• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05a
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• pp.106-109
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• 2004

Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of variability in scale, location, orientation and pose. In this paper, we present an efficient linear discriminant for multi-view face detection. Our approaches are based on linear discriminant. We define training data with fisher linear discriminant to efficient learning method. Face detection is considerably difficult because it will be influenced by poses of human face and changes in illumination. This idea can solve the multi-view and scale face detection problem poses. Quickly and efficiently, which fits for detecting face automatically. In this paper, we extract face using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected face and eye detect. The purpose of this paper is to classify face and non-face and efficient fisher linear discriminant..

• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.627-636
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• 2011

The purpose of this study is to develop the multi-modal interaction system. This system provides realistic and an immersive experience through multi-modal interaction. The system recognizes user behavior, intention, and attention, which overcomes the limitations of uni-modal interaction. The multi-modal interaction system is based upon gesture interaction methods, intuitive gesture interaction and attention evaluation technology. The gesture interaction methods were based on the sensors that were selected to analyze the accuracy of the 3-D gesture recognition technology using meta-analysis. The elements of intuitive gesture interaction were reflected through the results of experiments. The attention evaluation technology was developed by the physiological signal analysis. This system is divided into 3 modules; a motion cognitive system, an eye gaze detecting system, and a bio-reaction sensing system. The first module is the motion cognitive system which uses the accelerator sensor and flexible sensors to recognize hand and finger movements of the user. The second module is an eye gaze detecting system that detects pupil movements and reactions. The final module consists of a bio-reaction sensing system or attention evaluating system which tracks cardiovascular and skin temperature reactions. This study will be used for the development of realistic digital entertainment technology.

• Journal of Internet Computing and Services
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• v.14 no.6
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• pp.117-124
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• 2013

In this paper, we present a new method which efficiently estimates a face direction from a sequences of input video images in real time fashion. For this work, the proposed method performs detecting the facial region and major facial features such as both eyes, nose and mouth by using the Haar-like feature, which is relatively not sensitive against light variation, from the detected facial area. Then, it becomes able to track the feature points from every frame using optical flow in real time fashion, and determine the direction of the face based on the feature points tracked. Further, in order to prevent the erroneously recognizing the false positions of the facial features when if the coordinates of the features are lost during the tracking by using optical flow, the proposed method determines the validity of locations of the facial features using the template matching of detected facial features in real time. Depending on the correlation rate of re-considering the detection of the features by the template matching, the face direction estimation process is divided into detecting the facial features again or tracking features while determining the direction of the face. The template matching initially saves the location information of 4 facial features such as the left and right eye, the end of nose and mouse in facial feature detection phase and reevaluated these information when the similarity measure between the stored information and the traced facial information by optical flow is exceed a certain level of threshold by detecting the new facial features from the input image. The proposed approach automatically combines the phase of detecting facial features and the phase of tracking features reciprocally and enables to estimate face pose stably in a real-time fashion. From the experiment, we can prove that the proposed method efficiently estimates face direction.

• Atmosphere
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• v.17 no.2
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• pp.125-133
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• 2007

Cloud amount calculation algorithm was developed using MTSAT-1R satellite data. The cloud amount is retrieved at 5 km ${\times}$ 5 km over the Korean Peninsula and adjacent sea area. The algorithm consists of three steps that are cloud detection, cloud type classification, and cloud amount calculation. At the first step, dynamic thresholds method was applied for detecting cloud pixels. For using objective thresholds in the algorithm, sensitivity test was performed for TBB and Albedo variation with temporal and spatial change. Detected cloud cover was classified into 3 cloud types (low-level cloud, cirrus or uncertain cloud, and cumulonimbus type high-level cloud) in second step. Finally, cloud amount was calculated by the integration method of the steradian angle of each cloud pixel over $3^{\circ}$ elevation. Calculated cloud amount was compared with measured cloud amount with eye at surface observatory for the validation. Bias, RMSE, and correlation coefficient were 0.4, 1.8, and 0.8, respectively. Validation results indicated that calculated cloud amount was a little higher than measured cloud amount but correlation was considerably high. Since calculated cloud amount has 5km ${\times}$ 5km resolution over Korean Peninsula and adjacent sea area, the satellite-driven cloud amount could show the possibility which overcomes the temporal and spatial limitation of measured cloud amount with eye at surface observatory.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1385-1392
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• 2013

There is a variety of researches about recognition system using biometric data these days. In this study, we propose a new algorithm, uses simultaneous equation that made of the edge of objects, to segment an iris region without threshold values from an anterior eye image. The algorithm attempts to find a center area through calculated outskirts information of an iris, and decides the area where the most points are accumulated. To verify the proposed algorithm, we conducted comparative experiments to Hough transform and Daugman's method, based on 50 images anterior eye images. It was found that proposed algorithm is 5 and 75 times faster than on each algorithm, and showed high accuracy of detecting a center point (95.36%) more than Hough transform (92.43%). In foreseeable future, this study is expected to useful application in diverse department of human's life, such as, identification system using an iris, diagnosis a disease using an anterior image.

