room' was the theme, and the representative color was green, which affected new designs with yellow and pink.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.23-28
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• 2018

Next generation displays show a trend of evolving from the display device environment (represented by existing televisions) to the mobile environment. The mobile display corresponding to the personal display is similar to a home theatre; however, they are advantageous because they are small and have a relatively lower weight. Therefore, the display industry has an interest in diverse product applications of displays, reproducing more accurate colours, and offering improved image quality from display devices of various sizes. To address these interests, a psychophysical experiment was conducted in this research. The experiment compared the size of the colour stimulation corresponding to foveal vision by gradually increasing the lightness of the background. This was based on the assumption of possible differences in colours being recognized by the lightness of the background and the size of the colour stimulation. Contrary to the results of previous studies, where the colours are identified more clearly as the size of the colour stimulation increases (assuming that the lightness of the background is not considered) here the results of the experiment showed that the attributes of the identified colours were different depending on the lightness of the background and the size of the colour stimulation. Based on the experimental results, it is possible to resolve errors in colour conversion that can occur when the input image is switched from a large screen size to a mobile size display, and to reproduce the colours more accurately and improve the image quality.

• Journal of Broadcast Engineering
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• v.21 no.3
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• pp.425-435
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• 2016

In this paper, we propose a classification model by analyzing raw material images recorded using a color CCD camera to automatically classify good and defective agricultural products such as rice, coffee, and green tea, and raw materials. The current classifying agricultural products mainly depends on visual selection by skilled laborers. However, classification ability may drop owing to repeated labor for a long period of time. To resolve the problems of existing human dependant commercial products, we propose a vision based automatic raw material classification combining mean shift clustering and stepwise region merging algorithm. In this paper, the image is divided into N cluster regions by applying the mean-shift clustering algorithm to the foreground map image. Second, the representative regions among the N cluster regions are selected and stepwise region-merging method is applied to integrate similar cluster regions by comparing both color and positional proximity to neighboring regions. The merged raw material objects thereby are expressed in a 2D color distribution of RG, GB, and BR. Third, a threshold is used to detect good and defective products based on color distribution ellipse for merged material objects. From the results of carrying out an experiment with diverse raw material images using the proposed method, less artificial manipulation by the user is required compared to existing clustering and commercial methods, and classification accuracy on raw materials is improved.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.437-446
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• 2012

A face detection algorithms using two-dimensional (2-D) intensity or color images have been studied for decades. Recently, with the development of low-cost range sensor, three-dimensional (3-D) information (i.e., depth image that represents the distance between a camera and objects) can be easily used to reliably extract facial features. Most people have a similar pattern of 3-D facial structure. This paper proposes a face detection method using intensity and depth images. At first, adaboost algorithm using intensity image classifies face and nonface candidate regions. Each candidate region is divided into $5{\times}5$ blocks and depth values are averaged in each block. Then, $5{\times}5$ block rank pattern is constructed by sorting block averages of depth values. Finally, candidate regions are classified as face and nonface regions by matching the constructed depth map based block rank patterns and a template pattern that is generated from training data set. For template matching, the $5{\times}5$ template block rank pattern is prior constructed by averaging block ranks using training data set. The proposed algorithm is tested on real images obtained by Kinect range sensor. Experimental results show that the proposed algorithm effectively eliminates most false positives with true positives well preserved.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.801-806
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• 2022

In the modern era, the necessity of color capability in the digital era is the demand of the era, and research on improving color practice on the subdivided digital four areas that are not in the existing practice is needed. For digital majors who are difficult to solve in existing paint color practice, classes in digital color practice in four more specialized areas are needed, and the use of efficient artificial intelligence was studied for classes in digitized color and color sense. In this paper, we tried to show the expansion of the color practice area by suggesting digital color practice and color matching method based on Photoshop artificial intelligence and big data technology that existing color and color matching were practice that only CMYK could do. In addition, based on the color quantification data of individual users provided by the latest Adobe Sceney program artificial intelligence, the purpose of the practice was to improve learners' predictions of actual color combinations and random colors using filter effects. In conclusion, it is a study on the use of programs that eliminate ambiguity in the mixing process of existing paint practice, secure digital color details, and propose a practical method that can provide effective learning methods for beginners and intermediates to develop their senses through artificial intelligence support. The Adobe program practice method necessary for coloration and main color through theoretical consideration and improvement of teaching skills that are better than existing paint practice were presented.