• The Journal of the Korea Contents Association
• /
• v.8 no.9
• /
• pp.1-9
• /
• 2008

In this paper, we proposed automatic face detection and tracking which is robustness in rotation. To detect a face image in complicated background and various illuminating conditions, we used face skin color detection. we used Harris corner detector for extract facial feature points. After that, we need to track these feature points. In traditional method, Lucas-Kanade feature tracker doesn't delete useless feature points by occlusion in current scene (face rotation or out of camera). So we proposed the estimation function, which delete useless feature points. The method of delete useless feature points is estimation value at each pyramidal level. When the face was occlusion, we deleted these feature points. This can be robustness to face rotation and out of camera. In experimental results, we assess that using estimation function is better than traditional feature tracker.

• KIPS Transactions on Software and Data Engineering
• /
• v.3 no.11
• /
• pp.495-502
• /
• 2014

An effective solution to the variation on scale and rotation is required to recognize contactless palmprint. In this study, we firstly minimize the variation by extracting a region of interest(ROI) according to the size and orientation of hand and normalizing the ROI. This paper proposes a contactless palmprint recognition method based on KLT(Kanade-Lukas-Tomasi) feature points. To detect corresponding feature points, texture in local regions around KLT feature points are compared. Then, we recognize palmprint by measuring the similarity among displacement vectors which represent the size and direction of displacement of each pair of corresponding feature points. An experimental results using CASIA public database show that the proposed method is effective in contactless palmprint recognition. Especially, we can get the performance of exceeding 99% correct identification rate using multiple Gabor filters.

• Journal of Korea Multimedia Society
• /
• v.12 no.6
• /
• pp.804-814
• /
• 2009

In this paper, a novel system for avatar motion controlling by tracking face is presented. The system is composed of three main parts: firstly, LCS (Local Cluster Searching) method based face feature detection algorithm, secondly, HMM based feature points recognition algorithm, and finally, avatar controlling and animation generation algorithm. In LCS method, face region can be divided into many small piece regions in horizontal and vertical direction. Then the method will judge each cross point that if it is an object point, edge point or the background point. The HMM method will distinguish the mouth, eyes, nose etc. from these feature points. Based on the detected facial feature points, the 3D avatar is controlled by two ways: avatar orientation and animation, the avatar orientation controlling information can be acquired by analyzing facial geometric information; avatar animation can be generated from the face feature points smoothly. And finally for evaluating performance of the developed system, we implement the system on Window XP OS, the results show that the system can have an excellent performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.11
• /
• pp.2776-2788
• /
• 1996

A new feature-based motion parameter estimation for arbitrary-shaped regions is proposed. Existing motion parameter estimation algorithms such as gradient-based algorithm require iterations that are very sensitive to initial values and which often converge to a local minimum. In this paper, the motion parameters of an object are obtained by solving a set of linear equations derived by the motion of salient feature points of the object. In order to estimate the displacement of the feature points, a new process called the "bi-directional correspondence scheme" is proposed to ensure the robjstness of correspondence. The proposed correspondence scheme iteratively selects the feature points and their corresponding points until unique one-to-one correspondence is established. Furthermore, initially obtained motion paramerters are refined using an iterative method to give a better performance. The proposed algorithm can be used for motion estimationin object-based image coder, and the experimental resuls show that the proposed method outperforms existing schemes schemes in estimating motion parameters of objects in image sequences.sequences.

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.11
• /
• pp.68-78
• /
• 1999

A new algorithm for tracking feature points in an image sequence is presented. Most existing feature tracking algorithms often produce false trajectories, because the matching measures do not precisely reflect motion characteristics. In this paper, three attributes including spatial coordinate, motion direction and motion magnitude are used to calculate the feature point correspondence. The trajectories of feature points are determined by calculation the matching measure, which is defined as the minimum weighted Euclidean distance between two feature points. The weights of the attributes are updated reflecting the motion characteristics, so that the robust tracking of feature points is achieved. The proposed algorithm can find the trajectories correctly which has been shown by experimental results.

