• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.9 no.2
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• pp.49-56
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• 1991

In this study, 3D positioning of CCT digital imagery was done by using a personal computer image processing method to increase the economic and time efficiency of SPOT satellite imagery. Image matching technique which applies statistical theories, was applied to acqusition of satellite imagery. The reliability of these coordinates was anlysed to presente a new algorithm for three dimensional positioning necessary in digital elevation modelling and orthophoto production. In acquiring image coordinates from CCT digital satellite imagery, accuracy of planimetric and height coordinates was improved by applying the image matching technique and it was found through analysis of correlation factors between sizes of target window that 19$\times$19 pixels was the most suitable size for image coordinate acquisition. From these results, it was able to present an algorithm about utility of digital imagery in the analysis of SPOT satellite data.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1934-1935
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• 2011

In order for a service robot to perform several tasks, basically autonomous navigation technique such as localization, mapping, and path planning is required. The localization (estimation robot's pose) is fundamental ability for service robot to navigate autonomously. In this paper, we propose a new system for point pattern matching based visual global localization using spot lightings in ceiling. The proposed algorithm us suitable for system that demands high accuracy and fast update rate such a guide robot in the exhibition. A single camera looking upward direction (called ceiling vision system) is mounted on the head of the mobile robot and image features such as lightings are detected and tracked through the image sequence. For detecting more spot lightings, we choose wide FOV lens, and inevitably there is serious image distortion. But by applying correction calculation only for the position of spot lightings not whole image pixels, we can decrease the processing time. And then using point pattern matching and least square estimation, finally we can get the precise position and orientation of the mobile robot. Experimental results demonstrate the accuracy and update rate of the proposed algorithm in real environments.

• The KIPS Transactions:PartB
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• v.17B no.1
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• pp.79-84
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• 2010

Matching spots between two sets of 2-dimensional electrophoresis can make it possible to find out the generation, extinction and change of proteins. Generally protein spots are separated by 2-dimensional electrophoresis. This process makes the position of the same protein spot a little different according to the status of the tissue or the experimental environment. Matching the spots shows that the relation of spots is non-uniform and non-linear transformation. However we can also find that the local relation preserves the topology. This study proposes a matching method motivated by the preservation of the topology. To compare the similarity of the topology, we compared the distance and the angle between neighbour spots. Experimental result shows that the proposed method is effective.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.1
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• pp.29-37
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• 1993

The purpose of this paper is to estimate the attitude and the position of SPOT satellite which are needed in producing DEM(Digital Elevation Model) using SPOT satellite image pairs. DEM extraction is consists of three parts. First part is the modeling of satellite position and atitude, second part is the matching of two images to find corresponding point of them and third part is to calculate the elevation of each point by using the result of the first and second part. For modeling inaccessible area, extended modeling algorithm which removes the GCP(Ground Control Point) most errorneous from the GCPs extracted from map iteratively is proposed According to the experiments using a collinearity equation, the second order polynomials are shown to the optimal for .omega.(pitch), and Zs parameters while the first order ones for .kappa.(yaw) .PHI.(roll), Xs, and Ys parameters. The input images used in this paper are 6000*6000 level 1A panchromatic digital SPOT images of Chungchong-do, Korea. With 30 GCPs, experiments on SPOT images show that the planimetric and altimetric RMS errors are 7.11m and 7.10m, respectively, for test points.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.579-584
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• 2006

This paper proposes a nonlinear template matching measure, called counting measure, as a signal detection measure that is defined as the number of on pixels in the spot area. It is applied to classify probes for an on-off type DNA chip, where each probe spot is classified as hybridized or not. The counting measure also incorporates the maximum response search method, where the expected signal is obtained by taking the maximum among the measured responses of the various positions and sizes of the spot template. The counting measure was compared to existing signal detection measures such as the normalized correlation and the median for 2390 patient samples tested on the human papiliomavirus (HPV) DNA chip. The counting measure performed the best regardless of whether or not the maximum response search method was used. The experimental results showed that the counting measure combined with the positional search was the most preferable.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.323-328
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• 2006

