• Journal of the Semiconductor & Display Technology
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• v.12 no.3
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• pp.59-64
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• 2013

Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.

• MALSORI
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• no.65
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• pp.67-80
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• 2008

We have been building a text-independent speaker recognition system that is robust to unknown channel and noise environments. In this paper, we propose a linear transformation to obtain robust features. The transformation is optimized to maximize the distances between the Gaussian mixtures. We use rotation of the axes, to cope with the problem of scaling the transformation matrix. The proposed transformation is similar to PCA or LDA, but can achieve better result in some special cases where PCA and LDA can not work properly. We use YOHO database to evaluate the proposed method and compare the result with PCA and LDA. The results show that the proposed method outperforms all the baseline, PCA and LDA.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1991.10a
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• pp.77-80
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• 1991

This paper proposes a new feature extraction method which is effectively used in character recognition, and validate the effectiveness through various computational methods for similiarity degree. To get feature vectors used in this method, Hough transform is applied to character image, which is used for edge extraction in image processing. By that transformation technique, strokes could be extracted and feature vectors constructed suitably. The characteristic of this method is solving the difficulties in stroke extraction through transform space analysis, which is induced by noise and blurring, and representing high recognition rate 99.3% within 10 candidates in relative low dimension.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.04b
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• pp.267-271
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• 1993

Developed in this paper is a feature based modeling of welded plate construction(WPC) which is composed of flat or bended plates represented as reference plane with a constant thickness. First, the necessity and the characteristics of the feature based modeling of WPC as compared with the assemblies of mechanical parts are investigated. Second, feature library for the assemblied of WPC is shown which contains several types of features like joint feature, groove feature, materialfeature and precision feature. Third, the assembly procedures are presented whichmainly consist of both the assembly transformation and the correct assembly checking. Fourth, weld lines generated in the assembled WPC are defined so that those can be used in the process planning or the manufacturing stage. Finally, A prototype system isdeveloped by using a geometric modeling software Pro/Engineer, a graphic software GL(Graphic Library), and C language on a CAD workstation IRIS.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.13 no.1
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• pp.31-40
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• 2009

In the present work transformation of dimensionless heat diffusion equation for the solution of moving boundary problems have been formulated. The formulation is based on 1-D, 2-D and 3-D, unsteady heat diffusion equations. These equations are rst turned int dimensionless form by using dimensionless quantities and their transformation was formulated in liquid and solid phases. The salient feature of this work is that during the transformation of dimensionless heat diffusion equation there arises a convective term $\tilde{v}$ which is responsible for the motion of interface in liquid as well as solid phase. In the transformed heat equation, a correction factor $\beta$ also arises naturally which gives the correct transformed flux at interface.

• The Journal of Korea Robotics Society
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• v.11 no.1
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• pp.33-40
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• 2016

In this paper, we propose a method for estimating the pose of the camera using a rectangle feature utilized for the visual SLAM. A warped rectangle feature as a quadrilateral in the image by the perspective transformation is reconstructed by the Coupled Line Camera algorithm. In order to fully reconstruct a rectangle in the real world coordinate, the distance between the features and the camera is needed. The distance in the real world coordinate can be measured by using a stereo camera. Using properties of the line camera, the physical size of the rectangle feature can be induced from the distance. The correspondence between the quadrilateral in the image and the rectangle in the real world coordinate can restore the relative pose between the camera and the feature through obtaining the homography. In order to evaluate the performance, we analyzed the result of proposed method with its reference pose in Gazebo robot simulator.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.209-212
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• 2005

Hyperspectral data consists of more than 200 spectral bands that are highly correlated. In order to utilize hyperspectral data for classification, dimensional reduction or feature extraction is desired. By applying feature extraction, computational complexity of classification can be reduced and classification accuracy may be improved. In this paper, a genetic algorithm based feature extraction technique is proposed. Measure from discriminant analysis is utilized as optimization criterion. A subset of spectral bands is selected by genetic algorithm. Dimension of feature space is further reduced by linear transformation. Feasibility of the proposed technique is evaluated with AVIRIS data.

• Journal of Information Technology Applications and Management
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• v.13 no.1
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• pp.77-94
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• 2006

Case-based Reasoning (CBR) is widely used in business and industry prediction. It is suitable to solve complex and unstructured business problems. Recently, the prediction accuracy of CBR has been enhanced by not only various machine learning algorithms such as genetic algorithms, relative weighting of Artificial Neural Network (ANN) input variable but also data mining technique such as feature selection, feature weighting, feature transformation, and instance selection As a result, CBR is even more widely used today in business area. In this study, we investigated the usefulness of the CBR method in forecasting success in implementing ERP systems. We used a CBR method based on the feature weighting technique to compare the performance of three different models : MDA (Multiple Discriminant Analysis), GECBR (GEneral CBR), FWCBR (CBR with Feature Weighting supported by Analytic Hierarchy Process). The study suggests that the FWCBR approach is a promising method for forecasting of successful ERP implementation in Small and Medium sized Enterprises.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1257-1271
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• 2022

Recently, many Earth observation optical satellites have been developed, as their demands were increasing. Therefore, a rapid preprocessing of satellites became one of the most important problem for an active utilization of satellite images. Satellite image matching is a technique in which two images are transformed and represented in one specific coordinate system. This technique is used for aligning different bands or correcting of relative positions error between two satellite images. In this paper, we propose an automatic image matching method among satellite images with different ground sampling distances (GSDs). Our method is based on improved feature matching and robust estimation of transformation between satellite images. The proposed method consists of five processes: calculation of overlapping area, improved feature detection, feature matching, robust estimation of transformation, and image resampling. For feature detection, we extract overlapping areas and resample them to equalize their GSDs. For feature matching, we used Oriented FAST and rotated BRIEF (ORB) to improve matching performance. We performed image registration experiments with images KOMPSAT-3A and RapidEye. The performance verification of the proposed method was checked in qualitative and quantitative methods. The reprojection errors of image matching were in the range of 1.277 to 1.608 pixels accuracy with respect to the GSD of RapidEye images. Finally, we confirmed the possibility of satellite image matching with heterogeneous GSDs through the proposed method.

• Journal of the Semiconductor & Display Technology
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• v.4 no.1 s.10
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• pp.61-64
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• 2005

This study presents a new algorithm to detect the facial feature in a color image entered from the mobile device with complex backgrounds and undefined distance between camera's location and the face. Since skin color model with Hough transformation spent approximately 90$\%$ of running time to extract the fitting ellipse for detection of the facial feature, we have changed the approach to the simple geometric vector operation, called a TS(Triangle-Square) transformation. As the experimental results, this gives benefit of reduced run time. We have similar ratio of face detection to other methods with fast speed enough to be used on real-time identification system in mobile environments.