• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.77-85
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• 2006

In this paper, we propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet transformed images. We group the image classes into clusters which have similar texture features using Principal Component Analysis(PCA) and K-means. The hierarchical classifier has five layes which combine the clusters. The hierarchical classifier consists of 59 neural network classifiers learned with the back propagation algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 1000 training data and 1000 test data composed of 10 images from each of 100 classes shows classification rates of 81.5% and 75.1% correct, respectively.

• Journal of the Korea Society of Computer and Information
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• v.18 no.12
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• pp.11-19
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• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.595-600
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• 2008

본 논문에서는 위성영상 기반의 3차원 문화재 정보 시스템을 구현하기 위한 과정으로 고 해상 위성영상 제작을 위한 DEM(Digital Elevation Model)제작단계와 위성영상 자료를 수집하여 영상이 지니고 있는 왜곡을 보정하는 자료보정 단계, 사용 목적에 맞게 영상을 가공하여 저장하는 영상 생성단계를 통해 위성영상 기반의 3차원 지형정보를 생성하고, 3차원 스캐너를 활용한 대상지역 문화재의 DB 구축과 3차원 복원을 통해 3차원의 문화재 정보를 획득하고 질감 및 재질 사실적인 면을 강조하기 위한 텍스처 맵핑 과정을 통해 3차원 공간적인 조건 및 검색을 가능하게 함으로써 언제 어디서 든 누구나 쉽게 문화재에 대한 정보를 검색할 수 있도록 Web3D를 활용한 3차원 문화재 정보 시스템을 구현하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.10-16
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• 2007

In this paper, we propose a technique for retrieving images using spatial color correlation and texture characteristics based on local fourier transform. In order to retrieve images, two new descriptors are proposed. One is a color descriptor which represents spatial color correlation. The other is a descriptor combining the proposed color descriptor with texture descriptor. Since most of existing color descriptors including color correlogram which represent spatial color correlation considered just color distribution between neighborhood pixels, the structural information of neighborhood pixels is not considered. Therefore, a novel color descriptor which simultaneously represents spatial color distribution and structural information is proposed. The proposed color descriptor represents color distribution of Min-Max color pairs calculating color distance between center pixel and neighborhood pixels in a block with 3x3 size. Also, the structural information which indicates directional difference between minimum color and maximum color is simultaneously considered. Then new color descriptor(min-max color correlation descriptor, MMCCD) containing mean and variance values of each directional difference is generated. While the proposed color descriptor includes by far smaller feature vector over color correlogram, the proposed color descriptor improves 2.5 % ${\sim}$ 13.21% precision rate, compared with color correlogram. In addition, we propose a another descriptor which combines the proposed color descriptor and texture characteristics based on local fourier transform. The combined method reduces size of feature vector as well as shows improved results over existing methods.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.3
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• pp.260-272
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• 2001

In this paper, we have designed a region-based image retrieval system, FRIP(Finding Region In the Pictures). This system includes a robust image segmentation scheme using color and texture direction and retrieval scheme based on features of each region. For image segmentation, by using a circular filter, we can protect the boundary of round object and merge stripes or spots of objects into body region. It also combines scaled and shifted color coordinate and texture direction. After image segmentation, in order to improve the storage management effectively and reduce the computation time, we extract compact features from each region and store as index. For user interface, by the user specified constraints such as color-care / don't care. scale-care / dont care, shape-care / dont care and location-care / dont care, the overal/ matching score is estimated and the top Ie nearest images are reported in the ascending order of the final score.

• Journal of Korea Multimedia Society
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• v.17 no.3
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• pp.277-286
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• 2014

In this paper, we propose a texture feature-based language identification by fusion of Gabor, MDLC (multi-lag directional local correlation), and co-occurrence features. In the proposed method, for a test image, Gabor magnitude images are first obtained by Gabor transform followed by magnitude operator. Moments for the Gabor magniude images are then computed and vectorized. MDLC images are then obtained by MDLC operator and their moments are computed and vectorized. GLCM (gray-level co-occurrence matrix) is next calculated from the test image and co-occurrence features are computed using the GLCM, and the features are also vectorized. The three vectors of the Gabor, MDLC, and co-occurrence features are fused into a feature vector. In classification, the WPCA (whitened principal component analysis) classifier, which is usually adopted in the face identification, searches the training feature vector most similar to the test feature vector. We evaluate the performance of our method by examining averaged identification rates for a test document image DB obtained by scanning of documents with 15 languages. Experimental results show that the proposed method yields excellent language identification with rather low feature dimension for the test DB.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.4C
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• pp.226-231
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• 2011

To improve the conventional illustrated fish book, this paper introduces the concept of an electronic illustrated fish book which applies IT techniques to the conventional one, and proposes the image retrieval for it. The image retrieval is a core technology of the electronic illustrated fish book and make it overwhelm the conventional one. Since fishes, even if the same kind, have different features in shape, color, and texture and the same fish can even have different features by its pose or environment at that time for taking a picture, the conventional image retrieval, that uses simple features in shape, color, and texture, is not suitable for the electronic illustrated fish book. The proposed image retrieval adopts detail shape features extracted from head, body, and tail of a fish and different weights are given to the features depending on their invariability. The simulation results show that the proposed algorithm is far superior to the conventional algorithm.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.85-95
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• 2005

This study proposes a method of feature point extraction using scale-space filtering and a feature point tracking algorithm based on a texturedness similarity comparison, With well-defined operators one can select a scale parameter for feature point extraction; this affects the selection and localization of the feature points and also the performance of the tracking algorithm. This study suggests a feature extraction method using scale-space filtering, With a change in the camera's point of view or movement of an object in sequential images, the window of a feature point will have an affine transform. Traditionally, it is difficult to measure the similarity between correspondence points, and tracking errors often occur. This study also suggests a tracking algorithm that expands Shi-Tomasi-Kanade's tracking algorithm with texturedness similarity.

• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.187-199
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• 2007

The purpose of painterly rendering is to express real painting work from input image. For expression of real pain ting impression, drawing condition is one of main element. In this paper, we propose new algorithm for using density of edges. Drawing condition of new algorithm uses color difference and density of edges. And for finding next position from current position, we used dynamic grid. We did direction interpolation for coherence direction. Also using various texture brush, we express the feel of a material effective. We show results of images rendered more realistic oil painting effect, and discuss long-term goals for more effective result.

• Journal of the Korean Society of Radiology
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• v.10 no.8
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• pp.645-652
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• 2016

In this study, based on the analysis of texture feature values of statistical properties. And we examined the normal and the applicability of the computer-aided diagnosis of cerebral infarction in the brain computed tomography images. The experiment was analyzed to evaluate the ROC curve recognition rate of disease using six parameters representing the feature values of the texture. As a result, it showed average mean 88%, variance 92%, relative smoothness 94%, uniformity of 88%, a high disease recognition rate of entropy 84%. However, it showed a slightly lower disease recognition rate and 58% for skewness. In the analysis using ROC curve, the area under the curve for each parameter indicates 0.886 (p = 0.0001) or more, resulted in a meaningful recognition of the disease. Further, to determine the cut-off values for each parameter are determined to be the prediction of disease through the computer-aided diagnosis.