• Journal of KIISE:Databases
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• v.30 no.2
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• pp.125-133
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• 2003

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based texture features. The color features are obtained from soft-color histograms of the global image and the wavelet-based texture features are obtained from the invariant moments of the high-pass sub-band through the spatial-frequency analysis of the wavelet transform. The proposed system, called a color and texture based two-step retrieval(CTBTR), is composed of two-step query operations for an efficient image retrieval. In the first-step matching operation, the color histogram features are used to filter out the dissimilar images quickly from a large image database. The second-step matching operation applies the wavelet based texture features to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.1
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• pp.49-55
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• 2008

The face extraction is very important to provide the images of the roads and road sides without the problem of privacy. For face extraction form roadside images, we detected the skin color area by using HSI and YCrCb color models. Efficient skin color detection was achieved by using these two models. We used a connectivity and intensity difference for grouping, skin color regions further we applied shape conditions (rate, area, number and oval condition) and determined face candidate regions. We applied thresholds to region, and determined the region as the face if black part was over 5% of the whole regions. As the result of the experiment 28 faces has been extracted among 38 faces had problem of privacy. The reasons which the face was not extracted were the effect of shadow of the face, and the background objects. Also objects with the color similar to the face were falsely extracted. For improvement, we need to adjust the threshold.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.275-280
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• 2003

The method of simultaneous data acquisition of arteries and veins(SAAV) was suggested to obtain MR angiography of arteries and veins at 0.3T low filed MRI system (Magfinder, AlLab. Korea). Two separated artery- and vein-images were put together using AVCM(Artery-Vein Color Mapping) algorithm and presented in the same image. In this study, artery- and vein-separated angiograms of volunteer's neck were obtained. Two dimensioal blood-enhanced images wre sequentially obtained using SAAV pulse sequence based on time-of-flight(TOF) method with flow compensation. Imaging parameters were TR/TE=70/12msec. FOV=230mm, slice thickness = 3mm, flip angle=90$^{\circ}$, matrix size=256${\times}$256${\times}$64mm. TSat TH/SPA=15/20mm, Ts_v=10msec and Ts_a=40ms. 3D MRA images were reconstructed using the maximum intensity projection(MIP) and the artery-vein color mapping(AVCM) algorithm. This study showed good possibility of clinical applications of MRA in 0.3T which provides valuable diagnostic information of clinical vascular diseases.

• Journal of People, Plants, and Environment
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• v.24 no.6
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• pp.639-649
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• 2021

Background and objective: Various images from visual display terminals (VDTs) as well as living lighting are important parts of our daily life; thus, properly controlling the lighting environment - that is, illuminance, color temperature and good images from VDTs - can have a substantial effect on improving the mental health and work efficiency in everyday life. We examined electroencephalography (EEG) and heart rate variability (HRV) responses to various lighting conditions in 25 university students as they viewed images of a green landscape or traffic congestion. Methods: EEG was performed in darkness and when the room was illuminated with 10 different light-emitting diode (LED) color temperatures, while the EEG and HRV responses to green landscape or traffic congestion image stimuli were measured in darkness and during room illumination with three different LED color temperatures. Results: We found a significant difference between darkness and high LED illumination (400 lx) at 7 (CZ, F4, FZ, O1, O2, OZ, and T6) of 30 channels, while the alpha wave activity increased during darkness. In the second experiment, the green landscape image stimuli in the 30 lx-2600 K lighting condition elicited theta wave activity on the EEG, whereas the traffic congestion image stimuli under high LED illumination elicited high beta and gamma wave activities. Moreover, the subjects exhibited better stress coping ability and heart rate stability in response to green landscape image stimuli under illuminated conditions, according to their HRV. Conclusion: These results suggest that lower color temperatures and illumination levels alleviate tension, and that viewing green landscape image stimuli at low illumination, or in darkness, is effective for reducing stress. Conversely, high illumination levels and color temperatures are likely to increase tension and stress in response to traffic congestion image stimuli.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1657-1664
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• 2004

