• Journal of the Korean Society for Railway
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• v.6 no.2
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• pp.87-93
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• 2003

Serving a function of transportation information and geographic information together with proper composition of design elements, railway route maps have to be designed to easily recognized to the concerning passengers as they offer users to access to the guide information. This study analyzed visual aspects of the maps of 8 underground routes linking Seoul and urban cities(D1 - D8) and the maps of 8 different cities overseas (A1 - A8). As a source of information with condition of easy recognition, universality and aesthetics, route maps should be designed and consistently managed by the manual which establishes the principle of the logical system and the standardized form.

• International Journal of Advanced Culture Technology
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• v.9 no.4
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• pp.302-306
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• 2021

Indoor localization is one of the basic elements of Location-Based Service, such as indoor navigation, location-based precision marketing, spatial recognition of robotics, augmented reality, and mixed reality. We propose a Voxel Labeling-based visual positioning system using object simultaneous localization and mapping (SLAM). Our method is a method of determining a location through single image 3D cuboid object detection and object SLAM for indoor navigation, then mapping to create an indoor map, addressing it with voxels, and matching with a defined space. First, high-quality cuboids are created from sampling 2D bounding boxes and vanishing points for single image object detection. And after jointly optimizing the poses of cameras, objects, and points, it is a Visual Positioning System (VPS) through matching with the pose information of the object in the voxel database. Our method provided the spatial information needed to the user with improved location accuracy and direction estimation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.524-543
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• 2022

Correct facade image parsing is essential to the semantic understanding of outdoor scenes. Unfortunately, there are often various occlusions in front of buildings, which fails many existing methods. In this paper, we propose an end-to-end deep network for facade parsing with occlusions. The network learns to decompose an input image into visible and invisible parts by occlusion reasoning. Then, a context aggregation module is proposed to collect nonlocal cues for semantic segmentation of the visible part. In addition, considering the regularity of man-made buildings, a repetitive pattern completion branch is designed to infer the contents in the invisible regions by referring to the visible part. Finally, the parsing map of the input facade image is generated by fusing the results of the visible and invisible results. Experiments on both synthetic and real datasets demonstrate that the proposed method outperforms state-of-the-art methods in parsing facades with occlusions. Moreover, we applied our method in applications of image inpainting and 3D semantic modeling.

• Journal of the Korean Institute of Telematics and Electronics
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• v.23 no.2
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• pp.206-216
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• 1986

In this paper, the moving distance and velocity of a single moving object are measured by sampling three frames in a two-dimensional line sequence image. The brightness of each frame is analyzed, and the bit data of their pixel are rearranged so that the difference image may be extracted. The parameters for recognition of the object are the gray level of the object, the number of vertex points and the distance between the vertex points. The moving distance obtained from the coordinate which is constructed by the bit processing of the data in the memory map of a microcomputer, and the moving velocity is obtained from the moving distance and the time interval between the first and second sampled frames.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.1-9
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• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.354-359
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• 2013

In this paper, we suggested the method for implementation of unsupervised nonlinear classification using Binary Harmony Search (BHS) algorithm, which is known as a optimization algorithm. Various algorithms have been suggested for classification of feature vectors from the process of machine learning for pattern recognition or EEG signal analysis processing. Supervised learning based support vector machine or fuzzy c-mean (FCM) based on unsupervised learning have been used for classification in the field. However, conventional methods were hard to apply nonlinear dataset classification or required prior information for supervised learning. We solved this problems with proposed classification method using heuristic approach which took the minimal Euclidean distance between vectors, then we assumed them as same class and the others were another class. For the comparison, we used FCM, self-organizing map (SOM) based on artificial neural network (ANN). KEEL machine learning datset was used for simulation. We concluded that proposed method was superior than other algorithms.

• The Journal of Bigdata
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• v.5 no.1
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• pp.29-39
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• 2020

Purpose The atmosphere of respect for individual tastes that have spread throughout society has changed the consumption trend. As a result, the travel industry is also seeing customized travel as a new trend that reflects consumers' personal tastes. In particular, there is a growing interest in 'film-induced tourism', one of the areas of travel industry. We hope to satisfy the individual's motivation for traveling while watching movies with customized travel proposals, which we expect to be a catalyst for the continued development of the 'film-induced tourism industry'. Design/methodology/approach In this study, we implemented a methodology through 'OCR' of extracting and suggesting film location information that viewers want to visit. First, we extract a scene from a movie selected by a user by using 'OpenCV', a real-time image processing library. In addition, we detected the location of characters in the scene image by using 'EAST model', a deep learning-based text area detection model. The detected images are preprocessed by using 'OpenCV built-in function' to increase recognition accuracy. Finally, after converting characters in images into recognizable text using 'Tesseract', an optical character recognition engine, the 'Google Map API' returns actual location information. Significance This research is significant in that it provides personalized tourism content using fourth industrial technology, in addition to existing film tourism. This could be used in the development of film-induced tourism packages with travel agencies in the future. It also implies the possibility of being used for inflow from abroad as well as to abroad.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.4
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• pp.117-122
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• 2021

