• Journal of Digital Convergence
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• v.11 no.7
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• pp.157-163
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• 2013

In this paper, we propose a vision and depth information based real-time hand gesture interface method using finger joint estimation. For this, the areas of left and right hands are segmented after mapping of the visual image and depth information image, and labeling and boundary noise removal is performed. Then, the centroid point and rotation angle of each hand area are calculated. Afterwards, a circle is expanded at following pattern from a centroid point of the hand to detect joint points and end points of the finger by obtaining the midway points of the hand boundary crossing and the hand model is recognized. Experimental results that our method enabled fingertip distinction and recognized various hand gestures fast and accurately. As a result of the experiment on various hand poses with the hidden fingers using both hands, the accuracy showed over 90% and the performance indicated over 25 fps. The proposed method can be used as a without contacts input interface in HCI control, education, and game applications.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.1
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• pp.37-45
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• 2013

Almost of stroke patients who experience aftereffects such as motor, sensory and cognitive disorders have to take some rehabilitation therapies. It is known that the consistent training for rehabilitation therapy in their home is more effective than rehabilitation therapy in hospital. A few home training programs were developed but these programs don't give any appropriate feedback messages to the client. Therefore, we developed a home training program which can provide appropriate feedback message to the clients using the Kinect sensor which can analyze user's 3-D dimensional motion. With this development, the client can obtain some feedback messages such as the knowledges of performance, results and self-controlled feedback. The program will be more effective than any existing programs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.1
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• pp.106-112
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• 2015

In this paper, we propose an on-line signature recognition algorithm using fingertip point in the air from the depth image acquired by Kinect. We use ten statistical features for each X, Y, Z axis to react to changes in Shifting and Scaling of the signature trajectories in three-dimensional space. Artificial Neural Network is a machine learning algorithm used as a tool to solve the complex classification problem in pattern recognition. We implement the proposed algorithm to actual on-line signature recognition system. In experiment, we verify the proposed method is successful to classify 4 different on-line signatures.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.309-314
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• 2014

To generate a complete 3D model from depth images of multiple RGB-D cameras, it is necessary to find 3D transformations between RGB-D cameras. This paper proposes a convenient view calibration technique using a spherical object. Conventional view calibration methods use either planar checkerboards or 3D objects with coded-pattern. In these conventional methods, detection and matching of pattern features and codes takes a significant time. In this paper, we propose a convenient view calibration method using both 3D depth and 2D texture images of a spherical object simultaneously. First, while moving the spherical object freely in the modeling space, depth and texture images of the object are acquired from all RGB-D camera simultaneously. Then, the external parameters of each RGB-D camera is calibrated so that the coordinates of the sphere center coincide in the world coordinate system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1967-1972
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• 2016

This paper defines a distance graph for a hand region in a depth image and proposes an algorithm detecting finger using it. The distance graph is a graph expressing the hand contour with angles and Euclidean distances between the center of palm and the hand contour. Since the distance graph has local maximum at fingertips' position, we can detect finger points and recognize the number of them. The hand contours are always divided into 360 angles and the angles are aligned with the center of the wrist as a starting point. And then the proposed algorithm can well detect fingers without influence of the size and orientation of the hand. Under some limited recognition test conditions, the recognition test's results show that the recognition rate is 100% under 1~3 fingers and 98% under 4~5 fingers and that the failure case can also be recognized by simple conditions to be available to add.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.7
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• pp.866-872
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• 2020

Indoor location-based services provide a service based on the distance between an object and a person. Recently, indoor location-based services are often implemented using inexpensive depth sensors such as Kinect. The depth sensor provides a function to measure the position of a person, but the position of an object must be acquired manually using a tool. To acquire a 3D position of an object, it requires 3D interaction, which is difficult to a general user. GUI(Graphical User Interface) is relatively easy to a general user but it is hard to gather a 3D position. This study proposes the Interactive LOcation Context Authoring Toolkit(ILOCAT), which enables a general user to easily acquire a 3D position of an object in real space using GUI. This paper describes the interaction design and implementation of ILOCAT.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.808-811
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• 2020

This paper proposes a method to minimize the error of the Kinect camera sensor by using a random undirected Kalman filter. Kinect cameras, which provide RGB values and depth information, cause nonlinear errors in the sensor, causing problems in various applications such as skeleton detection. Conventional methods have tried to remove errors by using various filtering techniques. However, there is a limit to removing nonlinear noise effectively. Therefore, in this paper, a randomized unscented Kalman filter was applied to predict and update the nonlinear noise characteristics, we next tried to enhance a performance of skeleton detection. The experimental results confirmed that the proposed method is superior to the conventional method in quantitative results and reconstructed images on 3D space.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.135-144
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• 2018

The domestic pigsty environment is highly vulnerable to the spread of respiratory diseases such as foot-and-mouth disease because of the small space. In order to manage this issue, a variety of studies have been conducted to automatically analyze behavior of individual pigs in a pig pen through a video surveillance system using a camera. Even though it is required to correctly segment touching pigs for tracking each pig in complex situations such as aggressive behavior, detecting the correct boundaries among touching pigs using Kinect's depth information of lower accuracy is a challenging issue. In this paper, we propose a segmentation method using motion information of the touching pigs. In addition, our proposed method can be applied for detecting tracking errors in case of tracking individual pigs in the complex environment. In the experimental results, we confirmed that the touching pigs in a pig farm were separated with the accuracy of 86%, and also confirmed that the tracking errors were detected accurately.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.61-69
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• 2018

As the technology of information and communication has developed over recent years, an embedded system is applied in multiple industries and becomes more complicated. For this reason, embedded system development cost and time are also increased. For decreasing the cost and time, this paper suggests requirement evaluation method for concurrent development of an embedded system based on Discrete Event System(DEVS) Formalism. This paper proposes a method of describing the requirements specification in the form of DEVS atomic model. Also, the paper proposes the evaluator model that compares evaluation target system and the requirements model that is an implementation of requirement specification and proposes the evaluation method using them. In addition, we propose a method to utilize the requirement model created for requirements evaluation in the concurrent development process of the embedded system. As the case study, this paper proceeds requirement evaluation of Kinect depth data processing system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1686-1694
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• 2014

In this paper, a new scheme to recognize a finger shape in the depth image captured by Kinect is proposed. Rigid transformation of an input finger shape is pre-processed for its robustness against the shape angle of input fingers. After extracting contour map from hand region, observing the change of contour pixel location is performed to calculate rotational compensation angle. For the finger shape recognition, we first acquire three pixel points, the most left, right, and top located pixel points. In the proposed algorithm, we first acquire three pixel points, the most left, right, and top located pixel points for the finger shape recognition, also we use geometrical features of human fingers such as Euclidean distance, the angle of the finger and the pixel area of hand region between each pixel points to recognize the finger shape. Through experimental results, we show that the proposed algorithm performs better than old schemes.