• Journal of KIISE:Computing Practices and Letters
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• v.16 no.9
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• pp.889-897
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• 2010

Recently, a cutting-edge large-scale image database system has demanded these attributes: search with alarming speed, performs with high accuracy, archives efficiently and much more. An image identifier (descriptor) is for measuring the similarity of two images which plays an important role in this system. The extraction method of an image identifier can be roughly classified into two methods: a local and global method. In this paper, the proposed image identifier, LFH(Local Feature's Histogram), is obtained by a histogram of robust and distinctive local descriptors (features) constrained by a district sub-division of a local region. Furthermore, LFH has not only the properties of a local and global descriptor, but also can perform calculations at a magnificent clip to determine distance with pinpoint accuracy. Additionally, we suggested a way to extract LFH via GPU (OpenGL and GLSL). In this experiment, we have compared the LFH with SIFT (local method) and EHD (global method) via storage capacity, extraction and retrieval time along with accuracy.

• Journal of Korea Multimedia Society
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• v.7 no.5
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• pp.648-657
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• 2004

A method of extracting central objects in color images without any prior-knowledge is proposed in this paper, which uses basically information of significant color distribution. A central object in an image is defined as a set of regions that lie around center of the image and have significant color distribution against the other surround (or background) regions. Significant colors in an image are first defined as the colors that are distributed more densely around center of the image than near borders. Then core object regions (CORs) are selected as the regions a lot of pixels of which have the significant colors. Finally, the adjacent regions to the CORs are iteratively merged if they are similar to the CORs but not to the background regions in color distribution. The merging result is accepted as the central object that may include differently color-characterized regions and/or two or more objects of interest. Usefulness of the significant colors in extracting the central object was verified through experiments on several kinds of test images. We expect that central objects shall be used usefully in image retrieval applications.

• Journal of Soil and Groundwater Environment
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• v.19 no.4
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• pp.45-51
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• 2014

In accordance with the view on remediated soil as a resource, this study assessed the applicability of soil washing with the neutral phosphate for remediation of arsenic (As)-contaminated soil. Three soil samples of different land uses (i.e., rice paddy, upland field and forest land) were collected from the study site, and the aqua regia-extractable As concentrations were 59.2, 30.8 and 53.1 mg/kg, respectively. Among the neutral phosphate reagents, ammonium phosphate showed the highest As washing efficiency. The optimized washing condition was 2-hr washing with 0.5M ammonium phosphate solution (pH 6) and soil to liquid ratio of 1 : 5. The extraction efficiencies of As did not guarantee the residual soil As concentrations to satisfy the Korea soil regulatory level (i.e., Worrisome level) in the three soil samples. To enhance washing efficiency, the As-contaminated soil was submerged in washing solution (1 : 1, w/v) for 24 hr and 1-hr washing with 0.5M ammonium phosphate solution was tested. As extraction efficiencies of 36.1 (rice paddy), 21.4 (upland field) and 26.4% (forest land) were attained, which satisfied the Worrisome level for Region 1 (25 mg/kg of As) in rice paddy, but not in upland field and forest land.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.447-454
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• 2018

This paper proposes a depth image generation algorithm of stereo images using a deep learning model composed of a CNN (convolutional neural network). The proposed algorithm consists of a feature extraction unit which extracts the main features of each parallax image and a depth learning unit which learns the parallax information using extracted features. First, the feature extraction unit extracts a feature map for each parallax image through the Xception module and the ASPP(Atrous spatial pyramid pooling) module, which are composed of 2D CNN layers. Then, the feature map for each parallax is accumulated in 3D form according to the time difference and the depth image is estimated after passing through the depth learning unit for learning the depth estimation weight through 3D CNN. The proposed algorithm estimates the depth of object region more accurately than other algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1428-1434
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• 2018

