• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.4
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• pp.581-587
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• 2011

A wireless sensor network needs to consist of multi-sensors in order to infer a high level of information on circumstances. Data fusion, in turn, is required to utilize the data collected from multi-sensors for the inference of information on circumstances. The current paper, based on Dempster-Shafter's evidence theory, proposes data fusion in a wireless sensor network with different weights assigned to different sensors. The frequency of events per sensor is the crucial element in calculating different weights of the data of circumstances that each sensor collects. Data fusion utilizing these different weights turns out to show remarkable difference in reliability, which makes it much easier to infer information on circumstances.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.11
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• pp.1146-1150
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• 2014

Recently, there has been interests in smart vessel technology, and it needs to consider an intelligent control system for efficient vessel manangement. In a smart vessel, however, it can cause a network overload due to a number of data transmission from a variety of sensor nodes and bridge nodes. In this letter, we propose an data transmission scheme with data fusion to reduce the number of traffic from the sensor nodes. Hence it can decrease network overload for intelligent vessel management.

• International Journal of Contents
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• v.18 no.3
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• pp.11-20
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• 2022

Human Emotion Recognition is an exciting topic that has been attracting many researchers for a lengthy time. In recent years, there has been an increasing interest in exploiting contextual information on emotion recognition. Some previous explorations in psychology show that emotional perception is impacted by facial expressions, as well as contextual information from the scene, such as human activities, interactions, and body poses. Those explorations initialize a trend in computer vision in exploring the critical role of contexts, by considering them as modalities to infer predicted emotion along with facial expressions. However, the contextual information has not been fully exploited. The scene emotion created by the surrounding environment, can shape how people perceive emotion. Besides, additive fusion in multimodal training fashion is not practical, because the contributions of each modality are not equal to the final prediction. The purpose of this paper was to contribute to this growing area of research, by exploring the effectiveness of the emotional scene gist in the input image, to infer the emotional state of the primary target. The emotional scene gist includes emotion, emotional feelings, and actions or events that directly trigger emotional reactions in the input image. We also present an attention-based fusion network, to combine multimodal features based on their impacts on the target emotional state. We demonstrate the effectiveness of the method, through a significant improvement on the EMOTIC dataset.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2636-2644
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• 2013

This paper is proposed a wavelet-based the order statistics MAD(Median Absolute Deviation) method of image fusion of Multi-image contaminated with visible image and infrared image. also The method of compared and defined the threshold the wavelet coefficients using MAD of the wavelet coefficients of the detail subbands was proposed to effectively fusion which of selected the high quality image of the two images. The existed fusion rule may be possible to get the distorted fusion image especially by the distortion in the relation between the pixel and indicator of two images in the existed fusion rules. In order to complement the disadvantage, the threshold of the proposed method sets up the image statistic and excludes the distortion. The hardware design is used FPGA of Xilinx and DSP system for the image fusion and compressed encoding of the proposed algorithm. Therefore the proposed method is totally verified by comparing with the several other multi-image and the proposed image fusion.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.695-708
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• 2011

Image fusion is the process of combining multiple images of the same scene into a single fused image with application to many fields, such as remote sensing, computer vision, robotics, medical imaging and military affairs. The widely used image fusion rules that use wavelet transform have been based on a simple comparison with the activity measures of local windows such as mean and standard deviation. In this case, information features from the original images are excluded in the fusion image and distorted fusion images are obtained for noisy images. In this paper, we propose the use of a nonparametric squared ranks test on the quality of variance for two samples in order to overcome the influence of the noise and guarantee the homogeneity of the fused image. We evaluate the method both quantitatively and qualitatively for image fusion as well as compare it to some existing fusion methods. Experimental results indicate that the proposed method is effective and provides satisfactory fusion results.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.6
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• pp.635-644
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• 2020

Object recognition, detection and instance segmentation based on DL (Deep Learning) have being used in various practices, and mainly optical images are used as training data for DL models. The major objective of this paper is object segmentation and building detection by utilizing multimodal datasets as well as optical images for training Detectron2 model that is one of the improved R-CNN (Region-based Convolutional Neural Network). For the implementation, infrared aerial images, LiDAR data, and edges from the images, and Haralick features, that are representing statistical texture information, from LiDAR (Light Detection And Ranging) data were generated. The performance of the DL models depends on not only on the amount and characteristics of the training data, but also on the fusion method especially for the multimodal data. The results of segmenting objects and detecting buildings by applying hybrid fusion - which is a mixed method of early fusion and late fusion - results in a 32.65% improvement in building detection rate compared to training by optical image only. The experiments demonstrated complementary effect of the training multimodal data having unique characteristics and fusion strategy.

