• Journal of Korea Society of Industrial Information Systems
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• v.7 no.3
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• pp.23-31
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• 2002

In this paper we propose a new feature vector for recognition of the facial expression based on Gibbs distributions which are well suited for representing the spatial continuity. The extracted feature vectors are invariant under translation rotation, and scale of an facial expression imege. The Algorithm for recognition of a facial expression contains two parts： the extraction of feature vector and the recognition process. The extraction of feature vector are comprised of modified 2-D conditional moments based on estimated Gibbs distribution for an facial image. In the facial expression recognition phase, we use discrete left-right HMM which is widely used in pattern recognition. In order to evaluate the performance of the proposed scheme, experiments for recognition of four universal expression (anger, fear, happiness, surprise) was conducted with facial image sequences on Workstation. Experiment results reveal that the proposed scheme has high recognition rate over 95%.

• Journal of Korea Multimedia Society
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• v.12 no.1
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• pp.50-57
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• 2009

This paper proposes a extraction of feature lines on a polygonal model using local implicit surface fitting technique. To extract feature lines on a polygonal model, the previous technique addressed to compute the curvature and their derivatives at mesh vertices via global implicit surface fitting. It needs a user-specified precision parameter for finding an accurate projection of the mesh vertices onto an approximating implicit surface and requires high-time consumption. But we use a local implicit surface fitting technique to estimate the local differential information near a vertex by means of an approximating surface. Feature vertices are easily detected as zero-crossings, and can then be connected along the direction of principal curvature. Our method, demonstrated on several large polygonal models, produces a good fit which leads to improved visualization.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.11-18
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• 2001

In this paper, we proposed the image compression algorithm using triangular feature based GHA. In feature extraction, the input images are divided into eight areas of triangular shape, that has positional information for face image compression. The proposed algorithm reduces blocking effects in image reconstruction and contains informations of face feature and shapes of face as input images are divided into eight. We used triangular feature extraction for positional information and GHA for shape information of face images. Simulation results show that the proposed algorithm has a better performance than the block based K-means and non-parsed image based GHA in PSNR at the same bpp.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.38-47
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• 2000

In pattern classification, the Bhattacharyya distance has been used as a class separability measure. Furthemore, it is recently reported that the Bhattacharyya distance can be used to estimate error of Gaussian ML classifier within 1-2% margin. In this paper, we propose a feature extraction method utilizing the Bhattacharyya distance. In the proposed method, we first predict the classification error with the error estimation equation based on the Bhauacharyya distance. Then we find the feature vector that minimizes the classification error using two search algorithms: sequential search and global search. Experimental reslts show that the proposed method compares favorably with conventional feature extraction methods. In addition, it is possible to determine how man, feature vectors arc needed for achieving the same classification accuracy as in the original space.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.687-692
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• 2006

In a conventional ICA(Independent Component Analysis) based face recognition method, PCA(Principal Component Analysis) first is used for feature extraction, ICA learning method then is applied for feature enhancement in the reduced dimension. It is not considered that a necessary component can be located in the discarded feature space. In the new ICA(NICA), learning extracts features using the magnitude of kurtosis (4-th order central moment or cumulant). But, the pure ICA method can not discard noise effectively. The synergy effect of PCA and ICA can be achieved if PCA is used for noise reduction filter. Namely, PCA does whitening and noise filtering. ICA performs feature extraction. Experiment results show the effectiveness of the new ICA method compared to the conventional ICA approach.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.365-373
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• 1997

In this paper, a new method of the 3D(dimensional) image reconstruction is proposed to build up the 3D image from 2D images using digital image processing techniques and computer graphics. First, the new feature extraction algorithm that doesn't need various input parameters and is not affected by threshold is adopted This new algorithm extracts feature points by eliminating some undesirable points on the ground of the connectivity. Second, as the cast function to reconstruct surfaces using extracted feature points, the minimum distance measure between two plane images has been adopted According to this measure, the surface formation algorithm doesn't need complex calculation and takes the form of triangle or trapezoid To investigate usefulness, this approach has been applied to a head CT image and compared with other methods. Experimental comparisons show that the suggested algorithm yields better performance on feature extraction than others. In contrast with the other methods, the complex calculation for surface formation in the proposed algorithm is not necessary.

• Journal of the Semiconductor & Display Technology
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• v.14 no.4
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• pp.56-60
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• 2015

Mobile devices like smartphones and tablets are becoming increasingly capable in terms of processing power. Although they are already used in computer vision, no comparable measurement experiments of the popular feature extraction algorithm have been made yet. That is, local feature descriptors are widely used in many computer vision applications, and recently various methods have been proposed. While there are many evaluations have focused on various aspects of local features, matching accuracy, however there are no comparisons considering on speed trade-offs of recent descriptors such as ORB, FAST and BRISK. In this paper, we try to provide a performance evaluation of feature descriptors, and compare their matching precision and speed in KD-Tree setup with efficient computation of Hamming distance. The experimental results show that the recently proposed real valued descriptors such as ORB and FAST outperform state-of-the-art descriptors such SIFT and SURF in both, speed-up efficiency and precision/recall.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.404-406
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• 1996

The objective of this paper is to present a new feature-vector extraction method for the automatic detection and classification of power quality(PQ) disturbances, where FFT, DWT(Discrete Wavelet Transform), and data compression are utilized to extract an appropriate feature vector. In particular, the proposed classifier consists of three parts: i.e., (i) automatic detection of PQ disturbances, where the wavelet transform and signal power estimation method are utilized to detect each disturbance, (ii) feature vector extraction from the detected disturbance, and (iii) automatic classification, where Multi-Layer Perceptron(MLP) is used to classify each disturbance from the corresponding extracted feature vector. To demonstrate the performance and applicability of the proposed classification algorithm, some test results obtained by analyzing 7-class power quality disturbances generated by the EMTP are also provided.

• Korean Journal of Remote Sensing
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• v.17 no.4
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• pp.319-334
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• 2001

Extracting primitives from imagery plays an important task in visual information processing since the primitives provide useful information about characteristics of the objects and patterns. The human visual system utilizes features without difficulty for image interpretation, scene analysis and object recognition. However, to extract and to analyze feature are difficult processing. The ultimate goal of digital image processing is to extract information and reconstruct objects automatically. The objective of this study is to develop robust method to achieve the goal of the image processing. In this study, an adaptive strategy was developed by implementing Gabor filters in order to extract feature information and to segment images. The Gabor filters are conceived as hypothetical structures of the retinal receptive fields in human vision system. Therefore, to develop a method which resembles the performance of human visual perception is possible using the Gabor filters. A method to compute appropriate parameters of the Gabor filters without human visual inspection is proposed. The entire framework is based on the theory of human visual perception. Digital images were used to evaluate the performance of the proposed strategy. The results show that the proposed adaptive approach improves performance of the Gabor filters for feature extraction and segmentation.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.207-214
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• 2019

This paper proposes a new feature extraction method for automatically estimating the number of target and detecting the chaff using high range resolution profile(HRRP). Feature of one-dimensional range profile is expected to be limited or missing due to lack of information according to the time. The proposed method considers the dynamic movements of targets depending on the radial velocity. The observed HRRP sequence is used to construct a time-range distribution matrix, then assuming diverse radial velocities reflect the number of target and seduction chaff launch, the proposed method utilizes the characteristic of the gradient distribution on the time-range distribution matrix image, which is validated by electromagnetic computation data and dynamic simulation.