• The Research Journal of the Costume Culture
• /
• v.22 no.5
• /
• pp.688-701
• /
• 2014

This study examined the expression characteristics in pop art works of Jean-Charles de Castelbajac. The study here aimed at possibility to find a design development in building up the unique art world of creativity based on popularity, artistry, and originality without confinement to the trend only. For the research method, review of literature and analysis about Castelbajac's works reflecting the pop art feature in the collections from 2000S/S to 2012F/W were performed. The results of research are as follows. The external expression form of Castelbajac's works based on pop art was grouped roughly into use of mass culture image, appropriation of pop art expression technique, and parody of art works. First, his work appeared as application of the mass culture image such as symbolic thing in the modern consumer society, object in an ordinary life, character of well-known animation, national flag and famous star. Second, such appropriated pop art techniques showed as pop color in strong primary color and silk screen, photomontage, collage, assemblage, graffiti, and lettering. Third, a variety of images featured earlier in art works were shown in parody. These works are valuable in that they are expressed aesthetically through regeneration of popular culture's various images in view of fashion, they are described in the non-traditional value with frolic resistance and deviation out of existing fashion norm, and they are given the dynamic creativity integrated with art and fashion.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38A no.11
• /
• pp.928-937
• /
• 2013

This paper presents a novel contrast enhancement method based on singular value decomposition and image pyramid. The proposed method consists mainly of four steps. The proposed algorithm firstly decomposes image into band-pass images, including basis image and detail images, to improve both the global contrast and the local detail. In the global contrast process, singular value decomposition is used for contrast enhancement; the local detail scheme uses weighting factors. In the final image composition process, the proposed algorithm combines color and luminance components in order to preserve the color consistency. Experimental results show that the proposed algorithm improves contrast performance and enhances detail compared to conventional methods.

• Fashion & Textile Research Journal
• /
• v.12 no.5
• /
• pp.555-563
• /
• 2010

This research has attempted for a categorization by contemplating on concepts and characteristics of eco fashion design through analysis of precedent studies and to study the design characteristics, images, and internal values through keyword and design codes which appeared in the precedent studies. The subject of analysis was mostly focused on the theses published in international and domestic academic journals from 1990s to September 2009. The design characteristics of each eco fashion design were analyzed by classifying by form, detail, color, fabric and pattern. Method of analysis did content analysis. The results of the research can be summarized as follows. First of all, types of eco fashion design were human-ecology design, natural-ecology design, and social-ecology design. Secondly, the human-ecology design was presented a natural and comfortable form, color of the nature, and functional and new materials. The natural-ecology design was presented a natural silhouette, natural colors, and natural fiber. The social-ecology design were used a loose silhouette and over-size forms, natural colors, and recycled materials and bio fabric. Thirdly, the images per type of eco fashion design were Zen, sportive, natural, and modern image. And the internal values were presented efficiency, health-orientation, naturalness, and continuity.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1998.06b
• /
• pp.165-170
• /
• 1998

This paper presents a new prediction method RBP region-based prediction model where the context used for prediction contains regions instead of individual pixels. There is a meaningful property that RBP can partition a cartoon image into two distinctive types of regions, one containing full-color backgrounds and the other containing boundaries, edges and home-chromatic areas. With the development of computer techniques, synthetic images created with CG (computer graphics) becomes attactive. Like the demand on data compression, it is imperative to efficiently compress synthetic images such as cartoon animation generated with CG for storage of finite capacity and transmission of narrow bandwidth. This paper a lossy compression method to full-color regions and a lossless compression method to homo-chromatic and boundaries regions. Two criteria for partitioning are described, constant criterion and variable criterion. The latter criterion, in form of a linear function, gives the different threshold for classification in terms of contents of the image of interest. We carry out experiments by applying our method to a sequence of cartoon animation. We carry out experiments by applying our method to a sequence of cartoon animation. Compared with the available image compression standard MPEG-1, our method gives the superior results in both compression ratio and complexity.

