• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.1-8
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• 2005

Facial analysis is used in many applications like face recognition systems, human-computer interface through head movements or facial expressions, model based coding, or virtual reality. In all these applications a very precise extraction of facial feature points are necessary. In this paper we presents a method for automatic extraction of the facial features Points such as mouth corners, eye corners, eyebrow corners. First, face region is detected by AdaBoost-based object detection algorithm. Then a combination of three kinds of feature energy for facial features are computed; valley energy, intensity energy and edge energy. After feature area are detected by searching horizontal rectangles which has high feature energy. Finally, a corner detection algorithm is applied on the end region of each feature area. Because we integrate three feature energy and the suggested estimation method for valley energy and intensity energy are adaptive to the illumination change, the proposed feature extraction method is robust under various conditions.

• Journal of Forest and Environmental Science
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• v.37 no.1
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• pp.44-51
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• 2021

Rapid urbanization and population growth have caused substantial land use land cover (LULC) change in the Kathmandu valley. The lack of temporal and geographical data regarding LULC in the middle mountain region like Kathmandu has been challenging to assess the changes that have occurred. The purpose of this study is to investigate the changes in LULC in Chandragiri Municipality between 1996 and 2017 using geographical information system (GIS) and remote sensing. Using Landsat imageries of 1996 and 2017, this study analyzed the LULC change over 21 years. The images were classified using the Maximum Likelihood classification method and post classified using the change detection technique in GIS. The result shows that severe land cover changes have occurred in the Forest (11.63%), Built-up areas (3.68%), Agriculture (-11.26%), Shrubland (-0.15%), and Bareland (-3.91%) in the region from 1996 to 2017. This paper highlights the use of GIS and remote sensing in understanding the changes in LULC in the south-west part of Kathmandu valley.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.1
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• pp.185-194
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• 2014

One in every 10 persons suffer from chronic gastritis in Korea. Endoscopy is most commonly used to diagnose the chronic gastritis. Endoscopic diagnosis is precise but it is accompanied with pain and high cost. According to pulse diagnosis in Traditional East Asian Medicine, health problems in stomach can be diagnosed with radial pulse signals in 'Guan' location in the right wrist, which are non-invasive and cost-effective. In this study, we developed a classification model of chronic gastritis using pulse signals in right 'Guan' location. We used both linear discrimination method and logistic regression model with respect to pulse features obtained with a peak-valley detection algorithm and a Gaussian model. As a result, we obtained sensitivity ranged between 77%~89% and specificity ranged between 72%~83% depending on classification models and feature extraction methods, and the average classification rates were approximately 80%, irrespective of the models. Specifically, the Gaussian model were featured by superior sensitivities (89.1% and 87.5%) while the peak-valley detection method showed superior specificities (82.8% and 81.3%), and the average classification rate (sensitivity + specificity) of the Gaussian model was 80.9% which was 1.2% ahead of the peak-valley method. In conclusion, we obtained a reliable classification model for the chronic gastritis based on the radial pulse feature extraction algorithms, where the Gaussian model was featured by outperformed sensitivity and the peak-valley method was featured by outperformed specificity.

• Proceedings of the KOSOMBE Conference
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• v.1989 no.05
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• pp.15-18
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• 1989

This paper presents a new algorithm for P-wave detection in the ECG signal. We detect the peak and valley point using significant point extraction algorithm with 9-point derivative. Because P-wave duration is changed according to heart-rates, we search for the R-peak and calculate the R-R interval time prior to the determination of P-wave duration threshold values in order to actively adapt to the change of P duration. We determine the parameters for P-wave detection and then P-peak, P-onset and P-offset are detected by these parameters. The results obtained from the proposed algorithm have detected successively P-wave almost more than 90%.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.757-759
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• 2003

Understanding the area of the rice paddy fields is important, and suitable for it the remote sensing. SAR is effective to the monitor in Southeast Asia with the rainy season. The detection of the filled rice paddy fields by RADARSAT was tried in the north part of Bangkok Thailand, and in the Mekong river valley Cambodia, which ware the main rice production country in Southeast Asia. We get observation data by RADARSAT and fields all through a year around Bangkok. However, because the flood had occurred on the study area in 2002 observed, the detection only of the rice fields ware difficult.

