• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.12
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• pp.5073-5086
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• 2015

The rock fracture detection by image analysis is significant for fracture measurement and assessment engineering. The paper proposes a novel image segmentation algorithm for the centerline tracing of a rock fracture based on Hessian Matrix at Multi-scales and Steger algorithm. A traditional fracture detection method, which does edge detection first, then makes image binarization, and finally performs noise removal and fracture gap linking, is difficult for images of rough rock surfaces. To overcome the problem, the new algorithm extracts the centerlines directly from a gray level image. It includes three steps: (1) Hessian Matrix and Frangi filter are adopted to enhance the curvilinear structures, then after image binarization, the spurious-fractures and noise are removed by synthesizing the area, circularity and rectangularity; (2) On the binary image, Steger algorithm is used to detect fracture centerline points, then the centerline points or segments are linked according to the gap distance and the angle differences; and (3) Based on the above centerline detection roughly, the centerline points are searched in the original image in a local window along the direction perpendicular to the normal of the centerline, then these points are linked. A number of rock fracture images have been tested, and the testing results show that compared to other traditional algorithms, the proposed algorithm can extract rock fracture centerlines accurately.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1082-1088
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• 2009

Fundus images are highly useful in evaluating patients' retinal conditions in diagnosing eye diseases. In particular, vessel regions are essential in diagnosing diabetes and hypertension. In this paper, we used top-hat filter to compensate for non-uniform background. Image contrast was enhanced by using contrast limited adaptive histogram equalization (CLAHE) method. Hessian matrix was next applied to detect vessel regions. Results indicate that our method is 1.3% more accurate than matched filter method. Our proposed method is expected to contribute to diagnosing eye diseases.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.2
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• pp.148-153
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• 2004

The Hessian fly, Mayetiola destructor (Say), is known to be one of the major insect herbivores of wheat worldwide. In order to provide molecular events on interactions of the NIL with H21 and larvae of Hessian fly biotype L, the TaCR1 gene, Triticum aestivum cytokinin repressed 1, was isolated through the suppression subtractive hybridization, which was constructed using stems of the NIL with H21 at 6 days after infestation as tester and stems of the recurrent parent Coker797 without H21 at 6 days after infestation as driver. Transcript levels of TaCR1 mRNA in the NIL with H21 were highest at 6 days after infestation but in the Coker797 without H21 until 8 days were similar with those of non-infested plants. Expression of the TaCR1 gene was decreased at early time and then recovered after wounding or $H_2O$$_2$ treatment as well as 6-BAP treatment. Transcripts levels of the TaCR1 gene was changed after MeJA, SA, ethephone, or ABA treatment. In drought treatment, the TaCRl gene were increased at early stage of stress and then decreased at late stage. Expression of the TaCRl gene was continued to decrease through 24 h in the cold treatment. Although the TaCRl gene is increased through infestation in NIL with H21, further study was required to elucidate a role on resistance against larvae of Hessian fly. However, the TaCR1 gene could be used as marker gene on response of plants against abiotic stresses as well as application of plants with several hormones.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.1027-1035
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• 2012

This paper proposes a new Levenberg-Marquardt algorithm that is accelerated by adjusting a Jacobian matrix and a quasi-Hessian matrix. The proposed method partitions the Jacobian matrix into block matrices and employs the inverse of a partitioned matrix to find the inverse of the quasi-Hessian matrix. Our method can avoid expensive operations and save memory in calculating the inverse of the quasi-Hessian matrix. It can shorten the training time for fast convergence. In our results tested in a large application, we were able to save about 20% of the training time than other algorithms.

