• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.34 no.2
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• pp.13-21
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• 2002

A new method for measuring fiber length and fiber coarseness was developed using image analysis technique. Measured fibers were transferred to a glass slide on a filter paper placed on a wire of the laboratory paper machine. After staining the fibers on the slide, mean fiber lengths and coarseness were measured by a commercial image analysis software, named KS400. The resultant data obtained from the image analysis displayed a close correlation with those from FS-200 and also showed excellent reproducibility as well as those from FS-200. The length of synthetic fibers over 10 mm long could be readily measured by this new analysis technique. Finally, a substantial improvement in precision for measuring fiber length and coarseness was made with less operator's effort for a given time.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.64-75
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• 2003

Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.5
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• pp.623-629
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• 2004

In this paper, a Efficient method for the mixed image separation is presented using independent component analysis and the new fast expectation-maximization(EM) algorithm. In general, the independent component analysis (ICA) is one of the widely used statistical signal processing scheme in various applications. However, it has been known that ICA does not establish good performance in source separation by itself. So, Innovation process which is one of the methods that were employed in image separation using ICA, which produces improved the mixed image separation. Unfortunately, the innovation process needs long processing time compared with ICA or EM. Thus, in order to overcome this limitation, we proposed new method which combined ICA with the New fast EM algorithm instead of using the innovation process. Proposed method improves the performance and reduces the total processing time for the Image separation. We compared our proposed method with ICA combined with innovation process. The experimental results show the effectiveness of the proposed method by applying it to image separation problems.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.35-47
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• 2018

The 4th industrial revolution, digital image technology has developed beyond the limit of multimedia industry to advanced IT fusion and composite industry. Particularly, application technology related to HCI element algorithm in 3D image object recognition field is actively developed. 3D image object recognition technology evolved into intelligent image sensing and recognition technology through 3D modeling. In particular, image recognition technology has been actively studied in image processing using object recognition recognition processing, face recognition, object recognition, and 3D object recognition. In this paper, we propose a research method of human factor 3D image recognition technology applying human factor algorithm for 3D object recognition. 1. Methods of 3D object recognition using 3D modeling, image system analysis, design and human cognitive technology analysis 2. We propose a 3D object recognition parameter estimation method using FACS algorithm and optimal object recognition measurement method. In this paper, we propose a method to effectively evaluate psychological research techniques using 3D image objects. We studied the 3D 3D recognition and applied the result to the object recognition element to extract and study the characteristic points of the recognition technology.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1722-1725
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• 1997

This paper presents image analysis method using a Markov random field(MRF) model. Particulary, image esgmentation is to partition the given image into regions. This scheme is first segmented into regions, and the obtained domain knowledge is used to obtain the improved segmented image by a Markov random field model. The method is a maximum a posteriori(MAP) estimation with the MRF model and its associated Gibbs distribution. MAP estimation method is applied to capture the natural image by TMS320C80(MVP) and to realize the segmented image by a MRF model.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2006.04a
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• pp.297-304
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• 2006

This paper estimates the probability of image fusion to improve accuracy of NDVl analysis. NDVI has been utilized in monitoring extensive forest or forest fire, and image fusion is a method to improve the resolution of multi-spectra image same resolution as high resolution panchromatic image. In this paper wavelet, PCA, IHS, Brovey and multiplicative method was applied to improve spatial resolution of SPOT-4 satellite image. NDVI images were generated from original and fused images and the correlation coefficient of fused and original image was calculated. The results of their comparison, PCA method showed best performance.

• International Journal of Concrete Structures and Materials
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• v.9 no.4
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• pp.427-438
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• 2015

The present work proposes a new image analysis method for the evaluation of the multi-walled carbon nanotube (MWNT) distribution in a cement matrix. In this method, white cement was used instead of ordinary Portland cement with MWNT in an effort to differentiate MWNT from the cement matrix. In addition, MWNT-embedded cement composites were fabricated under different flows of fresh composite mixtures, incorporating a constant MWNT content (0.6 wt%) to verify correlation between the MWNT distribution and flow. The image analysis demonstrated that the MWNT distribution was significantly enhanced in the composites fabricated under a low flow condition, and DC conductivity results revealed the dramatic increase in the conductivity of the composites fabricated under the same condition, which supported the image analysis results. The composites were also prepared under the low flow condition (114 mm < flow < 126 mm), incorporating various MWNT contents. The image analysis of the composites revealed an increase in the planar occupation ratio of MWNT, and DC conductivity results exhibited dramatic increase in the conductivity (percolation phenomena) as the MWNT content increased. The image analysis and DC conductivity results indicated that fabrication of the composites under the low flow condition was an effective way to enhance the MWNT distribution.

• Ocean Systems Engineering
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• v.2 no.2
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• pp.137-145
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• 2012

Recent developments in underwater image recognition methods have received large attention by the ocean engineering researchers. In this paper, an improved bi-dimensional empirical mode decomposition (BEMD) approach is employed to decompose the given underwater image into intrinsic mode functions (IMFs) and residual. We developed a joint algorithm based on BEMD and Canny operator to extract multi-pixel edge features at multiple scales in IMFs sub-images. So the multiple pixel edge extraction is an advantage of our approach; the other contribution of this method is the realization of the bi-dimensional sifting process, which is realized utilizing regional-based operators to detect local extreme points and constructing radial basis function for curve surface interpolation. The performance of the multi-pixel edge extraction algorithm for processing underwater image is demonstrated in the contrast experiment with both the proposed method and the phase congruency edge detection.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.4 s.142
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• pp.421-426
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• 2005

Scattering center extraction schemes for radar cross section reduction of large complex targets, like warships, was developed, which are an 1-D radar image method(range profile), and a direct analysis based on an object precision method. The analysis result of partial dihedral model shows that the presented direct analysis method is more efficient than the 1-D radar image method for scattering center extraction of interested targets, in terms of radar cross section reduction design, not signal processing. In order to verify the accuracy of the direct analysis method, a scattering center analysis of an naval weapon system was carried out, and the result was coincident with that of another well-known RCS analysis program. Finally, an analysis result of RCS and its scattering center of an 120m class warship-like model presented that the direct analysis method can be an efficient and powerful tools for radar cross section reduction of large complex targets.

• Journal of Korean Society for Geospatial Information Science
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• v.12 no.2 s.29
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• pp.61-66
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• 2004

By the advent of the high-resolution Satellite imagery, there are increasing needs in image mosaicking technology which can be applied to various application fields such as GIS(Geographic Information system). To mosaic images, various methods such as image matching and histogram modification are needed. In this study, automated image mosaicking is performed using image matching method based on the multi-resolution wavelet analysis(MWA). Specifically, both area based and feature based matching method are embedded in the multi-resolution wavelet analysis to construct seam line.; seam points are extracted then polygon clipping method are applied to define overlapped area of two adjoining images. Before mosaicking, radiometric correction is proceeded by using histogram matching method. As a result, mosaicking area is automatically extracted by using polygon clipping method. Also, seamless image is acquired using multi-resolution wavelet analysis.