• Journal of IKEEE
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• v.22 no.4
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• pp.948-952
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• 2018

Haze removal is an object of scientific desire due to its various practical applications. Existing algorithms are founded upon histogram equalization, contrast maximization, or the growing trend of applying machine learning in image processing. Since machine learning-based algorithms solve problems based on the data, they usually perform better than those based on traditional image processing/computer vision techniques. However, to achieve such a high performance, one of the requisites is a large and reliable training database, which seems to be unattainable owing to the complexity of real hazy and haze-free images acquisition. As a result, researchers are currently using the synthetic database, obtained by introducing the synthetic haze drawn from the standard uniform distribution into the clear images. In this paper, we propose the enhanced equidistribution, improving upon our previous study on equidistribution, and use it to make a new database for training machine learning-based haze removal algorithms. A large number of experiments verify the effectiveness of our proposed methodology.

• International Journal of Control, Automation, and Systems
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• v.3 no.4
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• pp.542-551
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• 2005

A new distance measurement method with the use of a single camera and a rotating mirror is presented. A camera in front of a rotating mirror acquires a sequence of reflected images, from which distance information is extracted. The distance measurement is based on the idea that the corresponding pixel of an object point at a longer distance moves at a higher speed in a sequence of images in this type of system setting. Distance measurement based on such pixel movement is investigated. Like many other image-based techniques, this presented technique requires matching corresponding points in two images. To alleviate such difficulty, two kinds of techniques of image tracking through the sequence of images and the utilization of multiple sets of image frames are described. Precision improvement is possible and is one attractive merit. The presented approach with a rotating mirror is especially suitable for such multiple measurements. The imprecision caused by the physical limit could be improved through making several measurements and taking an average. In this paper, mathematics necessary for implementing the technique is derived and presented. Also, the error sensitivities of related parameters are analyzed. Experimental results using the real camera-mirror setup are reported.

• Journal of the Society of Naval Architects of Korea
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• v.47 no.6
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• pp.831-835
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• 2010

Currently, engineering design is carried out in a distributed manner geographically or physically. This imposes new requirements on the computational environments, such as efficient integration and collaboration in the Internet and network environments. Meanwhile, Web-based distributed design has led new paradigms in design and manufacturing fields. For example, Web-based technologies have reduced the product development time and to ensure a competitive product in order to exchange and interact of real-time design information that integrates the distributed design environment between departments as well as companies via Internet and Web. Hence, efficient data communication for design information sharing is the basis for collaborative systems in the distributed environments. Design data communication techniques such as CORBA, DCOM, and JAVA RMI have been considered in the existing research, but these techniques have some disadvantages such as limitations of interoperability and firewall problems. This paper presents the application of engineering design problems in which distributed design information resources are integrated and exchanged using Web Service for supporting XML and HTTP without interoperability and firewall problems.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.3
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• pp.15-20
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• 2015

After the US government declared 3D printing technology a next-generation manufacturing technology, there have been many practical studies conducted to expand 3D printing technology to manufacturing technologies, called AMERICA MAKES. In particular, the Keck Center, located at the University of Texas at El Paso, has studied techniques for easily combing the 3D stacking process with space mobility and expanded these techniques to simultaneous staking techniques for multiple materials. Additionally, it developed convergence manufacturing techniques, such as direct inking techniques, in order to produce a module structure that combines electronic circuits and components, such as CUBESET. However, in these studies, it is impossible to develop a unified system using traditional independent through simple sequencing connections. This is because there are many problems in the integration between the stacking modeling of 3D printers and post-machining, such as thermal deformations, the precision accuracy of 3D printers, and independently driven coordinate problems among process systems. Therefore, in this paper, the integration method is suggested, which combines these 3D printers and subsequent machining process systems through an Internet-based cloud. Additionally, the sequential integrated system of a 3D printer, an NC milling machine, machine vision, and direct inking are realized.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.7
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• pp.138-146
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• 2003

A critical issue in area-based stereo matching lies in selecting a fixed rectangular window size. Previous stereo methods doesn't deal effectively with occluding boundary due to inevitable window-based problems, and so give inaccurate and noisy matching results in areas with steep disparity variations. In this paper, a variable window approach is presented to estimate accurate, detailed and smooth disparities for three-dimensional structure reconstruction. It makes the smoothing of depth discontinuity reduced by evaluating corresponding correlation values and intensity gradient-based similarity in the three-dimensional disparity space. In addition, it investigates maximum connected match candidate points and then devise the novel arbitrarily shaped variable window representative of a same disparity to treat with disparity variations of various structure shapes. We demonstrate the performance of the proposed variable window method with synthetic images, and show how our results improve on those of closely related techniques for accuracy, robustness, matching density and computing speed.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1594-1599
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• 2008