We propose a method that classifies images into two object types man-made and natural objects. A central object is extracted from each image by using central object extraction method[1] before classification. A central object in an images defined as a set of regions that lies around center of the image and has significant color distribution against its surrounding. We define three measures to classify the object images. The first measure is energy of edge direction histogram. The energy is calculated based on the direction of only non-circular edges. The second measure is an energy difference along directions in Gabor filter dictionary. Maximum and minimum energy along directions in Gabor filter dictionary are selected and the energy difference is computed as the ratio of the maximum to the minimum value. The last one is a shape of an object, which is also represented by Gabor filter dictionary. Gabor filter dictionary for the shape of an object differs from the one for the texture in an object in which the former is computed from a binarized object image. Each measure is combined by using majority rule tin which decisions are made by the majority. A test with 600 images shows a classification accuracy of 86%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.818-819
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• 2014

Recently there are many works conducted on utilizing the DIBR (Depth-Image-Based Rendering) based intermediate images for the three-dimensional displays that do not require the use of stereoscopic glasses. However the prior works have used expensive depth cameras to obtain high-resolution depth images since DIBR-based intermediate image generation method requires the accuracy for depth information. In this study, we have developed the simulation to generate multi-view intermediate images based on the depth and color images using Microsoft Kinect. This simulation aims to support the acquisition of multi-view intermediate images utilizing the low-resolution depth and color image from Kinect, and provides the integrated service for the quality evaluation of the intermediate images. This paper describes the architecture and the system implementation of this simulation program.

• Journal of radiological science and technology
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• v.38 no.4
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• pp.443-449
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• 2015

An ultrasound probe has a big impact on Doppler images even though it has very high risk of frequent function-breakdowns occurring in medical ultrasound scanners. This study experimentally analyses the impacts of an ultrasonic probe's defected elements on power & color Doppler images. The results show that, the bigger the size of defected probe elements is, and the closer a group of action elements is to the center, the more the brightness of images and the velocity of Doppler diminish. When elements' defects increase in color & power Doppler images, false images are formed to be mistaken for blood-vessel plaque in neighboring regions. Accordingly, whenever element defects are suspected, we need check-up process in B-mode. From this respective, it is advisable to have primary interest in a probe and carry out continuous probe QA for ultrasonography.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.689-698
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• 2006

Pattern recognition is the study of how machines can observe the environment, learn to distinguish patterns of interest from their background, and make sound and reasonable decisions about the categories of the patterns. Color image consists of various color patterns. And most pattern recognition methods use the information of color which has been trained and extract the feature of the color. This thesis extracts adaptively specific color feature from images with several limited colors. Because the number of the color patterns is limited, the distribution of the color in the image is similar. But, when there are some noises and distortions in the image, its distribution can be various. Therefore we cannot extract specific color regions in the standard image that is well expressed in special color patterns to extract, and special color regions of the image to test. We suggest new method to reduce the error of recognition by extracting the specific color feature adaptively for images with the low distortion, and six test images with some degree of noises and distortion. We consequently found that proposed method shouws more accurate results than those of statistical pattern recognition.

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

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.697-702
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• 2008

Mobile display panel is small so that users are often difficult to perceive images clearly. About image we perceive much through colors and therefore we propose color fitting approach for clear perception even on the small and low quality LCD panels. Various color modifications have been studied and used in commercial software packages. For mobile usage, our approach instantly enhances color images by modifying colors in a way to contrast differences of them. The method includes tone enhancements (which contrast dark and bright sides) and color enhancements (which reduce saturation for pure colorants). Based on color theory, our method also modifies color values towards specified complementary and preference colors. We term this color fitting. This approach enables displaying photos, multimedia messages, videos and digital media broadcasting (DMB) for better perception in real-time on mobile devices. Index Terms.) color fitting, visualization on small display, mobile graphics, visual perception.