• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.4
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• pp.159-165
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• 2012

The accurate focusing position should be determined for accurate measurements In VMS. Camera lens focusing is an important problem in computer vision and video measuring systems (VMS) that use CCD cameras and high precision XYZ stages. Camera focusing is a very important step in high precision measurement systems that use computer vision technique. The auto focusing process consists of two steps, the focus value measurement step and the exact focusing position determination step. It is suitable for eliminating high frequency noises with lower processing time and without blurring. An automatic focusing technique is applied to measure a crater with a one-dimensional search algorithm for finding the best focus. Throughout this paper, the suggested algorithm for the Auto focusing was combined with the learning. As a result, it is expected that such a combination would be expanded into the system of recognizing voices in a noisy environment.

• Journal of Korea Society of Digital Industry and Information Management
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• v.7 no.4
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• pp.81-88
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• 2011

Mini Double Gears Frame is critical part of PDP and also produces couple hundred thousand every month. In the process of mass production, product inspection is very important process. Double Gear, one of the part of machine, was inspected by human eyes which caused mistakes and slow progress. To achieve the speed and accuracy the system was compensated by vision system which is inspecting automatically. The focus value is measured based on the fact that high contrast images have much high frequency edge information. High frequency term of the image is extracted using the high-pass filter and the sum of the high frequency term is used as the focus value. We used a Gaussian smoothing filter to reduce the noise and then measures the focus value using the modified Laplacian filter called a Sum modified Laplacian Focus values for the various lens positions are calculated and the position with the maximum focus value is decided as the focused position. The focus values calculated in various lens position showed the Gaussian distribution. We proposed a method to estimate the best focus position using the Gaussian curve fitting. Focus values of the uniform interval lens positions are calculated and the values are used to estimate the Gaussian distribution parameters to find the best focus position.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.29-35
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• 2014

The formula contains up between the text and the structural information, as well as their mathematical symbols. Research on-line or off-line recognition formula is underway actively used in various fields, and various forms of the equation are implemented recognition system. Although many documents are included in the various formulas, it is not easy to enter a formula into the computer. Recognition of the expression is divided into two processes of symbol recognition and structural analysis. After analyzing the location information of each character is specified to recognize the effective area after each symbol, and to the structure analysis based on the proximity between the characters is recognized as an independent single formula. Furthermore, analyzing the relationship between the front and back each time a combination of the position relationship between each symbol, and then to add the symbol which was able to easily update the structure of the entire formula. In this paper, by using a scanner to scan the book formula was used to interpret the meaning of the recognized symbol has a relative size and location information of the expression symbol. An algorithm to remove the formulas for calculation of the number of formula is present at the same time is proposed. Using the proposed algorithms to scan the books in the formula in order to evaluate the performance verification as 100% separation and showed the recognition rate equation.

• Journal of Embryo Transfer
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• v.19 no.3
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• pp.291-299
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• 2004

Characterization of quantitative trait loci (QTL) was investigated in the experimental cross population between Berkshire and Yorkshire breed. A total of 512 F$_2$ offspring from 65 matting of F$_1$ parents were phenotyped the carcass traits included average daily gain (ADG), average backfat thickness (ABF), tenth rip backfat thickness (TRF), loin eye area (LEA), and last rip backfat thickness (LRF). All animals were genotyped for 125 markers across the genome. Marker linkage maps were derived and used in QTL analysis based on line cross least squares regression interval mapping. A decision tree to identify QTL with imprinting effects was developed based on tests against the Mendelian mode of QTL expression. To set the evidence of QTL presence, empirical significance thresholds were derived at chromosome-wise and genome-wise levels using specialized permutation strategies. Significance thresholds derived by the permutation test were validated in the data set based on simulation of a pedigree and data structure similar to the Berkshire-Yorkshire population. Genome scan revealed significant evidences for 13 imprinted QTLs affecting growth and body compositions of which nine were identified to be QTL with paternally expressed inheritance mode. Four of QTLs in the loin eye area (LEA), and tenth rip backfat thickness (TRF), a maternally expressed QTL were found on chromosome 10 and 12. These results support the useful statistical models to analyse the imprinting far the QTLs related carcass trait.