• Journal of Internet Computing and Services
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• v.16 no.6
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• pp.11-21
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• 2015

As growing interest in Human-Computer Interaction(HCI), research on HCI has been actively conducted. Also with that, research on Natural User Interface/Natural User eXperience(NUI/NUX) that uses user's gesture and voice has been actively conducted. In case of NUI/NUX, it needs recognition algorithm such as gesture recognition or voice recognition. However these recognition algorithms have weakness because their implementation is complex and a lot of time are needed in training because they have to go through steps including preprocessing, normalization, feature extraction. Recently, Kinect is launched by Microsoft as NUI/NUX development tool which attracts people's attention, and studies using Kinect has been conducted. The authors of this paper implemented hand-mouse interface with outstanding intuitiveness using the physical features of a user in a previous study. However, there are weaknesses such as unnatural movement of mouse and low accuracy of mouse functions. In this study, we designed and implemented a hand mouse interface which introduce a new concept called 'Virtual monitor' extracting user's physical features through Kinect in real-time. Virtual monitor means virtual space that can be controlled by hand mouse. It is possible that the coordinate on virtual monitor is accurately mapped onto the coordinate on real monitor. Hand-mouse interface based on virtual monitor concept maintains outstanding intuitiveness that is strength of the previous study and enhance accuracy of mouse functions. Further, we increased accuracy of the interface by recognizing user's unnecessary actions using his concentration indicator from his encephalogram(EEG) data. In order to evaluate intuitiveness and accuracy of the interface, we experimented it for 50 people from 10s to 50s. As the result of intuitiveness experiment, 84% of subjects learned how to use it within 1 minute. Also, as the result of accuracy experiment, accuracy of mouse functions (drag(80.4%), click(80%), double-click(76.7%)) is shown. The intuitiveness and accuracy of the proposed hand-mouse interface is checked through experiment, this is expected to be a good example of the interface for controlling the system by hand in the future.

• The Korean Journal of Nuclear Medicine
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• v.33 no.3
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• pp.316-326
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• 1999

Purpose: We evaluated the feasibility of extracting pure left ventricular blood pool and myocardial time-activity curves (TACs) and of generating factor images from human dynamic N-13 ammonia PET using factor analysis. The myocardial blood flow (MBF) estimates obtained with factor analysis were compared with those obtained with the user drawn region-of-interest (ROI) method. Materials and Methods: Stress and rest N-13 ammonia cardiac PET imaging was acquired for 23 min in 5 patients with coronary artery disease using GE Advance tomograph. Factor analysis generated physiological TACs and factor images using the normalized TACs from each dixel. Four steps were involved in this algorithm: (a) data preprocessing; (b) principal component analysis; (c) oblique rotation with positivity constraints; (d) factor image computation. Area under curves and MBF estimated using the two compartment N-13 ammonia model were used to validate the accuracy of the factor analysis generated physiological TACs. The MBF estimated by factor analysis was compared to the values estimated by using the ROI method. Results: MBF values obtained by factor analysis were linearly correlated with MBF obtained by the ROI method (slope = 0.84, r = 0.91), Left ventricular blood pool TACs obtained by the two methods agreed well (Area under curve ratio: 1.02 ($0{\sim}1min$), 0.98 ($0{\sim}2min$), 0.86 ($1{\sim}2min$)). Conclusion: The results of this study demonstrates that MBF can be measured accurately and noninvasively with dynamic N-13 ammonia PET imaging and factor analysis. This method is simple and accurate, and can measure MBF without blood sampling, ROI definition or spillover correction.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.8 no.1
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• pp.20-30
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• 1975

It is matter of fact processing technology must affects the quality and yield of final product and these also depend on the selection of raw material directly or indirectly. So that the estimation of tile preprocessing condition of shellfish is of great importance for distributors and processors. This study was attempted to establish the basic data for evaluating the processing suitability of baby clam, which is one of the five important shellfishes for domestic use and export. The important results are as follows: 1. The ratio of meat volume and meat weight to the holding capacity by shells may be useful for measuring the condition index of baby clams. 2. Baby clams grown in the beds with the composition of a large quantity of gravel were low in condition index value than in sand and mud. 3. If the green feed phenomenon was the prime consideration for canned baby clams, tile most suitable harvest season if October. In this period, the digestive tract in body was almost colorless. 4. As a whole, seasonal changes of moisture and fat content in baby clams were reversely correlated. Protein content increased from April and slightly decreased for a while from July to August and increased again from September. In March, the content of glycogen was 6.3 to 6.8 percent. From this period to October, glycogen was rapidly decreased. In October, it was only 0.1 to 0.2 percent but increased from November. There were little seasonal changes in pH value and crude ash content. The pH value of meat was 6.0 to 6.2 and crude ash content was about 2 percent. 5. By the results of condition index and chemical composition of baby clams, the suitable harvest season as raw materials for processing was from March to June and from September to October. 6. The steamed baby clam meat was packed with 2 percent salt solution containing 0.15 percent, citric acid or 0.5 percent $Na_2$ EDTA in a round sanitary tin can coated with C-enamel that is 203.9 ml by volume and sterilized for 60 minutes at $112^{\circ}C$. The canned product has shown a good result to reserve the natural characteristics of baby clam through six month storage at room temperature.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1505-1514
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• 2022