Proteins in a cell appear as spots in a two dimensional gel image which is used in protein analysis. The spots from the same protein are in near position when comparing two gel images. Finding out the different proteins between a normal tissue and a cancer one is important information in drug development. Automatic matching of gel images is difficult because they are made from biological experimental processes. This matching problem is known to be NP-hard. Neural networks are usually used to solve such NP-hard problems. Hopfield neural network is selected since it is appropriate to solve the gel matching. An energy function with location and distance parameters is defined. The two spots which make the energy function minimum are matching spots and they came from the same protein. The energy function is designed to reflect the topology of spots by examining not only the given spot but also neighborhood spots.

• 한국정보컨버전스학회:학술대회논문집
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• 2008.06a
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• pp.113-116
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• 2008

In this paper we suggest two novel methods for an implementation of the spot detection phase in the 2-DE gel image analysis program. The one is the adaptive thresholding method for eliminating noises and the other is the asymmetric diffusion model for spot matching. Remained noises after the preprocessing phase cause the over-segmentation problem by the next segmentation phase. To identify and exclude the over-segmented background regions, il we use a fixed thresholding method that is choosing an intensity value for the threshold, the spots that are invisible by one's human eyes but mean very small amount proteins which have important role in the biological samples could be eliminated. Accordingly we suggest the adaptive thresholding method which comes from an idea that is got on statistical analysis for the prominences of the peaks. There are the Gaussian model and the diffusion model for the spot shape model. The diffusion model is the closer to the real spot shapes than the Gaussian model, but spots have very various and irregular shapes and especially asymmetric formation in x-coordinate and y-coordinate. The reason for irregularity of spot shape is that spots could not be diffused perfectly across gel medium because of the characteristics of 2-DE process. Accordingly we suggest the asymmetric diffusion model for modeling spot shapes. In this paper we present a brief explanation ol the two methods and experimental results.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.342-348
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• 1998

The purpose of this paper is to extract fast DEM (Digital Elevation Model) using satellite images. DEM extraction consists of three parts. First part is the modeling of satellite position and attitude, second part is the matching of two images to find corresponding poults of them and third part is to calculate the elevation of each point by using the result of the first and second part. The position and attitude modeling of satellite is processed by using GCPs. A area based matching method is used to find corresponding points between the stereo satellite images. In the DEM generation system, this procedure holds most of a processing time, therefore a new fast matching algorithm is proposed to reduce the time for matching. The elevation of each point is calculated using the exterior orientation obtained from modeling and disparity from matching. In this paper, the SPOT satellite images, level IA 6000 $\times$6000 panchromatic images are used to extract DEM. The experiment result shows the possibility of fast DEM. extraction with the satellite images.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2209-2211
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• 1998

This paper presents the matching algorithm with the satellite images using the image warping method. Two stereo images, which are used for the DEM(Digital Elevation Model) extraction, are generally distorted because the images are acquired at different locations and angles. Therefore, the matching Process can't be executed with the original images. To solve this problem, a dynamic triangular image warping method is proposed. At first, the initial matching is executed with seed point, and then, using the matched points from the initial matching, the distorted images is compensated. We experimented this algorithm with the parts of the $6000{\times}6000$ SPOT satellite images. The experiment results show this algorithm is superior to other warping algorithm.

• Journal of Korean Society for Geospatial Information Science
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• v.11 no.2 s.25
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• pp.29-37
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• 2003

An approach is described for automatic measuring of GCP's image coordinates from SPOT imagery and focused on the fulfillment an automatic orientation of satellite images. For the orientation of a stereopair of digital images, firstly, GCP(Ground Control Point) should be selected and then the work for measuring of image coordinates correspond to GCPs is required. In this study, we propose the method for extracting the GCP's image coordinates automatically using an image patch for control points and auxiliary points matching. For the evaluation of measurement accuracy, a comparison between points those are extracted manually and automatically by a proposed method have made. Finally, we shows the feasibility of automatic image coordinates measurment by applying in stereo modeling for SPOT images.