• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.117-124
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• 2008

This paper introduces a novel approach for automated mapping of a map feature that is vinyl green house in high spatial resolution imagery Some map features have their unique spatial patterns. These patterns are normally detected in high spatial resolution remotely sensed data by human recognition system. When spatial patterns can be applied to map feature identification, it will improve image classification accuracy and will be contributed a lot to feature identification. In this study, an automated feature identification approach using spatial aucorrelation is developed, specifically for the vinyl green house that has distinctive spatial pattern in its array. The algorithm aimed to develop the method without any human intervention such as digitizing. The method can investigate the characteristics of repeated spatial pattern of vinyl green house. The repeated spatial pattern comes from the orderly array of vinyl green house. For this, object-based approaches are essential because the pattern is recognized when the shapes that are consists of the groups of pixels are involved. The experimental result shows very effective vinyl house extraction. The targeted three vinyl green houses were exactly identified in the IKONOS image for a part of Jeju area.

• Journal of Forest and Environmental Science
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• v.27 no.3
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• pp.169-181
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• 2011

The purpose of this study was to understand the geographical and environmental distribution of animals and plants in Baekdudaegan region using field survey and GIS data. Crucial factors were selected and analyzed to understand the distributional characteristics of wild animals (16 species in 5 orders) and rare endemic plants (20 species in 12 orders). These crucial factors include stand factor (forest type, DBH class, and crown density), soil factor (bed rock, soil texture, and organic matter), geographical factor (elevation, slope, aspect) and climatic factor (temperature, rain fall, humidity). Finally, ten crucial factors were selected by statistical analysis and categorized for analyzing geographical and environmental features. Three orders such as Rodentia, Carnivora, and Artiodactula in wild animal showed the similar habitat characteristics with the small diameter and the elevation range from 801 to 1,000m. The Hydropotes inermis of Artiodactyla and Rattus norvegicus of Rodentia were different in the type of orders, but they had the similar habitat characteristics with the coniferous forest and loam. On the other hand, four orders such as Tubiflorales, Liliales, Ericales, and Rhamnales in the rare and endemic plants were showed high occurrence rate in the organic matter between 4 and 6%. The Rodgersia podophylla of Rosales and Gastrodia elata Blume of Microspermae were different in the type of orders, but they had the similar habitat characteristics with the stand factor and soil factor.

• Animal Bioscience
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• v.34 no.11
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• pp.1849-1858
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• 2021

Objective: Tenderness is a very complex feature, and the process of its formation is very complicated and not fully understood. Its diversification is one of the most important problems of beef production, as a result beef aging is widely used to improve tenderness as it is believed to provide a homogeneous product to consumers. While few studies have evaluated the muscle structure properties in relation to tenderness from early post-mortem, there little to no information available on how the muscle nanostructure of beef carcasses changes during post-mortem ageing to determine the appropriate aging time for acceptable tenderness. Methods: Muscle nanostructure (myofibril diameter [MYD], myofibril spacing [MYS], muscle fibre diameter [MFD], muscle fibre spacing [MFS], and sarcomere length [SL]), meat tenderness and cooking loss [CL]) were measured on 20 A2 longissimus muscles of Bonsmara, Beefmaster, Hereford, and Simbra at 45_mins, 1, 3, and 7 days post-slaughter. Muscle nanostructure was measured using a scanning electron microscope, while tenderness was measured using Warner Bratzler shear force. Results: At 45 minutes post-slaughter, breed affected MYD and MYS only, while at 24_hrs it also affected MFD and MFS. On day 3 breed effected MFS and SL, while on day 7 breed effected tenderness only. As the muscles matured, both MYD and MYS decreased while CL increased, and the muscles became tender. There was no uniformity on muscle texture features (surface structure, fibre separation, muscle contraction, and relaxation) throughout the ageing period. Conclusion: Meat tenderness can be directly linked to breed related myofibril structure changes during aging in particular the MYD, spacing between myofibrils and their interaction; while the MFD, spacing between muscle fibres, SL, and CL explain the non-uniformity in beef tenderness.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.13-21
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• 2019

The content based on image retrieval makes use of features of information within image such as color, texture and share for Retrieval data. we present a novel approach for improving retrieval accuracy based on DCT Filter-Bank. First, we perform DCT on a given image, and generate a Filter-Bank using the DCT coefficients for each color channel. In this step, DC and the limited number of AC coefficients are used. Next, a feature vector is obtained from the histogram of the quantized DC coefficients. Then, AC coefficients in the Filter-Bank are separated into three main groups indicating horizontal, vertical, and diagonal edge directions, respectively, according to their spatial-frequency properties. Each directional group creates its histogram after employing Otsu binarization technique. Finally, we project each histogram on the horizontal and vertical axes, and generate a feature vector for each group. The computed DC and AC feature vectors bins are concatenated, and it is used in the similarity checking procedure. We experimented using 1,000 databases, and as a result, this approach outperformed the old retrieval method which used color information.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.15-22
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• 2020

