• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.3 s.309
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• pp.39-49
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• 2006

This paper proposes a hand-posture recognition method using reinforcement learning for the performance improvement of vision-based hand-posture recognition. The difficulties in vision-based hand-posture recognition lie in viewing direction dependency and self-occlusion problem due to the high degree-of-freedom of human hand. General approaches to deal with these problems include multiple camera approach and methods of limiting the relative angle between cameras and the user's hand. In the case of using multiple cameras, however, fusion techniques to induce the final decision should be considered. Limiting the angle of user's hand restricts the user's freedom. The proposed method combines angular features and appearance features to describe hand-postures by a two-layered data structure and reinforcement learning. The validity of the proposed method is evaluated by appling it to the hand-posture recognition system using three cameras.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.5
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• pp.50-58
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• 1998

We propose an efficient face recognition system for controllinng the access to the restricted zone using both the face region detectors based on facial symmetry and the extended self-organizing maps (ESOM) which have sensory synapses and descriptive synapses. Based on the visual cues of the facial symmetry, we apply horizontal and vertical projections on elliptic regions detected by GHT(generalized hough transform) to identify all the face regions from the complex background.And we propose an ESOM which can exploit principal components and imitate an elastic similarity matching, to authenticate faces of the enlisted member. In order to cope with changes of facial experession or glasses wearing, etc, the facial descriptions of each member at the time of authentication are simultaneously updated on the discriptive synapses online using the incremental learning of the proposed ESOM. Experimental results prove the feasibility of our approach.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.2
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• pp.178-183
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• 2008

Human motion analysis is researched as a new method for human-robot interaction (HRI) because it concerns with the key techniques of HRI such as motion tracking and pose recognition. To analysis human motion, extracting features of human body from sequential images plays an important role. After finding the silhouette of human body from the sequential images obtained by CCD color camera, the skeleton model is frequently used in order to represent the human motion. In this paper, using the silhouette of human body, we propose the feature extraction method based on hybrid skeleton for detecting human motion. Finally, we show the effectiveness and feasibility of the proposed method through some experiments.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.6-10
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• 2023

In this paper, we proposed a software framework structure to apply ORB-SLAM, the most representative of SLAM algorithms, so that map creation and location estimation technology can be applied through tethered AR glasses. Since tethered AR glasses perform only the role of an input/output device, the processing of camera and sensor data and the generation of images to be displayed through the optical display module must be performed through the host. At this time, an Android-based mobile device is adopted as the host. Therefore, the major libraries required for the implementation of AR contents for AR glasses were hierarchically organized, and spatial recognition and location estimation functions using SLAM were verified.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1857-1862
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• 2016

Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.493-496
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• 2009

Many AR (Augmented Reality) applications have been interested in a marker-less tracking since the tracking methods give camera poses without attaching explicit markers. In this paper, we propose a new marker-less page recognition and tracking algorithm for an AR book application such as DigilogBook. The proposed method only requires orthogonal images of pages, which need not to be trained for a long time, and the algorithm works in real-time. The page recognition is done in two steps by using SIFT (Scale Invariant Feature Transform) descriptors and the comparison evaluation function. And also, the method provides real-time tracking with 25fps ~ 30fps by separating the page recognition and the frame-to-frame matching into two multi-cores. The proposed algorithm will be extended to various AR applications that require multiple objects tracking.

• Korean Journal of Artificial Intelligence
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• v.5 no.2
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• pp.43-53
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• 2017

In this paper, the pattern of extracting the same expression is proposed by using the Android intelligent device to identify the facial expression. The understanding and expression of expression are very important to human computer interaction, and the technology to identify human expressions is very popular. Instead of searching for the emoticons that users often use, you can identify facial expressions with acamera, which is a useful technique that can be used now. This thesis puts forward the technology of the third data is available on the website of the set, use the content to improve the infrastructure of the facial expression recognition accuracy, in order to improve the synthesis of neural network algorithm, making the facial expression recognition model, the user's facial expressions and similar e xpressions, reached 66%.It doesn't need to search for emoticons. If you use the camera to recognize the expression, itwill appear emoticons immediately. So this service is the emoticons used when people send messages to others, and it can feel a lot of convenience. In countless emoticons, there is no need to find emoticons, which is an increasing trend in deep learning. So we need to use more suitable algorithm for expression recognition, and then improve accuracy.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.64-72
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• 2013

This paper proposes a dynamic gesture recognition algorithm using SVM(Support Vector Machine) which is suitable for multi-dimension classification. First of all, the proposed algorithm locates the beginning and end of the gestures on the video frames at the Kinect camera, spots meaningful gesture frames, and normalizes the number of frames. Then, for gesture recognition, the algorithm extracts gesture features using body parts' positions and relations among the parts based on the human model from the normalized frames. C-SVM for each dynamic gesture is trained using training data which consists of positive data and negative data. The final gesture is chosen with the largest value of C-SVM values. The proposed gesture recognition algorithm can be applied to the interactive storybook as gesture interface.

• Smart Media Journal
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• v.7 no.2
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• pp.23-33
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• 2018

A musical sheet is read by optical music recognition (OMR) systems that automatically recognize and reconstruct the read data to convert them into a machine-readable format such as XML so that the music can be played. This process, however, is very challenging due to the large variety of musical styles, symbol notation, and other distortions. In this paper, we present a model for the recognition of musical symbols through the use of a mobile application, whereby a camera is used to capture the input image; therefore, additional difficulties arise due to variations of the illumination and distortions. For our proposed model, we first generate a line adjacency graph (LAG) to remove the staff lines and to perform primitive detection. After symbol segmentation using the primitive information, we use a covariance-matching method to estimate the similarity between every symbol and pre-defined templates. This method generates the three hypotheses with the highest scores for likelihood measurement. We also add a global consistency (time measurements) to verify the three hypotheses in accordance with the structure of the musical sheets; one of the three hypotheses is chosen through a final decision. The results of the experiment show that our proposed method leads to promising results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.191-197
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• 2015

In this paper, we propose the system which recognize the gaze using Random Forests in vehicular environment based on smart-phone. Proposed system is mainly composed of the following: face detection using Adaboost, face component estimation using Histograms, and gaze recognition based on Random Forests. We detect a driver based on the image information with a smart-phone camera, and the face component of driver is estimated. Next, we extract the feature vectors from the estimated face component and recognize gaze direction using Random Forest recognition algorithm. Also, we collected gaze database including a variety gaze direction in real environments for the experiment. In the experiment result, the face detection rate and the gaze recognition rate showed 82.02% and 84.77% average accuracies, respectively.