• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.515-522
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• 2001

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the highspeed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Map(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space. A topological feature map preserves the mutual relations (similarity) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented about k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Journal of KIISE:Databases
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• v.29 no.5
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• pp.358-366
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• 2002

Feature-based similarity retrieval become an important research issue in image database systems. The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps(SOM) provides a mapping from high dimensional feature vectors onto a two-dimensional space and generates a topological feature map. A topological feature map preserves the mutual relations (similarities) in feature spaces of input data, and clusters mutually similar feature vectors in a neighboring nodes. Therefore each node of the topological feature map holds a node vector and similar images that is closest to each node vector. We implemented a k-NN search for similar image classification as to (1) access to topological feature map, and (2) apply to pruning strategy of high speed search. We experiment on the performance of our algorithm using color feature vectors extracted from images. Promising results have been obtained in experiments.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.358-360
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• 2004

The digital images contain a significant amount of redundancy and require a large amount of data for their storage and transmission. Therefore, the image compression is necessary to treat digital images efficiently. The goal of image compression is to reduce the number of bits required for their representation. The image compression can reduce the size of image data using contractive mapping of original image. Among the compression methods, the mapping is affine transformation to find the block(called range block) which is the most similar to the original image. In this paper, we applied the neural network(SOM) in encoding. In order to improve the performance of image compression, we intend to reduce the similarities and unnecesaries comparing with the originals in the codebook. In standard image coding, the affine transform is performed with eight isometries that used to approximate domain blocks to range blocks.

• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.283-288
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• 2019

We propose a method to search for similar images by using outline lines and viewpoints. In the first test, the three-dimensional image, which can't control the motion, has lower search accuracy than the static flat image. For the cause analysis, six specific tropical fish data were selected. We made a 3D graphics of tropical fishes of each kind, and we made 144 image outline lines with 12 stage viewpoints of top, bottom, left and right. Tropical fish by type were collected and sorted by time of search through similar search. Studies have shown that there are many unique viewpoints for each species of tropical fish. To increase the accuracy of the search, a User Interface was created to select the user's point of view. When the user selects the viewpoint of the image, a method of showing the result in consideration of the range of the related viewpoint is proposed.

• Fashion & Textile Research Journal
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• v.13 no.2
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• pp.155-161
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• 2011

This thesis, concerning Femme Fatale Images Costume Form in paintings viewed from DeLong's Theory, analyzed frequency and traits on the basis of DeLong's Form Observation Theory and drew out the result as followings. First, Costume Form was mostly open, whole, planer integration, rounded, and indeterminate. The styles were similar each other. Second, surface structure was expressed by line & shape, color, texture, and pattern of which color had the highest priority. Next, line & shape and texture had the priority in order. They were expressed through intereaction. Third, the traits of Costume Form were a similar color harmony or large light and darkness contrast+soft quality+interaction by ambiguous line & shape. So, Femme Fatale Image in paintings was expressed by soft and pure images and erotic images.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1377-1382
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• 2018

In the marine environment, many clutters have similar features with the marine targets due to the diverse changes of the air temperature, water temperature, various weather and seasons. Also, the clutters in the ground environment have similar features due to the same reason. In this paper, we proposed a robust Hybrid Machine Character (HMC) method to classify the targets from the clutters in the infrared images for the various environments. The proposed HMC method adopts human's multiple personality utilization and the CNN ensemble method to classify the targets in the ground and marine environments. This method uses an advantage of the each environmental training model. Experimental results demonstrate that the proposed method has better success rate to classify the targets and clutters than previously proposed CNN classification method.

• Macromolecular Research
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• v.16 no.4
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• pp.314-319
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• 2008

An image analysis processing method for the measurement of nanofiber diameter was developed. For the analysis, scanning electron microscopy (SEM) images of electrospun fiber were prepared and applied to the individual measurement of the fiber diameter by using the developed and the traditional manual methods. Both methods provided a similar fiber distribution. The fiber average diameters were similar but the variance of the new method was larger than that of the manual method. The average diameters from the two methods exhibited a linear relationship with a high coefficient. The developed method can be used as a practical tool to estimate the fiber diameter of the electro spun web.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1140-1145
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• 2003

Image normalization is one of the important areas in pattern recognition. Also, log-polar images are useful in the sense that their image data size is reduced dramatically comparing with conventional images and it is possible to develop faster pattern recognition algorithms. Especially, the log-polar image is very similar with the structure of human eyes. However, there are almost no researches on pattern recognition using the log-polar images while a number of researches on visual tracking have been executed. We propose an image normalization technique of log-polar images using momentums applicable for affine-invariant pattern recognition. We handle basic distortions of an image including translation, rotation, scaling, and skew of a log-polar image. The algorithm is experimented in a PC-based real-time vision system successfully.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.169-171
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• 2009

The histogram specification is to change the histogram shape of the image into the already defined shape. This technique can be applied usefully in various image processing fields which include a machine vision. However, the histogram specification technique has its basic limits. For example, the histogram does not have location information of pixel within the image and receives the digital image, which is stored through a quantization process, as an input. Namely, the accuracy of specification falls in the high-resolution image because the larger the resolution of image is becoming, the more the pixels having similar value are becoming. Therefore, we proposed the multiresolution histogram specification method for improving the accuracy of specification. Consequently, we can know that if the histogram specification is accomplished by using the proposed algorithm, destination image and source image were changed almost similarly.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.3
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• pp.49-58
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• 2020

Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.