• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.58-63
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• 2022

Recently, the improvement of computational processing ability due to the rapid development of computing technology has greatly advanced the field of artificial intelligence, and research to apply it in various domains is active. In particular, in the national defense field, attention is paid to intelligent recognition among machine learning techniques, and efforts are being made to develop object identification and monitoring systems using artificial intelligence. To this end, various image processing technologies and object identification algorithms are applied to create a model that can identify friendly and enemy weapon systems and personnel in real-time. In this paper, we conducted image processing and object identification focused on tanks among various weapon systems. We initially conducted processing the tanks' image using a convolutional neural network, a deep learning technique. The feature map was examined and the important characteristics of the tanks crucial for learning were derived. Then, using YOLOv5 Network, a CNN-based object detection network, a model trained by labeling the entire tank and a model trained by labeling only the turret of the tank were created and the results were compared. The model and labeling technique we proposed in this paper can more accurately identify the type of tank and contribute to the intelligent recognition system to be developed in the future.

• Journal of Environmental Policy
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• v.13 no.3
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• pp.109-133
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• 2014

With a growing concern about climate change and green house gases mitigation, carbon labeling policy has been launched in several countries as an environmental policy which connects low carbon production to low carbon consumption. This research aims to propose a model that explains consumers' attitude and brand loyalty toward carbon labeling products. This model specifies the consumer's psychological processes by which consumer values, such as autonomy and environmental values, affect carbon labeling product and corporate images and finally form brand loyalty toward carbon labeling products. Panel data were collected in two separate surveys and analyzed using a structural equation technique. Results are summarized as follows. First, consumers' autonomy value(AV) positively affects locus of control(LC) and corporate image(CI). Second, consumers' environmental value(EV) positively influences perceived consumer effectiveness(PCE), which in turn has a negative effect on perceived barriers(PB). Perceived barriers finally affect product image(PI) negatively. Third, both corporate image and product image have causal relationships with brand loyalty. Our results suggest that carbon labeling policy contributes not only to the reduction of greenhouse gases but also to the increase of consumers' attitude and brand loyalty toward carbon labeling products. This research also provides governments with directions for efficient environmental policy and firms with guidance on effective marketing strategies about carbon labeling.

• Journal of KIISE:Databases
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• v.29 no.1
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• pp.27-33
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• 2002

There are two significant drawbacks in triple image indexing; one is that is cannot support concept-based image retrieval and the other is that it fails to allow disjunctive labeling of images. To remedy the drawbacks, we propose a new technique supporting a concept-based retrieval against images indexed by indefinite fuzzy triples (I-fuzzy triples). The I-fuzzy triples allow not only a disjunctive image labeling, but also a concept-based matching against images labeled disjunctively. The disjunctive labeling is based on the expended closed world assumption and the concept-based image retrieval is based on fuzzy matching. In this paper, we also propose a concept-based query evaluation against the image database to extract desired answers with the degree of certainty $\alpha$$\in$[1,0].

• Current Optics and Photonics
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• v.3 no.3
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• pp.220-226
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• 2019

A novel speckle-shifting ghost imaging (SSGI) technique is proposed in this paper. This method can effectively extract the edge of an unknown object without achieving its clear ghost image beforehand. However, owing to the imaging mechanism of SSGI, the imaging result generally contains serious noise. To solve the problem, we further propose a simple and effective method to remove noise from the speckle-shifting ghost image with a connected-region labeling (CRL) algorithm. In this method, two ghost images of an object are first generated according to SSGI. A threshold and the CRL are then used to remove noise from the imaging results in turn. This method can retrieve a high-quality image of an object with fewer measurements. Numerical simulations are carried out to verify the feasibility and effectiveness.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.1
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• pp.1-8
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• 2015

In many image processing tasks, connected component labeling (CCL) is performed to extract regions of interest. CCL was usually done in a sequential fashion when image resolution was relatively low and there are small number of input channels. As image resolution gets higher up to HD or Full HD and as the number of input channels increases, sequential CCL is too time-consuming to be used in real time applications. To cope with this situation, parallel CCL framework was introduced where multiple cores are utilized simultaneously. Several parallel CCL methods have been proposed in the literature. Among them are NSZ label equivalence (NSZ-LE) method[1], modified 8 directional label selection (M8DLS) method[2], and HYBRID1 method[3]. Soh [3] showed that HYBRID1 outperforms NSZ-LE and M8DLS, and argued that HYBRID1 is by far the best. In this paper we propose an improved hybrid parallel CCL algorithm termed as HYBRID2 that hybridizes M8DLS with label backtracking (LB) and show that it runs around 20% faster than HYBRID1 for various kinds of images.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.56-63
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• 2024

