• Journal of Biomedical Engineering Research
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• v.28 no.5
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• pp.665-675
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• 2007

The morphological change of ECG is the important diagnostic parameter to finding the malfunction of a heart. Generally ST segment deviation is concerned with myocardial abnormality. The aim of this study is to detect the change of ST in shape using a polynomial approximation method and the reference ST type. The developed algorithm consists of feature point detection, ST level detection and ST shape classification. The detection of QRS complex is accomplished using it's the morphological characteristics such as the steep slope and high amplitude. The developed algorithm detects the ST level change, and then classifies the ST shape type using the polynomial approximation. The algorithm finds the least squares curve for the data between S wave and T wave in ECG. This curve is used for the classification of the ST shapes. ST type is classified by comparing the slopes of the specified points between the reference ST set and the least square curve. Through the result from the developed algorithm, we can know when the ST level change occurs and what the ST shape type is.

• Journal of the Korean Society of Costume
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• v.58 no.6
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• pp.35-41
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• 2008

The purposes of this study lied in the analysis and classification of neck-base circumference shapes of the women in their twenties, by the application of three-dimensional automatic measurement data of human body, and thereby in the understanding of neck-base circumference shapes by the classified type. The findings are as follows: 1. The comparison of three-dimensional human body measurement items relating to the neck-base circumference part of the women in their twenties indicated that the largest individual difference was found in cervicale-center-anterior neck radius than in other items. 2. The factor analysis, which was conducted to extract the factors constituting the neck-base circumference, showed the shape of cervicale(factor 1), the shape of section neck(factor 2), the thickness of neck(factor 3), the shape of anterior neck(factor 4), and the shape of side neck(factor 5). 3. The classification of the neck-base circumference shapes resulted in three types. Type 1 was the shape of a reverse triangle hanging forward, Type 2 was that of a circle, and Type 3 was that of an oval open to the sides.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.5 s.311
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• pp.38-47
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• 2006

Conventional classification method uses a single classifier based on shape or motion feature. However this method exhibits a weakness if naively used since the classification performance is highly sensitive to the accuracy of moving region to be detected. The detection accuracy, in turn, depends on the condition of the image background. In this paper, we propose to resolve the drawback and thus strengthen the classification reliability by employing a Bayesian decision fusion and by optimally combining the decisions of three classifiers. The first classifier is based on shape information obtained from Fourier descriptors while the second is based on the shape information obtained from image gradients. The third classifier uses motion information. Our experimental results on the classification Performance of human and vehicle with a static camera in various directions confirm a significant improvement and indicate the superiority of the proposed decision fusion method compared to the conventional Majority Voting and Weight Average Score approaches.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.362-371
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• 2003

In this paper, we propose an optimal interest point extraction method to support shape-base image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of the shape contour. The threshold is determined dynamically by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For n points of the contour, the proposed algorithm has O(nlogn) computational cost on an average to extract the number of m optimal interest points. Experiments were performed on the 70 synthetic shapes of 7 different contour types and 1100 fish shapes. It shows the average optimization ratio up to 0.92 and has 14% improvement, compared to the fixed threshold method. The shape features extracted from our proposed method can be used for shape-based image classification, indexing, and similarity search via normalization.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.1-8
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• 1994

In this paper, we propose an efficient two-step pattern matching method which promises shape distortion-tolerant recognition of handwritten of handwritten Hangul syllables. In the first step, nonlinear shape normalization is carried out to compensate for global shape distortions in handwritten characters, then a preliminary classification based on simple pattern matching is performed. In the next step, nonlinear pattern matching which achieves best matching between input and reference pattern is carried out to compensate for local shape distortions, then detailed classification which determines the final result of classification is performed. As the performance of recognition systems based on pattern matching methods is greatly effected by the quality of reference patterns. we construct reference patterns by combining the proposed nonlinear pattern matching method with a well-known averaging techniques. Experimental results reveal that recognition performance is greatly improved by the proposed two-step pattern matching method and the reference pattern construction scheme.

