• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.1
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• pp.96-110
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• 2017

Most of vehicle detection studies using the existing general lens or wide-angle lens have a blind spot in the rear detection situation, the image is vulnerable to noise and a variety of external environments. In this paper, we propose a method that is detection in harsh external environment with noise, blind spots, etc. First, using a fish-eye lens will help minimize blind spots compared to the wide-angle lens. When angle of the lens is growing because nonlinear radial distortion also increase, calibration was used after initializing and optimizing the distortion constant in order to ensure accuracy. In addition, the original image was analyzed along with calibration to remove fog and calibrate brightness and thereby enable detection even when visibility is obstructed due to light and dark adaptations from foggy situations or sudden changes in illumination. Fog removal generally takes a considerably significant amount of time to calculate. Thus in order to reduce the calculation time, remove the fog used the major fog removal algorithm Dark Channel Prior. While Gamma Correction was used to calibrate brightness, a brightness and contrast evaluation was conducted on the image in order to determine the Gamma Value needed for correction. The evaluation used only a part instead of the entirety of the image in order to reduce the time allotted to calculation. When the brightness and contrast values were calculated, those values were used to decided Gamma value and to correct the entire image. The brightness correction and fog removal were processed in parallel, and the images were registered as a single image to minimize the calculation time needed for all the processes. Then the feature extraction method HOG was used to detect the vehicle in the corrected image. As a result, it took 0.064 seconds per frame to detect the vehicle using image correction as proposed herein, which showed a 7.5% improvement in detection rate compared to the existing vehicle detection method.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.256-262
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• 2004

A key component of an autonomous mobile robot is to localize itself and build a map of the environment simultaneously. In this paper, we propose a vision-based localization and mapping algorithm of mobile robot using fisheye lens. To acquire high-level features with scale invariance, a camera with fisheye lens facing toward to ceiling is attached to the robot. These features are used in mP building and localization. As a preprocessing, input image from fisheye lens is calibrated to remove radial distortion and then labeling and convex hull techniques are used to segment ceiling and wall region for the calibrated image. At the initial map building process, features we calculated for each segmented region and stored in map database. Features are continuously calculated for sequential input images and matched to the map. n some features are not matched, those features are added to the map. This map matching and updating process is continued until map building process is finished, Localization is used in map building process and searching the location of the robot on the map. The calculated features at the position of the robot are matched to the existing map to estimate the real position of the robot, and map building database is updated at the same time. By the proposed method, the elapsed time for map building is within 2 minutes for 50㎡ region, the positioning accuracy is ±13cm and the error about the positioning angle of the robot is ±3 degree for localization.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.7-18
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• 2005

A key component of an autonomous mobile robot is to localize itself accurately and build a map of the environment simultaneously. In this paper, we propose a vision-based mobile robot localization and mapping algorithm using scale-invariant features. A camera with fisheye lens facing toward to ceiling is attached to the robot to acquire high-level features with scale invariance. These features are used in map building and localization process. As pre-processing, input images from fisheye lens are calibrated to remove radial distortion then labeling and convex hull techniques are used to segment ceiling region from wall region. At initial map building process, features are calculated for segmented regions and stored in map database. Features are continuously calculated from sequential input images and matched against existing map until map building process is finished. If features are not matched, they are added to the existing map. Localization is done simultaneously with feature matching at map building process. Localization. is performed when features are matched with existing map and map building database is updated at same time. The proposed method can perform a map building in 2 minutes on $50m^2$ area. The positioning accuracy is ${\pm}13cm$, the average error on robot angle with the positioning is ${\pm}3$ degree.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.41-49
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• 2009

In this paper, the multi-camera calibration algorithm for optical motion capture system is proposed. This algorithm performs 1st camera calibration using DLT(Direct linear transformation} method and 3-axis calibration frame with 7 optical markers. And 2nd calibration is performed by waving with a wand of known length(so called wand dance} throughout desired calibration volume. In the 1st camera calibration, it is obtained not only camera parameter but also radial lens distortion parameters. These parameters are used initial solution for optimization in the 2nd camera calibration. In the 2nd camera calibration, the optimization is performed. The objective function is to minimize the difference of distance between real markers and reconstructed markers. For verification of the proposed algorithm, re-projection errors are calculated and the distance among markers in the 3-axis frame and in the wand calculated. And then it compares the proposed algorithm with commercial motion capture system. In the 3D reconstruction error of 3-axis frame, average error presents 1.7042mm(commercial system) and 0.8765mm(proposed algorithm). Average error reduces to 51.4 percent in commercial system. In the distance between markers in the wand, the average error shows 1.8897mm in the commercial system and 2.0183mm in the proposed algorithm.

• Journal of Korean Society of Forest Science
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• v.78 no.2
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• pp.119-131
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• 1989

This experiment was executed to investigate and compare qualitative and quantitative anatomical features in compression wood, opposite wood, and side wood formed in a bent stem, a straight branch, and an exposed horizontal root of Korean red pine(Pinus densiflora S. et Z.). The respective four discs containing compression wood taken at 20cm interval both in stem and branch as well as a disc containing well developed compression wood from horizontal root were analyzed. Percentage of compression wood and eccentricity showed decreasing tendency with the increasing distance in height direction of stem and length direction of branch. The qualitative anatomical features of compression wood appeared to differ from those of side and opposite wood in very gradual tracheid transition from earlywood to latewood, roundish tracheid shape on cross surface, tracheid distortion at tip on radial surface, existence of intercellular space, and helical cavity in tracheid wall. And the differences in these qualitative features among the compression wood, opposite wood, and side wood became less intensive with the decreasing trends in percentage of compression wood and eccentricity. The quantitative anatomical features in compression wood also appeared to be wider in that respective widths of fusiform and uniseriate ray than those of opposite and side wood, but the heights of fusiform and uniseriate ray in compression wood were smaller than in opposite and side wood. The number of horizontal resin canal(fusiform ray) and uniseriate ray, however, showed no differences among the compression wood, opposite wood, and side wood. And the number of vertical resin canal in unit area, $4{\pi}mm^2$ of compression wood was fewer than that in opposite wood, whereas numerous vertical resin canals contained in a growth ring. These rays of compression wood seemed to be characterized by smaller height and wider width than those of opposite and side wood.