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

• Journal of information and communication convergence engineering
• /
• v.8 no.1
• /
• pp.51-58
• /
• 2010

This paper proposes an edge adaptive color interpolation for an ultra-small HD-grade complementary metal-oxide semiconductor (CMOS) video sensor in camera phones that can process 720-p/30-fps videos. Recently, proposed methods with great image quality perceptually reconstruct the green component and then estimate the red/blue component using the reconstructed green and neighbor red and blue pixels. However, these methods require the bulky memory line buffers in order to temporally store the reconstructed green components. The edge adaptive color interpolation method uses seven or nine patterns to calculate the six edge directions. At the same time, the threshold values are adaptively adjusted by the sum of the color values of the selected pixels. This method selects the suitable one among the patterns using two flowcharts proposed in this paper, and then interpolates the missing color values. For verification, we calculated the peak-signal-to-noise-ratio (PSNR) in the test images, which were processed by the proposed algorithm, and compared the calculated PSNR of the existing methods. The proposed color interpolation is also fabricated with the 0.18-${\mu}m$ CMOS flash memory process.

• Korean Institute of Interior Design Journal
• /
• v.17 no.3
• /
• pp.43-50
• /
• 2008

Commercial space, the types of which have been increasingly various and changing rapidly, has been generating new marketing concepts for space. Especially as the environmentally friendly lifestyle spreads around, the component of natural landscape such as plant, stone, or water has been used as an important part in space design, freshly imprinting the brand image beyond the idea of simple interior property. By combining commercial space design with 'the component of the natural landscape', we can improve the brand image, create the newness in the space, lead customers to stay longer in the space, and reinforce the decorative effect. The parts of the commercial space to which we can apply the element of natural landscape are facades, walls, floors, and show windows. Various examples of real application are found according to the areas of business and goods displayed. This thesis attempts to maximize the effect of commercial space by examining and analyzing various instances of space and provide the ways of applying the space that contains an aesthetic value. For the research, 60 articles, theses, reports that have the keyword related to interior landscape and marketing strategy in commercial space were used as references. From the references, 70 cases were selected and analyzed to find landscape application patterns. Also, 4 store cases that landscape application have been the key to their success were selected for the survey. In doing this, I presented the readers with the packaging technique which improves brand image, the effect of stage direction which helps sensitive communication with users, the application as interior structure and the effect of an object that is useful to aesthetic effect in the commercial space. Finally, I endeavored to provide possible problems to be produced when applying the natural element in the commercial space and matters to be attended to in the management.

• Journal of Internet Computing and Services
• /
• v.16 no.2
• /
• pp.49-55
• /
• 2015

According to the statistics of traffic accidents over recent 5 years, traffic accidents during the night times happened more than the day times. There are various causes to occur traffic accidents and the one of the major causes is inappropriate or missing street lights that make driver's sight confused and causes the traffic accidents. In this paper, with smartphones, we designed and implemented a lane luminance measurement application which stores the information of driver's location, driving, and lane luminance into database in real time to figure out the inappropriate street light facilities and the area that does not have any street lights. This application is implemented under Native C/C++ environment using android NDK and it improves the operation speed than code written in Java or other languages. To measure the luminance of road, the input image with RGB color space is converted to image with YCbCr color space and Y value returns the luminance of road. The application detects the road lane and calculates the road lane luminance into the database sever. Also this application receives the road video image using smart phone's camera and improves the computational cost by allocating the ROI(Region of interest) of input images. The ROI of image is converted to Grayscale image and then applied the canny edge detector to extract the outline of lanes. After that, we applied hough line transform method to achieve the candidated lane group. The both sides of lane is selected by lane detection algorithm that utilizes the gradient of candidated lanes. When the both lanes of road are detected, we set up a triangle area with a height 20 pixels down from intersection of lanes and the luminance of road is estimated from this triangle area. Y value is calculated from the extracted each R, G, B value of pixels in the triangle. The average Y value of pixels is ranged between from 0 to 100 value to inform a luminance of road and each pixel values are represented with color between black and green. We store car location using smartphone's GPS sensor into the database server after analyzing the road lane video image with luminance of road about 60 meters ahead by wireless communication every 10 minutes. We expect that those collected road luminance information can warn drivers about safe driving or effectively improve the renovation plans of road luminance management.

• Journal of KIISE:Computing Practices and Letters
• /
• v.14 no.1
• /
• pp.1-12
• /
• 2008

In this paper, we present a real-time algorithm for recognizing the vehicle color from the indoor and outdoor vehicle images based on GPU (Graphics Processing Unit) acceleration. In the preprocessing step, we construct feature victors from the sample vehicle images with different colors. Then, we combine the feature vectors for each color and store them as a reference texture that would be used in the GPU. Given an input vehicle image, the CPU constructs its feature Hector, and then the GPU compares it with the sample feature vectors in the reference texture. The similarities between the input feature vector and the sample feature vectors for each color are measured, and then the result is transferred to the CPU to recognize the vehicle color. The output colors are categorized into seven colors that include three achromatic colors: black, silver, and white and four chromatic colors: red, yellow, blue, and green. We construct feature vectors by using the histograms which consist of hue-saturation pairs and hue-intensity pairs. The weight factor is given to the saturation values. Our algorithm shows 94.67% of successful color recognition rate, by using a large number of sample images captured in various environments, by generating feature vectors that distinguish different colors, and by utilizing an appropriate likelihood function. We also accelerate the speed of color recognition by utilizing the parallel computation functionality in the GPU. In the experiments, we constructed a reference texture from 7,168 sample images, where 1,024 images were used for each color. The average time for generating a feature vector is 0.509ms for the $150{\times}113$ resolution image. After the feature vector is constructed, the execution time for GPU-based color recognition is 2.316ms in average, and this is 5.47 times faster than the case when the algorithm is executed in the CPU. Our experiments were limited to the vehicle images only, but our algorithm can be extended to the input images of the general objects.