• The Journal of the Korea Contents Association
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• v.8 no.1
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• pp.236-245
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• 2008

Robust face recognition under various illumination environments is very difficult and needs to be accomplished for successful commercialization. In this paper, we propose an efficient illumination preprocessing method for face recognition. illumination preprocessing algorithm based on anisotropic smoothing is well known to be effective among illumination normalization methods but deteriorates the intensity contrast of the original image, and incurs less sharp edges. The proposed method in this paper improves the previous anisotropic smoothing based illumination normalization method so that it increases the intensity contrast and enhances the edges while diminishing effects of illumination. Due to the result of these improvements, face images preprocessed by the proposed illumination preprocessing method becomes to have more distinctive feature vectors(Gabor feature vectors). Through experiments of face recognition using Gabor jet similarity, the effectiveness of the proposed illumination preprocessing method is verified.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.12
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• pp.609-619
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• 2015

A preprocessing scheme utilizing multi-division of the ROI (region of interest) in a chemiluminescence image during inversion is proposed. The resulting inverted image shows the flame's structure, which can be useful for studying combustion instability. The flame structure is often quantitatively visualized with PLIF (planar laser-induced fluorescence) images as well. The chemiluminescence image, which is a line-integral of the flame, needs to be preprocessed before inversion, mainly due to the inherent noise and the assumption of axisymmetry during the inversion. The feasibility of the multi-division preprocessing technique has been tested with experimentally-obtained OH PLIF and $OH^*$ chemiluminescence images of jet and swirl-stabilized flames burning substitute natural gas (SNG). It turns out that the technique outperforms two conventional methods, specifically, the technique without preprocessing and the one with uni-division, reconstructing the SNG flame structures much better than its two counterparts when compared using corresponding OH PLIF images. The characteristics of the optimum degree of polynomials to be applied for curve-fitting of the flame region data for the multi-division method involving two flames has also been investigated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3490-3507
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• 2014

License plate detection (LPD) is one of the most important aspects of an automatic license plate recognition system. Although there have been some successful license plate recognition (LPR) methods in past decades, it is still a challenging problem because of the diversity of plate formats and outdoor illumination conditions in image acquisition. Because the accurate detection of license plates under different conditions directly affects overall recognition system accuracy, different methods have been developed for LPD systems. In this paper, we propose a license plate detection method that is rapid and robust against variation, especially variations in illumination conditions. Taking the aspects of accuracy and speed into consideration, the proposed system consists of two stages. For each stage, Haar-like features are used to compute and select features from license plate images and a cascade classifier based on the concatenation of classifiers where each classifier is trained by an AdaBoost algorithm is used to classify parts of an image within a search window as either license plate or non-license plate. And it is followed by connected component analysis (CCA) for eliminating false positives. The two stages use different image preprocessing blocks: image preprocessing without adaptive thresholding for the first stage and image preprocessing with adaptive thresholding for the second stage. The method is faster and more accurate than most existing methods used in LPD. Experimental results demonstrate that the LPD rate is 98.38% and the average computational time is 54.64 ms.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.271-276
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• 2024

The success of construction projects is influenced by various factors, with accurate management and prediction of the construction period playing a crucial role. The construction period is determined through contracts between the client and the contractor, and it is considered a key element in the management of construction projects, alongside cost management. To ensure the successful completion of projects, accurate prediction of the construction period is essential, as it aids in the efficient allocation of time and resources. The main objective of this study is to maximize the performance of construction period prediction models by applying and comparing various methods for handling missing data. Optimizing the model's performance requires accuracy and completeness of data, with the process of outlier removal and missing data imputation potentially having a significant impact on the model's predictive capability. During this process, the effect of changes in the dataset on model performance will be closely examined to identify the most effective method for handling missing data. Outlier removal and missing data imputation are crucial steps in the data preprocessing phase, and they can significantly improve the model's accuracy and reliability. This research aims to apply these data preprocessing methods and analyze their outcomes to find the most effective missing data imputation method for construction period prediction. After the selection process, considering the model's performance and stability, the mode imputation method was identified as the most suitable for predicting the construction period. The findings of this research are expected to contribute not only to improving the accuracy of construction period predictions but also to enhancing the overall efficiency and success rate of construction project management.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.481-490
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• 2002

