• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.05a
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• pp.816-819
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• 2011

The image recognition system using CCTV camera has been introduced to minimize not only loss of life and property but also traffic jam in the tunnel. In this paper, multi-object detection algorithm is proposed to track multi vehicles. The proposed algorithm is to detect multi cars based on Adaboost and to track multi vehicles to use template matching. As results of simulations, it is shown that proposed algorithm is useful for tracking multi vehicles.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.7
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• pp.2616-2622
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• 2010

This paper implements the algorithm for Janggi game robot based on network for the disabled people. The movement of target on janggi for them is designed using wireless robot and voice recognition. To remove obstacles, we implement the algorithm for moving object based on $A^{\ast}$ algorithm. We test the janggi game and certify effectiveness of moving object through the implementation of algorithm for them.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.51-57
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• 2008

In recent computer vision or robotics fields, the research area of object recognition from image using low cost web camera or other video device is performed actively. As study for this, there are various methodologies suggested to retrieve objects in robotics and vision research areas. Also, robotics is designed and manufactured to aim at doing like human being. For instance, a person perceives apples as one see apples because of previously knowing the fact that it is apple in one's mind. Like this, robotics need to store the information of any object of what the robotics see. Therefore, in this paper, we propose an methodology that we can rapidly recognize objects which is stored in object database by using SIFT (scale invariant feature transform) algorithm to get information about the object. And then we implement the methodology to enable to recognize simultaneously multiple objects in an image.

• Journal of the Korea Convergence Society
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• v.6 no.6
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• pp.1-8
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• 2015

In this paper, we proposed image enhancement method using sharpening filter for improving the accuracy of object detection using the existing Grabcut algorithm. GrabCut algorithm is the excellent performance extracting an object within a rectangular window range, but it has the drawback of the inferior performance in image with no clear distinction between background and objects. So, in this paper, reinforcing the brightness and clarity through histogram equalization, and tightening the border of the object using the sharpening filter look better than that extracted result of existing GrabCut algorithm in a similar image of the object and the background. Based on improved Grabcut algorithm, it is possible to obtain an improved result in the image processing convergence technique of character recognition, real-time object tracking and so on.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1439-1446
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• 2011

This paper describes the algorithm that lowers the dimension, maintains the object recognition and significantly reduces the eigenspace configuration time by combining the higher correlation feature information and Principle Component Analysis. Since the suggested method doesn't require a lot of computation than the method using existing geometric information or stereo image, the fact that it is very suitable for building the real-time system has been proved through the experiment. In addition, since the existing point to point method which is a simple distance calculation has many errors, in this paper to improve recognition rate the recognition error could be reduced by using several successive input images as a unit of recognition with K-Nearest Neighbor which is the improved Class to Class method.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.603-609
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• 2010

This paper introduces an objects recognition algorithm based on SURF(Speeded Up Robust Features) and GP(Genetic Programming) based gaits generation. Combining both methods, a recognition based intelligent walking for quadruped robots is proposed. The gait of quadruped robots is generated by means of symbolic regression for each joint trajectories using GP. A position and size of target object are recognized by SURF which enables high speed feature extraction, and then the distance to the object is calculated. Experiments for objects recognition and autonomous walking for quadruped robots are executed for ODE based Webots simulation and real robot.

• Journal of the Korea Society of Computer and Information
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• v.23 no.1
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• pp.41-48
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• 2018

Recently, surveillance systems are widely used, and one of the key technologies in this surveillance system is to recognize and track objects. In order to track a moving object robustly and efficiently in a complex environment, it is necessary to extract the feature points in the interesting object and to track the object using the feature points. In this paper, we propose a method to track interesting objects in real time by eliminating unnecessary information from objects, generating feature point descriptors using only key feature points, and reducing computational complexity for object recognition. Experimental results show that the proposed method is faster and more robust than conventional methods, and can accurately track objects in various environments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.11
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• pp.2875-2882
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• 2000

Typically, the ultrasonic sensors can be used in navigation systems for modeling of the enviornment, obstacle avoidance, and map building. In this paper, we tried to approach an object classification method using the range data of the ultrasonic sensors. A characterization of the sonar scan is described that allows the differentiation of planes, corners, edges, cylindrical and rectangular pillars by processing the scanned data from three sonars. To use the data from the ultrasonic sensors as input to the neural networks, we have introduced a clustering, threshold, and bit operation algorithm for the obtained raw data, After repeated training of the neural network, the performance of the proposed method was obtained through experiments. Also, the recognition ranges of the proposed method were investigated. As a result of experiments, we found that the proposed method successfully recognized the objects within the accuracy of 78%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.712-717
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• 2013

In this paper, we propose a performance-enhanced object recognition by using nonlinear 3D correlator based on pixel restoration. In the proposed method, elemental images of the 3D target that are partially occluded by a foreground object are picked up and transformed into sub-images. By using the block-matching algorithm, the occluded target regions of each sub-image are estimated and removed. After that, the missing pixels in each sub-image are reestablished by using the pixel-restoration method. Finally, through the nonlinear cross-correlations between the reconstructed reference and the target plane images, the improved object recognition can be performed. To show the feasibility of the proposed method, some preliminary experiments are carried out and results are presented by comparing the conventional method.

• The Journal of Korea Robotics Society
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• v.3 no.2
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• pp.91-98
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• 2008

This paper describes the recognition method of moving objects in mobile robot with an omnidirectional camera. The moving object is detected using the specific pattern of an optical flow in omnidirectional image. This paper consists of two parts. In the first part, the pattern of an optical flow is investigated in omnidirectional image. The optical flow in omnidirectional image is influenced on the geometry characteristic of an omnidirectional camera. The pattern of an optical flow is theoretically and experimentally investigated. In the second part, the detection of moving objects is presented from the estimated optical flow. The moving object is extracted through the relative evaluation of optical flows which is derived from the pattern of optical flow. In particular, Focus-Of-Expansion (FOE) and Focus-Of-Contraction (FOC) vectors are defined from the estimated optical flow. They are used as reference vectors for the relative evaluation of optical flows. The proposed algorithm is performed in four motions of a mobile robot such as straight forward, left turn, right turn and rotation. Experimental results using real movie show the effectiveness of the proposed method.