• 한국전산유체공학회:학술대회논문집
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• 2009.11a
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• pp.44-48
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• 2009

In this study, a vortex visualization method based on the vorticity magnitude is developed. One of the simplest models for a vortex is a vortex filament with the maximum vorticity on its center. The proposed method is based on the observation of this ideal distribution of vorticity magnitude. Laplacian and Hessian matrix of vorticity magnitude are tested for detecting the local maximum of vorticity magnitude. These ideas were applied to wake flow past a sphere. It was found that the Laplacian method is not able to distinguish vortices from the underlying shear layer clearly, while the Hessian matrix method does not suffer from this problem.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.1
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• pp.71-79
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• 1999

We consider multi-step quasi-Newton methods for unconstrained optimization. These methods were introduced by Ford and Moghrabi [1, 2], who showed how interpolating curves could be used to derive a generalization of the Secant Equation (the relation normally employed in the construction of quasi-Newton methods). One of the most successful of these multi-step methods makes use of the current approximation to the Hessian to determine the parameterization of the interpolating curve in the variable-space and, hence, the generalized updating formula. In this paper, we investigate new parameterization techniques to the approximate Hessian, in an attempt to determine a better Hessian approximation at each iteration and, thus, improve the numerical performance of such algorithms.

• Communications for Statistical Applications and Methods
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• v.23 no.2
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• pp.105-117
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• 2016

In the paper, as a sequence of the first tutorial, we discuss sufficient dimension reduction methodologies used to estimate central subspace (sliced inverse regression, sliced average variance estimation), central mean subspace (ordinary least square, principal Hessian direction, iterative Hessian transformation), and central $k^{th}$-moment subspace (covariance method). Large-sample tests to determine the structural dimensions of the three target subspaces are well derived in most of the methodologies; however, a permutation test (which does not require large-sample distributions) is introduced. The test can be applied to the methodologies discussed in the paper. Theoretical relationships among the sufficient dimension reduction methodologies are also investigated and real data analysis is presented for illustration purposes. A seeded dimension reduction approach is then introduced for the methodologies to apply to large p small n regressions.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.414-416
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• 2012

본 논문에서는 X-선 혈관 조영 영상 내 심혈관의 추출 방법을 제안한다. 본 방법은 불균일 조명 보정 필터를 사용함으로써 X-선 영상 내에서 나타나는 일정하지 않은 contrast, 낮은 명암도 및 불균일 조명 문제를 해결한다. 또한 영상의 지역적인 밝기 값의 변화의 특징을 고려하면서 분할 대상영역의 각 픽셀들의 2 차 미분((second partial derivation)을 행렬의 요소(element)로 갖는 Hessian 행렬의 고유치 (eigenvalue)를 영역확장의 문턱치 결정에 이용하여 전역적인 밝기값(intensity)만을 사용하는 분할의 단점을보완하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.1641-1644
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• 2004

3차원 볼륨데이터에서 분할 대상영역의 밝기 값이 다양하면서 밝기 값이 유사한 영역과 인접한 경우 3차원 영역확장(region growing) 방법을 사용하여 영역을 분할하기 위해서는 영역확장의 중요한 요인인 동질성 기준 값의 적절한 선택이 요구된다. 본 논문에서는 영역 복셀(voxel)의 1차 미분 값의 크기인 기울기 크기(gradient magnitude)만으로 영역의 경계를 찾기가 쉽지않은 대상의 분할을 위해 볼륨데이터의 지역적인 밝기 값의 변화의 특징을 고려하면서 분할 대상영역의 복셀의 2차 미분(second partial derivation)을 행렬의 요소(element)로 갖는 Hessian 행렬의 고유치(eigenvalue)를 영역확장의 문턱치 결정에 이용하였다. 제안한 알고리즘은 3차원 영역확장의 결과에 가장 큰 영향을 미치는 적절한 문턱치의 선택으로 대상영역의 분할을 성공적으로 수행하여 3차원 영역확장의 단점을 보완하였다.

• Bulletin of the Korean Mathematical Society
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• v.35 no.4
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• pp.757-767
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• 1998

For a Riemannian manifold $(M^n, g)$ we consider the space $V(M^n, g)$ of all smooth functions on $M^n$ whose Hessian is proportional to the metric tensor $g$. It is well-known that if $M^n$ is a space form then $V(M^n)$ is of dimension n+2. In this paper, conversely, we prove that if $V(M^n)$ is of dimension $\ge{n+1}$, then $M^n$ is a Riemannian space form.