• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1446-1452
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• 2014

Existing LDA uses the transform matrix that maximizes distance between classes. So we have to convert from an image to one-dimensional vector as training vector. However, in 2D-LDA, we can directly use two-dimensional image itself as training matrix, so that the classification performance can be enhanced about 20% comparing LDA, since the training matrix preserves the spatial information of two-dimensional image. However 2D-LDA uses same calculation schema for transformation matrix and therefore both LDA and 2D-LDA has the heteroscedastic problem which means that the class classification cannot obtain beneficial information of spatial distances of class clusters since LDA uses only data correlation-based covariance matrix of the training data without any reference to distances between classes. In this paper, we propose a new method to apply training matrix of 2D-LDA by using WPS-LDA idea that calculates the reciprocal of distance between classes and apply this weight to between class scatter matrix. The experimental result shows that the discriminating power of proposed 2D-LDA with weighted between class scatter has been improved up to 2% than original 2D-LDA. This method has good performance, especially when the distance between two classes is very close and the dimension of projection axis is low.

• Journal of KIISE:Databases
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• v.29 no.2
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• pp.128-137
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• 2002

In this paper, we propose the LPC+-file for efficient indexing of high-dimensional image data. With the proliferation of multimedia data, there Is an increasing need to support the indexing and retrieval of high-dimensional image data. Recently, the LPC-file (5) that based on vector approximation has been developed for indexing high-dimensional data. The LPC-file gives good performance especially when the dataset is uniformly distributed. However, compared with for the uniformly distributed dataset, its performance degrades when the dataset is clustered. We improve the performance of the LPC-file for the strongly clustered image dataset. The basic idea is to adaptively partition the data space to find subspaces with high-density clusters and to assign more bits to them than others to increase the discriminatory power of the approximation of vectors. The total number of bits used to represent vector approximations is rather less than that of the LPC-file since the partitioned cells in the LPC+-file share the bits. An empirical evaluation shows that the LPC+-file results in significant performance improvements for real image data sets which are strongly clustered.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.7C
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• pp.663-670
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• 2009

In this paper, we propose the image encryption method which gradually uses NFSR(Non-linear Feedback Shift Register) and 20 CAT(Two-Dimensional Cellular Automata Transform). The encryption method is processed in the following order. First, NFSR is used to create a PN(pseudo noise) sequence, which matches the size of the original image. Then, the created sequence goes through a XOR operation with the original image and process the encipherment. Next, the gateway value is set to produce a 20 CAT basis function. The produced basis function is multiplied by encryption image that has been converted to process the 20 CAT encipherment. Lastly, the results of the experiment which are key space analysis, entropy analysis, and sensitivity analysis verify that the proposed method is efficient and very secure.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.6B
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• pp.663-673
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• 2011

Applications of two dimensional image codes like QR and color code on mobile phone are spreading widely and in lieu of that, better decoding technology and platform designs are required. Futhermore "Bokode" technology which recognizes the defocus-blur image information from imperceptible visual tiny code on ordinary digital camera has been announced newly. There is an urgent need for competitive strategies to activate 2D image code industry and R&D under smart environment. This paper focused on suggesting valuable idea and practical task through investigation and analysis required for supporting of thus strategy construction. Description consist of introduction, technological tendency for R&D, patent and market, implementation task, conclusion and suggestion.

• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.3
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• pp.37-49
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• 2012

The purpose of this study is to provide basic data of dress design by researching three-dimensional decorative techniques representing flowers, a popular motif in dress design. This study analyzed 44 dresses that were presented in Valentino collection. The result of this study is as follows: First, the detailing expression method can show various patterns and colors through embroidery technique, sub-materials, and bonding techniques in embroidery, beads flower, and ribbon flowers. Second, the fabric flower technique expressed flower image in three-dimension in more various images than do either the ribbon or beads flower technique. Fabric flowers are mostly made from the same material as dresses. The expression technique of fabric flower is classified into the following three classifications: 1) A technique underlying a flower image with one, two and three large flowers, 2) a technique decorating a large area with a number of an identical motif, and 3) a technique highlighting a part of the body or design line by attracting eyes to one point, creating a corsage decorated where a designer wants to emphasize. Third, a silhouette technique realizes a flower image with a silhouette of a dress like sculpted structure and it is thus used less than a detailing expression technique. A flower image is expressed by making use of body lines or emphasizing the face. Finally, flower images on a dress were realized as a full blossomed flower, through visually streamlined shapes and curved lines.

• Archives of Plastic Surgery
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• v.34 no.5
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• pp.605-610
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• 2007

Purpose: Facial trauma is increasing along with increasing popularity in sports, and increasing exposure to crimes or traffic accidents. Compared to the 3D CT of 1990s, the latest CT has made significant improvement thus resulting in higher accuracy of diagnosis. The objective of this study is to compare 64 channel 3 dimensional volume CT(3D VCT) with conventional 3D CT in the diagnosis and treatment of facial bone fractures. Methods: 45 patients with facial trauma were examined by 3D VCT from Jan. 2006 to Feb. 2007. 64 channel 3D VCT which consists of 64 detectors produce axial images of 0.625 mm slice and it scans 175 mm per second. These images are transformed into 3 dimensional image using software Rapidia 2.8. The axial image is reconstructed into 3 dimensional image by volume rendering method. The image is also reconstructed into coronal or sagittal image by multiplanar reformatting method. Results: Contrasting to the previous 3D CT which formulates 3D images by taking axial images of 1-2 mm, 64 channel 3D VCT takes 0.625 mm thin axial images to obtain full images without definite step ladder appearance. 64 channel 3D VCT is effective in diagnosis of thin linear bone fracture, depth and degree of fracture deviation. Conclusion: In its expense and speed, 3D VCT is superior to conventional 3D CT. Owing to its ability to reconstruct full images regardless of the direction using 2 times higher resolution power and 4 times higher speed of the previous 3D CT, 3D VCT allows for accurate evaluation of the exact site and deviation of fine fractures.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.288-293
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• 2002

It is possible to classify image data into two types according to the internal representation: one is bitmap, the other is vector. A bitmap image is represented by the two dimensional pixels whereas a vector image is represented by mathematical functions to draw vector objects such as line, rectangle and circle on the two or three dimensional space. So it is necessary for users to use a individual application program for each different image. In this paper, we present a method for design and implementation of image editing tool based on combining of bitmap and vector image.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.99-107
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• 2010

In this paper a blind watermarking algorithm for digital image is presented. The proposed method operates in wavelet domain. The watermark is decomposed into 2D-IMFs using BEMD which is the 2-dimensional extension of 1 dimensional empirical mode decomposition. The CDMA based on SS technique is applied to watermark embedding and detection process. In the watermark embedding process, each IMF of watermark is embedded into middle frequency subimages in wavelet domain, so subimages just include partial information about embedded watermark. By characteristics of BEMD, when the partial information of watermark is synthesized, the original watermark is reconstructed. The experimental results show that the proposed watermarking algorithm is imperceptible and moreover is robust against JPEG compression, common image processing distortions.

• Journal of the Optical Society of Korea
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• v.17 no.2
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• pp.148-161
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• 2013

We propose one- and two-dimensional reference functions for processing of integral/multiview imaging. The functions provide the synthesis/analysis of the integral image by distance, as an alternative to the composition/decomposition by view images (directions). The synthesized image was observed experimentally. In analysis confirmed by simulation in a qualitative sense, the distance was obtained by convolution of the integral image with the reference functions.

• Journal of applied mathematics & informatics
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• v.9 no.2
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• pp.793-806
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps (SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.