• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.179-182
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• 2002

In this paper, this system makes use of the analog sensor and converts the feature of fish analog signal when sensor is operating with CPU(80C196KC). Then, After signal processing, this feature Is classified a special feature and a outline of fish by using the neural network, one of the artificial intelligence scheme. This neural network classifies fish pattern of very simple and short calculation. This has linear activation function and the error backpropagation is used as a learning algorithm. And the neural network is learned in off-line process. Because an adaptation period of neural network is too long time when random initial weights are used, off-line learning Is induced to decrease the Progress time We confirmed this method has better performance than somewhat outdated machines.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.287-290
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• 2000

In this paper we have presented approach to automatic the direction feature vectors detection, which detects the ridge line directly in gray scale images. In spite of a greater conceptual complexity, we have shown that our technique has less computational complexity than the complexity of the techniques which require binarization and thinning. Afterwards a various direction feature vectors is changed four direction feature vectors. In this paper used matching method is four direction feature vectors based matching. This four direction feature vectors consist feature patterns in fingerprint images. This feature patterns were used for identification of individuals inputed multilayer Neural Networks(NN) which has capability of excellent pattern identification.

• 한국정밀공학회지
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• 제25권2호
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• pp.35-42
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• 2008

This study introduces a robust feature detector for sonar data from a general fixed-type of sonar ring. The detector is composed of a data association filter and a feature extractor. The data association filter removes false returns provided frequently from sonar sensors, and classifies set of data from various objects and robot positions into a group in which all the data are from the same object. The feature extractor calculates the geometries of the feature for the group. We show the possibility of extracting circle feature as well as a line and a point features. The proposed method was applied to a real home environment with a real robot.

• 천문학회보
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• 제35권2호
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• pp.40.2-40.2
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• 2010

Polycyclic aromatic hydrocarbon (PAH) features have emerged as one of the most important infrared (IR) features since these PAH features dominate mid-IR spectra of galaxies and are ubiquitous within galactic and extragalactic objects. These PAH features have the potentials to become reliable star formation rate (SFR) proxies and diagnostics of physical conditions of interstellar medium, such as ionization states of dust grains and grain sizes. While constructing an unbiased library of 44 sample galaxies selected from 5MUSES sample, AKARI mJY Unbiased Survey of Extragalactic Sources in 5MUSES (AMUSES) intends to measure and to calibrate the PAH 3.3 mm emission feature which has not been studied extensively due to its weak strength and dearth of capable instruments. Out of 20 target galaxies, we detected the 3.3mm feature from eight galaxies and measured their line strengths, line widths and line ratios with other PAH emission features. Sample galaxies whose spectral energy distributions (SEDs) are classified as starburst-type have clearly stronger 3.3mm emission features than ones with AGN-type SEDs. We also found that there is a correlation between the PAH 3.3mm luminosity and total IR luminosity within our sample galaxies, albeit a large scatter. We further discuss implications of our results.

• 대한기계학회논문집A
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• 제25권5호
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• pp.841-849
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• 2001

The pixel information of the object was obtained sequentially and pixels were clustered to a label by the line labeling method. Feature points were determined by finding the slope for edge pixels after selecting the fixed number of edge pixels. The slope was estimated by the least square method to reduce the detection error. Once a matching point was determined by comparing the feature information of the object and the pattern, the parameters for translation, scaling and rotation were obtained by selecting the longer line of the two which passed through the matching point from left and right sides. Finally, modified Hausdorff Distance has been used to identify the similarity between the object and the given pattern. The multi-label method was developed for recognizing the patterns with more than one label, which performs the modified Hausdorff Distance twice. Experiments have been performed to verify the performance of the proposed algorithm and method for simple target image, complex target image, simple pattern, and complex pattern as well as the partially hidden object. It was proved via experiments that the proposed image matching algorithm for recognizing the object had a good performance of matching.

• Journal of Ubiquitous Convergence Technology
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• 제1권1호
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• pp.9-17
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• 2007

Feature based stereo matching is an effective way to perform 3D building reconstruction. However, in urban scene, the cluttered background and various building structures may interfere with the performance of building reconstruction. In this paper, we propose a novel method to robustly reconstruct buildings on the basis of rectangle regions. Firstly, we propose a multi-scale linear feature detector to obtain the salient line segments on the object contours. Secondly, candidate rectangle regions are extracted from the salient line segments based on their local information. Thirdly, stereo matching is performed with the list of matching line segments, which are boundary edges of the corresponding rectangles from the left and right image. Experimental results demonstrate that the proposed method can achieve better accuracy on the reconstructed result than pixel-level stereo matching.

• Journal of the Optical Society of Korea
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• 제18권4호
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• 한국멀티미디어학회논문지
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• 제12권6호
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• pp.794-803
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• 2009

In this paper, we will present a container auto-landing system, the system use the stereo camera to measure the container depth information. And the container region can be detected by using its hough line feature. In the line feature detection algorithm, we will detect the parallel lines and perpendicular lines which compose the rectangle region. Among all the candidate regions, we can select the region with the same aspect-ratio to the container. The region will be the detected container region. After having the object on both left and right images, we can estimate the distance from camera to object and container dimension. Then all the detect dimension information and depth inform will be applied to reconstruct the virtual environment of crane which will be introduce in this paper. Through the simulation result, we can know that, the container detection rate achieve to 97% with simple background. And the estimation algorithm can get a more accuracy result with a far distance than the near distance.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권9호
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• pp.559-566
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• 2005

An effective do-blurring algorithm is proposed to remove the blur of the even and the odd line images of the interlaced scan type camera. n the object or the camera moves fast while the interlaced scan type digital camera is acquiring images, blur is often created due to the misalignment between two images of even and odd lines. In this paper, the blurred original image is separated into the even and the odd line images of the half size. Two full sized images are generated using interpolation technique based on these two in ages. Again, these images are signed and combined through the processes of feature extraction, matching, sub-pixel matching, outlier removal, and mosaicking. De-blurring simulations about the images of different camera motions have been done.

• Journal of Information Processing Systems
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• 제1권1호
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• pp.9-13
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• 2005

Computer security has become a critical issue with the rapid development of business and other transaction systems over the Internet. The application of artificial intelligence, machine learning and data mining techniques to intrusion detection systems has been increasing recently. But most research is focused on improving the classification performance of a classifier. Selecting important features from input data leads to simplification of the problem, and faster and more accurate detection rates. Thus selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not a suitable method for a real-time intrusion detection system. In this paper, we develop the real-time intrusion detection system, which combines an on-line feature extraction method with the Least Squares Support Vector Machine classifier. Applying the proposed system to KDD CUP 99 data, experimental results show that it has a remarkable feature extraction and classification performance compared to existing off-line intrusion detection systems.