• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1843-1859
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• 2013

The continuous increase of the spatial resolution of remote sensing images brings great challenge to image analysis and processing. Traditional prior knowledge-based region detection and target recognition algorithms for processing high resolution remote sensing images generally employ a global searching solution, which results in prohibitive computational complexity. In this paper, a more efficient region of interest (ROI) detection algorithm based on visual attention and threshold segmentation (VA-TS) is proposed, wherein a visual attention mechanism is used to eliminate image segmentation and feature detection to the entire image. The input image is subsampled to decrease the amount of data and the discrete moment transform (DMT) feature is extracted to provide a finer description of the edges. The feature maps are combined with weights according to the amount of the "strong points" and the "salient points". A threshold segmentation strategy is employed to obtain more accurate region of interest shape information with the very low computational complexity. Experimental statistics have shown that the proposed algorithm is computational efficient and provide more visually accurate detection results. The calculation time is only about 0.7% of the traditional Itti's model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.927-930
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• 2010

Recently, continuous k-nearest neighbor query(CkNN) which is defined as a query to find the nearest points of interest to all the points on a given path is widely used in the LBS(Location Based Service) and ITS(Intelligent Transportation System) applications. It is necessary to acquire results quickly in the above applications and be applicable to spatial network databases. This paper proposes a new method to search nearest POIs(Point Of Interest) for moving query objects on the spatial networks. The method produces a set of split points and their corresponding k-POIs as results. There is no order between the POIs. The analysis show that the proposed method outperforms the existing methods.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.14 no.1
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• pp.9-16
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• 1996

A new stereo matching method for the relative orientation and the photogrammetric triangulation has been pro-posed. It matches the pairs of conjugate points to be used as pass points and tie points in digital aerial photographs instead of selecting these points with manual point transfer and measurements. Three unique steps included in the proposed matching method are as followings. The first step is searching interest points, and designating them as candidate points to be matched. The second is matching the points from the pair of images by the Cross Correlation Method in both direction(left to right direction and right to left). The third is selecting consistent pairs in the both matching directions. Computer programs based on the proposed matching method have been developed, and with digital aerial photographs which have full ground coordinate information tests were performed to know reliabiliy and positional accuracy of proposed method. Results of the tests reveal that the proposed matching method can eliminate the in-correctly matched pairs more efficiently than other matching methods, thus this can be more reliably applied to the relative orientation and the photogrammetric triangulation.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.67-73
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• 1996

The objective of this research is to investigate the behavior of the $F\"{o}rstner$ interest operator, which has been widely used for detecting distinct points in the field of digital photogrammetry and computer vision, in scale space. Considering the hugh volume of digital image utilized in digital photogrammetry, the scale space (image pyramid) approach which appears to be a solution for enhancing image processing, began to gain its attention. The investigation of the $F\"{o}rstner$ interest operator in scale space generated by the Gaussian kernel shows its behavior and feasibility for being used in practice.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.1
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• pp.49-55
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• 2015

In this paper, we propose a fast interest point detection method using SURF algorithm. Since the SURF algorithm needs a great computations to detect the interest points and obtain the corresponding descriptors, it is not suitable for real-time based applications. In order to overcome this problem, the interest point detection step is parallelized by OpenMP and SIMD based on analysis of the scale space representation process and localization one in the step. The simulation results demonstrate that processing speed is enhanced about 55% by applying the proposed method.

• International Journal of Contents
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• v.13 no.4
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• pp.29-38
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• 2017

The objective of this study was to determine the status of current chapel courses and analyze the necessity of online chapel courses. Students' interest, failure experience, perceived problems, and advantages of current chapel courses were examined. Students' preference, intention of sincerity, and perceived effectiveness of online chapel courses were also determined. Finally, hypothesis tests for the differences of students' interest, failure experience, perceived problems and advantages of current chapel courses, preference, intention of sincerity, and perceived effectiveness of online chapel courses according to gender, school year grade, major of study, and religion were performed. Students' low interest in chapel courses was verified. Even Christian students' interest was below 3 points out of 5-point Likert scale. However, students whose religion was not Christianity felt more coercion and had less interest in chapel courses. They wanted virtualization of chapel courses more. They had more willingness to faithful participation in online chapel courses. This research suggests that virtualization of chapel courses as a solution to chapel resistance is dependent on student's characteristics such as religion, major field of study, and mindset.

• International Journal of Control, Automation, and Systems
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• v.1 no.3
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• pp.332-338
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• 2003

Content-based image retrieval is the research of images from database, that are visually similar to given image examples. Gabor functions and Gabor filters are regarded as excellent methods for feature extraction and texture segmentation. However, they have a disadvantage not to perform well in case of a rotated image because of its direction-oriented filter. This paper proposes a method of extracting local texture features from blocks with central interest points detected in an image and a rotation invariant Gabor wavelet filter. We also propose a method of comparing pattern histograms of features classified by VQ (Vector Quantization) among images.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.4
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• pp.327-332
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• 2002

The main problems of computational complexity in object tracking are definition of objects, segmentations and identifications in non-structured environments with erratic movements and collisions of objects. The object's information as a region that corresponds to objects without discriminating among objects are considered. This paper describes the algorithm that, automatically and efficiently, recognizes and keeps tracks of interest-regions selected by users in video or camera image sequences. The block-based feature matching method is used for the region tracking. This matching process considers only dominant feature points such as corners and curved-edges without requiring a pre-defined model of objects. Experimental results show that the proposed method provides above 96% precision for correct region matching and real-time process even when the objects undergo scaling and 3-dimen-sional movements In successive image sequences.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.04a
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• pp.279-284
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• 1992

This paper presents the theoretical development and qualitiative evaluation of a new concept in the mathematical modeling of dynamicstructures. We use both test data and analytical approximations to identify the parameters of an incomplete model. The model has the capability of prodicting the response of the points of interest on the structure over the frequency range of interest and can be used to predict the changes in natural frequencies and normal modes due to structural changes. The theory was tested by running simulated tests on a relatively simple structure, identifying the parameters of the incomplete model, and using this model to predict the effects on frequency and mode shapes of several mass and stiffness changes. The conditions of the test were varied by selecting different numbers of points of meansurement, varying the frequency range, and by including assumed measurement error. It is recommended that the theroetical development be continued and that applications to more complex structures be carried out in order todevelop a better understanding of the limitations and capabilites of the method. A successful, more definitive evaluation could lead to immediate practical applications.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.3
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• pp.176-184
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• 2023

Points of interest (POIs) are widely used in tourism recommendations and to provide information about areas of interest. Currently, situation judgement using POI and GPS data is mainly rule-based. However, this approach has the limitation that inferences can only be made using predefined POI information. In this study, we propose an algorithm that uses POI data, GPS data, and schedule information to calculate the current speed, location, schedule matching, movement trajectory, and POI coverage, and uses machine learning to determine whether to stay or go. Based on the input data, the clustered information is labelled by k-means algorithm as unsupervised learning. This result is trained as the input vector of the SVM model to calculate the probability of moving and staying. Therefore, in this study, we implemented an algorithm that can adjust the schedule using the travel schedule, POI data, and GPS information. The results show that the algorithm does not rely on predefined information, but can make judgements using GPS data and POI data in real time, which is more flexible and reliable than traditional rule-based approaches. Therefore, this study can optimize tourism scheduling. Therefore, the stay detection algorithm using GPS movement trajectories and POIs developed in this study provides important information for tourism schedule planning and is expected to provide much value for tourism services.