• Journal of Biosystems Engineering
• /
• v.42 no.3
• /
• pp.227-233
• /
• 2017

Purpose: Morphological properties of soybean roots are important indicators of the vigor of the seed, which determines the survival rate of the seedlings grown. The current vigor test for soybean seeds is manual measurement with the human eye. This study describes an application of a machine vision technique for rapid measurement of soybean seed vigor to replace the time-consuming and labor-intensive conventional method. Methods: A CCD camera was used to obtain color images of seeds during germination. Image processing techniques were used to obtain root segmentation. The various morphological parameters, such as primary root length, total root length, total surface area, average diameter, and branching points of roots were calculated from a root skeleton image using a customized pixel-based image processing algorithm. Results: The measurement accuracy of the machine vision system ranged from 92.6% to 98.8%, with accuracies of 96.2% for primary root length and 96.4% for total root length, compared to manual measurement. The correlation coefficient for each measurement was 0.999 with a standard error of prediction of 1.16 mm for primary root length and 0.97 mm for total root length. Conclusions: The developed machine vision system showed good performance for the morphological measurement of soybean roots. This image analysis algorithm, combined with a simple color camera, can be used as an alternative to the conventional seed vigor test method.

• 제어로봇시스템학회:학술대회논문집
• /
• 2003.10a
• /
• pp.250-255
• /
• 2003

The position of a 3-dimensional(3D) point can be measured by using calibrated stereo camera. To obtain more accurate measurement ,more accurate camera calibration is required. There are many existing methods to calibrate camera. The simple linear methods are usually not accurate due to nonlinear lens distortion. The nonlinear methods are accurate more than linear method, but it increase computational cost and good initial guess is needed. The multi step methods need to know some camera parameters of used camera. Recent years, these explicit model based camera calibration work with the development of more precise camera models involving correction of lens distortion. But these explicit model based camera calibration have disadvantages. So implicit camera calibration methods have been derived. One of the popular implicit camera calibration method is to use neural network. In this paper, we propose implicit stereo camera calibration method for 3D reconstruction using support vector machine. SVM can learn the relationship between 3D coordinate and image coordinate, and it shows the robust property with the presence of noise and lens distortion, results of simulation are shown in section 4.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2000.11a
• /
• pp.695-698
• /
• 2000

Round-cornering machines are mainly used for cornering of stiffners for ship buildings. In the present time they have been operated manually by operators. so they are need to be operated automatically without regard to any shapes of stiffners. We developed the automatic round cornering system which consists of CCd Camera, PC and laser diode to detect automatically the edge of stiffners to be processed

• Journal of Biosystems Engineering
• /
• v.27 no.2
• /
• pp.143-150
• /
• 2002

Several researches for site-specific weed control have tried to increase accuracy of weed detection with machine vision technique. However, there is a problem which needs substantial time to perform site-specific spraying. Therefore, new technology for real-time precision spraying system is needed. This research was executed to develope the new technology to estimate weed density and size in real time, and to conduct a real-time site-specific spraying. It would effectively reduce herbicide amounts applied for a crop field. The real-time precision spraying system consisted of a Differential Global Positioning System (DGPS) with an error of 2 cm, a machine vision system, a geomagnetic sensor for correction of view point of CCD camera and an automatic sprayer with separately controlled nozzle. The weed density was calculated with comparison between position information and a pre-designed electronic map. The position information was obtained in real time using the DGPS and the machine vision. The electronic map contained a position database of crops automatically constructed when seeding. The developed system was tested on an experimental field of Seoul National University. Success rate of the spraying was about 61%.

• Journal of the Semiconductor & Display Technology
• /
• v.6 no.1 s.18
• /
• pp.43-48
• /
• 2007

For exposure systems, very accurate alignment between the mask and the substrate is indispensable. In this paper, an automatic alignment system using machine vision for exposure systems is described. Machine vision algorithms are described in detail including extraction of an alignment mark's center position and camera calibration. Methods for extracting parameters for alignment are also presented with some compensation techniques to reduce alignment time. Our alignment system was implemented with a vision system and motion control stages. The performance of the alignment system has been extensively tested with satisfactory results. The performance evaluation shows alignment accuracy of lum within total alignment time of about $2{\sim}3$ seconds including stage moving time.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.1
• /
• pp.409-415
• /
• 2022

In computer vision, an image of a measurement target is acquired using a camera. And feature values, vectors, and regions are detected by applying algorithms and library functions. The detected data is calculated and analyzed in various forms depending on the purpose of use. Computer vision is being used in various places, especially in the field of automatically recognizing automobile parts or measuring the quality. Computer vision is being used as the term machine vision in the industrial field, and it is connected with artificial intelligence to judge product quality or predict results. In this study, a vision system for judging the quality of automobile parts was built, and the results were compared by applying five machine learning classification models to the produced data.

• Journal of the Korean Society for Precision Engineering
• /
• v.22 no.6 s.171
• /
• pp.127-134
• /
• 2005

The machine vision has been applied to a number of industrial applications for quality control and automations to improve the manufacturing processes. In this paper, the automation system using the machine vision is developed, which is applicable to the marking process in a steel production process line. The working environment is very harsh to workers so that the automatic system in the steel industry is required increasingly. The developed automatic marking system consists of several mechanical and electrical elements such as the laser position detecting sensor system fur a structured laser beam which is projected to the billet in order to detect the geometry of the billet. An image processing algorithm has been developed to percept the two center positions of a camera and a billet, respectively, and to align two centers. A series of experiments has been conducted to investigate the performance of the proposed algorithm. The results show that two centers of the camera and the billet could be detected very well and differences between two center positions could be also decreased via the proposed location error decreasing algorithm.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1996.11a
• /
• pp.733-737
• /
• 1996

This paper presents a method to measure tile position and orientation of a prismatic polyhedral object (of unknown width, length, height, and number of vertices) using machine vision. The width, length, and origin of workplace where an object is lying are defined as Preliminary operation. The edges of an object are detected from captured image using least sum of square error. The information of an object is determined from the geometric relationships between edges. As an user interface, a versatile image processing program is developed in several modules, and renders a very useful 3D measurement at a limited constraints when adopted in automation of production process. The flexibility of camera position from the algorithm developrf can be used for automated pick and place operations and feeding workpiece u: ;ing assembly robot.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 2002.04a
• /
• pp.179-183
• /
• 2002

This paper presents a new approach to the calibration of a SCARA robot orientation with a camera modeling that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortion. radial distortion causes an inward or outward displacement of a given Image point from its ideal location. Actual optical systems are subject to various degrees of decentering, that is, the optical centers of lens elements are not strictly collinear. Thin prism distortion arises from imperfection in lens design and manufacturing as well as camera assembly It is our purpose to develop the vision system for the pattern recognition and the automatic test of parts and to apply the line of manufacturing.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1997.10a
• /
• pp.65-69
• /
• 1997

This paper presents a new approach to the calibration of a SCARA robot orientation with a camera modeling that accounts for major sources of camera distortion, namely, radial, decentering, and thin prism distortion. Radial distortion causes an inward or outward displacement of a given image point from its ideal location. Actual optical systems are subject to various degrees of decentering, that is, the optical centers of lens elements are not strictly collinear. Thin prism distortion arises from imperfection in lens design and manufacturing as well as camera assembly. It is our purpose to develop the vision system for the pattern recognition and the automatic test of parts and to apply the line of manufacturing.