• The Research Journal of the Costume Culture
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• v.26 no.3
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• pp.377-389
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• 2018

Consumer interest in eco-friendly fashion products has been consistent. While most relevant research emphasizes individual morals and environmental concern as the most crucial determinants to eco-friendly consumption behavior, more recent studies point out that in so doing there has been somewhat a neglectance on the importance of fundamental marketing strategies. More specifically, the crucial role of interior colors in fashion retail stores has been managerially considered something certain yet no empirical results have been found to support such a strong managerial assumption. For instance, colors such as green, blue, and brown are believed to represent natural images and are more appropriate to the eco-friendly marketing and the relevant research has been lacking. Therefore, this study attempts to explore the effect of in-store interior design colors (green versus non-green) on consumer perception of green store images. A total of 382 respondents were gathered for an online survey using differing store images as the stimulus and used for testing hypotheses. In the results, respondents exposed to store images using green interior colors reported a higher evaluation of green store image of the store. The effect is found to be significantly moderated by respondent's environmental concern: to explain, respondents of high environmental concern are less influenced by green color interiors when they evaluate the brand's eco-friendly image. In sum, the positive influence of green interior colors on green store image is found statistically significant, with its stronger effect for consumers of low concern. Managerial and academic discussions are provided.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.93-96
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• 2015

The aim of the estimation of disparity map is to find the corresponding pixels from similar two or more images. However, it is a difficult problem to get precise and consistent disparity under a variety of real world situations. In other words, the color values of stereo images are easily influenced by radiometric properties such as illumination direction, illumination color, and camera exposure. Therefore, conventional stereo matching methods can have low performances under radiometric conditions. In this paper, we propose an approaching of disparity map estimation that is reliable in controlling various radiometric variations close to the real environment. This method is motivated by following constancy. Even though each other has different radiometric property in stereo images, intensity of pixels of object have general constancy in specific block. Experimental results show that the proposed method has better performances compared to the comparison group under different radiometric conditions between stereo images. Consequentially, the proposed method is able to estimate the disparity map in stable under various radiometric variations.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.11
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• pp.529-536
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• 2015

When rendering both three-dimensional objects and photo images together, the non-photorealistic rendering results are in visual discord since the two contents have their own independent color distributions. This paper proposes a non-photorealistic rendering technique which renders both three-dimensional objects and photo images such as cartoons and sketches. The proposed technique computes the color distribution property of the photo images and reduces the number of colors of both photo images and 3D objects. NPR is performed based on the reduced colormaps and edge features. To enhance the natural scene presentation, the image region segmentation process is preferred when extracting and applying colormaps. However, the image segmentation technique needs a lot of computational operations. It takes a long time for non-photorealistic rendering for large size frames. To speed up the time-consuming segmentation procedure, we use GPGPU for the parallel computing using the GPU. As a result, we significantly improve the execution speed of the algorithm.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.9
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• pp.1430-1440
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• 2009

This study is on the abstract expressionist techniques among the art activities variously expressed in modern fashion. Its significance lies in making fashion artistic through the combination of fashion and art in contributing to the development of creative fashion culture. In terms of method, documents are used to characterize the concept of abstract expressionism, the works of representative artists, and the panting techniques in relation to modem art based on existing literature. Fashion-related anthology, domestic, and foreign fashion magazines were used to analyze the abstract expressionism applied to modern fashion since 2000. According to the findings, the abstract expressionism applied to modern fashion showed artistic expressions with the abstract beauty by chance, using only the images or techniques similar to those in abstract expressionism. Expressionist works had abstract and dynamic images, as they were given a third dimension through the process of being worn on the human body. Second, details or decorative factors were excluded to ensure the maximum space for expression, modem images were displayed using the simple forms such as silhouettes (spacious or dense) and the beauty of harmony was shown that had beauty emphasized by the expression effects of textile design, the division of space, and the composition of colors. Third, the action painting techniques in modern fashion were used for textile designs printed on the surface of clothes, and the dynamic character of the design was shown by the duplication and juxtaposition of stains created by chance. The color field abstract techniques were shown through printing, texture, and dying, in addition the intense and pure abstract images were displayed by treating clothes like large screens.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• The Transactions of the Korea Information Processing Society
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• v.5 no.8
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• pp.1951-1959
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• 1998

