• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.520-522
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• 1998

A development and application of machine vision system is introduced, which automatically recognizes the serial number of nuclear fuel tube. For the recognition, a indirect back light illuminating system is designed and a pattern matching algorithm based on neural network is applied. The various operation and management functions are also developed, on a PC under windows OS, for easy operation and data management, respectively. By the successful application of the vision system the productivity of the nuclear fuel tube recognition process is highly improved.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.119-124
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• 1999

This paper presents new approach to technology pattern recognition of camera back cover and test of soldering. In real-time implementing of pattern recognition camera back cover and test of soldering, the MVB-03 vision board has been used. Image can be captured from standard CCD monochrome camera in resolutions up to 640$\times$480 pixels. Various options re available for color cameras, a synchronous camera reset, and linescan cameras. Image processing os performed using Texas Instruments TMS320C31 digital signal processors. Image display is via a standard composite video monitor and supports non-destructive color overlay. System processing is possible using c30 machine code. Application software can be written in Borland C++ or Visual C++

• Smart Structures and Systems
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• v.30 no.3
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• pp.255-261
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• 2022

When no extensometer is available in a generic tensile-compression test carried out by a universal testing machine (for instance the model BIONIX from Material Testing Systems (MTS)), the test results only provide the relative displacement between the machine grips. The test does not provide any information on the local behaviour of the material. This contribution presents the potential of an application of Digital Image Correlation (DIC) toward the reconstruction of the behaviour along the specimen. In particular, the authors test a Ni-Ti shape memory alloys (SMA) specimen with emphasis on the coupling of the two measurement techniques.

• Journal of Welding and Joining
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• v.16 no.3
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• pp.85-94
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• 1998

Welding fabrication invariantly involves three district sequential steps: preparation, actual process execution and post-weld inspection. One of the major problems in automating these steps and developing autonomous welding system, is the lack of proper sensing strategies. Conventionally, machine vision is used in robotic arc welding only for the correction of pre-taught welding paths in single pass. In this paper, developed vision processing techniques are detailed, and their application in welding fabrication is covered. The software for joint tracking system is finally proposed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.903-909
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• 2018

In this paper, performance analysis of machine vision techniques is presented to improve the convenience and speed of automatic inspection in the industrial field when machine vision is applied to the image not taken in the stationary state, but in the moving state on a conveyer. When the length of cylindrical rods used for automobiles was measured using the edge detection method, the conveying speed increased, and the uncertainty of the boundary between the background and the part image increased, which resulted in a shorter image of the object taken. This paper proposes a linear compensation method to predict the biased errors of the length measurements after examining the pattern of biased and random errors, respectively, with 6 different types of specimens and 7 velocity stages. The length measurement corrected by the linear compensation method had the same accuracy as the stationary state within the speed range of 30 cm/s and could enhance the application capability in automatic inspections.

• IEIE Transactions on Smart Processing and Computing
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• v.3 no.6
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• pp.366-371
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• 2014

This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

• Journal of Biosystems Engineering
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• v.21 no.3
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• pp.315-324
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• 1996

This study was one of a series of studies on application of machine vision and image processing to extract the geometrical features of plants and to analyze plant growth. Several algorithms were developed to measure morphological properties of plants and describing the growth development of in-situ lettuce(Lactuca sativa L.). Canopy, centroid, leaf density and fractal dimension of plant were measured from a top viewed binary image. It was capable of identifying plants by a thinning top viewed image. Overlapping the thinning side viewed image with a side viewed binary image of plant was very effective to auto-detect meaningful nodes associated with canopy components such as stem, branch, petiole and leaf. And, plant height, stem diameter, number and angle of branches, and internode length and so on were analyzed by using meaningful nodes extracted from overlapped side viewed images. Canopy, leaf density and fractal dimension showed high relation with fresh weight or growth pattern of in-situ lettuces. It was concluded that machine vision system and image processing techniques are very useful in extracting geometrical features and monitoring plant growth, although interactive methods, for some applications, were required.

• Journal of Biosystems Engineering
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• v.25 no.4
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• pp.287-292
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• 2000

The herbicide penetration on weed leaves was spatially analyzed by using chlorophyll fluorescent emission and machine vision technique. Velvetleaf and metribuzin were used as experimental materials in the study. The herbicide spray images were obtained by a combinaton of a fluorescent dye and a UV lighting system. The herbicide penetration was analyzed by means of detecting chlorophyll fluorescent emission under blue-green lighting. According to the experiment results, the number and the size of spray droplets decreased with coverage increasing. The herbicide penetrated mainly along leaf veins and the time for complete penetration over the whole leaf was approximately 100 minutes after herbicide spraying. When the coverage of herbicide droplets on the surface of leaves increased, the speed of herbicide penetration also increased. This study suggested a way of characerizing herbicide spatial penetration and distribution in leaves.

• International Journal of Quality Innovation
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• v.4 no.2
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• pp.50-64
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• 2003

During IC inspection, which includes the two parts of Mark and Lead, the deviation of IC on the tape occurring in high speed movements usually generates light reflection effect, which in turn causes errors in IC recognition as measured by machine vision system. This research filters the light reflection effect by developing standard components, identifies the correct position of IC Lead, hence fixes the measurement errors or non-measurability caused by light reflection, avoids the resulting discontinued operation of measuring system, and improves the productivity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.4103-4117
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• 2014

Image processing and computer vision algorithms are gaining larger concern in a variety of application areas such as robotics and man-machine interaction. Vision allows the development of flexible, intelligent, and less intrusive approaches than most of the other sensor systems. In this work, we determine the location and orientation of a mobile robot which is crucial for performing its tasks. In order to be able to operate in real time there is a need to speed up different vision routines. Therefore, we present and evaluate a method for introducing parallelism into the multiple non-overlapping camera pose estimation algorithm proposed in [1]. In this algorithm the problem has been solved in real time using multiple non-overlapping cameras and the Extended Kalman Filter (EKF). Four cameras arranged in two back-to-back pairs are put on the platform of a moving robot. An important benefit of using multiple cameras for robot pose estimation is the capability of resolving vision uncertainties such as the bas-relief ambiguity. The proposed method is based on algorithmic skeletons for low, medium and high levels of parallelization. The analysis shows that the use of a multiprocessor system enhances the system performance by about 87%. In addition, the proposed design is scalable, which is necaccery in this application where the number of features changes repeatedly.