• 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 Institute of Electronics and Information Engineers
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• v.50 no.1
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• pp.225-231
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• 2013

In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.417-421
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• 1992

Ultrasonic and vision data need to be fused for efficient object recognition, especially in mobile robot navigation. In the proposed approach, the whole ultrasonic echo signal is utilized and data fusion is performed based on each sensor's characteristic. It is shown to be effective through the experiment results.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1908-1909
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• 2011

Hand gesture recognition has been widely used in virtual reality and HCI (Human-Computer-Interaction) system, which is challenging and interesting subject in the vision based area. The existing approaches for vision-driven interactive user interfaces resort to technologies such as head tracking, face and facial expression recognition, eye tracking and gesture recognition. The purpose of this paper is to combine the finite state machine (FSM) and the gesture recognition method, in other to control Windows Media Player, such as: play/pause, next, pervious, and volume up/down.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.5C
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• pp.371-377
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• 2008

In this paper, we present a framework for vision-based two-arm gesture recognition. To capture the motion information of the hands, we perform color-based tracking algorithm using adaptive kernel for each frame. And a feature selection algorithm is performed to classify the motion information into four different phrases. By using gesture phrase information, we build a gesture model which consists of a probability of the symbols and a symbol sequence which is learned from the longest common subsequence. Finally, we present a similarity measurement for two-arm gesture recognition by using the proposed gesture models. In the experimental results, we show the efficiency of the proposed feature selection method, and the simplicity and the robustness of the recognition algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.500-503
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• 1997

In this paper, a method for recognizing 3-D objects using the 3-D Hough transform and the robust stereo vision is studied. A 3-D object is recognized through two steps; modeling step and matching step. In modeling step, features of the object are extracted by analyzing the IGES file. In matching step, the values of the sensed image are compared with those of the IGES file which is assumed to location and orientation in the 3-D Hough transform domain. Since we use the 3-D Hough transform domain of the input image directly, the sensitivity to the noise and the high computational complexity could be significantly allcv~ated. Also, the cost efficiency is improved using the robust stereo vision for obtaining depth map image which is needed for 3-D Hough transform. In order lo verify the proposed method, real telephone model is recognized. Thc results of the location and orientation of the model are presented.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.95-103
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• 2019

According to the Statistics Korea in 2017, the 10 leading causes of death contain a cardiac disorder disease, self-injury. In terms of these diseases, urgent assistance is highly required when people do not move for certain period of time. We propose an unusual event detection algorithm to identify abnormal user behaviors using dust, vision and activity sensors in their houses. Vision sensors can detect personalized activity behaviors within the CCTV range in the house in their lives. The pattern algorithm using the dust sensors classifies user movements or dust-generated daily behaviors in indoor areas. The accelerometer sensor in the smartphone is suitable to identify activity behaviors of the mobile users. We evaluated the proposed pattern algorithms and the fusion method in the scenarios.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.120-124
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• 2021

Recently, human attribute recognition (HAR) attracts a lot of attention due to its wide application in video surveillance systems. Recent deep-learning-based solutions for HAR require time-consuming training processes. In this paper, we propose a post-processing technique that utilizes the historical video frames to improve prediction results without invoking re-training or modifying existing deep-learning-based classifiers. Experiment results on a large-scale benchmark dataset show the effectiveness of our proposed method.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.553-556
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• 2005

We present a vision-based hand gesture recognition system for object manipulation in virtual space. Most conventional hand gesture recognition systems utilize a simpler method for hand detection such as background subtractions with assumed static observation conditions and those methods are not robust against camera motions, illumination changes, and so on. Therefore, we propose a statistical method to recognize and detect hand regions in images using geometrical structures. Also, Our hand tracking system employs multiple cameras to reduce occlusion problems and non-synchronous multiple observations enhance system scalability. Experimental results show the effectiveness of our method.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.1
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• pp.46-51
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• 2015

In this paper, we propose a vision-based method to recognize lane changes of an autonomous vehicle. The proposed method is based on six states of driving situations defined by the positional relationship between a vehicle and its nearest lane detected. With the combinations of these states, the lane change is detected. The proposed method yields 98% recognition accuracy of lane change even in poor situations with partially invisible lanes.