• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.246-249
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• 2003

The constraints necessary guarantee using the comparison of these extrinsic parameters, which each Rotation matrix and Translation Vector must be equal to the either, except the X-axis Translation Vector. Thus, we can not yet calculate the 3D-range measurement in the end of camera calibration. To minimize this disadvantage, the Epipolar Rectification has been proposed in the literature. This paper aims to present the development of Epipolar Rectification to calibrate Stereo cameras. The required computation of the transformation mapping between points in 3D-space is based on calculating the image point that appears on new image plane by using calibrated parameters. This computation is assumed from the rotating the old ones around their optical center until focal planes becomes coplanar, thereby containing the baseline, and the Z-axis of both camera coordinate to be parallel together. The optical center positions of the new extrinsic parameters are the same as the old camera, whereas the new orientation differs from the old ones by the suitable rotations. The intrinsic parameters are the same for both cameras. So that, after completed calibration process, immediately can calculate the 3D-range measurement. And the rectification determines a transformation of each image plane such that pairs of conjugate Epipolar lines become collinear and parallel to one of the image axis. From the experimental results verify the proposed technique are agreed with the expected specifications.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.73-81
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• 2004

To extract the creditable features in fingerprint image, many people use the thinning algorithm that has a very important position in the preprocessing. In this paper, we propose the robust parallel thinning algorithm that can preserve the connectivity of the binarized fingerprint image, make the thinnest skeleton with 1-pixel width and get near to the medial axis extremely. The proposed thinning method repeats three sub-iterations. The first sub-iteration takes off only the outer boundary pixel by using the interior points. To extract the one side skeletons, the second sub-iteration finds the skeletons with 2-pixel width. The third sub-iteration prunes the needless pixels with 2-pixel width existing in the obtained skeletons and then the proposed thinning algorithm has the robustness against the rotation and noise and can make the balanced medial axis. To evaluate the performance of the proposed thinning algorithm we compare with and analyze the previous algorithms.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1566-1576
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• 2007

Camera pose information from 2D face image is very important for making virtual 3D face model synchronize with the real face. It is also very important for any other uses such as: human computer interface, 3D object estimation, automatic camera control etc. In this paper, we have presented a camera position determination algorithm from a single 2D face image using the relationship between mouth position information and face region boundary information. Our algorithm first corrects the color bias by a lighting compensation algorithm, then we nonlinearly transformed the image into $YC_bC_r$ color space and use the visible chrominance feature of face in this color space to detect human face region. And then for face candidate, use the nearly reversed relationship information between $C_b\;and\;C_r$ cluster of face feature to detect mouth position. And then we use the geometrical relationship between mouth position information and face region boundary information to determine rotation angles in both x-axis and y-axis of camera position and use the relationship between face region size information and Camera-Face distance information to determine the camera-face distance. Experimental results demonstrate the validity of our algorithm and the correct determination rate is accredited for applying it into practice.

• Korean Journal of Human Ecology
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• v.14 no.1
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• pp.143-153
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• 2005

The purpose of this research is to study face images according to the difference of facial shape and make-up. A variety of face images can be formulated by computer graphic simulation, combining numerously different facial shapes and make-up styles. In order to check out the diverse images by make-up styles, we applied five forms of eye brows, two types of eye shadows, and three lip shapes to the round-shaped face of a model. The question sheet, used with a operational stimulant in the experiment, contained 28 articles, composed of a pair of bi-ended adjective in 7 point scale. Data were analyzed using Varimax perpendicular rotation method, Duncan's Multiple Range Test, and Three-way ANOVA. After comparing various results of make-up application to various face types, we could find that facial shape, eye-brows, eye-shadow, and lip shapes influence interactively on total facial images. As a result of make-up image perception analyses, a factor structure was divided into mildness, modernness, elegance, and sociableness. Speaking of make-up image in terms of those factors, round form make-up style showed the highest level of mildness. Upward and straight style of make-up had the highest of modernness. Elegance level went highest when eye shadow style was round form and lip style was straight. Lastly, an incurve lip make-up style showed the highest of sociableness.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.552-559
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• 2011