• Journal of the Ergonomics Society of Korea
• /
• v.33 no.6
• /
• pp.515-532
• /
• 2014

Objective: The purpose of this study is to define consumers' affection on the interior design of commercial cars in terms of its design factors: color, embossing and gloss as independent factors. Background: Existing affective studies related to interior of vehicle focus on just sedans. However, there is no affective study about the interior of commercial cars. In addition, it is hard to change levels to which manufactures want. Method: Representative design factors were drawn using ANOVA and SNK analysis and definitive affective vocabularies were drawn using factor analysis. Furthermore, the results of 3D experiment were analyzed using ANOVA and LSD analysis. 3D images for the experiment were made using 3D max program. The experiment revealed that consumers discerned the differences in levels of each design factor and affective vocabulary. Results: The ANOVA revealed that beige color "A" type and non-gloss were the most preferred design in terms of the affective vocabularies and total preference. Conclusion: The result of the experiment may help manufactures to design the interior of commercial cars in the near future. Furthermore, the ANOVA result of affective vocabularies evaluation is expected to suggest a meaningful guideline. Application: The study results may be utilized as a guideline for interior design of commercial cars.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.3
• /
• pp.277-285
• /
• 2010

This paper describes an approach to analyze geometrical information of building images for understanding outdoor environment of autonomous navigation robot. Line segments and color information are used to classily a building with the other objects such as sky, trees, and roads. The line segments and their two neighboring regions are extracted from detected edges in image. The model of line segment (MLS) consists of color information of neighbor regions. This model rules out the line segments of non-building face. A building face converges into dominant vanishing points (DVPs) which include one vertical point and one of five horizontal points in maximum. The intersection of vertical and horizontal lines creates a facet of building. The geometrical characteristics such as the center coordinates, area, aspect ratio and aligned coexistence are used for extracting the windows in the building facet. In experiments, 150 building faces and 1607 windows were detected from the database of outdoor environment. We found that this result shows 94.46% detection rate. These experimental images were all taken in Ulsan metropolitan city in Korea under difference of viewpoints, daytime, camera system and weather condition.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.42 no.3
• /
• pp.452-473
• /
• 2018

This study analyzed the expressions of racing girl uniforms that promote automobile brands shown at Seoul and Busan motor shows. The results are as follows. In each of the design components expression, the glossy and plain material, the achromatic color, and the dress style appeared most frequently in the uniform design of both domestic and foreign automobile brands. In the fashion image expression, sexy image appeared the most, followed by modern image, romantic image, active image, elegance image and ethnic image. Based on the analysis results, the following expressive characteristics were identified: First, sexy images were used in uniform design to express the streamline and speedy feeling of a car metaphorically. Second, modern image, glossy material, and achromatic color were used for a uniform design to express advanced technology and the future orientation of an automobile. Third, fashion images that match the automobile type emphasized the brand image of the car. Fourth, some of the manufacturers that prevailed in the automobile market promoted several automobile brands exhibited with a unified uniform design that expressed the design philosophy and concept. As such, the motor show racing girl uniform contributed to promoting automobile brand identity and the automobile industry.

• Journal of Korean Institute of Industrial Engineers
• /
• v.26 no.4
• /
• pp.363-375
• /
• 2000

In this paper we propose a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. Simulation results illustrate the above method provides 77.5 percent precision rate without relevance feedback and increased precision rate using relevance feedback for overall queries. We also present a new indexing method that supports fast retrieval in large image databases. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.9 percent of the images from direct comparison.

• Journal of Internet Computing and Services
• /
• v.10 no.6
• /
• pp.113-121
• /
• 2009

In order to retrieve images with different emotions in regions of the images, this paper proposes the image retrieval system using emotion sketch. The proposed retrieval system divides an image into $17{\times}17$ sub-regions and extracts emotion features in each sub-region. In order to extract the emotion features, this paper uses emotion colors on 160 emotion words from H. Nagumo's color scheme imaging chart. We calculate a histogram of each sub-region and consider one emotion word having the maximal value as a representative emotion word of the sub-region. The system demonstrates the effectiveness of the proposed emotion sketch and our experimental results show that the system successfully retrieves on the Corel image database.

• Journal of Korea Multimedia Society
• /
• v.22 no.9
• /
• pp.1000-1010
• /
• 2019

Image semantic segmentation and dehazing are key tasks in the computer vision. In recent years, researches in both tasks have achieved substantial improvements in performance with the development of Convolutional Neural Network (CNN). However, most of the previous works for semantic segmentation assume the images are captured in clear weather and show degraded performance under hazy images with low contrast and faded color. Meanwhile, dehazing aims to recover clear image given observed hazy image, which is an ill-posed problem and can be alleviated with additional information about the image. In this work, we propose a deep multi-task network for simultaneous semantic segmentation and dehazing. The proposed network takes single haze image as input and predicts dense semantic segmentation map and clear image. The visual information getting refined during the dehazing process can help the recognition task of semantic segmentation. On the other hand, semantic features obtained during the semantic segmentation process can provide cues for color priors for objects, which can help dehazing process. Experimental results demonstrate the effectiveness of the proposed multi-task approach, showing improved performance compared to the separate networks.