In this paper, we propose an efficient feature extaetion method for content-based approach and implement an image retrieval system in the Oracle database. First, we estract color feature by the modified Stricker's method from input images, and this color feature and ART2 neural network are used for the rough classification of images. Next, we extract texture feature using wavelet transform, and finally exeute the detailed classification on the rough classified images from the previous step. Exsing the proposed feature extraction methods, we implement a useful image retrieval system by Extended SQI, statement on the relational database. The proposed system is implemented on the Oracle DBMS, and in the experimental results with 200 sample images, it shows the retrieval rate 90% and 81% in Recall and Precision, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.11
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• pp.115-123
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• 2015

The infrared light is known to be less dependent on background light compared to the visible light, and thus many applications such as remote sensing and image surveillance use the infrared image. Similar to color images, infrared images can also be degraded by hazy weather condition, and consequently the performance of the infrared image-based applications can decrease. Nevertheless, infrared image dehazing has not received significant interest. In this paper, we analyze the characteristic of infrared images, especially near-infrared (NIR) images, and present an NIR dehazing algorithm using the analyzed characteristics. In particular, a machine learning framework is adopted to obtain an accurate transmission map and several post-processing methods are used for further refinement. Experimental results show that the proposed NIR dehazing algorithm outperforms the conventional color image dehazing method for NIR image dehazing.

• Journal of Gastric Cancer
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• v.21 no.3
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• pp.246-257
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• 2021

Purpose: It is unclear whether high-definition (HD) imaging improves visibility and diagnostic ability in early gastric cancer (EGC) compared with standard-definition (SD) imaging. We aimed to compare the diagnostic performance and visibility scores of HD and SD ultraslim endoscopes in EGC. Materials and Methods: We used HD and SD ultraslim endoscopes to obtain 60 images with similar compositions of gastric environments. Of the 60 images, 30 showed EGC (15 images for each modality) and 30 showed no EGC (15 images for each modality). Seventeen endoscopists evaluated the presence and location of the lesions in each image. Diagnostic ability was compared between modalities. The color difference between a lesion and the surrounding mucosa (ΔE) was measured and compared between the modalities. Results: The ability of HD to detect EGC was significantly higher than that of SD (accuracy: 80.8% vs. 71.6%, P=0.017; sensitivity: 94.9% vs. 76.5%, P<0.001; positive predictive value, 76.2% vs. 55.3%, P<0.001; and negative predictive value (NPV), 94.1% vs. 73.5%, P<0.001). The ability of HD to determine the horizontal extent of EGC was significantly higher than that of SD (accuracy: 71.0% vs. 57.8%, P=0.004; sensitivity: 75.3% vs. 49.0%, P<0.001; NPV, 72.9% vs. 55.9%, P<0.001; and area under the curve: 0.891 vs. 0.631, P=0.038). The mean ΔE was significantly higher for HD than for SD (10.3 vs. 5.9, P=0.011). Conclusions: The HD ultraslim endoscope showed a higher diagnostic performance in EGC than the SD endoscope because it provided good color contrast.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.44 no.3
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• pp.397-405
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• 2024

In order to improve the visibility of nighttime thermal infrared images, a simulation method with daytime color images was proposed. As a simulation method consisting of two steps, the daytime thermal infrared image was simulated by learning the unpaired nighttime thermal infrared image and daytime thermal infrared image, then the result was translated into a daytime color image. A temperature change regression equation was constructed and applied to reflect the systematic characteristics of temperature changes in daytime and nighttime images, and day and night simulation and colorization were trained and modeled by CycleGAN. For the experimental area, 100 images were captured and used for training. As a result, the simulation showed an average SSIM of 0.2449 and a PSNR of 51.2254. It was confirmed that the method could simulate complex and detailed features such as vegetation.

• The Transactions of the Korea Information Processing Society
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• v.4 no.4
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• pp.933-943
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• 1997

In this paper, a conernt-based image retrival system with color assigments has been stueide and implment-ed. The color of images has been extracted after changing RGB color space to HSV(hue, saturation, value)that is the most compatible color for peop]e's feeling. In the color extracting, an image is divided into 9 different areasand 3 major colors for each area are selected by using color histograms. It is possible to chose the class of umages by keywords. We are evaluate four different types of queries such as an image input, keywords with color assignments, combining an image input and keywords with color assinments, and selecting specific part of an umage. Experimental rusults show that four different query types privide precision/recall 0.55/0.37, 0.57/0.43, 0.59/0.45 and 0.63/0.61, respectively. With color assignments, the retrieval system has been able to obtain high performance and validity.