• The Journal of the Korea Contents Association
• /
• v.12 no.6
• /
• pp.9-20
• /
• 2012

In computer vision, it is very important to obtain reliable corresponding feature points. However, we know it is not easy to find the corresponding feature points exactly considering by scaling, lighting, viewpoints, etc. Lots of SIFT methods applies the invariant to image scale and rotation and change in illumination, which is due to the feature vector extracted from corners or edges of object. However, SIFT could not find feature points, if edges do not exist in the area when we extract feature points along edges. In this paper, we present a new placement method of marker to improve the performance of SIFT feature detection and matching between different view of an object or scene. The shape of the markers used in the proposed method is formed in a semicircle to detect dominant direction vector by SIFT algorithm depending on direction placement of marker. We applied De Bruijn sequence for the markers direction placement to improve the matching performance. The experimental results show that the proposed method is more accurate and effective comparing to the current method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.12
• /
• pp.2363-2371
• /
• 2015

In this paper, we propose a new feature matching algorithm which is robust to the viewpoint change by using the FAST(Features from Accelerated Segment Test) feature detector and the SIFT(Scale Invariant Feature Transform) feature descriptor. The original FAST algorithm unnecessarily results in many feature points along the edges in the image. To solve this problem, we apply the principal curvatures for refining it. We use the SIFT descriptor to describe the extracted feature points and calculate the homography matrix through the RANSAC(RANdom SAmple Consensus) with the matching pairs obtained from the two different viewpoint images. To make feature matching robust to the viewpoint change, we classify the matching pairs by calculating the Euclidean distance between the transformed coordinates by the homography transformation with feature points in the reference image and the coordinates of the feature points in the different viewpoint image. Through the experimental results, it is shown that the proposed algorithm has better performance than the conventional feature matching algorithms even though it has much less computational load.

• Genomics & Informatics
• /
• v.17 no.4
• /
• pp.39.1-39.20
• /
• 2019

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogeneous sets of biosequences (composite data points). A composite data point is a set of ordinary data points (e.g., set of feature vectors). We theoretically extend the derivation of the largest generalized eigenvalue-based distance metric D_ij(γ₁) in any linear and non-linear feature spaces. We prove that D_ij(γ₁) is a metric under any linear and non-linear feature transformation function. We show the sufficiency and efficiency of using the decision rule $\bar{{\delta}}_{{\Xi}i}$(i.e., mean of D_ij(γ₁)) in classification of heterogeneous sets of biosequences compared with the decision rules min_𝚵iand median_𝚵i. We analyze the impact of linear and non-linear transformation functions on classifying/clustering collections of heterogeneous sets of biosequences. The impact of the length of a sequence in a heterogeneous sequence-set generated by simulation on the classification and clustering results in linear and non-linear feature spaces is empirically shown in this paper. We propose a new concept: the limiting dispersion map of the existing clusters in heterogeneous sets of biosequences embedded in linear and nonlinear feature spaces, which is based on the limiting distribution of nucleotide compositions estimated from real data sets. Finally, the empirical conclusions and the scientific evidences are deduced from the experiments to support the theoretical side stated in this paper.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.383-388
• /
• 2005

Depth recovery in robot vision is an essential problem to infer the three dimensional geometry of scenes from a sequence of the two dimensional images. In the past, many studies have been proposed for the depth estimation such as stereopsis, motion parallax and blurring phenomena. Among cues for depth estimation, depth from lens translation is based on shape from motion by using feature points. This approach is derived from the correspondence of feature points detected in images and performs the depth estimation that uses information on the motion of feature points. The approaches using motion vectors suffer from the occlusion or missing part problem, and the image blur is ignored in the feature point detection. This paper presents a novel approach to the defocus technique based depth from lens translation using sequential SVD factorization. Solving such the problems requires modeling of mutual relationship between the light and optics until reaching the image plane. For this mutuality, we first discuss the optical properties of a camera system, because the image blur varies according to camera parameter settings. The camera system accounts for the camera model integrating a thin lens based camera model to explain the light and optical properties and a perspective projection camera model to explain the depth from lens translation. Then, depth from lens translation is proposed to use the feature points detected in edges of the image blur. The feature points contain the depth information derived from an amount of blur of width. The shape and motion can be estimated from the motion of feature points. This method uses the sequential SVD factorization to represent the orthogonal matrices that are singular value decomposition. Some experiments have been performed with a sequence of real and synthetic images comparing the presented method with the depth from lens translation. Experimental results have demonstrated the validity and shown the applicability of the proposed method to the depth estimation.

• Proceedings of the KIEE Conference
• /
• 1987.07b
• /
• pp.1427-1430
• /
• 1987

The feature points of an image play a very important role in understanding the image. Especially, when an image is composed of lines, vertices of the image offer informations about its property and structure. In this paper, a series of process for extracting feature points from actual IC image is described. This result can be used to acquire CIF ( Caltech Intermediate Form ) file.