• ETRI Journal
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• v.41 no.6
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• pp.797-810
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• 2019

Image saliency detection is the basis of perceptual image processing, which is significant to subsequent image processing methods. Most saliency detection methods can detect only a single object with a high-contrast background, but they have no effect on the extraction of a salient object from images with complex low-contrast backgrounds. With the prior knowledge, this paper proposes a method for detecting salient objects by combining the boundary contrast map and the geodesics-like maps. This method can highlight the foreground uniformly and extract the salient objects efficiently in images with low-contrast backgrounds. The classical receiver operating characteristics (ROC) curve, which compares the salient map with the ground truth map, does not reflect the human perception. An ROC curve with distance (distance receiver operating characteristic, DROC) is proposed in this paper, which takes the ROC curve closer to the human subjective perception. Experiments on three benchmark datasets and three low-contrast image datasets, with four evaluation methods including DROC, show that on comparing the eight state-of-the-art approaches, the proposed approach performs well.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.4
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• pp.1897-1903
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• 2011

This paper discusses a 2D/3D conversion of images using technologies like object extraction and depth-map creation. The general procedure for converting 2D images into a 3D image is extracting objects from 2D image, recognizing the distance of each points, generating the 3D image and correcting the image to generate with less noise. This paper proposes modified new methods creating a depth-map from 2D image and recognizing the distance of objects in it. Depth-map information which determines the distance of objects is the key data creating a 3D image from 2D images. To get more accurate depth-map data, noise filtering is applied to the optical flow. With the proposed method, better depth-map information is calculated and better 3D image is constructed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.219-221
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• 2022

In recent summer, as it is concentrated, even in mountainous areas, flooding and flooding cause casualties in pedestrian evacuation situations. To compensate for this, a system that detects the occurrence of flooding and allows pedestrians to evacuate safely is required. Therefore, in this paper, we propose a research on pedestrian path search based on the shortest distance algorithm using Map API. The pedestrian route search system outputs a map using the T Map API, selects nearby buildings as shelters, and stores data. A shelter close to the pedestrian's current location is selected, and the shortest route is output and the distance and time are provided. If there is a problem with the current route during evacuation, another shelter route is provided from the current location. Therefore, it is thought that the pedestrian route search evacuation system proposed in this paper will prevent accidents during evacuation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.2 s.332
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• pp.39-48
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• 2005

An HMIPv6 provides micro mobility management using MAP for decreasing handover delay and network load in a mobile IP networks. An HMIPv6 uses distance based algorithm for MAP selection when a mobile host enters a new network domain. However, since every mobile hosts select a farthest router as a MAP, a handover delay and packet loss will be increased. A new MAP selection scheme is herein proposed to solve the problems caused by the distance based MAP selection algorithm by using secondary MAP. We executed the performance evaluation by simulation about handover delay and packet loss of an HMIPv6 and our proposed scheme. The simulation results show that the performance of our proposed scheme is better than that of HMIPv6.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.4
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• pp.367-371
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• 2015

In this paper, a novel localization method for a monocular camera is proposed by using a feature-based probabilistic map. The localization of a camera is generally estimated from 3D-to-2D correspondences between a 3D map and an image plane through the PnP algorithm. In the computer vision communities, an accurate 3D map is generated by optimization using a large number of image dataset for camera pose estimation. In robotics communities, a camera pose is estimated by probabilistic approaches with lack of feature. Thus, it needs an extra system because the camera system cannot estimate a full state of the robot pose. Therefore, we propose an accurate localization method for a monocular camera using a probabilistic approach in the case of an insufficient image dataset without any extra system. In our system, features from a probabilistic map are projected into an image plane using linear approximation. By minimizing Mahalanobis distance between the projected features from the probabilistic map and extracted features from a query image, the accurate pose of the monocular camera is estimated from an initial pose obtained by the PnP algorithm. The proposed algorithm is demonstrated through simulations in a 3D space.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.5
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• pp.36-42
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• 2002

This paper presents the way of building an occupancy grid map which a mobile robot needs to autonomously navigate in the unknown environment. A disparity map resulting from stereo matching can be converted into the 2D distance information. If the stereo matching has some errors, however, the subsequent map becomes unreliable. In this paper, a new morphological filter is proposed to reject 'spikes' of the disparity map due to stereo mismatch by considering the fact that these spikes occur locally. The new method has advantages that it is simpler and more easily realized than existing similar algorithms. Several occupancy grid maps based on stereo vision using the proposed algorithm have been built and compared with the actual distance information to verify the validity of the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.67-73
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• 2012

