• Proceedings of the KOSOMBE Conference
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• v.1996 no.05
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• pp.111-114
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• 1996

Mucociliary transport is one of the essential defensive functions of the airway mucosa. In this paper, the objective and quantitative method of measuring CBF(Ciliary Beat Frequency) was developed based on the image processing method. Microscopic ciliary images are acquired through image processing board inside PC, and data necessary for the FFT(Fast Fourier Transform) analysis are extracted. By means of FFT analysis, maximum peak frequencies are found in each divided block of a whole acquired image. Finally using these frequencies, we compose a frequency map showing the spatial distribution of CBF's.

• Asia-Pacific Journal of Business
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• v.13 no.4
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• pp.1-10
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• 2022

Purpose - Despite the emergence of such destination regeneration as a key factor in urban tourism, little research was carried out on destination regeneration, especially on the impact of destination regeneration on revisit intention. The purpose of this study is to investigate the relationship between destination regeneration and revisit intention with moderating role of the destination image. Design/Methodology/Approach - Data were collected from tourists who visited the destination, after which a total of 250 usable surveys were analyzed. In order to examine the data, we used frequency analysis, exploratory factor analysis, regression analysis by using SPSS 25.0. Findings - Based on the results, first, there is a positive relationship between destination regeneration and revisit intention. In addition, there are significant moderating effects of destination image between destination regeneration and revisit intention. Research Implications or Originality - Those significant findings could contribute to destination development from destination regeneration and revisit intention.

• Science of Emotion and Sensibility
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• v.7 no.4
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• pp.57-65
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• 2004

In this paper, we present a hybrid approach which incorporates color, texture and shape in content-based image retrieval. Colors in each image are clustered into a small number of representative colors. The feature descriptor consists of the representative colors and their percentages in the image. A similarity measure similar to the cumulative color histogram distance measure is defined for this descriptor. The co-occurrence matrix as a statistical method is used for texture analysis. An optimal set of five statistical functions are extracted from the co-occurrence matrix of each image, in order to render the feature vector for eachimage maximally informative. The edge information captured within edge histograms is extracted after a pre-processing phase that performs color transformation, quantization, and filtering. The features where thus extracted and stored within feature vectors and were later compared with an intersection-based method. The content-based retrieval system is tested to be effective in terms of retrieval and scalability through experimental results and precision-recall analysis.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.113-120
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• 2004

In this paper, we propose an efficient feature extraction and image retrieval algorithm for content-based retrieval method. First, we extract the object using Gaussian edge detector for input image which is key frames of MPEG video and extract the object features that are location feature, distributed dimension feature and invariant moments feature. Next, we extract the characteristic color feature using the proposed HAQ(Histogram Analysis md Quantization) algorithm. Finally, we implement an retrieval of four features in sequence with the proposed matching method for query image which is a shot frame except the key frames of MPEG video. The purpose of this paper is to propose the novel content-based image retrieval algerian which retrieves the key frame in the shot boundary of MPEG video belonging to the scene requested by user. The experimental results show an efficient retrieval for 836 sample images in 10 music videos using the proposed algorithm.

• The Journal of the Acoustical Society of Korea
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• v.37 no.2
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• pp.118-128
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• 2018

In this paper, we have studied how to search an underwater object by learning the image generated by the side scan sonar in the convolution neural network. In the method of human side analysis of the side scan image or the image, the convolution neural network algorithm can enhance the efficiency of the analysis. The image data of the side scan sonar used in the experiment is the public data of NSWC (Naval Surface Warfare Center) and consists of four kinds of synthetic underwater objects. The convolutional neural network algorithm is based on Faster R-CNN (Region based Convolutional Neural Networks) learning based on region of interest and the details of the neural network are self-organized to fit the data we have. The results of the study were compared with a precision-recall curve, and we investigated the applicability of underwater object detection in convolution neural networks by examining the effect of change of region of interest assigned to sonar image data on detection performance.

• Archives of design research
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• v.16 no.3
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• pp.59-70
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• 2003

The consumer's feelings toward products is each evaluated and appears in different way that is to say there are various types of motives for deciding a purchase for example, there might be an interesting function and emotional reaction. How is the product image that induces motives like this metaphorized and evaluated to be existing in the mental image. This study inquired into, it by presenting problems conscious of both sides indicated above, the major points associated with image of the product, characteristic positions of product elements and evaluation of the product. To draw out a clear conclusion, first three hypotheses were established and case a study was performed as part of conclusive research for verification of this. The collected data was made, by simple tabulation to represent the overall flow, and based on this, concrete analysis was conducted on the three reserch items. The analysis method weighted on quantitative analysis with consideration to consumer's psychological aspect. As the result, three important conclusions could be drawn out or ratiocination for evaluation of mental image formation.

• Proceedings of the IEEK Conference
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• 2000.11d
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• pp.255-258
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• 2000

This thesis shows controlling the mobile robot with distance information gotten with ultrasonic sensors, and analysis of captured image. The ultrasonic sensors supplies more accurate distance data in limited area but shows unstable data unlimited area while image data generally shows stable data, but this requires so much time because of amounts of calculation. So this thesis considers the merits of ultrasonic sensors and image to implement robot system .

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.07a
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• pp.16-18
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• 2012

Haze is an extreme reason of the reduction of contrast when capturing image in the outdoor. Recently, there are several single image dehazing techniques, but they are not robust in dynamic variations of natural environment caused by the thickness, coverage of haze and appearance of sunlight. In this paper, we propose an effective and robust method to enhance luminance for image dehazing depending on histogram analysis. Compare with conventional methods, our proposal have better performance in term of contrast, and computation time.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.3
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• pp.27-35
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• 2002

A knowledge-based image retrieval technique is image searching methods using some features from the queried image. The materials in this study are automobile head lamps. The input data is composed of characters and images which have various pattern. The numbers, special symbols, and general letters are under the category of the character. The image informations are made up of the distribution of pixel data, statistical analysis, and state of pattern which are useful for the knowledge data. In this paper, we implemented a retrieval system for the scientific crime detection at traffic accident using the proposed multi knowledge-based image retrieval technique. The values for the multi knowledge-based image features were extracted from color and gray scale each. With this 22 features, we improved the retrieval efficiency about the color information and pattern information. Visual basic, crystal report and MS access DB were used for this application. We anticipate the efficient scientific detection for the traffic accident and the tracking of suspicious vehicle.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.91-102
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• 1994

For the analysis of textured image, it requires large storage space and computation time to calculate the matrix features such as SGLDM(Spatial Gray Level Dependence Matrix). NGLDM(Neighboring Gray Level Dependence Matrix). NSGLDM(Neighboring Spatial Gray Level Dependence Matrix) and GLRLM(Gray Level Run Length Matrix). In spite of a large amount of information that each matrix contains, a set of several correlated scalar features calculated from the matrix is not sufficient to approximate it. In this paper, we propose a new classifier for textured images based on these matrices in which the projected vectors of each matrix on the meaningful directions are used as features. In the proposed method, an unknown image is classified to the class of a known image that gives the maximum similarity between the projected model vector from the known image and the vector from the unknown image. In the experiment to classify images of agricultural products, the proposed method shows good performance as much as 85-95% of correct classification ratio.