• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.3
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• pp.503-515
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• 2018

In this research, the three-dimensional structural and colorimetrical modeling of yarn-dyed woven fabrics was conducted based on the Kubelka-Munk theory (K-M theory) for their accurate color predictions. In the K-M theory for textile color formulation, the absorption and scattering coefficients, denoted K and S, respectively, of a colored fabric are represented using those of the individual colorants or color components used. One-hundred forty woven fabric samples were produced in a wide range of structures and colors using red, yellow, green, and blue yarns. Through the optimization of previous two-dimensional color prediction models by considering the key three-dimensional structural parameters of woven fabrics, three three-dimensional K/S-based color prediction models, that is, linear K/S, linear log K/S, and exponential K/S models, were developed. To evaluate the performance of the three-dimensional color prediction models, the color differences, ${\Delta}L^*$, ${\Delta}C^*$, ${\Delta}h^{\circ}$, and ${\Delta}E_{CMC(2:1)}$, between the predicted and the measured colors of the samples were calculated as error values and then compared with those of previous two-dimensional models. As a result, three-dimensional models have proved to be of substantially higher predictive accuracy than two-dimensional models in all lightness, chroma, and hue predictions with much lower ${\Delta}L^*$, ${\Delta}C^*$, ${\Delta}h^{\circ}$, and the resultant ${\Delta}E_{CMC(2:1)}$ values.

• Food Science and Preservation
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• v.7 no.2
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• pp.155-159
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• 2000

Apple fruit grading is largely dependant on skin color degree. This work reports about the possibility of nondestructive assessment of apple fruit color using infrared(NIR) reflectance spectroscopy. NIR spectra of apple fruit were collected in wavelength range of 1100~2500nm using an InfraAlyzer 500C(Bran+Luebbe). Calibration as calculated by the standard analysis procedures MLR(multiple linear regression) and stepwise, was performed by allowing the IDAS software to select the best regression equations using raw spectra of sample. Color degree of apple skin was expressed as 2 factors, anthocyanin content by purification and a-value by colorimeter. A total of 90 fruits was used for the calibration set(54) and prediction set(36). For determining a-value, the calibration model composed 6 wavelengths(2076, 2120, 2276, 2488, 2072 and 1492nm) provided the highest accuracy : correlation coefficient is 0.913 and standard error of prediction is 4.94. But, the accuracy of prediction result for anthocyanin content determining was rather low(R of 0.761).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06b
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• pp.165-170
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• 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 Korean Digital Architecture Interior Association
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• v.5 no.1
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• pp.9-16
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• 2005

In Interior design, light is the most important factor in deciding color, texture and illumination level which are the basic factors of spatial design. To apply rendering technologies on prediction of illuminating effect, it is important to understand and analyse the basic properties of the illumination models that are local illumination model and global illumination model. The illumination models in computer graphics express the factors which determine the surface color, texture and light distribution through the reflection. The purpose of this study is to propose the best way of shading algorithm in interior space provided by the computer, based on the experimental analysis that 5 shading methods are applied to the interior space. The results of this study were as followed. 1) Local illumination models that are Lambert shading, Ground shading and Phong shading are not suitable to the prediction of interior illumination effect. 2) Ray tracing that is global illumination model could be adopted to interior illumination effects. Ray tracing is a very versatile algorithm because of the large range of lighting effects it can model. 3) Neither radiality nor ray tracing offers a complete solution for simulating all interior illumination effects. 4) Radiosity excels at rendering diffuse-to-diffuse inter-reflections and ray tracing excels at rendering specular reflections. By merging both shading techniques, that offers the best of both. Using computer technologies to simulate lighting in preliminary design stage which will provide information for designers and occupants to determine the effect of using artificial light sources at each stage of their design process. Further study in illumination analysis, prediction of illumination effect, and lighting calculation is required as computer media expands.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.1109-1122
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• 2020

The aim of this study was to develop a marbling classification and prediction model using small parts of sirloin images based on a deep learning algorithm, namely, a convolutional neural network (CNN). Samples were purchased from a commercial slaughterhouse in Korea, images for each grade were acquired, and the total images (n = 500) were assigned according to their grade number: 1++, 1+, 1, and both 2 & 3. The image acquisition system consists of a DSLR camera with a polarization filter to remove diffusive reflectance and two light sources (55 W). To correct the distorted original images, a radial correction algorithm was implemented. Color images of sirloins of Hanwoo (mixed with feeder cattle, steer, and calf) were divided and sub-images with image sizes of 161 × 161 were made to train the marbling prediction model. In this study, the convolutional neural network (CNN) has four convolution layers and yields prediction results in accordance with marbling grades (1++, 1+, 1, and 2&3). Every single layer uses a rectified linear unit (ReLU) function as an activation function and max-pooling is used for extracting the edge between fat and muscle and reducing the variance of the data. Prediction accuracy was measured using an accuracy and kappa coefficient from a confusion matrix. We summed the prediction of sub-images and determined the total average prediction accuracy. Training accuracy was 100% and the test accuracy was 86%, indicating comparably good performance using the CNN. This study provides classification potential for predicting the marbling grade using color images and a convolutional neural network algorithm.

