• Journal of the Korea Society of Computer and Information
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• v.18 no.6
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• pp.21-28
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• 2013

This paper proposes an effective fire detection approach that includes the following multiple heterogeneous algorithms: moving region detection using grey level histograms, color segmentation using fuzzy c-means clustering (FCM), feature extraction using a grey level co-occurrence matrix (GLCM), and fire classification using support vector machine (SVM). The proposed approach determines the optimal threshold values based on grey level histograms in order to detect moving regions, and then performs color segmentation in the CIE LAB color space by applying the FCM. These steps help to specify candidate regions of fire. We then extract features of fire using the GLCM and these features are used as inputs of SVM to classify fire or non-fire. We evaluate the proposed approach by comparing it with two state-of-the-art fire detection algorithms in terms of the fire detection rate (or percentages of true positive, PTP) and the false fire detection rate (or percentages of true negative, PTN). Experimental results indicated that the proposed approach outperformed conventional fire detection algorithms by yielding 97.94% for PTP and 4.63% for PTN, respectively.

• Journal of Intelligence and Information Systems
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• v.15 no.2
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• pp.49-59
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• 2009

Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM(Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM(Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.433-438
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• 2016

This paper proposes a grade prediction method to measure meat quality in Hanwoo (Korean Native Cattle) using classification and feature extraction algorithms. The applied classification algorithm is an AdaBoost and the texture features of the given ultrasound images are extracted using SFTA. In this paper, as an initial phase, we selected ultrasound images of Hanwoo for verifying experimental results; however, we ultimately aimed to develop a diagnostic decision support system for human body scan using ultrasound images. The advantages of using ultrasound images of Hanwoo are: accurate grade prediction without butchery, optimizing shipping and feeding schedule and economic benefits. Researches on grade prediction using biometric data such as ultrasound images have been studied in countries like USA, Japan, and Korea. Studies have been based on accurate prediction method of different images obtained from different machines. However, the prediction accuracy is low. Therefore, we proposed a prediction method of meat quality. From the experimental results compared with that of the real grades, the experimental results demonstrated that the proposed method is superior to the other methods.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.1-8
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• 2019

In this paper, we propose an improved facial skin color extraction method to solve the problem that facial surface is lost due to shadow or illumination in skin color extraction process and skin color extraction is not possible. In the conventional HSV method, when facial surface is brightly illuminated by light, the skin color component is lost in the skin color extraction process, so that a loss area appears on the face surface. In order to solve these problems, we extract the skin color, determine the elements in the H channel value range of the skin color in the HSV color space among the lost skin elements, and combine the coordinates of the lost part with the coordinates of the original image, To minimize the number of In the face detection process, the face was detected using the LBP Cascade Classifier, which represents texture feature information in the extracted skin color image. Experimental results show that the proposed method improves the detection rate and accuracy by 5.8% and 9.6%, respectively, compared with conventional RGB and HSV skin color extraction and face detection using the LBP cascade classifier method.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• The Journal of the Convergence on Culture Technology
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• v.9 no.2
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• pp.429-436
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• 2023

Traditional Chinese music has a deep indigenous color and has its own unique way of thinking and characteristics. A consensus has already been formed that linear thinking is a major feature of traditional Chinese music, and it has been implemented in both traditional multi-tone and single-tone music. It is mainly expressed in the form of single-tone music or single-tone music. This linear thought of traditional Chinese music is formed by influencing factors in various fields. For example, it is related to national culture, geographical and natural environment, religious and philosophical background, traditional Chinese notation, individual characteristics of traditional musical instruments, Yulje, composition, and transmission methods. This thinking is different from Western classical music that pursues three-dimensional thinking, and Western music emphasizes the harmony of harmony, harmony of tone and texture, logic and identity of structure, and emphasizes the aspect of space. However, traditional Chinese music emphasizes the horizontal development of melody, the fluency of ancestors, and the continuity of structure. We aims to analyze the causes of linear thinking of traditional Chinese music so that it can be more useful in educational aspects and promote the succession and development of traditional music by transferring knowledge of ethnic music.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.12
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• pp.519-524
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• 2023

In hashing-based image retrieval, the hash code of a manipulated image is different from the original image, making it difficult to search for the same image. This paper proposes and evaluates a self-supervised deephashing model that generates perceptual hash codes from feature information such as texture, shape, and color of images. The comparison models are autoencoder-based variational inference models, but the encoder is designed with a fully connected layer, convolutional neural network, and transformer modules. The proposed model is a variational inference model that includes a SimAM module of extracting geometric patterns and positional relationships within images. The SimAM module can learn latent vectors highlighting objects or local regions through an energy function using the activation values of neurons and surrounding neurons. The proposed method is a representation learning model that can generate low-dimensional latent vectors from high-dimensional input images, and the latent vectors are binarized into distinguishable hash code. From the experimental results on public datasets such as CIFAR-10, ImageNet, and NUS-WIDE, the proposed model is superior to the comparative model and analyzed to have equivalent performance to the supervised learning-based deephashing model. The proposed model can be used in application systems that require low-dimensional representation of images, such as image search or copyright image determination.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.14 no.3
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• pp.181-188
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• 2009

