• 한국멀티미디어학회논문지
• /
• 제14권6호
• /
• pp.719-729
• /
• 2011

본 논문은 무선 이동로봇의 영상에서 빠르고 정확하게 직선을 검출하는 방법에 대한 연구이다. 직선검출 처리시간 향상을 위하여 무선 이동로봇으로부터 전송 받은 영상의 특성을 분석하고 기존의 전처리 방법들 중 효율적인 전처리 방법을 선택하였다. 그리고 직선검출 정확도 향상을 위하여 하프변환의 결과를 저장하는 하프배열에서 지역 최대값을 선택하는 방법을 마스크를 설계하고 하프배열에 적용하여 개선하였다. 무선 이동로봇으로부터 획득한 실험영상을 가지고 실험을 실시하였고 제안방법은 처리시간과 직선검출에 있어 기존 방법들에 비해 좋은 성능을 보였다.

• 한국연소학회지
• /
• 제20권1호
• /
• pp.24-31
• /
• 2015

Flame structure, which contains a useful information for studying combustion instability of the flame, is often quantitatively visualized with PLIF (planar laser-induced fluorescence) and/or chemiluminescence images. The latter, a line-integral of a flame property, needs to be preprocessed before being inverted, mainly due to its inherent noise and the axisymmetry assumption of the inversion. A preprocessing scheme utilizing multi-division of ROI (region of interest) of the chemiluminescence image is proposed. Its feasibility has been tested with OH PLIF and $OH^*$ chemiluminescence images of SNG (synthetic natural gas) swirl-stabilized flames taken from a model gas turbine combustor. It turns out that the multi-division technique outperforms two conventional ones: those are, one without preprocessing and the other with uni-division preprocessing, reconstructing the SNG flame structure much better than its two counterparts, when compared with the corresponding OH PLIF images. It is also found that the Canny edge detection algorithm used for detecting edges in the multi-division method works better than the Sobel algorithm does.

• 한국산업융합학회 논문집
• /
• 제26권5호
• /
• pp.785-792
• /
• 2023

Because hyperspectral imaging provides detailed spectral information across a broad range of wavelengths, it can be utilized in numerous applications, including environmental monitoring, food quality inspection, medical diagnosis, material identification, art authentication, and crime scene analysis. However, hyperspectral images often contain various types of distortions due to the environmental conditions during image acquisition, which necessitates the proper removal of these distortions through a data preprocessing process. In this study, a preprocessing method was investigated to effectively correct the distortion caused by artificial light sources used in indoor hyperspectral imaging. For this purpose, a halogen-tungsten artificial light source was installed indoors, and hyperspectral images were acquired. The acquired images were then corrected for distortion using a preprocessing that does not require complex auxiliary equipment. After the corrections were made, the results were analyzed. According to the analysis, a statistical transformation technique using mean and standard deviation with reference to a reference signal was found to be the most effective in correcting distortions caused by artificial light sources.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2004년도 학술대회지
• /
• pp.814-818
• /
• 2004

Off-line handwritten character recognition is in difficulty of incomplete preprocessing because it has not dynamic and timing information besides 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 image and segmentation function for binarization by extracted watershed image. Besides it proposes thinning methods which effectively extracts skeleton through conditional test mask considering running time and quality. of skeleton, estimates efficiency of existing methods and this paper's methods as running time and quality. Watershed image conversion uses prewitt operator for gradient image conversion, extracts local minima considering 8-neighborhood pixel. And methods by using difference of mean value is used in region merging step, Converted watershed image by means of this methods separates effectively character region and background region applying to segmentation function. 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.

• 한국콘텐츠학회논문지
• /
• 제8권1호
• /
• pp.236-245
• /
• 2008

다양한 조명 환경 하에서, 얼굴인식이 잘 동작하도록 하는 것은 매우 어려운 일이며 성공적인 상업화를 위해서는 반드시 성취되어야 하는 작업이다. 본 논문에서는 얼굴 인식을 위한 효율적인 조명 전처리 방법을 제안한다. Anisotropic smoothing 기반 조명 전처리 방법은 조명 전처리 방법 가운데 효과적인 방법으로 잘 알려져 있으나, 원 이미지의 명도 대비를 감소시키며 에지 성분의 약화를 초래한다. 본 논문의 제안 방법은 기존 anisotropic smoothing 방법을 개선하여, 조명의 영향을 줄이면서 명도 대비를 증가시키고 에지 정보를 강화한다. 이러한 개선의 결과로, 본 논문의 제안 방법에 의해 조명 전처리된 같은 사람의 얼굴 이미지들은 보다 차별적인 특징 벡터(가버 특징 벡터)를 갖게 된다. 본 논문에서 제안한 조명 전처리 방법의 효율성은 가버젯 유사도를 이용한 얼굴 인식의 실험을 통하여 입증되었다.

• 한국콘텐츠학회논문지
• /
• 제2권4호
• /
• pp.46-50
• /
• 2002

본 논문은 지문 영상의 전처리중 이진화 수행과정에서 지문 영상의 국부적 밝기 차이에 따른 가장 큰 애로점인 임계치(threshold value) 설정을 대상 지문 영역의 밝기 등에 스스로 적응할 수 있도록 Silt Sum 방법을 응용한 적을 이진화를 수행하였다. 기존의 방법과 비교하여 본 연구에서 제시한 개선된 전처리 방법은 보다 높은 인식 정확도를 제공하며, 이에 따라 실험 결과에서 보는 바와 같이 지문 인식을 위한 특징점 추출 알고리즘에 적용될 수 있다.

• 전기학회논문지
• /
• 제58권1호
• /
• pp.173-180
• /
• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2017년도 춘계학술발표대회
• /
• pp.787-790
• /
• 2017

Data quality is the main issue in the classification problems; generally, the presence of noisy instances in the training dataset will not lead to robust classification performance. Such instances may cause the generated decision tree to suffer from over-fitting and its accuracy may decrease. Decision trees are useful, efficient, and commonly used for solving various real world classification problems in data mining. In this paper, we introduce a preprocessing technique to improve the classification accuracy rates of the C4.5 decision tree algorithm. In the proposed preprocessing method, we applied the naive Bayes classifier to remove the noisy instances from the training dataset. We applied our proposed method to a real e-commerce sales dataset to test the performance of the proposed algorithm against the existing C4.5 decision tree classifier. As the experimental results, the proposed method improved the classification accuracy by 8.5% and 14.32% using training dataset and 10-fold crossvalidation, respectively.

• 산업기술연구
• /
• 제28권B호
• /
• pp.101-109
• /
• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• 한국공작기계학회논문집
• /
• 제15권4호
• /
• pp.69-73
• /
• 2006

This paper present the optimal cleaning condition of the out-hull preprocessing robot by Taguchi method in design of experiments. A $L_8(2^4)$ orthogonal array is adopted to study the effect of adjustment parameters. The adjustment parameters consist of robot speed, motor torque, motor speed and tool angle. And the quality feature is selected as surface roughness of sheet metal. Taguchi analysis is performed in order to evaluate the effect of adjustment parameters of the quality feature of cleaning process by $Minitab^{(R)}$.