• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.575-591
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• 2011

We present a novel active contour-based two-pass approach to extract smooth feature regions from a triangular mesh. In the first pass, an active contour formulated in level-set surfaces is devised to extract feature regions with rough boundaries. In the second pass, the rough boundary curve is smoothed by minimizing internal energy, which is derived from its curvature. The separation of the extraction and smoothing process enables us to extract feature regions with smooth boundaries from a triangular mesh without user's initial model. Furthermore, smooth feature curves can also be obtained by skeletonizing the smooth feature regions. We tested our algorithm on facial models and proved its excellence.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.359-362
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• 2004

In multi-dimensional image, ICA-based feature extraction algorithm, which is proposed in this paper, is for the purpose of detecting target feature about pixel assumed as a linear mixed spectrum sphere, which is consisted of each different type of material object (target feature and background feature) in spectrum sphere of reflectance of each pixel. Landsat ETM+ satellite image is consisted of multi-dimensional data structure and, there is target feature, which is purposed to extract and various background image is mixed. In this paper, in order to eliminate background features (tidal flat, seawater and etc) around target feature (laver farm) effectively, pixel spectrum sphere of target feature is projected onto the orthogonal spectrum sphere of background feature. The rest amount of spectrum sphere of target feature in the pixel can be presumed to remove spectrum sphere of background feature. In order to make sure the excellence of feature extraction method based on ICA, which is proposed in this paper, laver farm feature extraction from Landsat ETM+ satellite image is applied. Also, In the side of feature extraction accuracy and the noise level, which is still remaining not to remove after feature extraction, we have conducted a comparing test with traditionally most popular method, maximum-likelihood. As a consequence, the proposed method from this paper can effectively eliminate background features around mixed spectrum sphere to extract target feature. So, we found that it had excellent detection efficiency.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.6
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• pp.104-110
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• 1994

We proposed a high speed feature extraction algorithm and developed a feature vector extraction chip for high speed character recognition. It is hard to implement a high speed OCR by software alone with statistical method . Thus, the whole recognition process is divided into functional steps, then pipeline processed so that high speed processing is possible with temporal parallelism of the steps. In this paper we discuss the feature extraction step of the functional steps. To extract feature vector, a character image is normalized to 40$\times$40 pixels. Then, it is divided into 5$\times$5 subregions and 4x4 subregions to construct 41 overlapped subregions(10x10 pixels). It requires to execute more than 500 commands to extract a feature vector of a subregion by software. The proposed algorithm, however, requires only 10 cycles since it can extract a feature vector of a columm of subregion in one cycle with array structure. Thus, it is possible to process 12.000 characters per second with the proposed algorithm. The chip is implemented using EPLD and the effectiveness is proved by developing an OCR using it.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.113-120
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• 2004

In this paper, we propose an efficient feature extraction and image retrieval algorithm for content-based retrieval method. First, we extract the object using Gaussian edge detector for input image which is key frames of MPEG video and extract the object features that are location feature, distributed dimension feature and invariant moments feature. Next, we extract the characteristic color feature using the proposed HAQ(Histogram Analysis md Quantization) algorithm. Finally, we implement an retrieval of four features in sequence with the proposed matching method for query image which is a shot frame except the key frames of MPEG video. The purpose of this paper is to propose the novel content-based image retrieval algerian which retrieves the key frame in the shot boundary of MPEG video belonging to the scene requested by user. The experimental results show an efficient retrieval for 836 sample images in 10 music videos using the proposed algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.892-895
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• 1992

This paper presents a methodology for automatic feature extraction used in a vision system of FMC (flexible Manufacturing Cell). To implement a robot vision system, it is important to make a feature database for object recognition, location, and orientation. For industrial applications, it is necessary to extract feature information from CAD database since the detail information about an object is described in CAD data. Generally, CAD description is three dimensional information but single image data from camera is two dimensional information. Because of this dimensiional difference, many problems arise. Our primary concern in this study is to convert three dimensional data into two dimensional data and to extract some features from them and store them into the feature database. Secondary concern is to construct feature selecting system that can be used for part recognition in a given set of objects.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2041-2046
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• 2009

In this paper, we propose the feature points extraction method of hand region using vision. To do this, first, we find the HCbCr color model by using HSI and YCbCr color model. Second, we extract the hand region by using the HCbCr color model and the fuzzy color filter. Third, we extract the exact hand region by applying labeling algorithm to extracted hand region. Fourth, after finding the center of gravity of extracted hand region, we obtain the first feature points by using Canny edge, chain code, and DP method. And then, we obtain the feature points of hand region by applying the convex hull method to the extracted first feature points. Finally, we demonstrate the effectiveness and feasibility of the proposed method through some experiments.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.4
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• pp.466-471
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• 2006

IAn important problem of pattern recognition is to extract or select feature set, which is included in the pre-processing stage. In order to extract feature set, Principal component analysis has been usually used and SFS(Sequential Forward Selection) and SBS(Sequential Backward Selection) have been used as a feature selection method. This paper applies genetic algorithm which is a popular method for nonlinear optimization problem to the feature selection problem. So, we call it Genetic Algorithm Feature Selection(GAFS) and this algorithm is compared to other methods in the performance aspect.

• 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.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.214-217
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• 2000

Many researches have been performed for human recognition and coding schemes recently. For this situation, we propose an automatic facial feature extraction algorithm. There are two main steps: the face region evaluation from original background image such as office, and the facial feature extraction from the evaluated face region. In the face evaluation, Genetic Algorithm is adopted to search face region in background easily such as office and household in the first step, and Template Matching Method is used to extract the facial feature in the second step. We can extract facial feature more fast and exact by using over the proposed Algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.203-207
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• 2001

In this Paper, we propose the neural network based emotion recognition method for intelligently recognizing the human's emotion using CCD color image. To do this, we first acquire the color image from the CCD camera, and then propose the method for recognizing the expression to be represented the structural correlation of man's feature Points(eyebrows, eye, nose, mouse) It is central technology that the Process of extract, separate and recognize correct data in the image. for representation is expressed by structural corelation of human's feature Points In the Proposed method, human's emotion is divided into four emotion (surprise, anger, happiness, sadness). Had separated complexion area using color-difference of color space by method that have separated background and human's face toughly to change such as external illumination in this paper. For this, we propose an algorithm to extract four feature Points from the face image acquired by the color CCD camera and find normalization face picture and some feature vectors from those. And then we apply back-prapagation algorithm to the secondary feature vector. Finally, we show the Practical application possibility of the proposed method.