• The KIPS Transactions:PartB
• /
• v.9B no.5
• /
• pp.653-662
• /
• 2002

Fingerprint recognition is a task to find a matching pattern in a database for a specific persons fingerprint. To accomplish this task, preprocessing, classification, and matching steps are taken for a large-scale fingerprint database but only the matching step is taken without classification for a small-scale database. The primary matching method is based on minutiae (ridge ending point, bifurcation). This matching method, however, requires a very complex computation to extract minutiae and match minutiae-to-minutiae accurately due to translation, rotation, nonlinear deformation of fingerprint and occurrence of spurious minutiae. In addition, this method requires a laborious preprocessing step in order to improve the quality of fingerprint Images. This paper proposes a new simple method to eliminate these problems. With this method, Gabor variance is used instead of minutiae for fingerprint recognition. The Gabor variance is computed from Gabor features that result from filtering a fingerprint image through Gabor filter. In this paper, this method is described and its test result is shown, demonstrating the potential of using this new method for fingerprint recognition.

• Journal of the Korean Institute of Telematics and Electronics T
• /
• v.36T no.4
• /
• pp.24-29
• /
• 1999

In this paper, a simple contour-preserving filtering algorithm is proposed. The goal of the contour-preserving filtering method is to remove noise ad granularity as the preprocessing for the image segmentation procedure. Our method finds edge map and separates the image into the edge region and the non-edge region using this edge map. For the non-edge region, typical smoothing filters could be used to remove the noise and the small areas during the segmentation procedure. The result of simulation shows that our method is slightly better than the typical methods such as the median filtering and gradient inverse weighted filtering in the point of view of analysis of variance (ANOVA).

• Journal of IKEEE
• /
• v.18 no.2
• /
• pp.234-239
• /
• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• Proceedings of the KIEE Conference
• /
• 1995.07b
• /
• pp.745-747
• /
• 1995

A new digital method of anti-aliasing is presented and is applied to the development of an electric vehicle controller. A layered processing structure and some finite-bit approximation technique, devised in this paper, are the key attributions to the design and implementation of the anti-aliasing filter. The performance of the implemented preprocessing system is tested with several experimental results.

• Journal of the Semiconductor & Display Technology
• /
• v.10 no.3
• /
• pp.99-103
• /
• 2011

The stereoscopic vision system is the algorithm to obtain the depth of target object of stereo vision image. This paper presents an efficient disparity matching method using blue edge filter and graph cut algorithm. We do recommend the use of the simple sobel edge operator. The application of B band sobel edge operator over image demonstrates result with somewhat noisy (distinct border). The basic technique is to construct a specialized graph for the energy function to be minimized such that the minimum cut on the graph also minimizes the energy (either globally or locally). This method has the advantage of saving a lot of data. We propose a preprocessing effective stereo matching method based on sobel algorithm which uses blue edge information and the graph cut, we could obtain effective depth map.

• Proceedings of the KIEE Conference
• /
• 2003.11c
• /
• pp.920-923
• /
• 2003

An wearable electrocardiogram (ECG) monitoring system is a widely used non-invasive diagnostic tool for ambulatory patient who may be at risk from latent life-threatening cardiac abnormalities. In this paper, we have a portable ECG monitoring system with conductive fiber which was characterized by the small-size and the low power consumption. The system consists of conductive fibers, one-chip microcontroller, ECG preprocessing circuit, and monitoring software to be able to record and analyze in PC. ECG preprocessing circuit is made of pre-amplifier with gain of 10, band-pass filter with bandwidth of 0.5-120Hz and 2.5V offset circuit for A/D conversion. ECG signals obtained by sensor are included with corrupted noises such as a baseline wandering, 60 Hz power noise and interference noise by body movement. For cancellation corrupted noises in signals obtained by conductive fiber, we used the wavelet decomposition of wavelet transforms in MATLAB toolbox.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.2
• /
• pp.133-138
• /
• 2016

This paper describes the track initiation and target-tracking filter for ship tracking in a marine environment by using Light Detection And Ranging (LiDAR). LiDAR with three-dimensional scanning capability is more useful for target tracking in the short to medium range compared to RADAR. LiDAR has rotating multi-beams that return point clouds reflected from targets. Through preprocessing the cluster of the point cloud, the center point can be obtained from the cloud. Target tracking is carried out by using the center points of targets. The track of the target is initiated by investigating the normalized distance between the center points and connecting the points. The regular track obtained from the track initiation can be maintained by the target-tracking filter, which is commonly used in radar target tracking. The target-tracking filter is constructed to track a maneuvering target in a cluttered environment. The target-tracking algorithm including track initiation is experimentally evaluated in a sea-trial test with several boats.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.7
• /
• pp.1773-1793
• /
• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1998.03a
• /
• pp.100-105
• /
• 1998

Image processing was used to recognize parts at various disposition. Non-transpatent tachometer panel for automobile and semi-transparent panel have been used as test specimen. Laplacian filter and various threshold values have been applied for preprocessing and edge following algorithm has been applied. Series of length data between edges have been generated from each image and compared using cross correlation coefficient. The result using cross correlation coefficient. The result using both edge following and cross correlation coefficient was proven to be the best fit for the proposed parts.

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.637-640
• /
• 1999

In this Paper we show the LCD simulator for defect inspection using image processing algorithm and neural network. The defect inspection algorithm of the LCD consists of preprocessing, feature extraction and defect classification. Preprocess removes noise from LCD image, using morphology operator and neural network is used for the defect classification. Sample images with scratch, pinhole, and spot from real LCD color filter image are used. The proposed algorithms show that defect detected and classified in the ratio of 92.3% and 94.6 respectively.