• Electronics and Telecommunications Trends
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• v.35 no.5
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• pp.112-122
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• 2020

In 3D computer graphics, a depth map is an image that provides information related to the distance from the viewpoint to the subject's surface. Stereo sensors, depth cameras, and imaging systems using an active illumination system and a time-resolved detector can perform accurate depth measurements with their own light sources. The 3D image information obtained through the depth map is useful in 3D modeling, autonomous vehicle navigation, object recognition and remote gesture detection, resolution-enhanced medical images, aviation and defense technology, and robotics. In addition, the depth map information is important data used for extracting and restoring multi-view images, and extracting phase information required for digital hologram synthesis. This study is oriented toward a recent research trend in deep learning-based 3D data analysis methods and depth map information extraction technology using a convolutional neural network. Further, the study focuses on 3D image processing technology related to digital hologram and multi-view image extraction/reconstruction, which are becoming more popular as the computing power of hardware rapidly increases.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.327-330
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• 2016

Currently, the world's elderly population continues to grow at a dramatic rate. As the number of senior citizens increases, detection of someone falling has attracted increasing attention for visual surveillance systems. This paper presents a novel fall-detection algorithm using motion estimation and an integrated spatiotemporal energy map of the object region. The proposed method first extracts a human region using a background subtraction method. Next, we applied an optical flow algorithm to estimate motion vectors, and an energy map is generated by accumulating the detected human region for a certain period of time. We can then detect a fall using k-nearest neighbor (kNN) classification with the previously estimated motion information and energy map. The experimental results show that the proposed algorithm can effectively detect someone falling in any direction, including at an angle parallel to the camera's optical axis.

• Korean Journal of Remote Sensing
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• v.34 no.4
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• pp.671-680
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• 2018

Compared to the imagery produced by traditional satellites, PlanetScope satellite imagery has made it possible to easily capture remotely-sensed imagery every day through dozens or even hundreds of satellites on a relatively small budget. This study aimed to detect changed areas and update a land cover map using a PlanetScope image. To generate a classification map, pixel-based Random Forest (RF) classification was performed by using additional features, such as the Normalized Difference Water Index (NDWI) and the Normalized Difference Vegetation Index (NDVI). The classification result was converted to vector data and compared with the existing land cover map to estimate the changed area. To estimate the accuracy and trends of the changed area, the quantitative quality of the supervised classification result using the PlanetScope image was evaluated first. In addition, the patterns of the changed area that corresponded to the classification result were analyzed using the PlanetScope satellite image. Experimental results found that the PlanetScope image can be used to effectively to detect changed areas on large-scale land cover maps, and supervised classification results can update the changed areas.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.43-54
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• 2005

This paper proposes a novel texture segmentation method using Bayesian image segmentation method and SOM(Self Organization feature Map). Multi-scale wavelet coefficients are used as the input of SOM, and likelihood and a posterior probability for observations are obtained from trained SOMs. Texture segmentation is performed by a posterior probability from trained SOMs and MAP(Maximum A Posterior) classification. And the result of texture segmentation is improved by context information. This proposed segmentation method shows better performance than segmentation method by HMT(Hidden Markov Tree) model. The texture segmentation results by SOM and multi-sclae Bayesian image segmentation technique called HMTseg also show better performance than by HMT and HMTseg.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.161-172
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• 1996

The objective of this research is to develope a process of integrating video image into digital map. In order to reach the research objective, the work includes the development of georeferencing technique for video images, the development of pilot system and the assesment process. Georeferencing technique for video images is composed of DGPS positioning, filtering of abnormal points, map conflation, indexing locations for key frames via time tag and indexing locations for total frames. By using the proposed building process, we could find the result that the accuracy of image capturing test points is $92.8%({\pm}2\;frames)$. The eventual meaning of this study is that it is possible to find a new conception of digital map, which overcomes a limitation of exiting two dimensional digital map.

• Culinary science and hospitality research
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• v.13 no.2
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• pp.275-291
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• 2007

The growth of foodservice industry has caused keen competition in the family restaurant industry. For a differentiation strategy to be competitive, on the preferential basis, it is necessary for each family restaurant business to analyze its position in the market. Therefore, this study is to certify customers' perceived servicescape and image similarity and analyzes positioning of seven family restaurants. For this research, multidimensional scaling(MDS) is performed to determine how they are positioned relative to competitors. According to the result of the positioning map(ALSCAL and PROFIT map), it is found that Outback steakhouse, T.G.I. Friday's, Bennigans' have more strong competitiveness than VIPS, Marche and Sizzler in servicescape factors and restaurant selection attribute except menu diversity. But the preference order does not follow the order of competitiveness. This result from the positioning map means that each business needs various marketing strategies that cannot be copied easily.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2004.04a
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• pp.275-278
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• 2004

The geo-referencing of satellite imagery is a key task in remote sensing. GCPs are points the position of which is known both in the image and in the supporting maps. Mapping function makes the determination of map coordinates of all image pixels possible. Generally manual operations are done to identify image points corresponding to the points on a digital topographic map. In order to accurately measure ground coordinates of GCPs, differential global positioning system (DGPS) surveying are used. To acquire the sufficient number of well distributed GCPs is one of the most time-consuming and cost-consuming tasks. This paper describes the procedure of automatically extracting GCOs using GCP database. GCP image chips and image matching technique are used for automatic extraction of GCPs. We developed image processing tool for making image chip GCPs and Web Server for management of GCPs.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.53-61
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• 2016

Extracting focused object with saliency map is still remaining as one of the most highly tasked research area around computer vision for it is hard to estimate. Through this paper, we propose enhanced object extraction method based on multi-channel saliency map which could be done automatically without machine learning. Proposed Method shows a higher accuracy than Itti method using SLIC, Euclidean, and LBP algorithm as for object extraction. Experiments result shows that our approach is possible to be used for automatic object extraction without any previous training procedure through focusing on the main object from the image instead of estimating the whole image from background to foreground.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.173-184
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• 2010

The advancement of ubiquitous networks facilitates and increases access to the Internet via mobile devices, which offers the advantages of enhanced mobility and accessibility. While Mobile Internet is largely free of temporal and spatial restrictions, producing content for mobile websites is a challenging work due to the existence of multiple device types and markup languages. This paper proposes a technique that makes use of image maps to improve the visual consistency of mobile web content displayed on a variety of mobile devices. The proposed image map method captures several objects comprising the webpage into a single image, and provides the image in Mobile Web services. Mobile devices employing the proposed method can reorganize webpages with multiple objects more easily and obtain the visually identical content on their screen.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.8C
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• pp.788-794
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• 2003

It is very important to construct statistical model in order to exactly estimate the signal variance from the noisy image. By using estimated variance of original image, in general, Wiener filter is constructed, and it is applied to the noisy image. In this paper, we propose a new statistical mixture modeling of wavelet coefficients for image denoising. Firstly, a simple classification method is used to construct a significance map that captures significant property of wavelet coefficients. Based upon the significance map, the state probabilities of mixture model is computed, and signal variance is estimated by using them. Experimental results show that the proposed method yields 0.1-0.2㏈ higher PSNR than conventional methods for image denoising.