• Journal of the Korean Society for Precision Engineering
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• v.16 no.2 s.95
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• pp.39-49
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• 1999

This paper describes the development of a vision-aided position and orientation recognition system for automatically adjusting electronic tuners which control the waveform by rotating variable resisters. The position and orientation recognition system estimates the center and the angle of the tuner grooves so that the main controller may correct the difference from the ideal position and thereby manipulate the variable resisters automatically. In this paper a robust algorithm is suggested which estimates the center and the angle of the tuner grooves fast and precisly from the source image with lighting variance and video noise. In the algorithm morphological filtering, 8-chain coding, and invariant moments are sequentially used to figure out image segments concerned. The performance of the proposed system was evaluated using a set of real specimens. The results indicate the system works well enough to be used practically in real manufacturing lines. If the system adopts a high speed frame grabber which enables real time image processing, it can also be applied to positioning of robot manipulators as well as automated PCB adjusters.

• Korean Journal of Computational Design and Engineering
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• v.14 no.6
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• pp.404-414
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• 2009

In the computational molecular analysis, 3D structural comparison for protein searching plays a very important role. As protein databases have been grown rapidly in size, exhaustive search methods cannot provide satisfactory performance. Because exhaustive search methods try to handle the structure of protein by using sphere set which is converted from atoms set, the similarity calculation about two sphere sets is very expensive. Instead, the filter-and-refine paradigm offers an efficient alternative to database search without compromising the accuracy of the answers. In recent, a very fast algorithm based on the inter-atomic distance has been suggested by Ballester and Richard. Since they adopted the moments of distribution with inter-atomic distance between atoms which are rotational invariant, they can eliminate the structure alignment and orientation fix process and perform the searching faster than previous methods. In this paper, we propose a new 3D shape descriptor. It has properties of the general shape distribution and useful property in screening the molecular database. We show some experimental results for the validity of our method.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1636-1645
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• 1996

In this paper we proposed a new scheme for conditional two dimensional (2-D)moment-based classification of patterns on the basis of Gibbs random fields which are will suited for representing spatial continuity that is the characteristic of the most images. This implementation contains two parts: feature extraction and pattern classification. First of all, we extract feature vector which consists of conditional 2-D moments on the basis of estimated Gibbs parameter. Note that the extracted feature vectors are invariant under translation, rotation, size of patterns the corresponding template pattern. In order to evaluate the performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on 486 66Mhz PC. Experiments reveal that the proposed scheme has high classification rate over 94%.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.267-274
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• 2015

In this paper the optimal structure of a neural network based on OpenCV for a golf ball recognition and the intensity of ROI(Region Of Interest) are calculated. The system is composed of preprocess, image processing and machine learning, and a learning model is obtained by multi-layer perceptron using the inputs of 7 Hu's invariant moments, box ration extracted by vertical and horizontal length or ${\pi}$ calculated by area of ROI. Simulation results show that optimal numbers of hidden layer and the node of neuron are selected to 2 and 9 respectively considering the recognition rate and running time, and optimal intensity of ROI is selected to 200.

• Journal of the Society of Naval Architects of Korea
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• v.46 no.4
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• pp.382-390
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• 2009

The submerged body near the free surface is disturbed by the 1st and 2nd order wave forces, which results in unstable movements when no control is applied. In this paper, the vertical motions of the submerged body are analyzed, and the time-variant nonlinear system for the vertical motions of the submerged body is transformed to the time-invariant linear system in state space. Next, depth controller of the submerged body is designed by using LQR control, one of the modern optimal control technique. Numerical simulation shows that effective depth controls can be achieved by LQR control.

• Journal of the Optical Society of Korea
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• v.17 no.4
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• pp.328-335
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• 2013

Due to the rapid advancement of auto-refractor technology, most optometry shops provide refraction services. Despite their speed and convenience, the measurement values provided by auto-refractors include a significant degree of error due to psychological and physical factors. Therefore, there is a need for repetitive testing to obtain a smaller mean error value. However, even repetitive testing itself might not be sufficient to ensure accurate measurements. Therefore, research on a method of measurement that can complement auto-refractor measurements and provide confirmation of refraction results needs to be conducted. The customized optometry model described herein can satisfy the above requirements. With existing technologies, using human eye measurement devices to obtain relevant individual optical feature parameters is no longer difficult, and these parameters allow us to construct an optometry model for individual eyeballs. They also allow us to compute visual images produced from the optometry model using the CODE V macro programming language before recognizing the diffraction effects visual images with the neural network algorithm to obtain the accurate refractive diopter. This study attempts to combine the optometry model with the back-propagation neural network and achieve a double check recognition effect by complementing the auto-refractor. Results show that the accuracy achieved was above 98% and that this application could significantly enhance the service quality of refraction.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.125-133
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• 2003

In this paper we propose an efficient content-based image retrieval method using the color and wavelet based texture features. The color features are obtained from soft-color histograms of the global image and the wavelet-based texture features are obtained from the invariant moments of the high-pass sub-band through the spatial-frequency analysis of the wavelet transform. The proposed system, called a color and texture based two-step retrieval(CTBTR), is composed of two-step query operations for an efficient image retrieval. In the first-step matching operation, the color histogram features are used to filter out the dissimilar images quickly from a large image database. The second-step matching operation applies the wavelet based texture features to the retained set of images to retrieve all relevant images successfully. The experimental results show that the proposed algorithm yields more improved retrieval accuracy with computationally efficiency than the previous methods.