In this paper, a new image watermarking algorithm is proposed which can hide specific information of an ID card's owner in photo image for preventing ID's photo forgery. Proposed algorithm uses the image segmentation and the correlation peak position modulation of spread spectrum. The watermark embedded in photo ensures not only robustness against printing and scanning but also sufficient information capacity hiding unique number such as social security numbers in small-sized photo. Another advantage of proposed method is extracting accurate information with error tolerance within some rotation range by using $2^h{\times}2^w$ unit sample space not instead $1{\times}1$ pixels for insertion and extraction of information. 40 bits information can be embedded and extracted at $256{\times}256$ sized ID photo with BER value of 0 % when the test condition is 300dpi scanner and photo printer with 22 photos. In conclusion, proposed algorithm shows the robustness for noise and rotational errors occured during printing and scanning.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.675-678
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• 2018

Since the face has a unique property to identify human, the face recognition is actively used in a security area and an authentication area such as access control, criminal search, and CCTV. The frontal face image has the most face information. Therefore, it is necessary to acquire the front face image as much as possible for face recognition. In this study, the face region is detected using the Adaboost algorithm using Haar-like feature and tracks it using the mean-shifting algorithm. Then, the feature points of the facial elements such as the eyes and the mouth are extracted from the face region, and the ratio of the two eyes and degree of rotation of the face is calculated using their geographical information, and the approximate front face image is presented in real time.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.178-180
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• 2006

In general, camera calibration has to be gone ahead necessarily to estimate a position and an orientation of the object exactly using a camera. Autonomous land system in order to run a vehicle autonomously needs a camera calibration method appling a camera and various road environment. Camera calibration is to prescribe the confrontation relation between third dimension space and the image plane. It means to find camera calibration parameters. Camera calibration parameters using the paved road and the unpaved road are estimated. The proposed algorithm has been detected through the image processing after obtaining the paved road and the unpaved road. There is able to detect easily edges because the road lanes exist in the raved road. Image processing method is two. One is a method on the paved road. Image is segmentalized using open, dilation, and erosion. The other is a method on the unpaved road. Edges are detected using blur and sharpening. So it has been made use of Hough transformation in order to detect the correct straight line because it has less error than least-square method. In addition to, this thesis has been used vanishing point' principle. an algorithm suggests camera calibration method using Hough transformation and vanishing point. When the algorithm was applied, the result of focal length was about 10.7[mm] and RMS errors of rotation were 0.10913 and 0.11476 ranges. these have the stabilized ranges comparatively. This shows that this algorithm can be applied to camera calibration on the raved and unpaved road.

• Journal of the Korean Institute of Telematics and Electronics T
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• v.36T no.4
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• pp.1-8
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• 1999

This paper presents a new algorithm for surface segmentation and feature description. In the first stage of proposed algorithm, the signature of an edge image of object is extracted. The signature technique represents a surface using the distance from the mass center to the boundary of the image as a function of angle rotating counterclockwise. If there exists a range in the angle axis where more than two signatures form a closed curve, we can conclude there is a surface inside the range. Using this feature of the signature, surface can be segmented. The surface features such as number of vertices, number of edges, convex and type of surface can also be extracted from segmented surfaces. This algorithm has distinguished advantages; it can easily recover the lost part in the edge image using the curve fitting method; it extracts surface features correctly regardless of the rotation of the surface in 3-D space.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.11
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• pp.79-88
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• 1999

In this paper we address the tracking method which tracks only target object in image sequence including moving object. We use a contour tracking algorithm based on intensity and motion boundaries. The motion of the moving object contour in the image is assumed to be well describable by an affine motion model with a translation, a change in scale and a rotation. The moving object contour is represented by B-spline, the position and motion of which is estimated along the image sequence. we use pattern recognition to identify target object. In order to use linear Kalman Filters we decompose the estimation process into two filters. One is estimating the affine motion parameters and the other the shape of moving object contour. In some experiments with dial plate we show that this method enables us to obtain the robust motion estimates and tracking trajectories even in case of including obstructive object.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.37-48
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• 2005

In this paper, a new object detection method for FLIR images is proposed. The proposed method consists of intensity ranking feature and a classification algerian using the feature. The intensity ranking feature is a representation of an image, from which intensity distribution is regularized. Each object candidate region is classified as object or non-object by the proposed classification algorithm which is based on the intensity ranking similarity between the candidate and object training images. Using the proposed algorithm pixel-wise detection results can be obtained without any additional candidate selection algorithm. In experimental results, it is shown that the proposed ranking feature is appropriate for object detection in a FLIR image and some vehicle detection results in the situation of existing noise, scale variation, and rotation of the objects are presented.