• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.31-34
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• 2000

In this paper, we proposed multispectral image compression method using CIP (classified inter-channel prediction) and SVQ (selective vector quantization) in wavelet domain. First, multispectral image is wavelet transformed and classified into one of three classes considering reflection characteristics of the subband with the lowest resolution. Then, for a reference channel which has the highest correlation with other channels, the variable VQ is performed in the classified intra-channel to remove spatial redundancy. For other channels, the CIP is performed to remove spectral redundancy. Finally, the prediction error is reduced by performing SVQ. Experiments are carried out on a multispectral image. The results show that the proposed method reduce the bit rate at higher reconstructed image quality and improve the compression efficiency compared to conventional method.

• The Journal of the Acoustical Society of Korea
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• v.16 no.2
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• pp.58-65
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• 1997

A new automatic classification method with high and stable accuracy for time-series image data is presented in this paper. This method is based on prior condition that a classified map of the target area already exists, or at least one of the time-series image data had been classified. The classified map is used as a reference map to specify training areas of classification categories. The new automatic classification method consists of five steps, i.e., extraction of training data using reference map, detection of changed pixels based upon the homogeneity of training data, clustering of changed pixels, reconstruction of training data, and classification as like maximum likelihood classifier. In order to evaluate the performance of this method qualitatively, four time-series Landsat TM image data were classified by using this method and a conventional method which needs a skilled operator. As a results, we could get classified maps with high reliability and fast throughput, without a skilled operator.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.609-612
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• 1999

In this paper, we propose wavelet transform image compression method such that an image is decomposed into multiresolutions using biorthogonal wavelet transform with linear phase response property and decomposed subbands are classified by maximum classification gain. The classified data is quantized by allocating bits in accordance with classified class informations within subbands through arbitrary set bit allocation algorithm. And then, quantized data in each subband are entropy coded. The proposed coding method is that the quantized data perform shuffling before entropy coding in order to remove sign bit plane. And the context is assigned by maximum correlation direction for bit plane coding.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.8
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• pp.1227-1237
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• 1989

In this paper, we propose a lossless progressive image transmission scheme using hierarchical pyramid structure and classified vector quantizer in DCT domain. By adopting DCT to the hierarchical pyramid signals, we can reduce the spatial redundance. Moreover, the DCT coefficients can be encoded efficiently by using classified vector quantizer in DCT domain. The classifier is simply based on the variance of a subblock. Also, the mirror set of training set of images can improve the robustness of codebooks. Progressive image transmission can be achieved through following processes: from top to bottom level of planes in a pyramid, and from high to low AC variance class in a plane. Some simulation results with real images show that the proposed coding scheme yields a good performance at below 0.3 bpp and an excellent result at 0.409 bpp. The proposed coding scheme is well suited for lossless progressive image transmission as well as image data compression.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2108-2111
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• 2003

This paper introduces a new local feature extraction method and image matching method for the localization and classification of targets. Proposed method is based on the block-by-block projection associated with directional pattern of blocks. Each pattern has its own eigen-vertors called as CEBs(Classified Eigen-Blocks). Also proposed block-based image matching method is robust to translation and occlusion. Performance of proposed feature extraction and matching method is verified by the face localization and FLIR-vehicle-image classification test.

• Journal of the Korean Society of Costume
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• v.54 no.7
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• pp.91-106
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• 2004

The purpose of this study was to review the previous researches, analyze the clothing image, the make-up image and compare the analyses of clothing image and make-up image. The previous researches of clothing image and make-up image were reviewed in 6 kinds of Journal. The results of previous research review and analysis were followed as : Measuring mean of image are used to similarly that semantic differential technique and summated rating technique. Attention to proposed researcher abstraction image in make-up image, but there is problem that this hard to explain objectivity of image abstraction. There are a lot of occasions that 4 or 5 image factors were extracted by factor analysis. The make-up image researches that presented image stimulus were more than study that do not present. Image words were classified which were compiled words have similar sub image. Grace, activeness, lively. unique, modernity attractive, feminine. sexy and ripeness clothing images were classified factors. which were representative clothing image. Elegance, Sophisticate. romantic, natural, modern and youthfulness make-up image for factor were representative make-up image factors. However the problems were found that some representative image factor included the sub images which were different from some factor image. Compared with representation image words, same image words were used to not agree what clothing image and make-up image. Standardization of word should be made that show that clothing image and make-up image.