Canine cataract is developed with aging and can cause the blindness or surgical treatment if not treated timely. In this paper, we propose a method for extracting cataract suspicious areas automatically with FCM(Fuzzy C_Means) algorithm to overcome the weakness of previously attempted ART2 based method. The proposed method applies the fuzzy stretching technique and the Max-Min based average binarization technique to the dog eye images photographed by simple devices such as mobile phones. After applying the FCM algorithm in quantization, we apply the brightness average binarization method in the quantized region. The two binarization images - Max-Min basis and brightness average binarization - are ANDed, and small noises are removed to extract the final cataract suspicious areas. In the experiment with 45 dog eye images with canine cataract, the proposed method shows better performance in correct extraction rate than the ART2 based method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1807-1822
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• 2023

Innovation and rapid increased functionality in user friendly smartphones has encouraged shutterbugs to have picturesque image macros while in work environment or during travel. Formal signboards are placed with marketing objectives and are enriched with text for attracting people. Extracting and recognition of the text from natural images is an emerging research issue and needs consideration. When compared to conventional optical character recognition (OCR), the complex background, implicit noise, lighting, and orientation of these scenic text photos make this problem more difficult. Arabic language text scene extraction and recognition adds a number of complications and difficulties. The method described in this paper uses a two-phase methodology to extract Arabic text and word boundaries awareness from scenic images with varying text orientations. The first stage uses a convolution autoencoder, and the second uses Arabic Character Segmentation (ACS), which is followed by traditional two-layer neural networks for recognition. This study presents the way that how can an Arabic training and synthetic dataset be created for exemplify the superimposed text in different scene images. For this purpose a dataset of size 10K of cropped images has been created in the detection phase wherein Arabic text was found and 127k Arabic character dataset for the recognition phase. The phase-1 labels were generated from an Arabic corpus of quotes and sentences, which consists of 15kquotes and sentences. This study ensures that Arabic Word Awareness Region Detection (AWARD) approach with high flexibility in identifying complex Arabic text scene images, such as texts that are arbitrarily oriented, curved, or deformed, is used to detect these texts. Our research after experimentations shows that the system has a 91.8% word segmentation accuracy and a 94.2% character recognition accuracy. We believe in the future that the researchers will excel in the field of image processing while treating text images to improve or reduce noise by processing scene images in any language by enhancing the functionality of VGG-16 based model using Neural Networks.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.69-84
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• 2019

The purpose of this study is to extract lineaments in the southeastern part of the Gaeum Fault System, and to understand their characteristics and a relationship between them and fault activation. The lineaments were extracted using a multi-layered analysis based on a digital elevation model (5 m resolution), aerial photos, and satellite images. First-grade lineaments inferred as an high-activity along them were classified based on the displacement of the Quaternary deposits and the distribution of fault-related landforms. The results of classifying the first-grade lineaments were verified by fieldwork and electrical resistivity survey. In the study area of 510 km², a total of 222 lineaments was identified, and their total length was 333.4 km. Six grade lineaments were identified, and their total length was 11.2 km. The lineaments showed high-density distribution in the region along the Geumcheon, Gaeum, Ubo fault, and a boundary of the Hwasan cauldron consisting the Gaeum Fault System. They generally have WNW-ESE trend, which is the same direction with the strike of Gaeum Fault System. Electrical resistivity survey was conducted on eight survey lines crossing the first-grade lineament. A low-resistivity zone, which is assumed to be a fault damage zone, has been identified across almost all survey lines (except for only one survey line). The visual (naked eyes) detecting of the lineament was evaluated to be less objectivity than the automatic extraction using the algorithm. However, the results of electrical resistivity survey showed that first-grade lineament extracted by visual detecting was 83% reliable for inferred fault detection. These results showed that objective visual detection results can be derived from multi-layered analysis based on tectonic geomorphology.

