Joon Hwan Choi

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.

A new action potential detector using the MTEO and its effects on spike sorting systems at low signal-to-noise ratios

This paper considers neural signal processing applied to extracellular recordings, in particular, unsupervised action potential detection at a low signal-to-noise ratio. It adopts the basic framework of the multiresolution Teager energy operator (MTEO) detector, but presents important new results including a significantly improved MTEO detector with some mathematical analyses, a new alignment technique with its effects on the whole spike sorting system, and a variety of experimental results. Specifically, the new MTEO detector employs smoothing windows normalized by noise power derived from mathematical analyses and has an improved complexity by utilizing the sampling rate. Experimental results prove that this detector achieves higher detection ratios at a fixed false alarm ratio than the TEO detector and the discrete wavelet transform detector. We also propose a method that improves the action potential alignment performance. Observing that the extreme points of the MTEO output are more robust to the background noise than those of the action potentials, we use the MTEO output for action potential alignment. This brings not only noticeable improvement in alignment performance but also quite favorable influence over the classification performance. Accordingly, the proposed detector improves the performance of the whole spike sorting system. We verified the improvement using various modeled neural signals and some real neural recordings.

Relationship between deposition and recrystallization textures of copper and chromium electrodeposits

AbstractThe 〈100〉, 〈111〉 and 〈110〉 textures of copper electrodeposits obtained from copper sulfate baths changed to the 〈100〉, 〈100〉 and 〈

Finger vein extraction using gradient normalization and principal curvature

\sqrt {\text{3}} 10

A New Overlapping Resolution Method for Multi-channel Spike Sorting

〉 textures, respectively, after recrystallization. The textures of chromium electrodeposits obtained from the standard Sargent bath remained unchanged after recrystallization. The results are in agreement with the prediction of the strain energy release maximization model, in which the recrystallized grains orient themselves so that their minimum elastic modulus direction can be parallel to the absolute maximum internal stress direction due to dislocations in the non-recrystallized grains.

Precipitation characteristics of Cu-Mn-P alloy

The principal component analysis (PCA) is a popular projection method in neural spike sorting. When the waveforms extracted from a spike train are aligned incorrectly, however, the projection performance of the PCA deteriorates drastically, and the clustering errors multiply. This drawback is taken care of by the frequency domain PCA in this paper. By experiments, it is shown that the proposed approach maintains good projection performance under considerable alignment errors of the waveforms.

Perception-based fingerprint image enhancement

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.

A Wavelet(MZ-DWT) Based Action Potential Detector at Low Signal-to-Noise Ratios

Spike sorting is a prerequisite for all researches on multi-channel extracellular neural signal recordings. In this paper, we develop a new method for action potential classification. We introduce a mathematical model consisting of three Gaussian waveforms, which appropriately represents the general shapes of action potentials. Then we search for the best-fit waveform for each noise-corrupted spike based on the model, using peak fitting method. These processes result in increased separability among different classes of action potentials. The performance of the proposed method is assessed with synthesized neural recordings composed by spike templates and white Gaussian noise in various SNR environments.