• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.367-370
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• 2005

This paper proposes a novel method for content-based watermarking based on feature points of an image. At each feature point, watermark is embedded after affine normalization according to the local characteristic scale and orientation. The characteristic scale is the scale at which the normalized scale-space representation of an image attains a maximum value, and the characteristic orientation is the angle of the principal axis of an image. By binding watermarking with the local characteristics of an image, resilience against affine transformations can be obtained. Experimental results show that the proposed method is robust against various image processing steps including affine transformations, cropping, filtering, and JPEG compression.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.83-86
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• 2004

The variation of the directional properties of an image is used to extract the iris code for human identification. In order to conserve the original information while minimizing the effect of noise, scale-space filtering is applied. Resulting binary codes have been tested on a set of 272 iris images obtained from 18 persons.

• Journal of the Korea Computer Graphics Society
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• v.2 no.2
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• pp.21-28
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• 1996

The nature of complexity of medical images makes them difficult to segment using standard techniques. Therefore the usual approaches to segment images continue to predominantly involve manual interaction. But it tediously consumes a good deal of time and efforts of the experts. Hereby a nonmanual parameters estimation which can replace the manual interaction is needed to solve the problem of redundant manual works for an image segmentation. This paper attempts to estimate parameters for an image region segmentation using Scale Space Filtering. This attempt results in estimating the number of regions, their boundary and each representatives to be segmented 2-dimensionally and 3-dimensionally. Using this algorithm, we may diminish the problem of wasted time and efforts for finding prerequisite segmentation parameters, and lead the relatively reasonable result of region segmentation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2555-2575
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• 2017

This paper presents a dual exposure fusion method for image enhancement. Images taken with a short exposure time usually contain a sharp structure, but they are dark and are prone to be contaminated by noise. In contrast, long-exposure images are bright and noise-free, but usually suffer from blurring artifacts. Thus, we fuse the dual exposures to generate an enhanced image that is well-exposed, noise-free, and blur-free. To this end, we present a new scale-space patch-match method to find correspondences between the short and long exposures so that proper color components can be combined within a proposed dual non-local (DNL) means framework. We also present a residual filtering method that eliminates the structure component in the estimated noise image in order to obtain a sharper and further enhanced image. To this end, the entropy is utilized to determine the proper size of the filtering window. Experimental results show that our method generates ghost-free, noise-free, and blur-free enhanced images from the short and long exposure pairs for various dynamic scenes.

• Journal of Biomedical Engineering Research
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• v.21 no.4
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• pp.333-338
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• 2000

In this paper, an automated segmentation algorithm is proposed for MR brain images using T1-weighted, T2-weighted, and PD images complementarily. The proposed segmentation algorithm is composed of 3 step. In the first step, cerebrum images are extracted by putting a cerebrum mask upon the three input images. In the second step, outstanding clusters that represent inner tissues of the cerebrum are chosen among 3-dimensional(3D) clusters. 3D clusters are determined by intersecting densely distributed parts of 2D histogram in the 3D space formed with three optimal scale images. Optimal scale image is made up of applying scale space filtering to each 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram and searching graph structure. Optimal scale image best describes the shape of densely distributed parts of pixels in 2D histogram. In the final step, cerebrum images are segmented using FCM algorithm with its initial centroid value as the outstanding clusters centroid value. The proposed cluster's centroid accurately. And also can get better segmentation results from the proposed segmentation algorithm with multi spectral analysis than the method of single spectral analysis.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.482-486
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• 2009

Scale-invariant feature is an effective method for retrieving and classifying images. In this study, we analyze a scale-invariant planar curve features for developing 2D shapes. Scale-space filtering is used to determine contour structures on different scales. However, it is difficult to track significant points on different scales. In mathematics, curvature is considered to be fundamental feature of a planar curve. However, the curvature of a digitized planar curve depends on a scale. Therefore, automatic scale detection for curvature analysis is required for practical use. We propose a technique for achieving automatic scale detection based on difference of curvature. Once the curvature values are normalized with regard to the scale, we can calculate difference in the curvature values for different scales. Further, an appropriate scale and its position are detected simultaneously, thereby avoiding tracking problem. Appropriate scales and their positions can be detected with high accuracy. An advantage of the proposed method is that the detected significant points do not need to be located in the same contour. The validity of the proposed method is confirmed by experimental results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.8
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• pp.1613-1621
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• 1997

