• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.405-408
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• 2010

This paper presents a new feature extraction technique, correlation coefficient and Manhattan distance (MD) based method for recognition of rotated object in an image. This paper also represented a new concept of intensity invariant. We extracted global features of an image and converts a large size image into a one-dimensional vector called circular feature vector's (CFVs). An especial advantage of the proposed technique is that the extracted features are same even if original image is rotated with rotation angles 1 to 360 or rotated. The proposed technique is based on fuzzy sets and finally we have recognized the object by using histogram matching, correlation coefficient and manhattan distance of the objects. The proposed approach is very easy in implementation and it has implemented in Matlab7 on Windows XP. The experimental results have demonstrated that the proposed approach performs successfully on a variety of small as well as large scale rotated images.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.429-430
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• 2011

Roughness set theory is a popular topic to use in color-image segmentation. A new popular color image segmentation algorithm is proposed by scientists with the point using traditional histogram and Histon construct roughness set histogram. But, there is still a problem about that is the correlativity of color vector in roughness set histogram, which take an inactive effect in the process of color-image segmentation. Therefore, this paper represents further research based on this and proposed an improved method proved through lot of experiments. The experimental result reduces the correlativity of color vector in roughness set histogram and calculation time remarkably.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.1
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• pp.35-45
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• 1987

For the registration problem in which the matching of two images is made, a new algorithm using an 1-D LPC model was proposed. The proposed algorithm employed LPC coefficients as feature vector of an image. The similarity of two images was measured using an LPC distance, proposed by Itakura, between each image's feature vector. The comparision of performance with normalized correlation method and template matching method was made by a computer simulation with several real images. The results of simulation showed that the proposed algorithm was more robust to image intensity variation and computationall efficient.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.28 no.2
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• pp.247-254
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• 2010

In this paper, a new approach has been studied correcting the geometric distortion of SPOT 4 imagery. Two new equations were induced by the relationship between satellite and the Earth in the space. line-of-sight (LOS) vector adjustment model for SPOT 4 imagery was implemented in this study. This model is to adjust LOS vector under the assumption that the orbital information of satellite provided by receiving station is uncertain and this uncertainty makes a constant error over the image. This model is verified using SPOT 4 satellite image with high look angle and thirty five ground points, which include 10 GCPs(Ground Control Points) and 25 check points, measured by the GPS. In total thirty five points, the geometry of satellite image calculated by given satellite information(such as satellite position, velocity, attitude and look angles, etc) from SPOT 4 satellite image was distorted with a constant error. Through out the study, it was confirmed that the LOS vector adjustment model was able to be applied to SPOT4 satellite image. Using this model, RMSEs (Root Mean Square Errors) of twenty five check points taken by increasing the number of GCPs from two to ten were less than one pixel. As a result, LOS vector adjustment model could efficiently correct the geometry of SPOT4 images with only two GCPs. This method also is expected to get good results for the different satellite images that are similar to the geometry of SPOT images.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.156-158
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• 2003

Analysis of high-resolution satellite image becomes important for cartography, surveillance, and remote sensing. However, there are lots of problems to be solved for automatic analysis of high-resolution satellite image especially in urban area. The problems are originated from the increased complexity due to the unnecessary details and shadows, and time-varying illuminations. Because of such obstacles, it seems impossible to make automatic analysis. This paper proposes a way of change detection of buildings in urban area by using digital vector map. The proposed way makes the buildings on the vector map parameterized, and searches them in the preprocessed high-resolution image by using generalized Hough transform. The searched building objects are overlaid on the satellite image. The overlaid image can help to detect the change of building rapidly.

• The Korean Journal of Applied Statistics
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• v.24 no.2
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• pp.335-345
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• 2011

In this paper, we propose a new hybrid speed function for image segmentation using level set. A new proposed speed function uses the region and boundary information of image object for the exact result of segmentation. The region information is defined by the probability information of pixel intensity in a ROI(region-of-interest), and the boundary information is defined by the gradient vector flow obtained from the gradient of image. We show the results of experiment for an various artificial image and real medical image to verify the accuracy of segmentation using proposed method.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.305-312
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• 1998

A small PACS based on PC is designed for CR. To receive the digital image from CR, a DICOM Interface Unit (DIU) is designed that complied with the medical image standard, DICOM V3.0. The CR images acquired through the DIU are stored in a file-server; the patient information of the images is stored in a database. To improve the performance of PC and to use it easily, multiresolution images are constructed by wavelet transform and displayed progressively. Wavelet compression method is newly adopted to store the images hierarchically to storage units. In this compression method, the image is decomposed into subclasses of image by wavelet transform, and then the subclasses of the image are vector quantized using a multiresolution codebook. The storage units for CR images were divided into the short-term storage in file-server and the harddisk in viewing station. Image processing tools supported by general PACS is implemented based on PC.

• Korea Information Processing Society Review
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• v.12 no.1
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• pp.70-76
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• 2005

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.3
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• pp.13-20
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• 1990

This paper describes an interframe image coding using motion compensated and classified vector quantizer (MC-CVQ). It is essential to carefully encode blocks with significant pels in motion compensated vector quantizers (MCVQ). In this respect, we propose a new CVQ algorithm which is appropriate to the coding of interframe prediction error after motion compensation. In order to encode an image efficiently at a low bit rate, we partition each block, which is the processing element in MC, into equally sized 4 vectors, and classify vectors into 15 classes according to the position of significant pels. Vectors in each class are then encoded by the vector quantizer with the codebook independently designed for the class. The computer simulation shows that the signal-to-noise ratio and the average bit rate of MC-CVQ are 35-37dB and 0.2-0.25bit/pel, respectively, for the videophone or video conference type image.

• Journal of Biosystems Engineering
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• v.36 no.1
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• pp.40-47
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• 2011

Image analysis algorithm for the quality evaluation of ginseng seedling was investigated. The images of ginseng seedling were acquired with a color CCD camera and processed with the image analysis methods, such as binary conversion, labeling, and thinning. The processed images were used to calculate the length and weight of ginseng seedlings. The length and weight of the samples could be predicted with standard errors of 0.343 mm, and 0.0214 g respectively, $R^2$ values of 0.8738 and 0.9835 respectively. For the evaluation of the three quality grades of Gab, Eul, and abnormal ginseng seedlings, features from the processed images were extracted. The features combined with the ratio of the lengths and areas of the ginseng seedlings efficiently differentiate the abnormal shapes from the normal ones of the samples. The grade levels were evaluated with an efficient pattern recognition method of support vector machine analysis. The quality grade of ginseng seedling could be evaluated with an accuracy of 95% and 97% for training and validation, respectively. The result indicates that color image analysis with support vector machine algorithm has good potential to be used for the development of an automatic sorting system for ginseng seedling.