• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.1 s.108
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• pp.33-48
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• 2005

The purpose of this study was to discover the landscape visual image of the slope scene and their harmony with surrounding sceneries. This research utilized the basic study tool of psycho-physics and processed the case study of ten types of slope construction scene along the highway. The analysis was performed by the data obtained from the questionnaires and the photos for the slope construction scene. The questionnaires for analysis the image of the slope construction scene and their harmony with surrounding sceneries were designed using semantic differential scale and 5 point Likert-scale. The major findings were as follows. 1. At the part of the visual preferences analysis, the slope revegetation methods showed high level of preferences generally than on the slope structure methods. While the slope revegetation methods were estimated friendly, continuity, harmonious, soft, light and wide, the slope revegetation methods were estimated unstable, female, static, simple, omnipresent, appeared as policeman of weak inclination. Also the slope structure methods were estimated stable, manly, complicated, steep and healthy but rough, unharmonious, unfamiliar and heavy. 2. Psychological factors, related to the satisfaction for the slope revegetation methods were composed of three factors, aesthetic, individuality and physical character. And the slope structure methods were composed of five factors, aesthetic, individuality, stability, physical character, and complexity. 3. At the part of harmony with surrounding landscapes, the slope revegetation methods were evaluated highly but the slope structure methods received the lowest evaluation. Also the harmony analysis with surrounding view on the slope revegetation methods showed degree of high more than average in all texture, form, color and scale but the slope structure methods showed degree of fewer than average degree in form, scale, color and texture.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.6
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• pp.715-724
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• 2003

The Korean clothing culture has shown rapid changes along with influences of western cultures, with social changes due to the liberation from Japan, the Korean War, and the economic growth concentrated on export since the westernization of Korea had begun. Also clothing materials, as a phase of such culture, have been changed. Among silk fabrics, "Dan (단: damask of satin texture with Korean traditional patterns are inwoven)", which has been concerned one of the most beautiful and precious fabrics due to its complexity and difficulty of processing. Since the beginning of the Era of Enlightenment(Kae-hwa-gi), Dan fabric materials and weaving methods have changed. It seems that the figured texture using the Jacquard loom began in 1936. when the Jacquard machine began to be imported from Japan into Korea. From that time on, new fabric materials, such as yangdan (양단), and popdan (법단), hobakdan (호박단), silk and rayon mixed Dan(교직양단) began to be produced. Before 1950's there were some restrictions about the Number of wefts, so the production of the one colored dan(단색단) was more common than that of the multi colored dan(다색단). But with the spread of 4$\times$4 shuttle box loom (양사정직기), various kinds of the multi colored dan have been produced after 1960's. Around the end of 1980's, automatic shuttle change loom have been generalized and 7 color dan(칠색단), 9 color gumsadan (구색금사단) have been current in multi colored dan. In terms of materials, synthetic and chemical textiles had been used widely and alter 1980's most fabrics, of which the ground weave is not being woven with satin-weave but being woven with plain or twill-weave, are named Dan in general.

• Journal of Fashion Business
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• v.7 no.5
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• pp.108-118
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• 2003

Surface properties, including the texture and the luster, of cotton fibers and yarns thereof play an important role in textile technology. The convolutions and the cross-sectional shape of the cotton fiber affect the fabric texture and the luster accordingly. Mercerization of the cotton fabric affects the luster, strength, and other properties of the fabric. In this study, the effect of mercerization was examined on the luster of the cotton fabric, together with the effect of polishing treatment. One of the traditional methods determining the fabric luster is the use of glossmeter or goniometric glossmeter. The use of glossmeter gives successful results in determining the gloss of rather flat and continuous surface such as plastic sheet, painted surface, or paper products. Since the textile fabrics have diverse surface structures and textures, these could be regarded as having three-dimensional surface. Such complexity imposes some difficulties for differentiating subtle surface luster properties of diverse textile fabrics. The advancement in the area of imaging technologies has enabled the micro-scale analysis of the surface textures and the fabric luster recently. Using a CCD camera, the surface luster images were taken at various incident illumination conditions. Microscale analysis, including the blob analysis, of the images could differentiate the subtle luster properties present in a group of cotton fabric samples comprising mercerized cotton fabric, non-mercerized cotton fabric, polished cotton fabric, and a 'standard' cotton fabric. The glossmeter measurement gave satisfactory but limited differentiation among the samples, whose luster differences are easily recognizable with visual observation, except for the mercerized cotton fabric sample and the non-mercerized cotton fabric. The microscale analysis of the fabric luster could, therefore, help understand the nature of diverse textile fabric luster.

• Journal of Broadcast Engineering
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• v.7 no.2
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• pp.145-152
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• 2002

As the number of digital images available to users is exponentially growing due to the rapid development of digital technology, content-based image retrieval (CBIR) has been one of the most active research areas. A variety of image retrieval methods have been proposed, where, given an input query image, the images that are similar to the input are retrieved from an image database based on low-level features such as colors and textures. However, most of the existing retrieval methods did not consider the case when an input query image is a part of a whole image in the database due to the high complexity involved in partial matching. In this paper, we present an efficient method for partial image matching by using the histogram distribution relationships between query image and whole image. The proposed approach consists of two steps: the first step prunes the search space and the second step performs block-based retrieval using partial image matching to rank images in candidate set. The experimental results demonstrate the feasibility of the proposed algorithm after assuming that the response tune of the system is very high while retrieving only by using partial image matching without Pruning the search space.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.765-770
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• 2012

