• Journal of Information Processing Systems
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• v.8 no.3
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• pp.389-398
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• 2012

Texture is an important visual feature for image analysis. Many approaches have been proposed to model and analyze texture features. Although these approaches significantly contribute to various image-based applications, most of these methods are sensitive to the changes in the scale and orientation of the texture pattern. Because textures vary in scale and orientations frequently, this easily leads to pattern mismatching if the features are compared to each other without considering the scale and/or orientation of textures. This paper suggests an Orientation Matching Scheme (OMS) to ease the problem of mismatching rotated patterns. In OMS, a pair of texture features will be compared to each other at various orientations to identify the best matched direction for comparison. A database including rotated texture images was generated for experiments. A synthetic retrieving experiment was conducted on the generated database to examine the performance of the proposed scheme. We also applied OMS to the similarity computation in a K-means clustering algorithm. The purpose of using K-means is to examine the scheme exhaustively in unpromising conditions, where initialized seeds are randomly selected and algorithms work heuristically. Results from both types of experiments show that the proposed OMS can help improve the performance when dealing with rotated patterns.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.913-921
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• 1993

Texture mapping is mostly used as an image synthesis method in the model-based coding system. An image synthesis using this method uses only the texture information of a front face-view. Therefore, when the model is rotated, texture mapping may produce an awkward image in point of shading. In this paper. a new texture mapping method considering shading effect is studied, and also the ear's wireframe and changes of hair are suplemented for the relation. The experimental results show that the proposed method yields the synthesized images with reasonably natural quality.

• 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.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.332-338
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• 2003

Content-based image retrieval is the research of images from database, that are visually similar to given image examples. Gabor functions and Gabor filters are regarded as excellent methods for feature extraction and texture segmentation. However, they have a disadvantage not to perform well in case of a rotated image because of its direction-oriented filter. This paper proposes a method of extracting local texture features from blocks with central interest points detected in an image and a rotation invariant Gabor wavelet filter. We also propose a method of comparing pattern histograms of features classified by VQ (Vector Quantization) among images.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.7
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• pp.323-326
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• 2002

As multimedia database and digital image libraries are enlarged, CBIR(Content Based Image Retrieval) has been getting importance for the efficient search. Generally, CBIR uses primitive features such as color, shape, texture and so on. Among various methods of CBIR, Gabor wavelet has good image retrieval performance with texture features but it has a disadvantage which does not perform well for a rotated image because of its direction oriented filter. In this paper, we propose a new method to solve this problem by modifying Gabor filter for all directions. And then we will compare the searching performance of the proposed method with those of conventional image retrieval methods through experiments with trademarks.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.575-576
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• 2023

본 논문에서는 텍스처 합성할 때 방향을 고려하여 합성의 품질의 개선시킬 수 있는 방법을 제안한다. 또한 고정된 회전 각도가 아닌, 다양한 각도를 자동으로 샘플링하여 효율적으로 예제 이미지를 생성할 수 있도록 하였고, 이를 통해 합성 경계간의 차이를 자연스럽게 완화시킬 수 있는 결과를 실험을 통해 보여준다.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.349-364
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• 2013

The big challenge in current content-based image retrieval systems is to reduce the semantic gap between the low level-features and high-level concepts. In this paper, we have proposed a novel framework for efficient image retrieval to improve the retrieval results significantly as a means to addressing this problem. In our proposed method, we first extracted a strong set of image features by using the dual-tree rotated complex wavelet filters (DT-RCWF) and dual tree-complex wavelet transform (DT-CWT) jointly, which obtains features in 12 different directions. Second, we presented a relevance feedback (RF) framework for efficient image retrieval by employing a support vector machine (SVM), which learns the semantic relationship among images using the knowledge, based on the user interaction. Extensive experiments show that there is a significant improvement in retrieval performance with the proposed method using SVMRF compared with the retrieval performance without RF. The proposed method improves retrieval performance from 78.5% to 92.29% on the texture database in terms of retrieval accuracy and from 57.20% to 94.2% on the Corel image database, in terms of precision in a much lower number of iterations.

• Proceedings of the Korean Society of Computer Information Conference
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• 2024.01a
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• pp.415-418
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• 2024

본 논문에서는 텍스처 합성을 할 때 CNN을 사용하여 효율성을 높이고 방향을 고려하여 동적인 결과로 품질을 개선시킬 수 있는 방법을 제안한다. 자유로운 회전 각도로 방향성을 고려하여 동적인 결과물을 생성할 수 있도록 하였으며, 기존 접근법인 사각형 형태의 마스크 블록이 아닌 다양한 회전 각도를 고려하여 학습을 했기 때문에 텍스처 합성 과정에서 방향성 특징을 좀 더 잘 표현할 수 있다.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1057-1068
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• 2022

This paper compares the combination performance of feature point-based matching algorithms as a study to confirm the matching possibility between image taken by a user and a virtual texture image with the goal of developing mobile-based real-time image positioning technology. The feature based matching algorithm includes process of extracting features, calculating descriptors, matching features from both images, and finally eliminating mismatched features. At this time, for matching algorithm combination, we combined the process of extracting features and the process of calculating descriptors in the same or different matching algorithm respectively. V-World 3D desktop was used for the virtual indoor texture image. Currently, V-World 3D desktop is reinforced with details such as vertical and horizontal protrusions and dents. In addition, levels with real image textures. Using this, we constructed dataset with virtual indoor texture data as a reference image, and real image shooting at the same location as a target image. After constructing dataset, matching success rate and matching processing time were measured, and based on this, matching algorithm combination was determined for matching real image with virtual image. In this study, based on the characteristics of each matching technique, the matching algorithm was combined and applied to the constructed dataset to confirm the applicability, and performance comparison was also performed when the rotation was additionally considered. As a result of study, it was confirmed that the combination of Scale Invariant Feature Transform (SIFT)'s feature and descriptor detection had the highest matching success rate, but matching processing time was longest. And in the case of Features from Accelerated Segment Test (FAST)'s feature detector and Oriented FAST and Rotated BRIEF (ORB)'s descriptor calculation, the matching success rate was similar to that of SIFT-SIFT combination, while matching processing time was short. Furthermore, in case of FAST-ORB, it was confirmed that the matching performance was superior even when 10° rotation was applied to the dataset. Therefore, it was confirmed that the matching algorithm of FAST-ORB combination could be suitable for matching between virtual texture image and real image.

• Journal of KIISE:Databases
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• v.34 no.2
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• pp.99-108
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• 2007

In this paper, we design and implement the Film Defect Inspection System (FDIS) that detects film defects and determines their types which can be used for producing polarized films of TFT-LCD. The proposed system is designed to detect film defects from polarized film images using image segmentation techniques and to determine defect types through the image analysis of detected defects. To determine defect types, we extract features such as shape and texture of defects, and compare those features with corresponding features of referential images stored in a template database. Experimental results using FDIS show that the proposed system detects all defects of test images effectively (Precision 1.0, Recall 1.0) and efficiently (within 0.64 second in average), and achieves the considerably high correctness in determining defect types (Precision 0.96 and Recall 0.95 in average). In addition, our system shows the high robustness for rotated transformation of images, achieving Precision 0.95 and Recall 0.89 in average.