• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.506-508
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• 1998

본 논문에서는 경치 영상들에 대해 영상의 내용(색상, texture)에 기반하여 검색하는새로운 방법을 제안한다. 경치 영상들은 색상이 주 특징이며 결합되는 texture 특징들에 의해 영상 정보간의 유사성의 파악이 더 용이하다. 색상 특징은 HSV 색상 히스토그램에 의해 특징을 나타내며 영상의 전역적 색상 특징과 지역적 색상 특징으로 세분화되고 texture 특징은 2차원 Garbor filter에 의해 영상별 특징을 나타낸다. 시스템의 검색은 예제 영상에 의한 질의 방식으로 예제 영상을 주면 이와 유사한 영상들이 유사도가 높은 순위대로 출력하게 된다.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.10-18
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• 2012

Automatic smoke detection systems using a surveillance camera requires a reliable smoke detection method. When an image sequence is captured from smoke spreading over in the air, not only has each smoke image frame a special texture, called static texture, but the difference between two smoke image frames also has a peculiar texture, called dynamic texture. Even though an object has a static texture similar to that of the smoke, its dynamic texture cannot be similar to that of the smoke if its movement differs from the diffraction action of the smoke. This paper presents a reliable smoke detection method using these two textures. The proposed method first detects change regions using accumulated frame difference, and then picks out smoke regions using Haralick features extracted from two textures.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.7
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• pp.597-603
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• 2004

In retrieving large database of image data, the clustering is essential for fast retrieval. However, it is difficult to cluster a number of image data adequately. Moreover, current retrieval methods using similarities are uncertain of retrieval accuracy and take much retrieving time. In this paper, a suggested image retrieval system combines Fuzzy ART neural network algorithm to reinforce defects and to support them efficiently. This image retrieval system takes color and texture as specific feature required in retrieval system and normalizes each of them. We adapt Fuzzy ART algorithm as neural network which receive normalized input-vector and propose improved Fuzzy ART algorithm. The result of implementation with 200 image data shows approximately retrieval ratio of 83%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.211-227
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• 2011

Content Based Image Retrieval (CBIR) is an approach for retrieving similar images from an image database based on automatically-derived image features. The quality of a retrieval system depends on the features used to describe image content. In this paper, we propose an image clustering system that takes a database of images as input and clusters them using k-means clustering algorithm taking into consideration color, texture and shape features. Experimental results show that the combination of the three features brings about higher values of accuracy and precision.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1234-1257
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• 2023

In this paper, we propose a framework for multi-focus image fusion called PATN. In our approach, by aggregating deep features extracted based on the U-type Transformer mechanism and shallow features extracted using the PSA module, we make PATN feed both long-range image texture information and focus on local detail information of the image. Meanwhile, the edge-preserving information value of the fused image is enhanced using a dense residual block containing the Sobel gradient operator, and three loss functions are introduced to retain more source image texture information. PATN is compared with 17 more advanced MFIF methods on three datasets to verify the effectiveness and robustness of PATN.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.807-810
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• 2003

This research uses high-resolution satellite images as a source of collecting farmland information. For effectively extract the paddy area, we use texture information and different classify methods to assist the satellite image classification. First, using maximum likelihood classifier to extract paddy information from images. The results show that User Accuracy and Procedure Accuracy of the paddy area can increase from 80.60% to 95.45% and 84.38% to 95.45%. Second, establishing a paddy Knowledge Base and using Knowledge Base Classifier to extract paddy area, and result shows the User Accuracy and Producer Accuracy to be 92.16% and 90.06%. Finally, The result shows we can effectively contribute to the paddy field information extraction from high-resolution satellite images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1371-1378
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• 2017

In this paper, we present an upsampling technique for depth map image using selective bilateral weights and a color weight using laplacian function. These techniques prevent color texture copy problem, which problem appears in existing upsamplers uses bilateral weight. First, we construct a high-resolution image using the bicubic interpolation technique. Next, we detect a color texture region using pixel value differences of depth and color image. If an interpolated pixel belongs to the color texture edge region, we calculate weighting values of spatial and depth in $3{\times}3$ neighboring pixels and compute the cost value to determine the boundary pixel value. Otherwise we use color weight instead of depth weight. Finally, the pixel value having minimum cost is determined as the pixel value of the high-resolution depth image. Simulation results show that the proposed algorithm achieves good performance in terns of PSNR comparison and subjective visual quality.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.601-606
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• 2006

The image data amount that used in medical institution with great development of medical technology is increasing rapidly. Therefore, people need automation method that use image processing description than macrography of doctors for analysis many medical image. In this paper. we propose that acquire texture information to using GLCM about liver area of abdomen CT image, and automatically detects liver tumor using PCA from this data. Method by one feature as intensity of existent liver humor detection was most but we changed into 4 principal component accumulation images using GLCM's texture information 8 feature. Experiment result, 4 principal component accumulation image's variance percentage is 89.9%. It was seen this compare with liver tumor detecting that use only intensity about 92%. This means that can detect liver tumor even if reduce from dimension of image data to 4 dimensions that is the half in 8 dimensions.

• 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 the Korea Academia-Industrial cooperation Society
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• v.9 no.6
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• pp.1889-1893
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• 2008

This paper explores the classification performance of applying to support vector machines (SVMs) for the image classification problems. In this study, we extract the color, texture and shape features of natural images and compare the performance of image classification using each individual feature and integrated features. The experiment results show that classification accuracy on the basis of color feature is better than that based on texture and shape features and the results of the integrating features also provides a better and more robust performance than individual feature. In additions, we show that the proposed classifier of SVM based approach outperforms BPNN to corporate the image classification problems.