• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2420-2422
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• 2003

In this paper, two Fuzzy Logics for path planning of an autonomous mobile robot are proposed. If a target point is given, such problems regarding the velocity and object recognition are closely related with path to which the mobile robot navigates. Therefore, to ensure safety navigation of the mobile robot for two fuzzy logic parts, path planning considering the surrounding environment was performed in this paper. First, feature points for local and global path are determined by utilizing Cell Decomposition off-line computation. Second, the on-line robot using two Fuzzy Logics navigates around path when it tracks the feature points. We demonstrated optimized path planning only for local path using object recognition fuzzy logic corresponds to domestic situation. Furthermore, when navigating, the robot uses fuzzy logic for velocity and target angle. The proposed algorithms for path planning has been implemented and tested with pioneer-dxe mobile robot.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.468-471
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• 2004

In this paper, a method for classifying fingerprint ridge lines using runlength codes is proposed. To detect feature points(minutiae) in automatic fingerprint identification system(AFIS), classification of fingerprint ridge lines are essential process. The fingerprint ridge lines are classified by run-length coding, and also the end and bifurcation regions in ridge lines are separated. To evaluate the performance of the proposed method, detected feature regions including minutiae points and classified fingerprint ridge lines are shown.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.9
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• pp.3196-3210
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• 2014

This paper presents a novel video metrology approach based on robust tracking. From videos acquired by an uncalibrated stationary camera, the foreground likelihood map is obtained by using the Codebook background modeling algorithm, and the multiple moving objects are tracked by a combined tracking algorithm. Then, we compute vanishing line of the ground plane and the vertical vanishing point of the scene, and extract the head feature points and the feet feature points in each frame of video sequences. Finally, we apply a single view mensuration algorithm to each of the frames to obtain height measurements and fuse the multi-frame measurements using RANSAC algorithm. Compared with other popular methods, our proposed algorithm does not require calibrating the camera, and can track the multiple moving objects when occlusion occurs. Therefore, it reduces the complexity of calculation and improves the accuracy of measurement simultaneously. The experimental results demonstrate that our method is effective and robust to occlusion.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.549-550
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• 2019

In this paper, we propose a method to detect the scene change using deep learning. To extract feature points, we use a deep neural network and express extracted feature points as 128 dimensional vectors using SIFT descriptor. If it is less than 25%, it is determined that the scene is changed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.488-489
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• 2019

We propose a method to identify vehicle type by the formula of distance between feature points of vehicle and proportional rate of size. Car images are converted from the basic RGB model to the gray color model. Perform Canny Edge Direction to remove the background image of the car. The desired feature points are obtained through contour extraction.

• Current Optics and Photonics
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• v.8 no.3
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• pp.300-306
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• 2024

To solve the nozzle swing angle non-contact measurement problem, we present a nozzle pose estimation algorithm involving weighted measurement uncertainty based on rotation parameters. Firstly, the instantaneous axis of the rocket nozzle is constructed and used to model the pivot point and the nozzle coordinate system. Then, the rotation matrix and translation vector are parameterized by Cayley-Gibbs-Rodriguez parameters, and the novel object space collinearity error equation involving weighted measurement uncertainty of feature points is constructed. The nozzle pose is obtained at this step by the Gröbner basis method. Finally, the swing angle is calculated based on the conversion relationship between the nozzle static coordinate system and the nozzle dynamic coordinate system. Experimental results prove the high accuracy and robustness of the proposed method. In the space of 1.5 m × 1.5 m × 1.5 m, the maximum angle error of nozzle swing is 0.103°.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.381-391
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• 2001

This paper presents an efficient approach to build an image mosaics from image sequences. Unlike general panoramic stitching methods, which usually require some geometrical feature points or solve the iterative nonlinear equations, our algorithm can directly recover the 8-parameter planar perspective transforms. We use four quasi-feature points in order to compute the projective transform between two images. This feature is based on the graylevel distribution and defined in the overlap area between two images. Therefore the proposed algorithm can reduce the total amount of the computation. We also present an algorithm lot efficiently matching the correspondence of the extracted feature. The proposed algorithm is applied to various images to estimate its performance and. the simulation results present that our algorithm can find the correct correspondence and build an image mosaics.

• Journal of Korea Game Society
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• v.11 no.2
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• pp.161-166
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• 2011

In this paper, we propose a novel background segmentation and feature point extraction method of a human motion for the augmented reality game. First, our method transforms input image from RGB color space to HSV color space, then segments a skin colored area using double threshold of H, S value. And it also segments a moving area using the time difference images and then removes the noise of the area using the Hessian affine region detector. The skin colored area with the moving area is segmented as a human motion. Next, the feature points for the human motion are extracted by calculating the center point for each block in the previously obtained image. The experiments on various input images show that our method is capable of correct background segmentation and feature points extraction 12 frames per second.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.173-182
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• 2005

In this paper, we propose a landmark recognition method which is irrelevant to the camera viewpoint on the navigation for localization. Features in previous research is variable to camera viewpoint, therefore due to the wealth of information, extraction of visual landmarks for positioning is not an easy task. The proposed method in this paper, has the three following stages; first, extraction of features, second, learning and recognition, third, matching. In the feature extraction stage, we set the interest areas of the image. where we extract the corner points. And then, we extract features more accurate and resistant to noise through statistical analysis of a small eigenvalue. In learning and recognition stage, we form robust feature models by testing whether the feature model consisted of five corner points is an invariant feature irrelevant to viewpoint. In the matching stage, we reduce time complexity and find correspondence accurately by matching method using similarity evaluation function and Graham search method. In the experiments, we compare and analyse the proposed method with existing methods by using various indoor images to demonstrate the superiority of the proposed methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.6
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• pp.646-650
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• 2014

In this paper, in order to avoid the deterioration of the pattern classification performance which results from the curse of dimensionality, we propose a new feature selection method. The newly proposed feature selection method is based on Fuzzy C-Means clustering algorithm which analyzes the data points to divide them into several clusters and the concept of a function with fuzzy numbers. When it comes to the concept of a function where independent variables are fuzzy numbers and a dependent variable is a label of class, a fuzzy number should be related to the only one class label. Therefore, a good feature is a independent variable of a function with fuzzy numbers. Under this assumption, we calculate the goodness of each feature to pattern classification problem. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.