The macro tidal flat of the Gochanggun Myongsasipri, located on the southwestern coast of Korea, is studied in terms of seasonal variations of surface sediment and sedimentary environment. Surface sediments of 45 sites in the winter (February) and the summer (August) are sampled across three survey lines (15 sites in each survey line), respectively. The tidal flat of open-coast Myongsasipri is mainly composed of fine to medium sand, the distribution of which shows a coast-parallel trend. Grain-size distribution has a bi-modal trend, and grain size in the winter is coarser than that in the summer. During the winter, the upper tidal flat is dominated by medium sand, while the lower tidal flat is dominated by find sand. Such a feature is attributed to wave-dominated sedimentation in the winter. The finer grains of the summer rather than that of the winter and relationship between texture parameters suggest that tidal energy plays an important role in tidal-flat sedimentation during the summer. This study represents an environmental change from wave-dominated conditions in the winter to tide-dominated conditions in the summer as a result of the seasonal variation in the intensity of onshore-directed winds and waves in the Myongsasipri tidal flat.

• Journal of the Mineralogical Society of Korea
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• v.16 no.1
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• pp.33-47
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• 2003

Various applied-mineralogical characterization including measurements of surface area, size distribution, swelling index, and viscosity were done for some domestic bentonites in order to decipher the rheological properties and their controlling factors. The bentonites, which are Ca-type and relatively low-grade (rnontmorillonite contents: 30 ∼ 75 wt%), occur mostly as subhedral lamellas with the size range of 2 ∼ 4 $\mu\textrm{m}$. The size distribution of mineral fractions in bentonite suspension is dominant in the range of 10 ∼ 100 $\mu\textrm{m}$, and though rather complicated, exhibits roughly bimodal patterns. The feature is more conspicuous in the case of zeolitic bentonite. The bentonites have surface areas ranging 269 ∼ 735 $\m^2$/g, which are measured by EGME adsorption method. The EGME surface areas are nearly proportional to the rnontmorillonite contents, moisture contents, or total CEC. In the surface area measurements, zeolitic bentonites have slightly higher values than those zeolite- free types. The measured swelling index and viscosity of domestic bentonites are comparatively low in values. The swelling values of bentonites were measured to be 250∼500% at maximum by progressively mixing amounts of 2 ∼ 5 wt% Na$_2$CO$_3$, which varies depending on the contents of rnontmorillonite and other impurities, especially zeolite. Much amount of sodium carbonate is required for optimum swelling property of zeolitic bentonited which has usually strong Na- exchanged capacity. The bentonites, which are comparatively feldspar-rich and low in size and crystallinity, tend to be higher in viscosity values. Tn addition, the viscosity is largely higher in case of the bentonites with higher pH in suspension. However, the rheological properties of bentonites such as swelling index and viscosity do not show any obvious relationships with rnontmorillonite contents and mean particle size in suspension. In contrast, roughly speaking, the swelling index of bentonites is reversely proportional to the values of surface area which can be regarded as a collective physico-chemical parameter encompassing all the effects caused by mineral composition, surface charge, particle size, morphological farm, and etc. in bentonites. Thus, the rheological properties in bentonite suspension appear to be rather complicated characteristics which mainly depend on the flocculation of clay particles and the mode of particle association, i.e. quasicrystals, controlled by surface charge, morphology, size, and texture of rnon-tmorillonite, and which partly affected by the finer impurities such as zeolite.

• The Korean Journal of Mycology
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• v.26 no.2 s.85
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• pp.182-186
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• 1998

The result of study on the morphological feature of fruitbody, soil characteristics and enviromental condition of habitat of Tricholoma giganteum in Korea was as follows: The Pileus was $5.5{\sim}28.0\;cm$ in diameter and $1.5{\sim}3.7\;cm$ in thickness, indicating of yellowish white, beige, or ivory color. The lamellae were $18{\sim}20\;mm$ in diameter; the stipe was $9.0{\sim}35.7\;cm$ long and $1.0{\sim}3.0\;cm$ wide. The spores were $3.5{\sim}4.8{\times}5.7{\sim}7.4\;{\mu}m$ in size, ellipsoid, hyaline and the spore deposit white. The basidia were $6.1{\sim}7.0{\times}32.2{\sim}39.2\;{\mu}m$ in size. Cheiloystidia were $3.5{\sim}4.4{\times}30.5{\sim}33.1\;{\mu}m$ in size. Pileipellis were $3.3{\sim}4.4{\times}33.0{\sim}55.0\;{\mu}m$ wide and had clamp connection. Stipitipellis were $2.2{\sim}3.3{\times}88.0{\sim}93.1\;{\mu}m$ in size. Texture of soil in habitate of T. giganteum was silty loam, and organic materials content and available phosphate content in this soil were high as comparision to general soil in Korea. In the time of the mushrooms sprout, the room temperature indicated $25{\sim}27^{\circ}C$, their humidity showed $80{\sim}83%$, and the brightnesses were 328 Lux.