In recent years, human damage and loss of money due to various disasters such as typhoons, earthquakes, forest fires, landslides, and wars are steadily occurring, and a lot of manpower and funds are required to prevent and recover them. In this paper, we designed and developed a disaster drone system based on artificial intelligence in order to monitor these various disaster situations in advance and to quickly recognize and respond to disaster occurrence. In this study, multiple disaster drones are used in areas where it is difficult for humans to monitor, and each drone performs an efficient search with an optimal path by applying a deep learning-based optimal path algorithm. In addition, in order to solve the problem of insufficient battery capacity, which is a fundamental problem of drones, the optimal route of each drone is determined using Ant Colony Optimization (ACO) technology. In order to implement the proposed system, it was applied to a forest fire situation among various disaster situations, and a forest fire map was created based on the transmitted data, and a forest fire map was visually shown to the fire fighters dispatched by a drone equipped with a beam projector. In the proposed system, multiple drones can detect a disaster situation in a short time by simultaneously performing optimal path search and object recognition. Based on this research, it can be used to build disaster drone infrastructure, search for victims (sea, mountain, jungle), self-extinguishing fire using drones, and security drones.

• International Journal of Contents
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• v.10 no.4
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• pp.1-10
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• 2014

In this paper, we propose a method to build an accurate initial trimap for the GrabCut algorithm without the need for human interaction. First, we identify a rough candidate for the label region of a bottle by applying a saliency map to find a salient area from the image. Then, the Hough Transformation method is used to detect the left and right borders of the label region, and the k-means algorithm is used to localize the upper and lower borders of the label of the bottle. These four borders are used to build an initial trimap for the GrabCut method. Finally, GrabCut segments accurate regions for the label. The experimental results for 130 wine bottle images demonstrated that the saliency map extracted a rough label region with an accuracy of 97.69% while also removing the complex background. The Hough transform and projection method accurately drew the outline of the label from the saliency area, and then the outline was used to build an initial trimap for GrabCut. Finally, the GrabCut algorithm successfully segmented the bottle label with an average accuracy of 92.31%. Therefore, we believe that our method is suitable for product label recognition systems that automatically segment product labels. Although our method achieved encouraging results, it has some limitations in that unreliable results are produced under conditions with varying illumination and reflections. Therefore, we are in the process of developing preprocessing algorithms to improve the proposed method to take into account variations in illumination and reflections.

• Genomics & Informatics
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• v.17 no.4
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• pp.39.1-39.20
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• 2019

Analyzing patterns in data points embedded in linear and non-linear feature spaces is considered as one of the common research problems among different research areas, for example: data mining, machine learning, pattern recognition, and multivariate analysis. In this paper, data points are heterogeneous sets of biosequences (composite data points). A composite data point is a set of ordinary data points (e.g., set of feature vectors). We theoretically extend the derivation of the largest generalized eigenvalue-based distance metric D_ij(γ₁) in any linear and non-linear feature spaces. We prove that D_ij(γ₁) is a metric under any linear and non-linear feature transformation function. We show the sufficiency and efficiency of using the decision rule $\bar{{\delta}}_{{\Xi}i}$(i.e., mean of D_ij(γ₁)) in classification of heterogeneous sets of biosequences compared with the decision rules min_𝚵iand median_𝚵i. We analyze the impact of linear and non-linear transformation functions on classifying/clustering collections of heterogeneous sets of biosequences. The impact of the length of a sequence in a heterogeneous sequence-set generated by simulation on the classification and clustering results in linear and non-linear feature spaces is empirically shown in this paper. We propose a new concept: the limiting dispersion map of the existing clusters in heterogeneous sets of biosequences embedded in linear and nonlinear feature spaces, which is based on the limiting distribution of nucleotide compositions estimated from real data sets. Finally, the empirical conclusions and the scientific evidences are deduced from the experiments to support the theoretical side stated in this paper.