• Korean Journal of Human Ecology
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• v.18 no.1
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• pp.247-257
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• 2009

The purpose of this study was to examine the effects of interior color and product volume of clothing shop on consumer's store image and preference for a clothing shop. The research methodology was a survey questionnaire and the subjects were 516 university students in Daejeon, Korea. The measuring instruments were stimuli and self-administrated questionnaire. The stimuli were 7 pictures of clothing shop including interior color and product volume variables, and the self-administrated questionnaire consisted of semantic differential scales for store image, store preference, and subject's demographic attribution. The data were analyzed by t-test, ANOVA, MANOVA, Duncan's multiple range test, based on SPSS program The results were as follows: first, in clothing shop's interior colors affecting store image and preference for clothing shop, white color gave a casual, sophisticated, characteristic, and attractive image to the shops, and brown color gave an elegant, sophisticated image, while black color gave a sophisticated, uncomfortable image, and gray color gave a less positive image to them than other colors. Subjects preferred white, brown, and black color in the order. Second, clothing shop's products volume also affected consumers' store image and preference. Its small volume gave a more sophisticated, elegant image than other volume levels, and subjects preferred small and medium volume of clothing products to their large volume. Third, the effects of shop's interior color and clothes' product volume on store image were different depending on subject's sex. The results revealed that clothing shop's interior color and product volume are important VMD components affecting consumer's store image and preference, and consumer's sex has to be considered to understand the effects of VMD components on clothing shop image.

• IEMEK Journal of Embedded Systems and Applications
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• v.12 no.2
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• pp.97-103
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• 2017

Thermal imaging is mainly used in military equipment required for night observation. In particular, technologies of uncooled thermal imaging detectors are being developed as applied to low-cost night observation system. Many system integrators require different specifications of the uncooled thermal imaging camera but their development time is short. In this approach, EOSYSTEM has developed a small size, TEC-less uncooled thermal imaging camera module with $32{\times}32mm$ size and low power consumption. Both domestic detector and import detector are applied to the EOSYSTEM's thermal imaging camera module. The camera module contains efficient infrared image processing algorithms including : Temperature compensation non-uniformity correction, Bad/Dead pixel replacement, Column noise removal, Contrast/Edge enhancement algorithms providing stable and low residual non-uniformity infrared image.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.2_2
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• pp.255-261
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• 2020

We consider the problem of autonomously flying a quadcopter in indoor environments. Navigation in indoor settings poses two major issues. First, real time recognition of the marker captured by the camera. Second, The combination of the distributed images is used to determine the position and orientation of the quadcopter in an indoor environment. We autonomously fly a miniature RC quadcopter in small known environments using an on-board camera as the only sensor. We use an algorithm that combines data-driven image classification with image-combine techniques on the images captured by the camera to achieve real 3D localization and navigation.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.12
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• pp.1581-1590
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• 1995

Recently, advanced video coding techniques using segmentation technique have been actively researched as candidates for video coding of MPEG-4 standard. The conventional segmentation techniques are unsuitable for real-time process because they have sequential structure. In this paper, we propose a new image segmentation technique using competitive learning neural network for vector quantization. The proposed segmentation procedure consist of prefiltering, primary and secondary segmentation, and a small region ellimination process. Primary segmentation segments input image in detail. Secondary segmentation merges similar region using a repetitive FSCL(Frequency sensitive competive learning) neural network. In this process, it is possible to segment an image from high resolution to low resolution by adjusting the number of repetition. Finally, small regions are merged into adjacent regions. Experimental results show that the procedure described yields reconstructed images of reasonably acceptable quality at bit rates of 0. 25 - 0.3 bit/pel.

• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1508-1517
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• 2021

In CCTV environments, a lot of learning image data is required to monitor illegal dumping of garbage with a typical image-based object detection using deep learning method. In this paper, we propose a system to monitor unauthorized dumping of garbage by learning the articular points of the person using only a small number of images without immediate use of the image for deep learning. In experiment, the proposed system showed 74.97% of garbage dumping detection performance with only a relatively small amount of image data in CCTV environments.

• Journal of Ocean Engineering and Technology
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• v.16 no.6
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• pp.32-36
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• 2002

This paper presents the results of wave profile detection from video image using the Mexican hat function. The Mexican hat function has been extensively used in the field of signal processing to detect discontinuity in the images. The analysis was done on the numerical image and video images of waves that were taken in the small wave flume. The results show that the Mexican hat function is an excellent tool for wave profile detection.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.6
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• pp.760-768
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• 2000

Filter efficiency of electrically charged particle in uncharged fibrous filter was measured. In previous studies, the effect of charged particle on filter efficiency was investigated but there was difficulty in measuring of image effect that is appeared at the charged small particle. We could easily measure the image effect with charging small particles by photoelectric charging. The spark discharge aerosol generator and a differential mobility analyzer (DMA) were used to generate sub-micron monodisperse particles (${\leq}200$ nm). The generated particles were charged in photoelectric charging process using ultraviolet lamp and electric field. The filter efficiency of the charged particles, classified by another DMA, was measured in filter tester using a condensation nucleus counter (CNC) as function of particle diameter, particle charge and airflow velocity. It is shown that the filter efficiency increases with increasing charge number of the particle and is affected by particle size and flow velocity. Single fiber filter efficiency mainly depends on image force parameter and peclet number. The peclet number was not considered at previous other papers. We propose a modi fied experimental correlation as function of image force parameter and peclet number.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.404-406
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• 2002

In this paper, the method of image feature extraction is proposed. This method employ the energy field analysis, outlier removal algorithm and ring projection. Using this algorithm, we achieve rotation-translation-scale invariant feature extraction. The force field are exploited to automatically locate the extrema of a small number of potential energy wells and associated potential channels. The image feature is acquired from relationship of local extrema using the ring projection method.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.76-82
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• 2011

In this paper, we propose the technique to detect the location and size of the small target in multi-resolution image using cubic facet model. The input image is reduced by the multi-resolution and we obtain the multi-resolution images. We apply the facet model and the local maxima conditions to the multi-resolution images of each level. And then, we detect the location of the small target. We estimate that the location at the maximum of the $D_2$ which means the local maxima value of the facet model in the multi-resolution images is the location of the small target. We can detect the small target of the various size about the multi-resolution images of each level. In this paper, we experimented in the various infrared images with the small target. The method using the typical facet model applies a mask. However, the proposed method applies a mask in the multi-resolution images. We verified to vary the mask size and differ the size of the small target. The proposed algorithm can detect the location and size of the small target.

• Journal of Internet Computing and Services
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• v.6 no.2
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• pp.59-68
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• 2005

In this paper, for compression and transmission of 3D image, we propose an algorithm which executes 3D discrete cosine transform(DCT) for 3D images, hierarchically segments 3D blocks of an image in comparison with the original image and executes finite-state vector quantization(FSVQ) for each 3D block. Using 3D DCT coefficient feature, a 3D image is segmented hierarchically into large smooth blocks and small edge blocks, then the block hierarchy informations are transmitted. The codebooks are constructed for each hierarchical blocks respectively, the encoder transmits codeword index using FSVQ for reducing encoded bit with hierarchical segmentation information. The new algorithm suggested in this paper shows that the quality of Small Lobster and Head image increased by 1,91 dB and 1.47 dB respectively compared with those of HFSVQ.