• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.145-150
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• 2003

고해상도 위성영상을 이용한 산림의 분석은 기존의 중ㆍ저해상도 영상의 분석과 다른 접근이 필요하다. 본 연구는 임분 특성을 해석하는데 중요한 판독기준인 texture를 이용하여 영상 안에서 임상, 임목직경급, 수관울폐도 등에 따른 Texture 정보를 비교 분석하고자 한다. 울산 일부 산림지역을 대상으로 3개의 가시광선 밴드와 1개의 근적외선 밴드의 1m IKONOS 영상을 이용하여 Texture 정보를 추출하는데 일반적으로 사용되는 통계적인 방법 중에 하나인 GLCM(Gray-Level Co-occurrence matrix)을 통해 Texture 분석을 하였다. 또한 1996년도에 제작된 4차 임상도를 통해 추출된 산림 특성별 Texture 정보를 비교 검토하여 고해상도 위성영상을 활용하여 산림 특성을 해석하는데 최적의 Texture 정보를 제시하고자 하였다. 고해상도 영상에서 나타나는 임분의 특성별 질감정보는 임상, 직경, 임목밀도에 따라 다양하게 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.10
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• pp.3642-3667
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• 2021

Texture synthesis technology has the advantages of repairing texture and structure at the same time. However, during the filling process, the size of the patch is fixed, and the content of the filling is not fully considered. In order to be able to adaptively change the patch size, we used the exemplar-based inpainting technique as the test algorithm, considering the image structure and texture, calculated the image structure patch size and texture patch size, and comprehensively determined the image patch size. This can adaptively change the patch size according to the filling content. In addition, we use multi-layer images to calculate the priority, so that the order of image repair was more stable. The proposed repair algorithm is compared with other image repair algorithms. The experimental results showed that the proposed adaptive image repair algorithm can better repair the texture and structure of the image, which proved the effectiveness of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.36-45
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• 2006

Three-dimensional (3-D) texture coordinates mean the position information of torture segments that are mapped into polygons in a 3-D mesh model. In order to compress texture coordinates, previous works reused the same linear predictor that had already been employed to code geometry data. However, the previous approaches could not carry out linear prediction efficiently since texture coordinates were discontinuous along a coding order. Especially, discontinuities of texture coordinates became more serious in the 3-D mesh model including a non-atlas texture. In this paper, we propose a new scheme to code 3-D texture coordinates using as a texture image rearrangement. The proposed coding scheme first extracts texture segments from a texture. Then, we rearrange the texture segments consecutively along the coding order, and apply a linear prediction to compress texture coordinates. Since the proposed scheme minimizes discontinuities of texture coordinates, we can improve coding efficiency of texture coordinates. Experiment results show that the proposed scheme outperforms the MPEG-4 3DMC standard in terms of coding efficiency.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1987.04a
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• pp.22-26
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• 1987

Texture provides an important acurce of information about the threedicensfornarry information of visible surface particulary for stationary conccular views. To recover three dicmensinoary information, the distorging effects of pro jection must be distinguished from properties of the texture on which the distrortion acts. In this paper, we show an approximated maximum likelihood estimation method by which we find surface oriemtation of the visible surface in gaussian sphere using local analysis of the texture, In addition assuming that an orthographic projection and a circle is an image formation system and a texel(texture element)respectively we derive the surface orientation from the distribution of variation by means of orthographic pro jemction of a tangent directon which exstis regulary in the are length of a circle we present the orientation parameters of textured surface with saint and tilt and also the surface normal of the resvlted surface orimentation as needle map. This algorithm was applied to geograghic contour and synthetic textures.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.3
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• pp.149-157
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• 2001

In the video retrieval system, visual clues of still images and motion information of video are employed as feature vectors. We generate the temporal textures to express the motion information whose properties are simple expression, easy to compute. We make those temporal textures of wavelet coefficients to express motion information, M components. Then, temporal texture feature vectors are extracted using spatial texture feature vectors, i.e. spatial gray-level dependence. Also, motion amount and motion centroid are computed from temporal textures. Motion trajectories provide the most important information for expressing the motion property. In our modeling system, we can extract the main motion trajectory from the temporal textures.

• ETRI Journal
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• v.23 no.2
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• pp.41-51
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• 2001

MPEG-7 standardization work has started with the aims of providing fundamental tools for describing multimedia contents. MPEG-7 defines the syntax and semantics of descriptors and description schemes so that they may be used as fundamental tools for multimedia content description. In this paper, we introduce a texture based image description and retrieval method, which is adopted as the homogeneous texture descriptor in the visual part of the MPEG-7 final committee draft. The current MPEG-7 homogeneous texture descriptor consists of the mean, the standard deviation value of an image, energy, and energy deviation values of Fourier transform of the image. These are extracted from partitioned frequency channels based on the human visual system (HVS). For reliable extraction of the texture descriptor, Radon transformation is employed. This is suitable for HVS behavior. We also introduce various matching methods; for example, intensity-invariant, rotation-invariant and/or scale-invariant matching. This technique retrieves relevant texture images when the user gives a querying texture image. In order to show the promising performance of the texture descriptor, we take the experimental results with the MPEG-7 test sets. Experimental results show that the MPEG-7 texture descriptor gives an efficient and effective retrieval rate. Furthermore, it gives fast feature extraction time for constructing the texture descriptor.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.4
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• pp.675-683
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• 1987

In general, texture images with regular patterns can be described by using the standard texture model regularity vectors for their shape analysis. Early methods not only take much time but also have computational complexity in obtaining regularity vectors. The proposed some improved preprocessing algorithms for texture analysis. Finally, we showed the utility of the proposed method through texture synthesis by making use of the results of texture analysis.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.327-336
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• 2002

Texture analysis is an important technique in many image understanding areas, such as perception of surface, object, shape and depth. But the previous works are intend to the issue of only texture segment, that is not capable of acquiring recognition information. No unsupervised method is basased on the recognition of texture in image. we propose a novel approach for efficient texture image analysis that uses unsupervised learning schemes for the texture recognition. The self-organization neural network for multiple texture image identification is based on block-based clustering and merging. The texture features used are the angle and magnitude in orientation-field that might be different from the sample textures. In order to show the performance of the proposed system, After we have attempted to build a various texture images. The final segmentation is achieved by using efficient edge detection algorithm applying to block-based dilation. The experimental results show that the performance of the system Is very successful.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.11
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• pp.4043-4064
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• 2021

Night-time image quality evaluation is an urgent requirement in visual inspection. The lighting environment of night-time results in low brightness, low contrast, loss of detailed information, and colour dissonance of image, which remains a daunting task of delicately evaluating the image quality at night. A new blind quality assessment metric is presented for realistic night-time scenario through a comprehensive consideration of contrast, texture, and colour in this article. To be specific, image blocks' color-gray-difference (CGD) histogram that represents contrast features is computed at first. Next, texture features that are measured by the mean subtracted contrast normalized (MSCN)-weighted local binary pattern (LBP) histogram are calculated. Then statistical features in Lαβ colour space are detected. Finally, the quality prediction model is conducted by the support vector regression (SVR) based on extracted contrast, texture, and colour features. Experiments conducted on NNID, CCRIQ, LIVE-CH, and CID2013 databases indicate that the proposed metric is superior to the compared BIQA metrics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.