• Communications for Statistical Applications and Methods
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• v.31 no.3
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• pp.309-322
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• 2024

Identifying algae in water is important for managing algal blooms which have great impact on drinking water supply systems. There have been various microscopic approaches developed for algae classification. Many of them are based on the morphological features of algae. However, there have seldom been mathematical frameworks for comparing the shape of algae, represented as a planar continuous curve obtained from an image. In this work, we describe a recent framework for computing shape distance between two different algae based on the elastic metric and a novel functional representation called the square root velocity function (SRVF). We further introduce statistical procedures for multiple shapes of algae including computing the sample mean, the sample covariance, and performing the principal component analysis (PCA). Based on the shape distance, we classify six algal species in watersheds experiencing algal blooms, including three cyanobacteria (Microcystis, Oscillatoria, and Anabaena), two diatoms (Fragilaria and Synedra), and one green algae (Pediastrum). We provide and compare the classification performance of various distance-based and model-based methods. We additionally compare elastic shape distance to non-elastic distance using the nearest neighbor classifiers.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.543-549
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• 2008

Inspired by using a robust loss function in the support vector machine regression to control training error and the idea of robust template matching with M-estimator, Chen (2004) applies M-estimator techniques to gaussian radial basis functions and form a new class of robust kernels for the support vector machines. We are specially interested in the shape of the Huber's M-estimator in this context and propose a way to find the shape parameter of the Huber's M-estimating function. For simplicity, only the two-class classification problem is considered.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1115-1127
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• 2010

In order to increase the productivity in manufacturing TFT-LCD(thin film transistor-liquid crystal display), it is essential to classify defects that occur during the production and make an appropriate decision on whether the product with defects is scrapped or not. The decision mainly depends on classifying the defects accurately. In this paper, we present an effective classification method for film defects acquired in the panel production line by analyzing the intensity distribution and shape feature of the defects. We first generate a binary image for each defect by separating defect regions from background (non-defect) regions. Then, we extract various features from the defect regions such as the linearity of the defect, the intensity distribution, and the shape characteristics considering intensity, and construct a referential image database that stores those feature values. Finally, we determine the type of a defect by matching a defect image with a referential image in the database through the matching cost function between the two images. To verify the effectiveness of our method, we conducted a classification experiment using defect images acquired from real TFT-LCD production lines. Experimental results show that our method has achieved highly effective classification enough to be used in the production line.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.7
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• pp.1026-1035
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• 2002

The purposes of this study were to classify the front view of trunk of adult males into several kinds of shape and to provide the characteristics and silhouette of each group which has same shape. As the sample, subjects were l290 males of 20 to 54 year-old. The procedure and results were follows; l. The principal component analysis was used to obtain the shape factor of the front of the trunk 8 factors in the front which explained 86.8% of total variance were extracted. 2. As the result of the cluster analysis of factor scores, the font of body was classified in 4 types. 4 types were named X, A, Y and H type in the front considering the characteristics of each type.

• The Research Journal of the Costume Culture
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• v.13 no.3 s.56
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• pp.344-360
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• 2005

The purpose of the study is to identify the physical characteristics of early adolescent boys, to classify body shapes by physical characteristic. The subjects were 549 boys in the capital area. Their body shapes were identified and classified based on 47 anthropometric measurements, 43 photographic measurements and 10 indexed measurements. For data analysis were performed descriptive statistics, factor analysis, cluster analysis, ANOVA and Duncan test using SPSS Ver. 10. According to the result of extracting factors indicating the characteristics of body shape, horizontal size, vertical length, lateral posture, the lateral shape of the abdomen and the hip, the shape of the back protrusion, the front shape of the trunk and was the shape of the shoulders. According to the result of classifying body shapes, four types of shape - T(Tall) type, P(Petite) type, L(Large) type and R(Regular) type were identified. The results of this study are expected to contribute to planning sizes according to the type of body shape and improving the fitness of ready-made clothes in apparel and school uniform manufacturers.