Many studies on internal data expression to process logic functions efficiently on computer have been doing actively. In this paper, we propose an efficient logic function manipulation system made on the Objected-Oriented manner, where Binary Decision Diagrams(BDD's) are adopted for internal data espressionof logic functions. Thus it is easy to make BDD's presenting combinational problems. Also, we propose a method of applying filtering function for reducing the size of BDD's instead of attributed bits, and add it to the mainpultion system. As a resutls, the space of address is expanded so that the number of node that can be used in the mainpulation system is increased up to 2/sup 27/. Finally, we apply the implemented system to One-Shot state assignment problems of asynchronous sequential circuits and show that it is efficient for the filtering method to reduce the size of BDD's.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.159-162
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• 1998

Land cover and land use classification on a huge scale, e.g. national or continental scale, has become more and more important because environmental researches need land cover: And land use data on such scales. We developed a crop field extraction method, which is one of the steps in our land cover classification system for a huge area. Firstly, a crop field model is defined to characterize "crop field" in terms of NDVI value and textual information Textual information is represented by the density of straight lines which are extracted by wavelet transform. Secondly, candidates of NDVI threshold value are determined by "scale-space filtering" method. The most appropriate threshold value among the candidates is determined by evaluating the line density of the area extracted by the threshold value. Finally, the crop field is extracted by applying level slicing to Landsat TM image with the threshold value determined above. The experiment demonstrates that the extracted area by this method coincides very well with the one extracted by visual interpretation.

• Journal of Korea Multimedia Society
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• v.3 no.4
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• pp.339-346
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• 2000

Medical image is analyzed to get an anatomical information for diagnostics. Segmentation must be preceded to recognize and determine the lesion more accurately. In this paper, we propose automatic segmentation algorithm for MR brain images using T1-weighted, T2-weighted and PD images complementarily. The proposed segmentation algorithm is first, extracts cerebrum images from 3 input images using cerebrum mask which is made from PD image. And next, find 3D clusters corresponded to cerebrum tissues using scale filtering and 3D clustering in 3D space which is consisted of T1, T2, and PD axis. Cerebrum images are segmented using FCM algorithm with its initial centroid as the 3D cluster's centroid. The proposed algorithm improved segmentation results using accurate cluster centroid as initial value of FCM algorithm and also can get better segmentation results using multi spectral analysis than single spectral analysis.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2004.11a
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• pp.283-288
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• 2004

The measurement of ultra low aspect ratio fluid film thickness is very crucial technique both for the verification of lubrication media characteristics and for the clearance design in many precision components such as MEMS, precision bearings and other slideways. Many technologies are applied to the measurement of ultra low aspect ratio fluid film thickness (i.e. elastohydrodynamic lubrication film thickness). In particular, in-situ optical interferometric method has many advantages in making the actual contact behaviors realized with the experimental apparatus. This measurement method also does the monitoring of the surface defects and fractures happening during the contact behavior, which are delicately influenced by the surface conditions such as load, velocity, lubricant media as well as surface roughness. Careful selection of incident lights greatly enhances the fringe resolutions up to $\~1.0$ nanometer scale with digital image processing technology. In this work, it is found that coaxial aligning trichromatic incident light filtering system developed by the author can provide much finer resolution of ultra low aspect ratio fluid film thickness than monochromatic or dichromatic incident lights, because it has much more spectrums of color components to be discriminated according the variations of film thickness. For the measured interferometric images of ultra low aspect ratio fluid film thickness it is shown how the film thickness is finely digitalized and measured in nanometer scale with digital image processing technology and space layer method. The developed measurement system can make it possible to visualize the contact deformations and possible fractures of contacting surface under the repeated loading condition.