• The Journal of Natural Sciences
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• v.5 no.2
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• pp.33-38
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• 1992

A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented for multime-dia teleconference. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes; perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. We compare the coding efficiency of this technique with that of a well established technique (discrete cosine transform (DCT) image coding).

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.9
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• pp.76-90
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• 1999

In this paper, a color image retrieval algorithm is proposed based on color histogram and color texture. The representative color vectors of a color image are made from k-means clustering of its color histogram, and color texture is generated by centering around the color of pixels with its color vector. Thus the color texture means texture properties emphasized by its color histogram, and it is analyzed by Gaussian Markov Random Field (GMRF) model. The proposed algorithm can work efficiently because it does not require any low level image processing such as segmentation or edge detection, so it outperforms the traditional algorithms which use color histogram only or texture properties come from image intensity.

• Journal of the Korean Society of Clothing and Textiles
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• v.32 no.11
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• pp.1771-1781
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• 2008

The purpose of this study was to classify the casual image by clothing design elements. This research was done by survey method with 30 kinds of casual image photos selected in fashion magazines. The data was analyzed by Reliability Analysis, Factor Analysis, ANOVA, Duncan's test and MDS. The results of this study are as follows: 1. Casual image was classified by 6 factors. Those were classic-casual, modern-casual, romantic-casual, vintage-casual, sexy-casual and active-casual images. 2. Classic-casual image was well-expressed by A silhouette, fit, chromatic and chromatic color coordinations and hard texture. Modern-casual image was well-expressed by H silhouette, fit and achromatic and achromatic color coordinations. Romantic-casual image was well-expressed by A silhouette, fit and soft texture. Vintage-casual image was well-expressed by H silhouette, combination apparel-fit, chromatic and chromatic color coordinations and fade-out texture. Sexy-casual image was well-expressed by fitted silhouette, tight apparel-fit and combination texture. 3. Casual image was positioned into mostly dynamic and modern on image scale.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.4
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• pp.141-147
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• 1996

In this paper an improved morphological algorithm for directional defect detection is proposed, where the defect is parallel to the texture image. The algorithm is based on obtaining the background image while removing the defect by comparing every directional morphological result with max or min except that of defect. The defect can of defect and the background image. For a computer simulation, it is shown that the proposed method has better performance than the conventional algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.43-54
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• 2005

This paper proposes a novel texture segmentation method using Bayesian image segmentation method and SOM(Self Organization feature Map). Multi-scale wavelet coefficients are used as the input of SOM, and likelihood and a posterior probability for observations are obtained from trained SOMs. Texture segmentation is performed by a posterior probability from trained SOMs and MAP(Maximum A Posterior) classification. And the result of texture segmentation is improved by context information. This proposed segmentation method shows better performance than segmentation method by HMT(Hidden Markov Tree) model. The texture segmentation results by SOM and multi-sclae Bayesian image segmentation technique called HMTseg also show better performance than by HMT and HMTseg.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.3
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• pp.59-65
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• 2004

This paper explores an image segmentation and representation method using Vector Quantization(VQ) on color and texture for content-based image retrieval system. The basic idea is a transformation from the raw pixel data to a small set of image regions which are coherent in color and texture space. These schemes are used for object-based image retrieval. Features for image retrieval are three color features from HSV color model and five texture features from Gray-level co-occurrence matrices. Once the feature extraction scheme is performed in the image, 8-dimensional feature vectors represent each pixel in the image. VQ algorithm is used to cluster each pixel data into groups. A representative feature table based on the dominant groups is obtained and used to retrieve similar images according to object within the image. The proposed method can retrieve similar images even in the case that the objects are translated, scaled, and rotated.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.6
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• pp.692-704
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• 2002

In this paper, we present am image retrieval method based on color-spatial features using quadtree and texture information extracted from object MBRs in an image. Tile proposed method consists of creating a DC image from an original image, changing a color coordinate system, and decomposing regions using quadtree. As such, conditions are present to decompose the DC image, then the system extracts representative colors from each region. And, image segmentation is used to search for object MBRs, including object themselves, object included in the background, or certain background region, then the wavelet coefficients are calculated to provide texture information. Experiments were conducted using the proposed similarity method based on color-spatial and texture features. Our method was able to refute the amount of feature vector storage by about 53%, but was similar to the original image as regards precision and recall. Furthermore, to make up for the deficiency in using only color-spatial features, texture information was added and the results showed images that included objects from the query images.

• Korean Journal of Remote Sensing
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• v.21 no.2
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• pp.121-133
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• 2005

Texture imaging, which means texture image creation by co-occurrence relation, has been known as one of the useful image analysis methodologies. For this purpose, most commercial remote sensing software provides texture analysis function named GLCM (Grey Level Co-occurrence Matrix). In this study, texture-imaging program based on GLCM algorithm is newly implemented. As well, texture imaging modules for GLDV (Grey Level Difference Vector) are contained in this program. As for GLCM/GLDV Texture imaging parameters, it composed of six types of second order texture functions such as Homogeneity, Dissimilarity, Energy, Entropy, Angular Second Moment, and Contrast. As for co-occurrence directionality in GLCM/GLDV, two direction modes such as Omni-mode and Circular mode newly implemented in this program are provided with basic eight-direction mode. Omni-mode is to compute all direction to avoid directionality complexity in the practical level, and circular direction is to compute texture parameters by circular direction surrounding a target pixel in a kernel. At the second phase of this study, some case studies with artificial image and actual satellite imagery are carried out to analyze texture images in different parameters and modes by correlation matrix analysis. It is concluded that selection of texture parameters and modes is the critical issues in an application based on texture image fusion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.763-781
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• 2011

We present a texture-based hatching technique for color images and video. Whereas existing approaches produce monochrome hatching effects in considering of triangular mesh models by applying strokes of uniform size, our scheme produces color hatching effects from photographs and video using strokes with a range of sizes. We use a Delaunay triangulation to create a mesh of triangles with sizes that reflect the structure of an input image. At each vertex of this triangulation, the flow of the image is analyzed and a hatching texture is then created with the same alignment, based on real pencil strokes. This texture is given a modified version of a color sampled from the image, and then it is used to fill all the triangles adjoining the vertex. The three hatching textures that accumulate in each triangle are averaged and the result of this process across all the triangles forms the output image. We can also add a paper texture effect and enhance feature lines in the image. Our algorithm can also be applied to video. The results are visually pleasing hatching effects similar to those seen in color pencil drawings and oil paintings.

• Journal of IKEEE
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• v.24 no.1
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• pp.254-259
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• 2020

This paper is the implementation of the conversion system from image to music based on intentional synesthesia. The input image based on color, texture, and shape was converted into melodies, harmonies and rhythms of music, respectively. Depending on the histogram of colors, the melody can be selected and obtained probabilistically to form the melody. The texture in the image expressed harmony and minor key with 7 characteristics of GLCM, a statistical texture feature extraction method. Finally, the shape of the image was extracted from the edge image, and using Hough Transform, a frequency component analysis, the line components were detected to produce music by selecting the rhythm according to the distribution of angles.