Despite the rapid strides in content-based image retrieval, a notable disparity persists between the visual features of images and the semantic features discerned by humans. Hence, image retrieval based on the association of semantic similarities recognized by humans with visual similarities is a difficult task for most image-retrieval systems. Our study endeavors to bridge this gap by refining image semantics, aligning them more closely with human perception. Deep learning techniques are used to semantically classify images and retrieve those that are semantically similar to personalized images. Moreover, we introduce a keyword-based image retrieval, enabling automatic labeling of images in mobile environments. The proposed approach can improve the performance of a mobile device with limited resources and bandwidth by performing retrieval based on the visual features and keywords of the image on the mobile device.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.7
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• pp.7-15
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• 2020

Recently, the fourth industrial revolution has been presented as a new paradigm and additive manufacturing (AM) has become one of the most important topics. For this reason, process monitoring for each cross-sectional layer of additive metal manufacturing is important. Particularly, deep learning can train a machine to analyze, optimize, and repair defects. In this paper, image classification is proposed by learning images of defects in the metal cross sections using the convolution neural network (CNN) image labeling algorithm. Defects were classified into three categories: crack, porosity, and hole. To overcome a lack-of-data problem, the amount of learning data was augmented using a data augmentation algorithm. This augmentation algorithm can transform an image to 180 images, increasing the learning accuracy. The number of training and validation images was 25,920 (80 %) and 6,480 (20 %), respectively. An optimized case with a combination of fully connected layers, an optimizer, and a loss function, showed that the model accuracy was 99.7 % and had a success rate of 97.8 % for 180 test images. In conclusion, image labeling was successfully performed and it is expected to be applied to automated AM process inspection and repair systems in the future.

• The Journal of Korean Institute of Next Generation Computing
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• v.15 no.3
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• pp.16-24
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• 2019

Recently, research on emotion recognition based on EEG has attracted great interest from human-robot interaction field. In this paper, we propose a method of labeling using image-based EEG topography instead of evaluating emotions through self-assessment and annotation labeling methods used in MAHNOB HCI. The proposed method evaluates the emotion by machine learning model that learned EEG signal transformed into topographical image. In the experiments using MAHNOB-HCI database, we compared the performance of training EEG topography labeling models of SVM and kNN. The accuracy of the proposed method was 54.2% in SVM and 57.7% in kNN.

• Journal of information and communication convergence engineering
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• v.6 no.2
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• pp.204-206
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• 2008

In this paper, we proposed the recognition method that automatically extracts cracks from a surface image acquired by a digital camera and recognizes the directions (horizontal, vertical, -45 degree, and 45 degree) of cracks using the fuzzy single layer perceptron. We compensate an effect of light on a concrete surface image by applying the closing operation, which is one of the morphological techniques, extract the edges of cracks by Sobel masking, and binarize the image by applying the iterated binarization technique. Two times of noise reduction are applied to the binary image for effective noise elimination. After the specific regions of cracks are automatically extracted from the preprocessed image by applying Glassfire labeling algorithm to the extracted crack image, the cracks of the specific region are enlarged or reduced to $30{\times}30$ pixels and then used as input patterns to the fuzzy single layer perceptron. The experiments using concrete crack images showed that the cracks in the concrete crack images were effectively extracted and the fuzzy single layer perceptron was effective in the recognition of the extracted cracks directions.

• Asia Marketing Journal
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• v.20 no.4
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• pp.65-94
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• 2019

The purpose of this study was to analyze the brand loyalty formation by positive labeling. Affecting such factors as involvement, self-image, community engagement, preference, and choice cutback, positive labeling can be seen as one of psychological factors that shapes consumer's behavior and their decision. This study was carried out because little research was done to examine the influence of positive labeling toward brand loyalty, and also to find out the benefits that consumers can get from being labeled in positive terms. Data were collected through survey questionnaire and 151 usable responses were used. Following a series of pretests and confirmatory factor analysis helped to purify measures and verify the psychometric properties of the scale. Structural equation modeling with AMOS was used for testing of research hypotheses. The result of data analysis demonstrated the positive relationship between labeling and brand loyalty, i.e. positive labeling indirectly leads to consumers' loyalty toward a brand. Findings revealed significant relationship between involvement and emotional attachment, as well as the relationship between community engagement and choice cutback. The results gave support for the hypothesis of moderating effect of buzz on the relationship between involvement and emotional attachment, even though the hypothesis of moderating effect of distinction was rejected. Taking Apple's rivalry strategy as initial point, this study highlights the role of labeling in creating social identity. The study attempts to show the positive consequences of labeling strategy for firms that seeks ways of good competition without engaging into conflicts.