• Analytical Science and Technology
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• v.37 no.1
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• pp.47-62
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• 2024

Abu Gurdi area is located in the South-eastern Desert of Egypt which considered as volcanic massive sulfide deposits (VMS). The present work aims at investigating the ore mineralogy of Abu Gurdi region in addition to the effectiveness of the hydrometallurgical route for processing these ores using alkaline leaching for the extraction of Zn, Cu, and Pb in the presence of hydrogen peroxide, has been investigated. The factors affecting the efficiency of the alkaline leaching of the used ore including the reagent composition, reagent concentration, leaching temperature, leaching time, and Solid /Liquid ratio, have been investigated. It was noted that the sulfide mineralization consists mainly of chalcopyrite, sphalerite, pyrite, galena and bornite. Gold is detected as rare, disseminated crystals within the gangue minerals. Under supergene conditions, secondary copper minerals (covellite, malachite, chrysocolla and atacamite) were formed. The maximum dissolution efficiencies of Cu, Zn, and Pb at the optimum leaching conditions i.e., 250 g/L NaCO₃ - NaHCO₃ alkali concentration, for 3 hr., at 250 ℃, and 1/5 Solid/liquid (S/L) ratio, were 99.48 %, 96.70 % and 99.11 %, respectively. An apparent activation energy for Zn, Cu and Pb dissolution were 21.599, 21.779 and 23.761 kJ.mol^-1, respectively, which were between those of a typical diffusion-controlled process and a chemical reaction-controlled process. Hence, the diffusion of the solid product layer contributed more than the chemical reaction to control the rate of the leaching process. High pure Cu(OH)₂, Pb(OH)₂, and ZnCl₂ were obtained from the finally obtained leach liquor at the optimum leaching conditions by precipitation at different pH. Finally, highly pure Au metal was separated from the mineralized massive sulfide via using adsorption method.

• International Journal of Industrial Entomology and Biomaterials
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• v.48 no.3
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• pp.139-146
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• 2024

For stable silkworm breeding and high-quality sericulture product production, the detection of Pebrine disease in silkworm eggs is critical. Current diagnostic methods can be timeconsuming and complex. This study aimed to develop a simplified and rapid diagnostic method using loop-mediated isothermal amplification (LAMP) technology to detect pebrine infection in silkworm mother moths. Eight primer candidates targeting the ribosomal gene region of microsporidia were designed and evaluated for specificity and detection sensitivity. A simplified nucleic acid extraction method was established, and isothermal amplification was performed using the selected primers. Of these, primers ID30 and ID45 showed no polymerization, while ID5, ID18, and ID76 exhibited nonspecific reactions, making them unsuitable. Primers ID1, ID6, ID45, and ID82 successfully amplified DNA only in the presence of pebrine, with ID82 demonstrating the best reproducibility and sensitivity, detecting as low as 2.5 pg/ul of DNA through electrophoresis and 5 pg/ul via a colorimetric change with phenol red. The entire process, from nucleic acid extraction to detection, was completed within 60 min. The use of the ID82 primer set in LAMP technology offers a promising and efficient approach for the rapid diagnosis of pebrine disease, potentially enhancing quality control in sericulture.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
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• v.32 no.5
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• pp.401-409
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• 2006

Infections of the oral and maxillofacial region are one of the most common conditions for which a patient presents to a maxillofacial surgeons. Although these infections can arise from a variety of source, dental disease is the most common etiology. So, odontogenic infection are frequently encountered in the practice of oral and maxillofacial surgery. These infections often respond to antimicrobial chemotherapy or surgical intervention, such as extraction of teeth, incision and drainage through clinical features. But, odontogenic infections have the potential to spread via the fascial spaces in the head and neck region, and, they spread to cavernous sinus, deep musculofascial space and other vital structure. We have undertaken clinical studies on infections in the oral and maxillofacial regions by analyzing retrospectively hospitalized patients in the Dept. of Oral and Maxillofacial Surgery, Chonbuk National University Hospital past 5 years from 2000 to 2004. And, the patients' age, sex, medical history, causes of the infection, surgical intervention, and other clinical parameters were reviewed. The obtained results were as follows : 1. The most frequent cause of oral and maxillofacial infection was odontogenic. And in the odontogenic cause, dental caries was the most common cause (47.2%). 2. The most common fascial space involved was the submandibular space (15.7%), followed by the buccal space (14.8%). 3. 60.4% of all patients required surgical drainage of the abscess, endodontic treatment or tooth extraction or periodontal treatment with drainage. 4. The most causative organism isolated from the pus culture were streptococcus viridans (53.9%). 5. Underlying medical problems were found in 136 patients (41.9%), the most common being hypertension (27.9%) and diabetes (14.7%).