• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.363-366
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• 2005

In this paper, for detecting human faces under variations of lighting condition and facial expression, distance ranking feature and detection algorithm based on the feature are proposed. Distance ranking is the intensity ranking of a distance transformed image. Based on statistically consistent edge information, distance ranking is robust to lighting condition change. The proposed detection algorithm is a matching algorithm based on FFT and a solution of discretization problem in the sliding window methods. In experiments, face detection results in the situation of varying lighting condition, complex background, facial expression change and partial occlusion of face are shown

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.621-624
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• 1999

This paper describes a distance estimation method of object's motion in soccer image sequence by tracking field features. And we quantitatively evaluate the estimation accuracy We suppose that the input image sequence is taken with a camera on static axis and includes only zooming and panning transformation between frames. Adaptive template matching is adopted for non-rigid object tracking. For background compensation, feature templates selected from reference frame image are matched in following frames and the matched feature point pairs are used in computing Affine motion parameters. A perspective displacement field model is used for estimating the real distance between two position on Input Image. To quantitatively evaluate the accuracy of the estimation, we synthesized a 3 dimensional virtual stadium with graphic tools and experimented on the synthesized 2 dimensional image sequences. The experiment shows that the average of the error between the actual moving distance and the estimated distance is 1.84%.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.129-136
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• 2015

We propose a hybrid face recognition to deal with illumination variation. For this, we extract discriminant features by using the different illumination invariant feature extraction methods. In order to utilize both advantages of each method, we evaluate the discriminant power of each feature by using the discriminant distance and then construct a composite feature with only the features that contain a large amount of discriminative information. The experimental results for the Multi-PIE, Yale B, AR and yale databases show that the proposed method outperforms an individual illumination invariant feature extraction method for all the databases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.9C
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• pp.876-883
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• 2003

In this paper, we propose an algorithm for organ recognition in ultrasound images using log power spectrum. The main procedure of the algorithm consists of feature extraction and feature classification. In the feature extraction, as a translation invariant feature, log power spectrum is used for extracting the information on echo of the organs tissue from a preprocessed input image. In the feature classification, Mahalanobis distance is used as a measure of the similarity between the feature of an input image and the representative feature of each class. Experimental results for real ultrasound images show that the proposed algorithm yields the improvement of maximum 30% recognition rate than the recognition algorithm using power spectrum and Euclidean distance, and results in better recognition rate of 10-40% than the recognition algorithm using weighted quefrency complex cepstrum.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.14 no.6
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• pp.650-662
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• 1989

In this paper, a new algorithm for feature extraction and classification of recognizing Hanguel patterns is proposed. Inputed patterns classify into six basic formal patterns and divided into subregion of Hanguel phoneme and extract the crook feature from position information of the each subregion. Hanguel patterns are defined and are made of the indexed-sequence file using these crook features points. Hanguel patterns are recognized by retrievignt ehses two files such as feature indexed-sequence file and standard dictionary file. Thi paper show that the algorithm is very simple and easily construct the software system. Experimental result presents the output of feature extraction and grouping of input patterns. Proposed algorithm extract the crooked feature using distance transformation method within the rectangle of enclosure the characters. That uses the informationof relative position feature. It represents the 97% of recognition ratio.

• Journal of Korea Multimedia Society
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• v.8 no.2
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• pp.181-191
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• 2005

The classification error of 'ㅁ', 'ㅇ' is one of the main causes of incorrect recognition in Korean characters, but there haven't been enough researches to solve this problem. In this paper, a new feature extraction method from Korean grapheme is proposed to recognize 'ㅁ', 'ㅇ'effectively. First, we defined an optimal neighbor-distance selection measure using modified Hausdorff distance, which we determined the optimal neighbor-distance by. And we extracted neighbor-angle feature which was used as the effective feature to classify the two graphemes 'ㅁ', 'ㅇ'. Experimental results show that the proposed feature extraction method worked efficiently with the small number of features and could recognize the untrained patterns better than the conventional methods. It proves that the proposed method has a generality and stability for pattern recognition.

• International Journal of Quality Innovation
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• v.3 no.2
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• pp.113-131
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• 2002

The relationships among multi-dimensional data (such as medical examination data) with ambiguity and variation are difficult to explore. The traditional approach to building a data classification system requires the formulation of rules by which the input data can be analyzed. The formulation of such rules is very difficult with large sets of input data. This paper first describes two classification approaches using back-propagation (BP) neural network and Mahalanobis distance (MD) classifier, and then proposes two classification approaches for multi-dimensional feature selection. The first one proposed is a feature selection procedure from the trained back-propagation (BP) neural network. The basic idea of this procedure is to compare the multiplication weights between input and hidden layer and hidden and output layer. In order to simplify the structure, only the multiplication weights of large absolute values are used. The second approach is Mahalanobis-Taguchi system (MTS) originally suggested by Dr. Taguchi. The MTS performs Taguchi's fractional factorial design based on the Mahalanobis distance as a performance metric. We combine the automatic thresholding with MD: it can deal with a reduced model, which is the focus of this paper In this work, two case studies will be used as examples to compare and discuss the complete and reduced models employing BP neural network and MD classifier. The implementation results show that proposed approaches are effective and powerful for the classification.

• Journal of Korea Multimedia Society
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• v.9 no.6
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• pp.762-772
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• 2006

Instance based learning algorithm is the best known lazy learner and has been successfully used in many areas such as pattern analysis, medical analysis, bioinformatics and internet applications. However, its feature weighting scheme is too naive that many other extensions are proposed. Our version of IB3 named as eXtended IBL (XIBL) improves feature weighting scheme by backward stepwise regression and its distance function by VDM family that avoids overestimating discrete valued attributes. Also, XIBL adopts leave-one-out as its noise filtering scheme. Experiments with common artificial domains show that XIBL is better than the original IBL in terms of accuracy and noise tolerance. XIBL is applied to two important applications - intrusion detection and spam mail filtering and the results are promising.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1188-1193
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• 2013

In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and $H^*$ elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.5
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• pp.841-849
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• 2001

The pixel information of the object was obtained sequentially and pixels were clustered to a label by the line labeling method. Feature points were determined by finding the slope for edge pixels after selecting the fixed number of edge pixels. The slope was estimated by the least square method to reduce the detection error. Once a matching point was determined by comparing the feature information of the object and the pattern, the parameters for translation, scaling and rotation were obtained by selecting the longer line of the two which passed through the matching point from left and right sides. Finally, modified Hausdorff Distance has been used to identify the similarity between the object and the given pattern. The multi-label method was developed for recognizing the patterns with more than one label, which performs the modified Hausdorff Distance twice. Experiments have been performed to verify the performance of the proposed algorithm and method for simple target image, complex target image, simple pattern, and complex pattern as well as the partially hidden object. It was proved via experiments that the proposed image matching algorithm for recognizing the object had a good performance of matching.