Image matching is a crucial preprocessing step for effective utilization of multi-temporal and multi-sensor very high resolution (VHR) satellite images. Deep learning (DL) method which is attracting widespread interest has proven to be an efficient approach to measure the similarity between image pairs in quick and accurate manner by extracting complex and detailed features from satellite images. However, Image matching of VHR satellite images remains challenging due to limitations of DL models in which the results are depending on the quantity and quality of training dataset, as well as the difficulty of creating training dataset with VHR satellite images. Therefore, this study examines the feasibility of DL-based method in matching pair extraction which is the most time-consuming process during image registration. This paper also aims to analyze factors that affect the accuracy based on the configuration of training dataset, when developing training dataset from existing multi-sensor VHR image database with bias for DL-based image matching. For this purpose, the generated training dataset were composed of correct matching pairs and incorrect matching pairs by assigning true and false labels to image pairs extracted using a grid-based Scale Invariant Feature Transform (SIFT) algorithm for a total of 12 multi-temporal and multi-sensor VHR images. The Siamese convolutional neural network (SCNN), proposed for matching pair extraction on constructed training dataset, proceeds with model learning and measures similarities by passing two images in parallel to the two identical convolutional neural network structures. The results from this study confirm that data acquired from VHR satellite image database can be used as DL training dataset and indicate the potential to improve efficiency of the matching process by appropriate configuration of multi-sensor images. DL-based image matching techniques using multi-sensor VHR satellite images are expected to replace existing manual-based feature extraction methods based on its stable performance, thus further develop into an integrated DL-based image registration framework.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.337-345
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• 2023

Estimating the rice heading date is one of the most crucial agricultural tasks related to productivity. However, due to abnormal climates around the world, it is becoming increasingly challenging to estimate the rice heading date. Therefore, a more objective classification method for estimating the rice heading date is needed than the existing methods. This study, we aimed to classify the rice heading stage from various images using a CNN classification model. We collected top-view images taken from a drone and a phenotyping tower, as well as slanted-view images captured with a RGB camera. The collected images underwent preprocessing to prepare them as input data for the CNN model. The CNN architectures employed were ResNet50, InceptionV3, and VGG19, which are commonly used in image classification models. The accuracy of the models all showed an accuracy of 0.98 or higher regardless of each architecture and type of image. We also used Grad-CAM to visually check which features of the image the model looked at and classified. Then verified our model accurately measure the rice heading date in paddy fields. The rice heading date was estimated to be approximately one day apart on average in the four paddy fields. This method suggests that the water head can be estimated automatically and quantitatively when estimating the rice heading date from various paddy field monitoring images.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.189-198
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• 2024

Lipreading is one of the important parts of speech recognition, and several studies have been conducted to improve the performance of lipreading in lipreading systems for speech recognition. Recent studies have used method to modify the model architecture of lipreading system to improve recognition performance. Unlike previous research that improve recognition performance by modifying model architecture, we aim to improve recognition performance without any change in model architecture. In order to improve the recognition performance without modifying the model architecture, we refer to the cues used in human lipreading and set other regions such as chin and cheeks as regions of interest along with the lip region, which is the existing region of interest of lipreading systems, and compare the recognition rate of each region of interest to propose the highest performing region of interest In addition, assuming that the difference in normalization results caused by the difference in interpolation method during the process of normalizing the size of the region of interest affects the recognition performance, we interpolate the same region of interest using nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation, and compare the recognition rate of each interpolation method to propose the best performing interpolation method. Each region of interest was detected by training an object detection neural network, and dynamic time warping templates were generated by normalizing each region of interest, extracting and combining features, and mapping the dimensionality reduction of the combined features into a low-dimensional space. The recognition rate was evaluated by comparing the distance between the generated dynamic time warping templates and the data mapped to the low-dimensional space. In the comparison of regions of interest, the result of the region of interest containing only the lip region showed an average recognition rate of 97.36%, which is 3.44% higher than the average recognition rate of 93.92% in the previous study, and in the comparison of interpolation methods, the bilinear interpolation method performed 97.36%, which is 14.65% higher than the nearest neighbor interpolation method and 5.55% higher than the bicubic interpolation method. The code used in this study can be found a https://github.com/haraisi2/Lipreading-Systems.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.