• Korean Journal of Digital Imaging in Medicine
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• v.9 no.2
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• pp.39-43
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• 2007

In angiography, the global standard agreements of DICOM is lossless. But it brings on overload and takes too much store space in DICOM sever. Because of all those things we transmit images which is classified in subjective way. But this cause data loss and would be lead doctors to make wrong reading. As a result of that we try to transmit continued image (raw data) to reduce those mistakes. We got angiography images from the equipment(Allura FD20-Philips). And compressed it in two different methods(lossless & lossy fair). and then transmitted them to PACS system. We compared the quality of QC phantom images that are compressed by different compress method and compared spatial resolution of each images after CD copy. Then compared each Image's data volume(lossless & lossy fair). We measured spatial resolution of each image. All of them had indicated 401p/mm. We measured spatial resolution of each image after CD copy. We got also same conclusion (401p/mm). The volume of continued image (raw data) was 127.8MB(360.5 sheets on average) compressed in lossless and 29.5MB(360.5 sheets) compressed in lossy fair. In case of classified image, it was 47.35MB(133.7 sheets) in lossless and 4.5MB(133.7 sheets) in lossy fair. In case of angiography the diagnosis is based on continued image(raw data). But we transmit classified image. Because transmitting continued image causes some problems in PACS system especially transmission and store field. We transmit classified image compressed in lossless But it is subjective and would be different depend on radiologist. therefore it would make doctors do wrong reading when patients transfer another hospital. So we suggest that transmit continued image(raw data) compressed in lossy fair. It reduces about 60% of data volume compared with classified image. And the image quality is same after CD copy.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.37-41
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• 1995

In this paper, we proposed the 2-stage ultrasound liver image classifier which uses the fractal dimensions obtained from the original image and its 1/2 subsampled image, and the Normalized Fourier Power Spectrum. The fractal dimension based on Fractional Brownian Motion (FBM) is calculated from the variance of the same scale pixels instead of the mean of them. Since the actual ultrasound. liver images does not fully match the FBM, to get the fractal dimension, we use the scale vectors which satisfy the FBM model. In 2-stage classifier, we first classified normal and diffuse liver and then classified the fat liver and cirrhosis from the diffuse liver. For the test liver images. 70% of normal liver and 80% of fat liver and 90% of cirrhosis is classified classified with our 2-stage classifier.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.120-127
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• 2000

In this paper, we propose multispectral image compression using classified interband prediction and vector quantization in wavelet domain. This method classifies each region considering reflection characteristics of each band in image data. In wavelet domain, we perform the classified intraband VQ to remove intraband redundancy for a reference band image that has the lowest spatial variance and the best correlation with other band. And in wavelet domain, we perform the classifled interband prediction to remove interband redundancy for the remaining bands. Then error wavelet coefficients between original image and predicted image are intraband vector quantized to reduce prediction error. Experiments on remotely sensed satellite image show that coding efficiency of theproposed method is better than that of the conventional method.

• Journal of the Korean Society of Clothing and Textiles
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• v.25 no.8
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• pp.1367-1377
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• 2001

Cousumers developed self-image through clothing symbolic product for reveal one’self image. When consumers select particular brand of various brand in the market that congruent with one’self image. They have to continue image. In this purpose, the research model was constituted and the questionnaire was made, reviewing preceding studies on self-image, shopping orientation. As for the method of the research, 635 female consumers were the object for the data of this research. The major results of this were as follows: 1. The factor or real self-image was composed of four factors: the refined and deluxe image, casual and simple image, decorous and dressy image, quiet and feminine image. 2. The consumer with real self-image were classified three groups: the group of feminine and dressy image 288 persons, the group of casual image 167 persons and poor image 171 persons. In the consumer’s classified real purchasing brand-image the group of feminine and dressy image was given higher score at feminine and refined and deluxe image, the group of casual image was given higher score at casual and active. 3. The factor of shopping orientation was composed of four factors: pleasant, planned, loyal, recreational shopping orientation. The group of feminine and dressy image was given higher score at pleasant and planned shopping orientation, the group of casual image was given lower score loyal and confident shopping orientation than the other group.