• Science of Emotion and Sensibility
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• v.12 no.1
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• pp.109-120
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• 2009

This study was carried out in order to investigate color sensation and sensibility for yellowish natural dye fabrics and reddish ones and to establish prediction models for color sensibility factors of them by color sensation and the related physical measurements focusing on 40s middle-aged people. Eight fabric stimuli which were dyed with a variety of yellowish or reddish natural dyes was subjectively evaluated in terms of color sensation and sensibility by 40s aged participants. As results, three color sensibility factors including 'Active', 'Characteristic', and 'Relax' were extracted and they were examined in respect of their relationships with color sensation and physical color properties. Color sensibility factor 'Active', the dominant factor for the naturally dyed fabrics was explained by $L^*$ and sensation 'Deep' in its predictive model and a yellowish fabric dyed with 300% solution of armur cork unmordanted was perceived the strongest in the factor. Factor 'Characteristic' was predicted by both $a^*$ and sensation 'Light' and reddish natural dye fabrics tended to be felt more strongly for it. Color sensation 'Strong' was the only predictor for factor 'Relax' in that naturally dyed fabrics with lower values for the sensation seemed to show higher 'Relax' factor and a reddish fabric dyed with safflower 125% was the highest for the color sensibility factor. These results could be utilized to design color-sensible natural dye fabrics for middle-aged people.

• The KSFM Journal of Fluid Machinery
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• v.10 no.4
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• pp.39-46
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• 2007

In large pump station, vortex generation such as free-surface vortex and submerged vortex occurring around pump intake, or at bell-mouth inlet has been an important flow characteristics which should be considered always to keep away the suction of air-entrained or cavitated flow. In this study, a commercial CFD code was used to predict accurately the vortex generation for the specified intake design. These result shows the preliminary result of submerged vortex prediction for the Turbo-machinery Society of Japan Sump Test CFD standard model. At bottom wall, air volume fraction (red color) was found in a large scale to explain the submerged vortex generation at particular operation and configuration condition. And these indicate the free surface formation behind the bell mouth. Particularly, non-uniform approaching flow is a major parameter to govern the occurrence of the free-surface vortex. Futhermore the comparison between turbulence ($k-{\epsilon}$ & $k-{\omega}$ model) mode were executed in this study.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1577-1589
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• 2023

Recently, the necessity of predicting changes for monitoring ocean is widely recognized. In this study, we performed a time series prediction of remote-sensing reflectance (Rrs), which can indicate changes in the ocean, using Geostationary Ocean Color Imager (GOCI) data. Using GOCI-I data, we trained a multi-scale Convolutional Long-Short-Term-Memory (ConvLSTM) which is proposed in this study. Validation was conducted using GOCI-II data acquired at different periods from GOCI-I. We compared model performance with the existing ConvLSTM models. The results showed that the proposed model, which considers both spatial and temporal features, outperformed other models in predicting temporal trends of Rrs. We checked the temporal trends of Rrs learned by the model through long-term prediction results. Consequently, we anticipate that it would be available in periodic change detection.

• Journal of the Korean Society of Clothing and Textiles
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• v.45 no.1
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• pp.180-199
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• 2021

This study investigated natural aroma fragrance on the fashion image of Galchon, a traditional natural dyeing textile made with immature persimmon from the Jeju area, Korea. Nine fabric pairs consisting of differently colored cotton and silk Galchon with various tones and fabric types were used for subjective evaluation. Thirty five female college students evaluated the specimens using a 7-point scale questionnaire for fashion image-related adjectives. A specimen with three different presentation types that included fabric without fragrance (FO), fabric with citrus fragrance, and fabric with chamaecyparis (FCP) were randomly provided to a subject. As a result, color variables of Galchon were found to be the primary influence on fashion images for both cotton and silk Galchon that showed interaction effects with presentation types. The citrus fragrance increased the feeling of 'Active' while chamaecyparis tended to contribute to a stronger perception of 'Elegance' for cotton Galchon. Finally, these results were used to develop prediction models for fashion images of Galchon that employed color variables and presentation types.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2938-2956
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• 2020

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.