Local color transfer is the process of assigning a given color to a local region in a target image. The local region that contains the given color has to be segmented. Conventionally, the segmentation of the corresponding local region in a target image is based on the color distance. The region which is the closest in color distance is separated. However, since the close range of color distance separating a given color from target image is ambiguous and uncertain, color distortion is often generated around a separated local region. Therefore, this paper addresses the problem of segmentation in a local color transfer. To prevent color distortion, a modified color influence map is proposed with color categories. First, the target image is roughly segmented using a color category map, which groups similar colors in color space. It restricts the color transfer to a specific color category according to a given color. Second, modified color influence map assigning different weight to lightness and chroma, respectively, is used instead of Euclidian color distance. Then, by combining a modified color influence map and color category map filtered with anisotropic diffusion, a local region that contains a given color can be segmented more strictly than conventional method.

• Journal of Korean Elementary Science Education
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• v.40 no.3
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• pp.390-406
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• 2021

This study investigated how elementary students with different spatial ability constructed spatial thinking process about on volcanoes and earthquakes with GeoMapApp-based materials. Students' spatial thinking process was analyzed in terms of spatial concept recognized, tools of spatial representation, and their spatial reasoning to construct topographic structure. The student group with high-scored spatial ability showed the spatial reasoning based on internal representation of building mental images through sectional division of horizontal distance, directly connected with spatial concept, or distorting spatial concept. The student group with low-scored spatial ability built the spatial reasoning directly connected with spatial concept instead of transforming into internal representation, and partially recognized spatial concept on either distance or depth. Based on the results, we argued identifying spatial concepts such as distance, height, or depth from the GeoMapApp data would be funda- mental for the better spatial thinking.

• Journal of Environmental Impact Assessment
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• v.15 no.4
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• pp.271-277
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• 2006

In this study, we investigated the applicable plan of GIS on the environmental impact assessment of 60 Hz Powerline. So we assessed distance data based on calculations by use of 2D and 3D Geographical information systems(GIS) and distance data based on measurements on 1: 5000 maps accord with on site distance measurements to use input data for calculating magnetic field. One hundred eight of the on site measured addresses were selected from residences. The data were achieved by measuring the distance between residence and power line on maps with scales of 1: 5000. The digital map was obtained from National Geographic Information Institute with scales of 1: 5000, and we made 2D and 3D map. Correlation analyses were performed for statistical analyses. For the 3D GIS versus on site comparison of different exposure categories, 70 of 108 measurements were assigned to the correct category. Similarly for 2D GIS versus on site comparison, 71 of 108 were correctly categorized. When comparing map measurement with on site measurement, 62 of 108 were correctly categorized. When the correlation analysis was performed, best correlation was found between 3D GIS and on site measurements with r = 0.84947 (p<0.0001). The correlation between map and on site measurement yielded an r of 0.76517 (p<0.0001). Since the GIS measurements and map measurement were made from the center point in the building and the on site measurements had to be made from the closest wall on the building, this might introduce and additional error in urban areas. The difference between 2D and 3D calculations were resulted from the height of buildings.

• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.612-624
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• 2005

In this paper, we propose a surface-based registration using a gaussian weighted distance map for PET-CT brain image fusion. Our method is composed of three main steps: the extraction of feature points, the generation of gaussian weighted distance map, and the measure of similarities based on weight. First, we segment head using the inverse region growing and remove noise segmented with head using region growing-based labeling in PET and CT images, respectively. And then, we extract the feature points of the head using sharpening filter. Second, a gaussian weighted distance map is generated from the feature points in CT images. Thus it leads feature points to robustly converge on the optimal location in a large geometrical displacement. Third, weight-based cross-correlation searches for the optimal location using a gaussian weighted distance map of CT images corresponding to the feature points extracted from PET images. In our experiment, we generate software phantom dataset for evaluating accuracy and robustness of our method, and use clinical dataset for computation time and visual inspection. The accuracy test is performed by evaluating root-mean-square-error using arbitrary transformed software phantom dataset. The robustness test is evaluated whether weight-based cross-correlation achieves maximum at optimal location in software phantom dataset with a large geometrical displacement and noise. Experimental results showed that our method gives more accuracy and robust convergence than the conventional surface-based registration.