• Journal of Korea Multimedia Society
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• v.24 no.7
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• pp.890-902
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• 2021

Although the object detection accuracy with still images has been significantly improved with the advance of deep learning techniques, the object detection problem with video data remains as a challenging problem due to the real-time requirement and accuracy drop with occlusion. In this research, we propose a method in pig detection for video monitoring environment. First, we determine a motion, from a video data obtained from a tilted-down-view camera, based on the average size of each pig at each location with the training data, and extract key frames based on the motion information. For each key frame, we then apply YOLO, which is known to have a superior trade-off between accuracy and execution speed among many deep learning-based object detectors, in order to get pig's bounding boxes. Finally, we merge the bounding boxes between consecutive key frames in order to reduce false positive and negative cases. Based on the experiment results with a video data set obtained from a pig farm, we confirmed that the pigs could be detected with an accuracy of 97% at a processing speed of 37fps.

• Journal of information and communication convergence engineering
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• v.6 no.3
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• pp.270-274
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• 2008

This paper proposes an advanced signal processing circuit for a fingerprint sensor based on ridge resistivity. A novel fingerprint integrated sensor using ridge resistivity variation resulting from ridges and valleys on the fingertip is presented. The pixel level simple detection circuit converts from a small and variable sensing current to binary voltage out effectively. The sensor circuit blocks were designed and simulated in a standard CMOS 0.35 ${\mu}m$ process.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.5
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• pp.581-586
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• 2006

In this paper, describes ground detection method for removal earth field of magnet guidance system Magnetic guidance system is magnetic markers are installed just under the surface of roadway pavement and the magnetic fields generated these markers are detected by magnetic field sensor mounted of vehicles. vehicle is know lot lateral distance using magnetic field. But sensor is together measuring the magnetic field and earth field. It is operate error. Thus in this paper, proposed new method removing earth field or development experiment device via show the for practical and excellence.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.2
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• pp.279-284
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• 2010

The plane curve approach which was proposed by Boukharouba et. al. is a multi-threshold selection method through searching peak-valley based on histogram cumulative distribution function. However the method is required to select parameters to compose plane curve, and the shape of plane curve is affected according to parameters. Therefore detection of peak-valley is effected by parameters. In this paper, we propose an entropy maximizing-based method to select optimal plane curve parameters, and propose a multi-thresholding method based on the selected parameters. The effectiveness of the proposed method is demonstrated by multi-thresholding experiments on various images and comparison with other conventional thresholding methods based on histogram.

• Korean Journal of Veterinary Service
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• v.44 no.1
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• pp.15-19
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• 2021

Senecavirus A (SVA), previously known as Seneca Valley virus, can cause vesicular disease and neonatal losses in pigs that is clinically indistinguishable from foot-and-mouth disease virus (FMDV). After the first case report in Canada in 2007, it had been restrictively identified in North America including United States. But, since 2015, SVA emerged outside North America in Brazil, and also in several the Asian countries including China, Thailand, and Vietnam. Considering the SVA occurrence in neighboring countries, there has been a high risk that Korea can be introduced at any time. In particular, it is very important in terms of differential diagnosis in the suspected case of vesicular diseases in countries where FMD is occurring. So far, several different molecular detection methods for SVV have been published but not validated as the reference method, yet. In this study, seven different molecular methods for detecting SVA were evaluated. Among them, the method by Flowler et al, (2017) targeted to 3D gene region with the highest sensitivity and no cross reaction with other vesicular disease agents including FMDV, VSV and SVD, was selected and applied further to antigen surveillance of SVA. A total of 245 samples of 157 pigs from 61 farms submitted for animal disease diagnose nationwide during 2018 were tested all negative. In 2018, no sign of SVA occurrence have been confirmed in Korea, but the results of the surveillance for SVA needs to be continued and accumulated at a high risk of SVA in neighboring countries.