• Journal of the Korean Society of Floral Art and Design
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• no.43
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• pp.3-17
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• 2020

Through color analysis and survey of seasonal display cases using flower materials in department stores, hotels, and retailers, which are representative commercial spaces in Korea and abroad, it is designed to recognize the need for color planning that applies seasonal colors and emotional adjectives, away from the traditional method of relying on the season, shape and texture of materials, in the process of flower displays. The research method analyzed the colors used in the 48 domestic and foreign commercial space flower display cases collected. Based on this, the first expert questionnaire collected adjectives extraction and seasonal coordinates reminiscent of the case and examined the suitability of emotional adjectives extracted by the second public survey. The research results extracted typical colors and tones of spring, summer, fall, and winter, and recognized seasonal emotional adjectives. Based on these results, We could see that the color scheme should be advanced in the flower display, which used to depend solely on the shape or texture of the flower material, to produce the intended emotional design.

• Journal of Korean Society of Rural Planning
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• v.28 no.1
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• pp.57-69
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• 2022

Crop classification is very important for estimating crop yield and figuring out accurate cultivation area. The purpose of this study is to classify crops harvested in fall in Idam-ri, Goesan-gun, Chungcheongbuk-do by using unmanned aerial vehicle (UAV) images and support vector machine (SVM) model. The study proceeded in the order of image acquisition, variable extraction, model building, and evaluation. First, RGB and multispectral image were acquired on September 13, 2021. Independent variables which were applied to Farm-Map, consisted gray level co-occurrence matrix (GLCM)-based texture characteristics by using RGB images, and multispectral reflectance data. The crop classification model was built using texture characteristics and reflectance data, and finally, accuracy evaluation was performed using the error matrix. As a result of the study, the classification model consisted of four types to compare the classification accuracy according to the combination of independent variables. The result of four types of model analysis, recursive feature elimination (RFE) model showed the highest accuracy with an overall accuracy (OA) of 88.64%, Kappa coefficient of 0.84. UAV-based RGB and multispectral images effectively classified cabbage, rice and soybean when the SVM model was applied. The results of this study provided capacity usefully in classifying crops using single-period images. These technologies are expected to improve the accuracy and efficiency of crop cultivation area surveys by supplementing additional data learning, and to provide basic data for estimating crop yields.

• Korean Journal of Soil Science and Fertilizer
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• v.14 no.3
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• pp.130-136
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• 1981

Rice hull was reduced to ash by carbonization grades to illcuidate alkalinity increase and extract of inorganic nutrients in the rice hull charcoal. Alkaline reaction of water extraction was neutral at less carbonized charcoal, but much carbonized ash from 65% weight loss reached over 10 of pH value, also origin shape of rice hull was maintained until near 65% carbonized grade. Therefore, physical properties sustained good condition for seedling bed. The more charcoal carbonized to ash, the pH value and concentration of inorganic nutrient in their extracts were increased gradually. Nitric acid concentrations for neutralizing extract from charcoal were stronger in proportion to the carbonized grade but 0.1 N nitric acid solution was very reasonable to neutralize the 65% carbonized charcoal for mixing with heavy texture acidy soil(pH 5.3) of uncultivated deep horizon to transplant the tobacco seedlings. Volume ratio mixing for seedling bed is adequate at five of ash to one of acid solution. Neutralization with nitric acid solution also accelerated extraction of the inorganic nutrient in rice hull ash. Tobacco seedlings grown on bed mixed with neutralized rice hull charcoal and soil had shown better results on the agronomic measurement than alkaline ash bed, and phosphorus and cations were uptaken more amounts.

• Journal of Environmental Science International
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• v.17 no.2
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• pp.185-193
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• 2008

This study has been carried out to examine the feasibility of soil washing process for reducing arsenic contamination level of soil around $Dalch\hat{e}n$ Mine. The results of physicochemical tests of the target soil showed that pH was weak alkalic ($pH{\simeq}7.8$), soil texture was coarse sand, and organic contents (5.7%) and CEC (Cation exchange capacity; 21.5 meq/100 g) were similar with those of soils generally found in Korea. Contamination levels of arsenic were found to over 201 mg/kg which exceed the Korea standard levels of countermeasure and concern. To investigate chemical partitioning of heavy metals, sequential extraction procedures were adopted and it was found that arsenic was predominantly associated with the residual fraction among five fractional forms as much as over 85%, which is demonstrating that only less than 15% of all might be vulnerable to a selected washing agents. Among 6 kinds of washing agents applied on the screening for arsenic-contaminated soil, HCl and $H_3PO_4$ solution were selected as promising washing agents. In comparison with HCl and $H_3PO_4$ solutions for arsenic washing by kinetic experiment in the change of pH, soil-solution ratio, temperature, and washing solution concentration, $H_3PO_4$ solution was determined to best one of agents tested, which showed faster washing rate than HCl to accomplish regulatory goal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1086-1103
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• 2020

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.

• The KIPS Transactions:PartB
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• v.12B no.4 s.100
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• pp.395-402
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• 2005

Object extraction is needed to track objects in color traffic image sequence. To extract objects, we use background differencing method based on MOG(Mixture of Gaussians). In extracted objects, shadows may be included. Due to shadows, we may not find exact location of objects and sometimes we find adjacent objects are glued together. Many methods have been proposed to remove shadows. Conventional methods usually assume that color and texture information are preserved under the shadow. Thus these methods do not work well if these assumptions do not hold. In this paper, we propose a new robust shadow removal method which works well in those situations. First we extract shadow pixel candidates by analysing color information and compute the ratio of shadow pixel candidates over the total number of Pixels. W the ratio is reasonable, we remove shadow candidate Pixels and if not, we use data in history array containing Previous removal records. We applied the method to real color traffic image sequences and obtained good results.

• 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 Korea Multimedia Society
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• v.5 no.4
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• pp.415-425
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• 2002

In this paper, we present a content-based image retrieval method which is based on the feature extraction in the wavelet transform domain. In order to overcome the drawbacks of the feature vector making up methods which use the global wavelet coefficients in subbands, we utilize the energy value of wavelet coefficients, and the shape-based retrieval of objects is processed by moment which is invariant in translation, scaling, rotation of the objects The proposed methods reduce feature vector size, and make progress performance of classification retrieval which provides fast retrievals times. To offer the abilities of region-based image retrieval, we discussed the image segmentation method which can reduce the effect of an irregular light sources. The image segmentation method uses a region-merging, and candidate regions which are merged were selected by the energy values of high frequency bands in discrete wavelet transform. The region-based image retrieval is executed by using the segmented region information, and the images are retrieved by a color, texture, shape feature vector.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.