In this paper, we proposed an effective hardware structure using DCT-based inra-prediction mode selection to reduce computational complexity caused by intra mode decision. In this hardware structure, the input block is transformed at first and then analyzed to determine its texture directional tendency. the complexity has solved by performing intra prediction in only predicted edge direction. $4{\times}4$ DCT is calculated in one cycle using Multitransform_PE and Inta_pred_PE calculates one prediction mode in two cycles. Experimental results show that the proposed Intra prediction encoding needs only 517 cycles for one macroblock encoding. This architecture improves the performance by about 17% than previous designs. For hardware implementation, the proposed intra prediction encoder is implemented using Verilog HDL and synthesized with Megnachip $0.18{\mu}m$ standard cell library. The synthesis results show that the proposed architecture can run at 125MHz.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.1 s.307
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• pp.55-66
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• 2006

We propose a progressive image retrieval method based on an efficient combination of multiresolution color and torture features in wavelet transform domain. As a color feature, color autocorrelogram of the hue and saturation components is chosen. As texture features, BDIP and BVLC moments of the value component are chosen. For the selected features, we obtain multiresolution feature vectors which are extracted from all decomposition levels in wavelet domain. The multiresolution feature vectors of the color and texture features are efficiently combined by the normalization depending on their dimensions and standard deviation vector, respectively, vector components of the features are efficiently quantized in consideration of their storage space, and computational complexity in similarity computation is reduced by using progressive retrieval strategy. Experimental results show that the proposed method yields average $15\%$ better performance in precision vs. recall and average 0.2 in ANMRR than the methods using color histogram color autocorrelogram SCD, CSD, wavelet moments, EHD, BDIP and BVLC moments, and combination of color histogram and wavelet moments, respectively. Specially, the proposed method shows an excellent performance over the other methods in image DBs contained images of various resolutions.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.150-161
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• 2013

This paper presents a new system which produces high resolution 3D contents by capturing multiview images of an object using multiple cameras, and estimating geometric and texture information of the object from the captured images. Even though a variety of multiview image-based 3D reconstruction systems have been proposed, it was difficult to generate high resolution 3D contents because multiview image-based 3D reconstruction requires a large amount of memory and computation. In order to reduce computational complexity and memory size for 3D reconstruction, the proposed system predetermines the regions in input images where an object can exist to extract object boundaries fast. And for fast computation of a visual hull, the system represents silhouettes and 3D-2D projection/back-projection relations by chain codes and 1D homographies, respectively. The geometric data of the reconstructed object is compactly represented by a 3D segment-based data format which is called DoCube, and the 3D object is finally reconstructed after 3D mesh generation and texture mapping are performed. Experimental results show that the proposed system produces 3D object contents of $800{\times}800{\times}800$ resolution with a rate of 2.2 seconds per frame.

• Journal of Information Processing Systems
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• v.9 no.1
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• pp.173-188
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• 2013

In Facial Expression Recognition Systems (FERS), only particular regions of the face are utilized for discrimination. The areas of the eyes, eyebrows, nose, and mouth are the most important features in any FERS. Applying facial features descriptors such as the local binary pattern (LBP) on such areas results in an effective and efficient FERS. In this paper, we propose an automatic facial expression recognition system. Unlike other systems, it detects and extracts the informative and discriminant regions of the face (i.e., eyes, nose, and mouth areas) using Haar-feature based cascade classifiers and these region-based features are stored into separate image files as a preprocessing step. Then, LBP is applied to these image files for facial texture representation and a feature-vector per subject is obtained by concatenating the resulting LBP histograms of the decomposed region-based features. The one-vs.-rest SVM, which is a popular multi-classification method, is employed with the Radial Basis Function (RBF) for facial expression classification. Experimental results show that this approach yields good performance for both frontal and near-frontal facial images in terms of accuracy and time complexity. Cohn-Kanade and JAFFE, which are benchmark facial expression datasets, are used to evaluate this approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3268-3283
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• 2018

With the advent of the Information Age, the source identification of digital images, as a part of digital image forensics, has attracted increasing attention. Therefore, an effective technique to identify the source of digital images is urgently needed at this stage. In this paper, first, we study and implement some previous work on image source identification based on sensor pattern noise, such as the Lukas method, principal component analysis method and the random subspace method. Second, to extract a purer sensor pattern noise, we propose a sample selection method to improve the random subspace method. By analyzing the image texture feature, we select a patch with less complexity to extract more reliable sensor pattern noise, which improves the accuracy of identification. Finally, experiment results reveal that the proposed sample selection method can extract a purer sensor pattern noise, which further improves the accuracy of image source identification. At the same time, this approach is less complicated than the deep learning models and is close to the most advanced performance.

• Journal of Broadcast Engineering
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• v.5 no.1
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• pp.31-40
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• 2000

In this paper, we propose an edge histogram to efficiently represent the edge distribution in the image for MPEG-7. To this end, we adopt global, semi-global, and local edge histogram bins. Also, we extract the edge information from the image in terms of image blocks rather than pixels, which reduces the extraction complexity and is also applicable to the block-based compression standards such as MPEG-1, and 2. Experimental results show that the proposed method yields better retrieval accuracy and feature extraction speed comparing to other non-homogeneous texture descriptors of MPEG-7 including the wavelet-based descriptor and local edge-based descriptor.