Wonseok Song

A finger-vein verification system using mean curvature

The finger-vein pattern is one of the human biometric signatures that can be used for personal verification. The first task of a verification process using finger-vein patterns is extracting the pattern from an infrared finger image. As a robust extraction method, we propose the mean curvature method, which views the vein image as a geometric shape and finds the valley-like structures with negative mean curvatures. When the matched pixel ratio is used in matching vein patterns, experimental results show that, while maintaining low complexity, the proposed method achieves 0.25% equal error rate, which is significantly lower than what existing methods can achieve.

Finger vein extraction using gradient normalization and principal curvature

Finger vein authentication is a personal identification technology using finger vein images acquired by infrared imaging. It is one of the newest technologies in biometrics. Its main advantage over other biometrics is the low risk of forgery or theft, due to the fact that finger veins are not normally visible to others. Extracting finger vein patterns from infrared images is the most difficult part in finger vein authentication. Uneven illumination, varying tissues and bones, and changes in the physical conditions and the blood flow make the thickness and brightness of the same vein different in each acquisition. Accordingly, extracting finger veins at their accurate positions regardless of their thickness and brightness is necessary for accurate personal identification. For this purpose, we propose a new finger vein extraction method which is composed of gradient normalization, principal curvature calculation, and binarization. As local brightness variation has little effect on the curvature and as gradient normalization makes the curvature fairly uniform at vein pixels, our method effectively extracts finger vein patterns regardless of the vein thickness or brightness. In our experiment, the proposed method showed notable improvement as compared with the existing methods.

MAP-Based Motion Refinement Algorithm for Block-Based Motion-Compensated Frame Interpolation

A new motion vector field (MVF) refinement algorithm is proposed for block-based motion-compensated frame interpolation. Under the assumption that an observed MVF, such as the result of a block-based motion estimation (BME), is a degraded version of the true MVF, the true MVF is estimated from the observation through maximum a posteriori probability (MAP) estimation. To define the posterior probability of the true MVF, the degradation is modeled as a locally stationary additive Gaussian noise, so the variance of the noise represents the unreliability of the observed motion vector (MV). The noise variance is directly estimated from the observation vector and its select neighbors. The prior distribution of the true MVF is designed to rely on the distances between the MV and its neighbors and to properly smooth the false MVs in the observation. The MAP estimate of the true MVF is obtained via the iterative conditional mode method. The outcome is a set of iterative update equations, which produce the kth estimate of the true MV of a block by combining, according to the estimated noise variance, the observation and the neighboring (k - 1)th estimates. Experimental results prove that the proposed algorithm achieves performances comparable with those of several existing MAP-based BME algorithms at a much lower computational complexity.

The Recognition and Segmentation of the Road Surface State using Wavelet Image Processing

This study focus on segmentation process that classifies road surfaces into 5 different categories, dry, wet water, icy, and snowy surfaces by analyzing asphalt-paved road images taken in daylight. By using the polarization coefficients, the proportions of horizontally polarized components to vertically polarized components, regions with over 1.3 polarization coefficients are classified as wet surfaces. Except for wet surfaces, the decision process a lies time-frequency analysis to other parts by using the third order wavelet packet transform. In addition, by using the average frequency characteristics of dry and icy surfaces from image templates, decide which is closer to a test image, and finally identify dry and icy surfaces. It is confirmed that the reposed estimation and segmentation of recognition on various images. This can be interpreted as an indication that image-only mad surface condition supervision is probable.

A Study on Utilizing Instrumented Indentation Technique for Evaluating In-field Integrity of Nuclear Structures

Power generating unit structures are designed and built to meet standard to secure its safety for expected life time. As the structures have been exposed to combined environment, degradation of structure material is accelerated and it can cause unexpected damage; evaluating precise mechanical properties of weak site like welded area is an essential research area as it is directly connected to safety issues. Existing measuring technique like tensile test requires specific size in testing specimen yet it is destructive method which is hard to apply on running structures. To overcome above mentioned limitation, IIT is getting limelight as it is non-destructive and simple method. In this study, latest technique is introduced to evaluate tensile property and residual stress by analyzing stress field occurs under the indenter while IIT is performed. Test on welded area, the weak site of nuclear structures have been practiced and confirmed that IIT can be usefully applied to evaluate integrity in industry.

A Vanishing Point Detection Method Based on the Empirical Weighting of the Lines of Artificial Structures

A vanishing point is a point where parallel lines converge, and they become evident when a cameras lenses are used to project 3D space onto a 2D image plane. Vanishing point detection is the use of the information contained within an image to detect the vanishing point, and can be utilized to infer the relative distance between certain points in the image or for understanding the geometry of a 3D scene. Since parallel lines generally exist for the artificial structures within images, line-detection-based vanishing point-detection techniques aim to find the point where the parallel lines of artificial structures converge. To detect parallel lines in an image, we detect edge pixels through edge detection and then find the lines by using the Hough transform. However, the various textures and noise in an image can hamper the line-detection process so that not all of the lines converging toward the vanishing point are obvious. To overcome this difficulty, it is necessary to assign a different weight to each line according to the degree of possibility that the line passes through the vanishing point. While previous research studies assigned equal weight or adopted a simple weighting calculation, in this paper, we are proposing a new method of assigning weights to lines after noticing that the lines that pass through vanishing points typically belong to artificial structures. Experimental results show that our proposed method reduces the vanishing point-estimation error rate by 65% when compared to existing methods.

A statistical approach to motion vector field smoothing for block-based motion-compensated frame interpolation

This paper proposes a new approach to motion vector field smoothing for block-based motion-compensated frame interpolation (MCFI). Based on the assumption that an observed motion vector field, which is the result of a block-based motion estimation (BME), is a degraded version of the true motion vector field, we calculate the maximum a posteriori (MAP) estimate of the true motion vector field from the observed. The degradation and the true motion vector field are modeled as additive Gaussian noise and a Markov random field, respectively. Iterative conditional modes (ICM) method is used for calculating the MAP estimate. The experimental results show that the proposed algorithm not only smoothes MVFs but also preserves motion boundaries better than the existing methods.

Nondestructive Measurement of Non-Equibiaxial Welding Residual Stresses Using Instrumented Indentation Technique With Knoop Indenter

Welding residual stresses which are generated in almost all welded structures unavoidably can come to be serious cause of fracture and failure of in-service welded structures. Various techniques have been developed to measure and estimate welding residual stresses such as hole-drilling method, saw-cutting method, X-ray/neutron diffraction method and so on. The instrumented indentation technique (IIT) is being attracted to significant alternative as a measurement method of residual stresses because of it’s nondestructive characteristic and usefulness of measurement on local scales. Basic concept of IIT to evaluate residual stresses is to compare two indentation load-depth curves that are measured experimentally between under stress-free state and under stressed state. In case of using Vickers indenter, average surface residual stress can be measured quantitatively from analyzing measured load diffrerence. Each x, y directional residual stresses can be evaluate by using Knoop indenter. Indenting each directions with Knoop indenter, difference load-depth curves are measured under non-equibiaxial stress state. Residual stress directionality can be expressed as the function of the load-difference ratio calculated from the load-depth curves and the conversion factor ratio that is constant regardless of indentation depth. This function was verified with the experimental data and the results of finite element analyses on various biaxial stress states, Knoop indentation model showed good agreement between the experimental data and the simulation results.Copyright