• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1384-1390
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• 2019

This paper proposes a technological solution for improving the recognition direction of target objects for automatic vision inspection by machine vision. This paper proposes a technological solution for improving the recognition direction of target objects for automatic vision inspection by machine vision. This enables the automatic machine vision inspection to detect the image of the inspection object regardless of the position and orientation of the object, eliminating the need for a separate inspection jig and improving the automation level of the inspection process. This study develops the technology and method that can be applied to the wire harness manufacturing process as the inspection object and present the result of real system. The results of the system implementation was evaluated by the accredited institution. This includes successful measurement in the accuracy, detection recognition, reproducibility and positioning success rate, and achievement the goal in ten kinds of color discrimination ability, inspection time within one second and four automatic mode setting, etc.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.6
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• pp.605-611
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• 2019

3D Vision system has many applications recently but popularization have many problems that need to be overcome. Volumetric display may process a amount of visual data and design the high efficient vision system for display. In case of stereo system for volumetric display, disparity vectors from the stereoscopic sequences and residual images with the reference images has been transmitted, and the reconstructed stereoscopic sequences have been displayed at the receiver. So central issue for the design of efficient volumetric vision system lies in selecting an appropriate stereo matching and robust vision system. In this paper, we propose high efficient vision system with the reduction of rectification error which can perform the 3D data extraction efficiently with low computational complexity. In experimental results with proposed vision system, the proposed method can perform the 3D data extraction efficiently with reducing rectification error and low computational complexity.

• Electronics and Telecommunications Trends
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• v.39 no.4
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• pp.63-72
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• 2024

Vision is a process in which the brain and eyes collaborate to enable sight by analyzing light reflected from objects. Vision is also the most crucial among the five basic human senses for recognizing environments. The eyes contain 70% of the sensory receptors in the body, and 90% of the information processed by the brain is visual. Currently, approximately 2.2 billion people worldwide have vision impairments. A recent study estimated that the global economic productivity losses due to vision impairment and blindness amount to approximately $410 billion. Additionally, as people age, their ability to control their vision declines, leading to presbyopia, which typically starts in their 40s. Since people heavily rely on vision in their daily lives, vision problems can significantly reduce the quality of life. Approaches to solving vision problems can be broadly categorized into visual prostheses requiring surgery, sensory substitution based on neuroplasticity, and smart glasses for presbyopia. We present the trends and future development prospects for three key areas of research: visual prostheses, visual substitution technologies, and smart glasses technologies. These areas are being explored with the aim of addressing visual impairments and blindness.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.119-124
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• 2001

In this study, a vision system with image processing method have been introduced to find the edge radius of curvature. It was applied to inspect the edge quality of the deburring process product with brush grinding. Size of data was found to be critical in calculating the radius of curvature. Results using laser measurement system were compared.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.358-363
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• 1992

The tire manufacturing process demands classification of tire types when the tires are transferred between the inner processes. Though most processes are being well automated, the classification relies greatly upon the visual inspection of humen. This has been an obstacle to the factory automation of tire manufacturing companies. This paper proposes an effective vision systems which can be usefully applied to the tire classification process in real time. The system adopts a parallel architecture using multiple transputers and contains the algorithms of preprocesssing for character recognition. The system can be easily expandable to manipulate the large data that can be processed seperately.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.04b
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• pp.389-395
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• 1995

The bicycle production process mainly consists of welding process of pipes and its quality is determined by geometrical symmetry of arrangement of welded pipes. Hence, manual quality control of bicycle frames was very ineffecient and required much time in dimensional inspection and does not reserve high precision. In this paper we introduce a method of fast and accurate dimensional inspection of bicycle frame, using the Stereo Vision system and related some image processing techniques.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.54-63
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• 1993

Quantization error, generated by the quantization process of an image, is inherent in computer vision. Because, especially in stereo vision, the quantization error in a 2-D image results in position errors in the reconstructed 3-D scene, it is necessary to analyze it mathematically. In this paper, the analysis of the probability density function (pdf) of quantization error for a line-based stereo matching scheme is presented. We show that the theoretical pdf of quantization error in the reconstructed 3-D position information has more general form than the conventional analysis for pixel-based stereo matching schemes. Computer simulation is observed to surpport the theoretical distribution.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.86-88
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• 2005

An autonomous semantic-map building method is proposed, with the robot localized in the semantic-map. Our semantic-map is organized by objects represented as SIFT features and vision-based relative localization is employed as a process model to implement extended Kalman filters. Thus, we expect that robust SLAM performance can be obtained even under poor conditions in which localization cannot be achieved by classical odometry-based SLAM

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.780-785
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• 1992

We present a multi-sensor fusion method in positioning control of a robot by using fuzzy logic. In general, the vision sensor is used in the gross motion control and the force/torque sensor is used in the fine motion control. We construct a fuzzy logic controller to combine the vision sensor data and the force/torque sensor data. Also, we apply the fuzzy logic controller to the peg-in-hole process. Simulation results uphold the theoretical results.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.1021-1024
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• 2001

Out of all metal-cutting processes, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. In this paper, the vision system of the sensing methods of drill flank wear on the basis of image processing is used to detect the wear pattern by non-contact and direct method and get the reliable wear information about drill. In image processing of acquired image, median filter is applied for noise removal. The vision flank wear area of the drill was measured. Backpropagation neural networks (BPns) were used for no-line detection of drill wear. The neural network consisted of three layers: input, hidden and output. The input vectors comprised of spindle rotational speed, feed rates, vision flank wear, thrust and torque signals. The output was the drill wear state which was either usable or failure. Drilling experiments with various spindle rotational speed and feed rates were carried out. The learning process was peformed effectively by utilizing backpropagation. The detection of the abnormal states using BPNs achieved 96.4% reliability even when the spindle rotational speed and feedrate were changed.