• Journal of Advanced Navigation Technology
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• v.15 no.4
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• pp.672-679
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• 2011

In this paper, detection algorithm of face region based on skin color of in the TV images is proposed. In the first, reference image is set to the sampled skin color, and then the extracted of face region is candidated using the Euclidean distance between the pixels of TV image. The eye image is detected by using the mean value and standard deviation of the component forming color difference between Y and C through the conversion of RGB color into CMY color model. Detecting the lips image is calculated by utilizing Q component through the conversion of RGB color model into YIQ color space. The detection of the face region is extracted using basis of knowledge by doing logical calculation of the eye image and lips image. To testify the proposed method, some experiments are performed using front color image down loaded from TV color image. Experimental results showed that face region can be detected in both case of the irrespective location & size of the human face.

• International journal of thyroidology
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• v.11 no.2
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• pp.92-98
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• 2018

Intraoperative identification and localization of parathyroid glands are crucial step in preventing postoperative hypocalcemia during thyroid and parathyroid surgery. If there is a method to predict the parathyroid's location rather than detecting and verifying with naked eye, it would make the operator easier to find and identify the parathyroid. Recently, near-infrared light imaging technologies have been introduced in the fields of thyroid and parathyroid surgery to predict the localization of the parathyroid. These are being conducted in two ways: autofluorescence imaging with a unique intrinsic fluorophore in the parathyroid tissues and fluorescence imaging with external fluorescence materials specially absorbed into parathyroid tissues. We are suggest that parathyroid glands can be detected by surgeon with NIR autofluorescence imaging even if they are covered by fibrofatty tissues before they are detected by surgeon's naked eye. These novel techniques are very useful to identify and preserve parathyroid glands during thyroidectomy. In this article, we reviewed the latest papers that describe autofluorescence imaging and exogenous ICG fluorescence imaging of parathyroid glands during thyroid and parathyroid surgery.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2363-2374
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• 2016

Communication occurs through verbal elements, which usually involve language, as well as non-verbal elements such as facial expressions, eye contact, and gestures. In particular, among these non-verbal elements, gestures are symbolic representations of physical, vocal, and emotional behaviors. This means that gestures can be signals toward a target or expressions of internal psychological processes, rather than simply movements of the body or hands. Moreover, gestures with such properties have been the focus of much research for a new interface in the NUI/NUX field. In this paper, we propose a method for recognizing the number of fingers and detecting the hand region based on the depth information and geometric features of the hand for application to an NUI/NUX. The hand region is detected by using depth information provided by the Kinect system, and the number of fingers is identified by comparing the distance between the contour and the center of the hand region. The contour is detected using the Suzuki85 algorithm, and the number of fingers is calculated by detecting the finger tips in a location at the maximum distance to compare the distances between three consecutive dots in the contour and the center point of the hand. The average recognition rate for the number of fingers is 98.6%, and the execution time is 0.065 ms for the algorithm used in the proposed method. Although this method is fast and its complexity is low, it shows a higher recognition rate and faster recognition speed than other methods. As an application example of the proposed method, this paper explains a Secret Door that recognizes a password by recognizing the number of fingers held up by a user.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.3
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• pp.32-40
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• 2021

Underground information is difficult to visually check, which can lead to a huge accident in the event of a safety accident. Recently, the Ministry of Land, Infrastructure and Transport intends to reduce safety accidents caused by the aging or damage of underground facilities through the Special Act on Underground Safety Management. GPR is increasingly being used as a technology to acquire information in underground spaces that are difficult to see with the naked eye. However, GPR's location information is corrected by checking images of CCTV and GPS information acquired during exploration. This method has an average error of about 2 meters. In this works, We used LiDAR to calibrate the GPR information and found that the error was reduced from at least 7cm to up to 40cm. If accurate GPR information collected in the future is analyzed quickly using AI, etc., it will be able to collect and utilize underground information faster than it is now to secure safety.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.865-870
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• 2023

Recently, accidents caused by black ice, a road freezing phenomenon caused by natural power, are increasing. Black ice is difficult to identify directly with the human eye and is more likely to misunderstand it as standing water, so there is a high accident rate caused by car sliding. To solve this problem, this paper presents a method of detecting black ice centered on LiDAR sensors. With a small, inexpensive, and high-accuracy light detection and ranging (LiDAR) sensor, the temperature and inclination angle are set differently to detect black ice and asphalt by setting different reflection angles of asphalt and black ice differently in temperatures and inclinations. The LIDARO carried out in the study points out that additional research and improvement are needed to increase accuracy, and through this, more reliable black ice detection methods can be suggested. This method suggests a method of detecting black ice through early system design research by preventing accidents caused by black ice in advance.