• 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 Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10c
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• pp.263-266
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• 2007

We introduce a vision-based hand gesture recognition fer understanding musical time and patterns without extra special devices. We suggest a simple and reliable vision-based hand gesture recognition having two features First, the motion-direction code is proposed, which is a quantized code for motion directions. Second, the conducting feature point (CFP) where the point of sudden motion changes is also proposed. The proposed hand gesture recognition system extracts the human hand region by segmenting the depth information generated by stereo matching of image sequences. And then, it follows the motion of the center of the gravity(COG) of the extracted hand region and generates the gesture features such as CFP and the direction-code finally, we obtain the current timing pattern of beat and tempo of the playing music. The experimental results on the test data set show that the musical time pattern and tempo recognition rate is over 86.42% for the motion histogram matching, and 79.75% fer the CFP tracking only.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.5
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• pp.10-16
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• 1985

In this paper, a computer vision system, which catches printed music score image using CCTV camera and microcomputer, and then recognizes the image and performs tar music with speaker, is discussed. Integral projection method is adopted for feature detection and recognition of the music score image. The range of recognition is con(ined to staffs, perpen-dicular lines and musical notes including chord notes among the various kinds of elements of music score. The practical recognition algorithm considering noises, the preprocessing processes getting rid of noises are also showed, and simple hardware system playing chord is made, In the results, good recognition ratio and performance are obtained.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.365-366
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.934-937
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• 2003

In this paper, authors use a stereo vision system based on the visual model of human and establish inexpensive method that recognizes moving distance using characteristic points around the robot. With the stereovision. the changes of the coordinate values of the characteristic points that are fixed around the robot are measured. Self-displacement and self-localization recognition system is proposed from coordination reconstruction with those changes. To evaluate the proposed system, several characteristic points that is made with a LED around the robot and two cheap USB PC cameras are used. The mobile robot measures the coordinate value of each characteristic point at its initial position. After moving, the robot measures the coordinate values of the characteristic points those are set at the initial position. The mobile robot compares the changes of these several coordinate values and converts transformation matrix from these coordinate changes. As a matrix of the amount and the direction of moving displacement of the mobile robot, the obtained transformation matrix represents self-displacement and self-localization by the environment.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.270-278
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• 2000

A machine vision system using charge coupled device(CCD) camera for the weed detection in a radish farm was developed. Shape features were analyzed with the binary images obtained from color images of radish and weeds. Aspect, Elongation and PTB were selected as significant variables for discriminant models using the STEPDISC option. The selected variables were used in the DISCRIM procedure to compute a discriminant function for classifying images into one of the two classes. Using discriminant analysis, the successful recognition rate was 92% for radish and 98% for weeds. To recognize radish and weeds more effectively than the discriminant analysis, an artificial neural network(ANN) was used. The developed ANN model distinguished the radish from the weeds with 100%. The performance of ANNs was improved to prevent overfitting and to generalize well using a regularization method. The successful recognition rate in the farms was 93.3% for radish and 93.8% for weeds. As a whole, the machine vision system using CCD camera with the artificial neural network was useful to detect weeds in the radish farms.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.349-355
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• 2014

In this paper, we propose the artificial vision system using human visual information processing and wavelet. Artificial vision system may be used for the visually impaired person and the machine recognition system. In this paper, we have constructed the information compression process to ganglion cells from the human retina. And we have reconstructed the primary visual information using recovery process to primary visual cortex from ganglion. Primary visual information is constructed by wavelet transformation using a high frequency and low frequency response. In the experiment, we used the faces database of AT&T. And the proposed method was able to improve the accuracy of face recognition considerably. And it was verified through experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.3
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• pp.344-352
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• 1999

There has been requested to automate the welding process in shipyard due to its dependence on skilled operators and the inferior working environments. According to these demands, multiple robot welding system for sub-assembly welding line has been developed, realized and installed at Keoje shipyard. In order to realize automatic welding system, robots have to be equipped with a sensing system to recognize the position of the welding panels. In this research, a camera vision system(CVS) is developed to detect the position of base panels for sub-assembly line in shipbuilding. Two camera vision systems are used in two different stages (fitting and welding) to automate the recognition and positioning of welding lines. For automatic recognition of panel position, various image processing algorithms are proposed in this paper.

• 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.