• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.709-716
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• 2000

We present an efficient algorithm using region-based average of variations of local correlation coefficients (LCC) for the extraction of texture regions. The key idea of this algorithm for the classification of texture and shade regions is to utilize the fact that the averages of the variations of LCCs according to different orientations texture regions are clearly larger than those in shade regions. In order to evaluate the performance of the proposed algorithm, we use nine test images (Lena, Bsail, Camera Man, Face, Woman, Elaine, Jet, Tree, and Tank) of 8-bit 256$\times$256 pixels. Experimental results show that the proposed feature extracts well the regions which appear visually as texture regions.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.7
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• pp.53-61
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• 1998

In this paper, we propose an efficient multi-view images coding algorithm to find the optimal depth and texture from the set of multi-view images. The proposed algorithm consists of two consecutive steps, i) the depth estraction step, and ii) the texture extraction step, comparedwith the traditional algorithem which finds the depth and texture concurrently. The X-Y plane of the normalized object space is divided into traingular paatches and the Z value of the node is determined in the first step and then the texture of the each patch is extracted in the second step. In the depth extraction step, the depth of the node is determined by applying the block based disparity compensation method to the windowed area centered at the node. In the second step, the texture of the traingular patches is extracted from the multi-view images by applying the affine transformation based disparity compensation method to the traingular pateches with the depth extracted from the first step. Experimental results show that the SNR(Singnal-to- Noise Ratio) of images enconded by our algorithm is better than that of images encoded by the traditional algorithm by the amount about 4dB for for the test sets of multi-view images called dragon, kid, city and santa.

• Journal of the HCI Society of Korea
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• v.1 no.2
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• pp.27-33
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• 2006

This paper presents a new approach to translation and rotation invariant feature extraction for image texture retrieval. For the rotation invariant feature extraction, we invent angular projection along angular frequency in Polar coordinate system. The translation and rotation invariant feature vector for representing texture images is constructed by the averaged magnitude and the standard deviations of the magnitude of the Fourier transform spectrum obtained by the proposed angular projection. In order to easily implement the angular projection, the Radon transform is employed to obtain the Fourier transform spectrum of images in the Polar coordinate system. Then, angular projection is applied to extract the feature vector. We present our experimental results to show the robustness against the image rotation and the discriminatory capability for different texture images using MPEG-7 data set. Our Experiment result shows that the proposed rotation and translation invariant feature vector is effective in retrieval performance for the texture images with homogeneity, isotropy and local directionality.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.724-732
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• 2001

In this paper, a new method of feature extraction of major objects to represent an image using Vector Quantization(VQ) is proposed. The principal features of the image, which are used in a content-based image retrieval system, are color, texture, shape and spatial positions of objects. The representative color and texture features are extracted from the given image using VQ(Vector Quantization) clustering algorithm with a general feature extraction method of color and texture. Since these are used for content-based image retrieval and searched by objects, it is possible to search and retrieve some desirable images regardless of the position, rotation and size of objects. The experimental results show that the representative feature extraction time is much reduced by using VQ, and the highest retrieval rate is given as the weighted values of color and texture are set to 0.5 and 0.5, respectively, and the proposed method provides up to 90% precision and recall rate for 'person'query images.

• 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.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.613-616
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• 2004

To find the best way to distinguish sand dunes from urban building and rural patches, textural analysis has been performed in Kimnyeong sand dune, Jeju. An aerial photo was re-sampled into one-meter. Homomorphic filters were applied to the original sub-scene and then high-pass filtered one. The entropy filtered one proves to be the best extraction method after high pass filtered-homomorphic filters in urban areas. The spectral values of sand dune area were similar to open land in rural area. In contrast, the texture values of sand dune area are more homogeneous than those of open land in rural area.

• The Korean Journal of Food And Nutrition
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• v.33 no.2
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• pp.131-141
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• 2020

This purpose of this study was to investigate the quality characteristics of jellies added with lemon myrtle extract. Lemon myrtle leaves were extracted for 0, 3, 5, 7, 9 minutes, respectively, in 90℃ water and used for jelly preparation. The moisture content of control showed the lowest value and the content increased significantly as the extraction time of lemon myrtle increased. The pH of L0 was significantly high and increased significantly with the increase of extraction duration time. The lightness value was the lowest in the L3. The redness showed the lowest value in the L9. The yellowness showed the lowest value in the L0. In texture properties the hardness of L9 showed the highest value and the lemon myrtle extraction duration increased significantly. The cohesiveness was highest in the L0 and lowest in the L5. Gumminess and chewiness increased significantly with increasing extraction duration. Total polyphenol content was the highest in the L5 and the jellies with lemon myrtle extracts were significantly higher than the L0. DPPH radical scavenging activities increased significantly with increasing extraction duration. The ABTS radical scavenging activity of the L0 was the lowest. In the sensory evaluation overall preference, color, sweetness, texture, and lemon myrtle flavor did not show any significant differences among the samples.

• Journal of IKEEE
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• v.14 no.4
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• pp.332-338
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• 2010

Usually, mage feature extraction methods are performed as a pre-processing step in many applications including image retrieval, object recognition, and image indexing. Especially, in the image texture analysis, texture feature extraction methods attempt to increase texture contrast to make it easier to extract the texture features from the image. One of the distinct textures in microscopic skin image is the wrinkle, and its features could provide various useful information for the age-related applications. In this paper, we propose a scheme to extract age-related features from the skin images and improve its accuracy in the skin age estimation.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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• pp.125-133
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• 2004

In this paper, we present a new method that combines the characteristics of edge in-formation and second-order neural networks for the classification of structural textures. The edges of a texture are extracted using an edge detection approach. From this edge information, classification features called second-order features are obtained. These features are fed into a second-order neural network for training and subsequent classification. It will be shown that the main disadvantage of using structural methods in texture classifications, namely, the difficulty of the extraction of texels, is overcome by the proposed method.

• Journal of Digital Contents Society
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• v.18 no.6
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• pp.1041-1047
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• 2017

There are many methods for surveying the status of a road, and the use of unmanned aerial vehicle (UAV) photo is one such method. When the UAV images are too large to be processed and suspected to be redundant, a texture extraction technique is used to transform the data into a reduced set of feature representations. This is an important task in 3D simulation using UAV images because a huge amount of data can be inputted. This paper presents a texture extraction method from UAV images to obtain high-resolution images of bridges. The proposed method is in three steps: firstly, we use the 3D bridge model from the V-World database; secondly, textures are extracted from oriented UAV images; and finally, the extracted textures from each image are blended. The result of our study can be used to update V-World textures to a high-resolution image.