• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.494-500
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• 2004

This paper presents a machine vision system, which inspects the measurement of the ALC block on a real-time basis in the production process. The automatic measurement system was established with a CCD camera, an image grabber, and a personal computer without using assembled measurement equipment. Images obtained by this system was processed by an algorithm, specially designed for an enhanced measurement accuracy. For the realization of the proposed algorithm, a preprocessing method that can be applied to overcome uneven lighting environment, boundary decision method, unit length decision method in uneven condition with rocking objects, and a projection of region using pixel summation are developed. From our experimental results, we could find that the required measurement accuracy specification is sufficiently satisfied by using the proposed method.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.795-801
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• 1998

In this paper, we propose a machine vision algorithm for inspecting inserts which are used for milling and turning operations. Major defects of the inserts are breakage and crack on insert surfaces. Among the defects, breakages on the face of the inserts can be detected through three stages of the algorithm developed in this paper. In the first stage, a multi-layer perceptron is used to recognize the inserts being inspected. Edge detection of the insert image is performed in the second stage. Finally, in the third stage breakages on the insert face are identified using Hough transform. The overall algorithm is tested on real specimens and the results show that the algorithm works fairly well.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.5
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• pp.529-535
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• 2009

This paper describes a measuring device of glass thickness using machine vision and image processing techniques on real-time. Today, the machine vision enable to inspect fast and exactly than human's eyes. The presented system has advantages of continuous measurement, flexibility and good accuracy. The system consists of a laser diode, a CCD camera with PC. The camera located on the opposite side of the incident beam measures the distance between two reflected laser beams from the glass top and bottom surface. We apply a binary algorithm to convert and analyze the image from camera to PC. Laser point coordination by border tracing algorithm is used to find the center of beam circle. The measured result was compared with micrometer and showed 0.002mm accuracy. Finally, the errors were discussed how to minimize the influence of glass wedge angle and angular error of moving stage.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.354-362
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• 2000

In Korea, researches on year-round leaf vegetables production system are in progress, most of them focused on environmental control. Therefore, automation technologies for harvesting, transporting, and grading are in great demand. A robot system for harvesting lettuces, composed of a 3-DOF (degree of freedom) manipulator, an end-effector, a lettuce feeding conveyor, an air blower, a machine vision system, six photoelectric sensors, and a fuzzy logic controller, was developed. A fuzzy logic control was applied to determine appropriate grip force on lettuce. Leaf area index and height were used as input variables and voltage as an output variable for the fuzzy logic controller. Success rate of the lettuce harvesting was 94.12%, and average harvesting time was approximately 5 seconds per lettuce.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.266-270
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• 2016

In this paper, we propose a technique of drowsiness detection using machine vision. The drowsiness of vehicle driver is often the primary cause of motor vehicle accidents. Therefore, the checking of eye images for detecting drowsiness status of driver is critical for preventing these accidents. In our suggested method, we analyze the changes of histogram and edge of eye region images which are acquired using CCD camera. We developed a drowsiness detection system using the histogram and edge change information. The experimental results show that our proposed method enhances the accuracy of detecting drowsiness nearly to 98%, and can be used for preventing vehicle accidents due to the drowsiness of drivers.

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.3
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• pp.397-406
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• 1998

The purpose of this research is to develop an integrated quality inspection system using machine vision technology in the automotive engine assembly process. The system makes it possible for the inspected data to be entered directly from the machine vision system into the developed system without the need for intermediate operations. Such direct entry enables prompt corrective actions against process problems. An IVP-150 machine vision board is installed an the PC for image processing, and a template matching technology is implemented to precisely verify quality factors. The developed system is successfully installed in a manufacturing process, and it showed robustness to the problems of noise, distortion, and orientation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.3
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• pp.216-220
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• 2007

Camera calibration in machine vision is the process of determining the intrinsic camera parameters and the three-dimensional (3D) position and orientation of the camera frame relative to a certain world coordinate system. On the other hand, Takagi-Sugeno-Kang (TSK) fuzzy system is a very popular fuzzy system and approximates any nonlinear function to arbitrary accuracy with only a small number of fuzzy rules. It demonstrates not only nonlinear behavior but also transparent structure. In this paper, we present a novel and simple technique for camera calibration for machine vision using TSK fuzzy model. The proposed method divides the world into some regions according to camera view and uses the clustered 3D geometric knowledge. TSK fuzzy system is employed to estimate the camera parameters by combining partial information into complete 3D information. The experiments are performed to verify the proposed camera calibration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.211-213
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• 2019

This paper proposes an image detection method, which can detect images regardless of the location and the direction of an image, required for automatic inspection based on machine vision technologies. A cable harness is considered in this paper as an inspection object, and implementation results of a technology of being applicable to a real cable harness production process is presented.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.181-189
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• 2023

In this paper, we present a case study of developing MVIS (Machine Vision Inspection System) designed for exterior quality inspection of stamping dies used in the production of automotive exterior components in a small to medium-sized factory. While the primary processes within the factory, including machining, transportation, and loading, have been automated using PLCs, CNC machines, and robots, the final quality inspection process still relies on manual labor. We implement the MVIS with general-purpose industrial cameras and Python-based open-source libraries and frameworks for rapid and low-cost development. The MVIS can play a major role on improving throughput and lead time of stamping dies. Furthermore, the processed inspection images can be leveraged for future process monitoring and improvement by applying deep learning techniques.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.89-98
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• 2024

Due to the increase in population and consequently the increase in the production of plastic waste, recovery of this part of the waste is an undeniable necessity. On the other hand, the recycling of plastic waste, if it is placed in a systematic process and controlled, can be effective in creating jobs and maintaining environmental health. Waste collection in many large cities has become a major problem due to lack of proper planning with increasing waste from population accumulation and changing consumption patterns. Today, waste management is no longer limited to waste collection, but waste collection is one of the important areas of its management, i.e. training, segregation, collection, recycling and processing. In this study, a systematic method based on machine vision for sorting plastic bottles in different colors for recycling purposes will be proposed. In this method, image classification and segmentation techniques were presented to improve the performance of plastic bottle classification. Evaluation of the proposed method and comparison with previous works showed the proper performance of this method.