Haptic rendering is a process that provides force feedback during interactions between a user and an object. This paper presents a haptic rendering technique for a telemanipulation system of deformable objects using image processing and physically based modeling techniques. The interaction forces between an instrument driven by a haptic device and a deformable object are inferred in real time based on a continuum mechanics model of the object, which consists of a boundary element model and ${\alpha}$ priori knowledge of the object's mechanical properties. Macro- and micro-scale experimental systems, equipped with a telemanipulation system and a commercial haptic display, were developed and tested using silicone (macro-scale) and zebrafish embryos (micro-scale). The experimental results showed the effectiveness of the algorithm in different scales: two experimental systems applied the same algorithm provided haptic feedback regardless of the system scale.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.503-508
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• 2005

In the modern, distributed and dynamic construction environment it is important to exchange information from different sources and in different data formats in order to improve the processes supported by these systems. Previous research has demonstrated that (i) a significant percentage of construction data is stored in semi-structured or unstructured data formats (ii) locating and identifying such data that are needed for the important decision making processes is a very hard and time-consuming task. In this paper, an automated methodology for the classification and retrieval of construction images in AEC/FM model based systems will be presented. Specifically, a combination of techniques from the areas of image processing, computer vision, and content-based image retrieval have been deployed to develop a method that can retrieve related construction site image data from components of a project model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.3
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• pp.199-206
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• 2023

This study utilizes deep learning algorithms to automatically classify construction waste into three categories: wood waste, plastic waste, and concrete waste. Two models, VGG-16 and ViT (Vision Transformer), which are convolutional neural network image classification algorithms and NLP-based models that sequence images, respectively, were compared for their performance in classifying construction waste. Image data for construction waste was collected by crawling images from search engines worldwide, and 3,000 images, with 1,000 images for each category, were obtained by excluding images that were difficult to distinguish with the naked eye or that were duplicated and would interfere with the experiment. In addition, to improve the accuracy of the models, data augmentation was performed during training with a total of 30,000 images. Despite the unstructured nature of the collected image data, the experimental results showed that VGG-16 achieved an accuracy of 91.5%, and ViT achieved an accuracy of 92.7%. This seems to suggest the possibility of practical application in actual construction waste data management work. If object detection techniques or semantic segmentation techniques are utilized based on this study, more precise classification will be possible even within a single image, resulting in more accurate waste classification

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.4
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• pp.51-59
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• 2016

Measurements of the automobile manufacturers are available anywhere and anytime, directly based on the criterion of failure is measured. The maintenance of high-precision production activities is direct evidence of the fact that competitive manufacturing activities are very important in determining the success of companies to recall defective starting from raw material costs. The current manufacturing sites produce calipers and clearance gauge the degree of tool only specific. Therefore, judging the quality, including the number of errors, requires a lot of attention to the dimension failures in day-to-day measurements and measurement tasks and duties repeated in difficult situations. In this paper, we aim to develop a vehicle manufacturing plant site using each of the manufacturing processes while operating a measurement tool. We display it using the Image Processing PC-based S/W with all those visual facts by management and recorded as image information a more accurate and current situation to obtain information and share visual measurements. We carry out research on the design and development vision inspection algorithm applied for pattern-recognition techniques that can help manufacturing site quality control.

• Journal of Multimedia Information System
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• v.6 no.4
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• pp.225-234
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• 2019

Deep learning has become increasingly popular in both academic and industrial areas nowadays. Various domains including pattern recognition, Computer vision have witnessed the great power of deep neural networks. However, current studies on deep learning mainly focus on quality data sets with balanced class labels, while training on bad and imbalanced data set have been providing great challenges for classification tasks. We propose in this paper a method of data analysis-based data reduction techniques for selecting good and diversity data samples from a large dataset for a deep learning model. Furthermore, data sampling techniques could be applied to decrease the large size of raw data by retrieving its useful knowledge as representatives. Therefore, instead of dealing with large size of raw data, we can use some data reduction techniques to sample data without losing important information. We group PCB characters in classes and train deep learning on the ResNet56 v2 and SENet model in order to improve the classification performance of optical character recognition (OCR) character classifier.