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.635-644
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• 2020

Object recognition, detection and instance segmentation based on DL (Deep Learning) have being used in various practices, and mainly optical images are used as training data for DL models. The major objective of this paper is object segmentation and building detection by utilizing multimodal datasets as well as optical images for training Detectron2 model that is one of the improved R-CNN (Region-based Convolutional Neural Network). For the implementation, infrared aerial images, LiDAR data, and edges from the images, and Haralick features, that are representing statistical texture information, from LiDAR (Light Detection And Ranging) data were generated. The performance of the DL models depends on not only on the amount and characteristics of the training data, but also on the fusion method especially for the multimodal data. The results of segmenting objects and detecting buildings by applying hybrid fusion - which is a mixed method of early fusion and late fusion - results in a 32.65% improvement in building detection rate compared to training by optical image only. The experiments demonstrated complementary effect of the training multimodal data having unique characteristics and fusion strategy.

• The Journal of Korean Institute of Information Technology
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• v.17 no.9
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• pp.99-112
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• 2019

Cracks on pavement surfaces are critical signs and symptoms of the degradation of pavement structures. Image-based pavement crack detection is a challenging problem due to the intensity inhomogeneity, topology complexity, low contrast, and noisy texture background. In this paper, we address the problem of pavement crack detection and segmentation at pixel-level based on a Deep Neural Network (DNN) using gray-scale images. We propose a novel DNN architecture which contains a modified U-net network and a high-level features network. An important contribution of this work is the combination of these networks afforded through the fusion layer. To the best of our knowledge, this is the first paper introducing this combination for pavement crack segmentation and detection problem. The system performance of crack detection and segmentation is enhanced dramatically by using our novel architecture. We thoroughly implement and evaluate our proposed system on two open data sets: the Crack Forest Dataset (CFD) and the AigleRN dataset. Experimental results demonstrate that our system outperforms eight state-of-the-art methods on the same data sets.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.77-90
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• 2023

Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.

• The Research Journal of the Costume Culture
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• v.30 no.6
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• pp.880-897
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• 2022

The purpose of this study is to apply the traditional Chinese fretwork on the 'Faux Chenille' textile work method in a way of enhancing the decorative features of patterns and developing the fashion design. As for the method, it works on the historic background and advancement of the fretwork and it refers to the bibliographies pertinent to the traditional Chinese geometric pattern. The result are as follows. First, pure cotton and 100% rayon are mixed to make it feasible to produce the texture for the material to be tender and enhanced, and in the process of washing and drying the Faux Chenille textile. The Faux Chenille textile is an important material that is required to select materials with great absorption capability as the most effective material to re-visualize the lines and patterns by sustaining the diagonal lines. Second, the fretwork is designed to process the basic formation for 90° angle with the sense of unlimited extensive line and changes with straight line. It has been confirmed that, if the angle that controls the Faux Chenille textile and the tailoring interval are well aligned, the expression of traditional geometric pattern would be effective and it may be expressed in contemporary style. Third, through the fashion design application by utilizing the Faux Chenille textile of the fretwork, it is confirmed that the contemporary application of the traditional culture could be expressed uniquely and creatively while it is affirmed that the western technique and Asian culture can be blended in harmony.

• Journal of the Korea Fashion and Costume Design Association
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• v.25 no.2
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• pp.1-16
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• 2023

Due to the current seriousness of environmental pollution and the eco-friendly movement of the fashion industry, research on sustainable fashion design is being actively conducted. In this study, consumer perception of upcycling products, are divided into image, function, and meaning; and image is further divided into shape, color, and material. It was redefined as pattern, and image recognition was evaluated among men and women in their 20s and 30s, and men and women in their 40s and 50s used as subjects. First, factors that determine each image were extracted based on qualitative analysis of the precedent cases of upcycling bags, and quantitative analysis of the subjects was induced through a questionnaire. As a result of the analysis of evaluation items related to image association, the average frequency analysis of all subjects for each stimuli and the cognitive variance of the frequency analysis by generation by gender were found to be similar. However, awareness of some stimuli by generation showed a significant difference. Overall, in the three stimuli with high overall preference, common features, such as the basic box-shaped symmetrical structure, the monochromatic color of the Munsell system, solid and practical texture, and appropriate use of patterns were identified. In addition, it was confirmed that there was a difference with factors such as femininity, simplicity, touch, and splendor in the measurement factors. In conclusion, it is considered that the main significance of this study is that it excluded the recognition and meaning of upcycling products and explored the original design and image elements of products. Therefore, it is expected that this study will be used as a basic data for responding to the gender image of each generation as an alternative method of sustainable fashion design, and it will be an opportunity to expand the scope of the study to a detailed study beyond the biased topic.