Off-line handwritten character recognition is in difficulty of incomplete preprocessing because it has not dynamic information has various handwriting, extreme overlap of the consonant and vowel and many error image of stroke. Consequently off-line handwritten character recognition needs to study about preprocessing of various methods such as binarization and thinning. This paper considers running time of watershed algorithm and the quality of resulting image as preprocessing for off-line handwritten Korean character recognition. So it proposes application of effective watershed algorithm for segmentation of character region and background region in gray level character image and segmentation function for binarization by extracted watershed image. Besides it proposes thinning methods that effectively extracts skeleton through conditional test mask considering routing time and quality of skeleton, estimates efficiency of existing methods and this paper's methods as running time and quality. Average execution time on the previous method was 2.16 second and on this paper method was 1.72 second. We prove that this paper's method removed noise effectively with overlap stroke as compared with the previous method.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.26 no.4
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• pp.43-50
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• 2013

Objectives : This study aims to suggest preliminary standard procedure for face lift and correction of nasolabial folds using thread-embedding (Maeseon) of Korean medicine(KM). Methods : Three KM practitioners of facial correction and rejuvenation who have over hundred case of practice participated in establishment of standard procedure. Standard procedure contains preprocessing, main procedure for correction, and solution of side effects. Results : Standard procedure is comprised of twelve processes with preprocessing and postprocessing. Preprocessing has position, disinfection, and anesthesia. Main process consists of overall structure correction, face lifting, nasolabial folds correction, and mesh making on cheek. Postprocess covers disinfection, edema prevention. Conclusions : To our knowledge, this is the first work to suggest standard procedure of facial rejuvenation using Maeseon. It would contribute to standardized practice in clinical fields and future study of revealing Maeseon's effectiveness.

• Journal of IKEEE
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• v.27 no.4
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• pp.561-566
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• 2023

With the development of deep learning, it is possible to solve various computer non-specialized problems such as image processing. However, most image processing methods use only the visual information of the image to process the image. Text data such as descriptions and annotations related to images may provide additional tactile and visual information that is difficult to obtain from the image itself. In this paper, we intend to improve image classification accuracy through a deep learning model that analyzes images and texts using image-text pairs. The proposed model showed an approximately 11% classification accuracy improvement over the deep learning model using only image information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1680-1686
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• 2015

Edge detection is an essential preprocessing for most image processing application, and there are several existing detection methods such as Sobel, Roberts, Laplacian, LoG(Laplacian of Gaussian) operators, etc. Those existing edge detection methods have not given satisfactory results since they do not offer enough pixel brightness change in low light level environment. Therefore, in this study new algorithms using brightness transfer function in the preprocessing and for edge detection applying standard deviation and average-weighted local masks are proposed. In addition, the performance of proposed algorithms was evaluated in comparison with the existing edge detection methods such as Sobel, Roberts, Prewitt, Laplacian, LoG operators.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.7
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• pp.1732-1739
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• 2013

The identification of haplotypes, which encode SNPs in a single chromosome, makes it possible to perform a haplotype-based association test with diseases. Given a set of genotypes from a population, the process of recovering the haplotypes that explain the genotypes is called haplotype inference. We propose a new preprocessing algorithm for the haplotype inference by pure parsimony (HIPP). The proposed algorithm excludes a large amount of redundant candidate haplotypes by detecting some groups of haplotypes that are dispensable for optimal solutions. For the well-known synthetic and biological data, the experimental results of our method show that our method run much faster than other preprocessing methods. After applying our preprocessing results, the numbers of haplotypes of HIPP solvers are equal to or slightly larger than that of optimal solutions.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.