• Journal of the Korean Society of Food Culture
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• v.23 no.2
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• pp.204-213
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• 2008

In this age of information ruled by new technologies and knowledge, the world is interconnected as a single community, and within this trend of globalization, new cultural codes are emerging through temporal fusion between the past and the present and spatial fusion between different regions and countries. In this situation, it seems meaningful to review Korean fusion foods and restaurants serving such foods, as well as to consider their future directions. Thus, the objective of the present study was to survey and analyze Korean fusion restaurants representing Korean culture not only in Korea, but also in foreign cities, and to identify the expression characteristics of such restaurants. Based on restaurants recommended in relevant magazines and on Internet sites, 18 spaces were selected, visited, and surveyed, in which tradition and modernism were well-mixed. Data on the shapes, materials, colors, and patterns of spatial elements and food-related elements, including photographs, were collected and analyzed. The results are as follows. Of the 18 restaurants, 13 (72%) showed temporal fusion in both spatial and food-related elements, 4 showed temporal fusion in spatial elements and cultural fusion in food-related elements, and 1 showed cultural fusion in both spatial and food-related elements. In general, the spaces were mainly designed with modern elements and partially with traditional elements (ceilings, windows, furniture, articles), and the fusion of food-related elements was made in diverse forms that included temporal fusion restructuring traditional menus contemporarily, and cultural fusion harmonizing traditional food with Western cookery.

• Journal of Korea Spatial Information System Society
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• v.6 no.1 s.11
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• pp.87-100
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• 2004

When spatial data which a user requests are stored various data servers in web mapping environment, it is necessary to access one more map servers to gain data and then integrate them. This paper designs and implements a fusion map server for web mapping. The proposed fusion map server has three characteristics. First, it has customizing metadata which reflect various user's requests. Second, it provides fusion services which integrate spatial and non-spatial data. Last, it uses the dynamic liking method to support the fusion services efficiently. We design and implement a publisher client, a map server and a fusion map server for proposed map server.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.649-664
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• 2011

Cooperative spectrum sensing (CSS) with decision fusion is considered as a key technology for tackling the challenges caused by fading/shadowing effects and noise uncertainty in spectrum sensing in cognitive radio. However, most existing solutions assume an error-free decision transmission, which is obviously not the case in realistic scenarios. This paper extends the general decision-fusion-based CSS scheme by considering the fading/shadowing effects and noise corruption in the common control channels. With this more practical model, the fusion centre first estimates the local decisions using a binary minimum error probability detector, and then combines them to get the final result. Theoretical analysis and simulation of this CSS scheme are performed over typical channels, which suggest some performance deterioration compared with the pure case that assumes an error-free decision transmission. Furthermore, the fusion strategy optimization in the proposed cooperation model is also investigated using the Bayesian criteria. The numerical results show that the total error rate of noisy CSS is higher than that of the pure case, and the optimal values of fusion parameter in the counting rule under both cases decrease as the local detection threshold increases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.2
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• pp.174-182
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• 2013

Most methods for fusion-based finger vein recognition were to fuse different features or matching scores from more than one trait to improve performance. To overcome the shortcomings of "the curse of dimensionality" and additional running time in feature extraction, in this paper, we propose a finger vein recognition technology based on matching score-level fusion of a single trait. To enhance the quality of finger vein image, the contrast-limited adaptive histogram equalization (CLAHE) method is utilized and it improves the local contrast of normalized image after ROI detection. Gabor features are then extracted from eight channels based on a bank of Gabor filters. Instead of using the features for the recognition directly, we analyze the contributions of Gabor feature from each channel and apply a weighted matching score-level fusion rule to get the final matching score, which will be used for the last recognition. Experimental results demonstrate the CLAHE method is effective to enhance the finger vein image quality and the proposed matching score-level fusion shows better recognition performance.