• The Journal of the Korea Contents Association
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• v.17 no.1
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• pp.137-144
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• 2017

Face detection is still a challenging task under severe face pose variations in complex background. This paper proposes an effective algorithm which can detect single or multiple faces based on skin color detection and depth information. We introduce Gaussian mixture model(GMM) for skin color detection in a color image. The depth information is from three dimensional depth sensor of Kinect V2 device, and is useful in segmenting a human body from the background. Then, a labeling process successfully removes non-face region using several features. Experimental results show that the proposed face detection algorithm can provide robust detection performance even under variable conditions and complex background.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.6C
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• pp.593-603
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• 2009

In this paper, we propose a new integrated computer vision system designed to track multiple human beings and extract their silhouette with an active stereo camera. The proposed system consists of three modules: detection, tracking and silhouette extraction. Detection was performed by camera ego-motion compensation and disparity segmentation. For tracking, we present an efficient mean shift based tracking method in which the tracking objects are characterized as disparity weighted color histograms. The silhouette was obtained by two-step segmentation. A trimap is estimated in advance and then this was effectively incorporated into the graph cut framework for fine segmentation. The proposed system was evaluated with respect to ground truth data and it was shown to detect and track multiple people very well and also produce high quality silhouettes. The proposed system can assist in gesture and gait recognition in field of Human-Robot Interaction (HRI).

• Parasites, Hosts and Diseases
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• v.57 no.4
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• pp.435-437
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• 2019

Chagas disease is caused by the protozoan parasite Trypanosoma cruzi, and is endemic in many Latin American countries. Diagnosis is based on serologic testing and the WHO recommends two or more serological tests for confirmation. Acidic ribosomal P protein of T. cruzi showed strong reactivity against positive sera of patients, and we cloned the protein after fragmenting it to enhance its antigenicity and solubility. Twelve positive sera of Chagas disease patients were reacted with the fragmented ribosomal P protein using western blot. Detection rate and density for each fragment were determined. Fragments F1R1, F1R2, and F2R1 showed 100% rate of detection, and average density scoring of 2.00, 1.67, and 2.42 from a maximum of 3.0, respectively. Therefore, the F2R1 fragment of the ribosomal P protein of T. cruzi could be a promising antigen to use in the diagnosis of Chagas disease in endemic regions with high specificity and sensitivity.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.465-468
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• 2014

In this paper, we describe a method capable of measuring biological signals including respiration, heart rate, blood pressure, and blood sugar, using UWB (Ultra Wide Band) pulses, while does not contact the human body. Physiological signal is a basic data for checking the health. Because life is longer and active area of human becomes very broad, the medical system and the physical human resources which are focused on existing hospital must be located close patient, In that way, they hope be to engage in healthy life by stepping a quick step and treatment. Thus, it must be fitted closely to the patient. It is necessary to monitor the health without inconvenience on an ongoing basis. How to utilize radio waves in this way have been studied for a long time. However, the characteristics of radio waves on the human body has not been accurately grasped and developed as such. Accordingly, it is a level that can not be applied clinically. So, it is not widely put to practical use. In this paper, We analyzed and described the impact and characteristics of UWB pulses to the human body is a problem existing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.618-632
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• 2010

This paper presents a novel approach to control the augmented reality (AR) objects robustly in a marker-less AR system by fingertip tracking and hand pattern recognition. It is known that one of the promising ways to develop a marker-less AR system is using human's body such as hand or face for replacing traditional fiducial markers. This paper introduces a real-time method to manipulate the overlaid virtual objects dynamically in a marker-less AR system using both hands with a single camera. The left bare hand is considered as a virtual marker in the marker-less AR system and the right hand is used as a hand mouse. To build the marker-less system, we utilize a skin-color model for hand shape detection and curvature-based fingertip detection from an input video image. Using the detected fingertips the camera pose are estimated to overlay virtual objects on the hand coordinate system. In order to manipulate the virtual objects rendered on the marker-less AR system dynamically, a vision-based hand control interface, which exploits the fingertip tracking for the movement of the objects and pattern matching for the hand command initiation, is developed. From the experiments, we can prove that the proposed and developed system can control the objects dynamically in a convenient fashion.

• Journal of Sensor Science and Technology
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• v.7 no.3
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• pp.197-202
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• 1998

The human pulse wave is a vital biosignal that includes the diagnostic data related with the heart and the cardiovascular system of human body. Based on the mechanical transducing method, a pulse detection transducer using optical fiber was developed to acquire the pulses non-invasively. To improve the detection efficiency, we proposed a new design that consists of two combinational parts; detecting part, which is in contact with the pulsating skin and transmits the displacement motion of the pulsating skin to the sensing part, and sensing part, which converts the physical quantity transmitted from the detecting part to electronic signal. By using the new method, we confirmed that the proposed transducer can detect the C point(incisura) and the T wave(tidal wave) which is not easily detected by existing transducers.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.501-507
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• 2012

Human body motion is a non-verbal part for interaction or movement that can be used to involves real world and virtual world. In this paper, we explain a study on natural user interface (NUI) in human hand motion recognition using RGB color information and depth information by Kinect camera from Microsoft Corporation. To achieve the goal, hand tracking and gesture recognition have no major dependencies of the work environment, lighting or users' skin color, libraries of particular use for natural interaction and Kinect device, which serves to provide RGB images of the environment and the depth map of the scene were used. An improved Camshift tracking algorithm is used to tracking hand motion, the experimental results show out it has better performance than Camshift algorithm, and it has higher stability and accuracy as well.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.63-69
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• 2017

This study is about the development of a smart bed control system to be able to detect the human position and body signal on bed. The main control board in the bed control system consists of the human sensing part, motor driving part and MCU. Here, to increase the credibility to check the human presence on bed, the human sensing part is combined with the human position part by membrane sensor and the body-signal detecting part of EMFI sensor. Also, remotely connecting the two detected signal to the application program of the app mode makes it possible to monitor human information on bed. In this paper, the remote function monitoring of the on-bed human information by bluetooth communication will be abe to make it applicable to the technical prevention method of the bed fall and absence accident in hospital and care facilities.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.1
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• pp.111-118
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• 2022

In computer vision research, the two-dimensional human pose is a very extensive research direction, especially in pose tracking and behavior recognition, which has very important research significance. The acquisition of human pose targets, which is essentially the study of how to accurately identify human targets from pictures, is of great research significance and has been a hot research topic of great interest in recent years. Human pose recognition is used in artificial intelligence on the one hand and in daily life on the other. The excellent effect of pose recognition is mainly determined by the success rate and the accuracy of the recognition process, so it reflects the importance of human pose recognition in terms of recognition rate. In this human body gesture recognition, the human body is divided into 17 key points for labeling. Not only that but also the key points are segmented to ensure the accuracy of the labeling information. In the recognition design, use the comprehensive data set MS COCO for deep learning to design a neural network model to train a large number of samples, from simple step-by-step to efficient training, so that a good accuracy rate can be obtained.