• Smart Structures and Systems
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• v.24 no.5
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• pp.597-606
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• 2019

Damage detection based on dynamic characteristics of a structure is one of important roles in structural damage identification. It is difficult to detect local structural damage using traditional dynamic experimental methods due to a limited number of sensors used in an experiment. In this work, a non-contact test stand of fan blades is established, and a full-field noncontact test method, combined with three-dimensional digital image correlation, Bayesian operational modal analysis, and damage indices, is used to detect local damage of a fan blade under ambient excitation without use of baseline information before structural damage. The methodology is applied to detect invisible local damage on the fan blade. Such a method has a seemingly high potential as an alternative to detect local damage of blades with complex high-precision surfaces under extreme working conditions because it is a noncontact test method and can be used under ambient excitation without human participation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.333-336
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• 2002

This paper presents a new image segmentation method using fuzzy measures which reflect the local property of an image as well as the global property of an image An image is globally segmented into the crisp region and the ambiguous region in terms of the Index of fuzziness measured over all pixels of an image. The ambiguous region is luther partitioned into background and object in terms of the index of fuzziness computed over the set of neighboring pixels reflecting the local property most. From the experimental results, this method shows the effective ambiguity handling capability in segmenting an image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.54-60
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• 2010

The local appearance-based method is one of the face recognition methods that divides face image into small areas and extracts features from each area of face image using statistical analysis. It collects classification results of each area and decides identity of a face image using a voting scheme by integrating classification results of each area of a face image. The conventional local appearance-based method divides face images into small pieces and uses all the pieces in recognition process. In this paper, we propose a local appearance-based method that makes use of only the relatively important facial components. The proposed method detects the facial components such as eyes, nose and mouth that differs much from person to person. In doing so, the proposed method detects exact locations of facial components using support vector machines (SVM). Based on the detected facial components, a number of small images that contain the facial parts are constructed. Then it extracts features from each facial component image using principal components analysis (PCA). We compared the performance of the proposed method to those of the conventional methods. The results show that the proposed method outperforms the conventional local appearance-based method while preserving the advantages of the conventional local appearance-based method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1633-1649
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• 2017

In remote sensing image processing, the traditional fusion algorithm is based on the Intensity-Hue-Saturation (IHS) transformation. This method does not take into account the texture or spectrum information, spatial resolution and statistical information of the photos adequately, which leads to spectrum distortion of the image. Although traditional solutions in such application combine manifold methods, the fusion procedure is rather complicated and not suitable for practical operation. In this paper, an improved IHS transformation fusion algorithm based on the local variance weighting scheme is proposed for remote sensing images. In our proposal, firstly, the local variance of the SPOT (which comes from French "Systeme Probatoire d'Observation dela Tarre" and means "earth observing system") image is calculated by using different sliding windows. The optimal window size is then selected with the images being normalized with the optimal window local variance. Secondly, the power exponent is chosen as the mapping function, and the local variance is used to obtain the weight of the I component and match SPOT images. Then we obtain the I' component with the weight, the I component and the matched SPOT images. Finally, the final fusion image is obtained by the inverse Intensity-Hue-Saturation transformation of the I', H and S components. The proposed algorithm has been tested and compared with some other image fusion methods well known in the literature. Simulation result indicates that the proposed algorithm could obtain a superior fused image based on quantitative fusion evaluation indices.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.10
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• pp.69-76
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• 2016

In this paper, an efficient image denoising method using non-local means (NL-means) method in the transform domain is proposed. Survey for various image denoising methods has been given, and the performances of the image denoising method using NL-means method have been analyzed. We propose an efficient implementation method for NL-means method by calculating the weights for NL-means method in the DCT and LiftLT transform domain. By using the proposed method, the computational complexity is reduced, and the image denoising performance improves by using the characteristics of images in the tranform domain efficiently. Moreover, the proposed method can be applied efficiently for performing image denoising and image rescaling simultaneously. Extensive computer simulations show that the proposed method shows superior performance to the conventional methods.

• Journal of the Korea Safety Management & Science
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• v.19 no.2
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• pp.117-133
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• 2017

The purpose of this study is to consider the relation between the deciding factors of local identity and local image improvement. To achieve this purpose, the deciding factors of local identity have been set as the following four: historical identity, cultural identity, (nature) scenery identity, and industrial identity. The writer has established a model to represent the relation between these factors and the local image improvement, namely 'Visitor Satisfaction' and 'Revisit Intentions', and framed a hypothesis for empirical verification. The study results are as follows: First, the deciding factors of local identity which this study addressed are empirically proven to have a significant effect on the visitor satisfaction and the intention to revisit in which is it likely that the greater the impact of the deciding factors, the higher the visitor satisfaction and revisit intentions would be. Second, the deciding factors of local identity, it appears that the 'historical identity' factor had the largest impact on the visitor satisfaction, and the 'cultural identity' factor had the largest impact on the revisit intentions. Therefore, of the four deciding factors, the 'historical identity' should be considered the highest priority to increase the visitor satisfaction, and the 'cultural identity' should be considered the highest priority to increase the revisit intentions. Third, based on the verification of the relation between the visitor satisfaction and revisit intentions, it can be presumed that the greater the visitor satisfaction, the higher the revisit intentions would be.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.127-144
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• 2018

The differentiation of this study is largely based on the above discussion. First, I would like to examine the main factors that affect the image of the local government's special transport agencies in terms of users and suggest strategic options for promoting the local government's special traffic. Second, a public service approach is very necessary considering the concept of new public transport such as local government special transport, individual use experience and social impact. The purpose of this study is to demonstrate the characteristics of local government special traffic through the five important factors of the service quality which are not capable of objectively measuring the characteristics of the service.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.106-109
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• 2009

Recently, image matching becomes important in Computer Aided Diagnosis (CAD) due to the huge amount of medical images. Specially, texture feature is useful in medical image matching. However, texture features such as co-occurrence matrices can't describe well the spatial distribution of gray levels of the neighborhood pixels. In this paper we propose a frequency domain-based texture feature extractor that describes the local spatial distribution for medical image retrieval. This method is based on 2D Local Discrete Fourier transform of local images. The features are extracted from local Fourier histograms that generated by four Fourier images. Experimental results using 40 classes Brodatz textures and 1 class of Emphysema CT images show that the average accuracy of retrieval is about 93%.

• Journal of Broadcast Engineering
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• v.18 no.3
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• pp.372-379
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• 2013

In this paper, we proposed a content-based image retrieval algorithm using local binary patterns and HSV color histogram. Images are retrieved using image input in image retrieval system. Many researches are based on global feature distribution such as color, texture and shape. These techniques decrease the retrieval performance in images which contained background the large amount of image. To overcome this drawback, the proposed method extract background fast and emphasize the feature of object by shrinking the background. The proposed method uses HSV color histogram and Local Binary Patterns. We also extract the Local Binary Patterns in quantized Hue domain. Experimental results show that the proposed method 82% precision using Corel 1000 database.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.189-196
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• 2010

In this paper, we proposed the adaptive local sigma filter reducing noises generated by an image sensor. The small noises generated by the image sensor are amplified by increased an analog gain and an exposure time of the image sensor together with information. And the goal of this work was the system design that is reduce the these amplified noises. Edge data are extracted by Flatness Index Map algorithm. We made the threshold adaptively changeable by the luminance average in this algorithm that extracts the edge data not in high luminance, but just low luminance. The Local Sigma Filter performed only about the edge pixel that were extracted by Flatness Index Map algorithm. To verify the performance of the designed filter, we made the Window test program. The hardware was designed with HDL language. We verified the hardware performance of Local Sigma Filter system using FPGA Demonstration board and HD image sensor, $1280{\times}720$ image size and 30 frames per second.