• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.631-632
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• 2006

The assembly of camera lens modules fur the mobile phone has not been automated so far. They are still assembled manually because of high precision of all parts and hard-to-recognize lens by vision camera. In addition, the very short life cycle of the camera phone lens requires flexible and intelligent automation. This study proposes a fast and accurate identification system of the parts by distributing the camera for 4 degree of freedom assembly robot system. Single or multi-cameras can be installed according to the part's image capture and processing mode. It has an agile structure which enables adaptation with the minimal job change. The framework is proposed and the experimental result is shown to prove the effectiveness.

• Annual Conference of KIPS
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• 2021.11a
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• pp.942-943
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• 2021

The present work proposes a classification system for the HEp-2 cell images using an unsupervised deep feature learning method. Unlike most of the state-of-the-art methods in the literature that utilize deep learning in a strictly supervised way, we propose here the use of the deep convolutional autoencoder (DCAE) as the principal feature extractor for classifying the different types of the HEp-2 cell images. The network takes the original cell images as the inputs and learns to reconstruct them in order to capture the features related to the global shape of the cells. A final feature vector is constructed by using the latent representations extracted from the DCAE, giving a highly discriminative feature representation. The created features will be fed to a nonlinear classifier whose output will represent the final type of the cell image. We have tested the discriminability of the proposed features on one of the most popular HEp-2 cell classification datasets, the SNPHEp-2 dataset and the results show that the proposed features manage to capture the distinctive characteristics of the different cell types while performing at least as well as the actual deep learning based state-of-the-art methods.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.28 no.2
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• pp.89-95
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• 2017

Background and Objectives : High-speed videolaryngoscopy (HSV) is the only technique that captures the true intra-cycle vibratory behavior of the vocal folds by capturing full images of the vocal folds. However, it has problems of no immediate feedback during examination, considerable waiting time for digital kymography (DKG), recording duration limited to a few seconds, and extreme demands for storage space. Herein, we demonstrate a new post-processing method that converts HSV images to two-dimensional digital kymography (2D-DKG) images, which adopts the algorithm of 2D videokymography (2D VKG). Materials and Methods : HSV system was used to capture images of vocal folds. HSV images were post-processed in Kay image-process software (KIPS), and conventional DKG images were retrieved. Custom-made post-processing system was used to convert HSV images to 2D-DKG images. The quantitative parameters of the post-processed 2D-DKG images was validated by comparing these parameters with those of the DKG images. Results : Serial HSV images for all phases of vocal fold vibratory movement are included. The images were converted by the scanning method using U-medical image-process software. Similar to conventional DKG, post-processed 2D DKG image from the HSV image can provide quantitative information on vocal fold mucosa vibration, including the various vibratory phases. Differences in amplitude symmetry index, phase symmetry index, open quotient, and close quotient between 2D-DKG and DKG were analyzed. There were no statistical differences between the quantitative parameters of vocal fold vibratory movement in 2D-DKG and DKG. Conclusion : The post-processing method of converting HSV images to 2D DKG images could provide clinical information and storage economy.

• The KIPS Transactions:PartB
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• v.9B no.4
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• pp.453-458
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• 2002

It is not easy to analyze dynamic sports motion, but it can be done easily by using an interactive playback of the recorded motion. Everybody is able to capture a golf swing owing to the popularization of PC and digital camcorder in these days. In this study, the system that can provide golf swing analysis is selected and implemented. Furthermore, editing method that can represent motion is applied and moving object tracking algorithm that can make this profess easy is applied to provide easy golf swing analysis.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.833-836
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• 2013

In this paper, we proposed an intelligent surveillance system which can be determined by the overspeed of vehicle which continuously collects by video imaging device. Imaging device to capture images continuously, and filtering errors that occur as a natural, long-distance moving objects by comparing the images collected before and after the images. To measure the size of things, it proves that able to measure speed of the vehicle, depending on the amount of growing pixels using the pixel processing.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.454-461
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• 2008

This paper is regarding a new input interface based on displacement of mobile devices having a camera. The mobile device can capture consecutive images by the camera, the displacement of the device is estimated by computing the displacement between consecutive images in real-time. The proposed system extracts feature points based on SUSAN comer detector which has low computational complexity. We generate Voronoi domain by using the two-pass algorithm to match extracted features. Finally, the displacement of a mobile device is estimated by calculating SAD values between two consecutive images. We evaluated the performance of the proposed algorithm with 1500 images. True matching accuracy of the proposed algorithm is 90% and the computation for each image is conducted in 5m sec.

• Journal of Broadcast Engineering
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• v.11 no.1 s.30
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• pp.80-90
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• 2006

A simple 2D gaze tracking method using single camera and Purkinje image is proposed. This method employs single camera with infrared filter to capture one eye and two infrared light sources to make reflection points for estimating corresponding gaze point on the screen from user's eyes. Single camera, infrared light sources and user's head can be slightly moved. Thus, it renders simple and flexible system without using any inconvenient fixed equipments or assuming fixed head. The system also includes a simple and accurate personal calibration procedure. Before using the system, each user only has to stare at two target points for a few seconds so that the system can initiate user's individual factors of estimating algorithm. The proposed system has been developed to work in real-time providing over 10 frames per second with XGA $(1024{\times}768)$ resolution. The test results of nine objects of three subjects show that the system is achieving an average estimation error less than I degree.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.383-392
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• 2006

In this paper, we propose a new relevance feedback algorithm using Probabilistic Neural Networks(PNN) while supporting multi-class learning. Then, to validate the effectiveness of our feedback approach, we incorporate the proposed algorithm into our region-based image retrieval tool, FRIP(Finding Regions In the Pictures). In our feedback approach, there is no need to assume that feature vectors are independent, and as well as it allows the system to insert additional classes for detail classification. In addition, it does not have a long computation time for training because it only has four layers. In the PNN classification process, we store the user's entire past feedback actions as a history in order to improve performance for future iterations. By using a history, our approach can capture the user's subjective intension more precisely and prevent retrieval performance errors which originate from fluctuating or degrading in the next iteration. The efficacy of our method is validated using a set of 3000 images derived from a Corel-photo CD.

• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.12
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• pp.1853-1861
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• 2009

Body sizing of has been recognized as an important element affecting the degree of customer satisfaction in the apparel industry. Recent developments in IT technologies have enabled more studies in custom-made apparel systems that comply with the diverse demands from customers in many countries. Diverse methods to obtain personal physical size are being studied. This study estimates the accuracy by developing the system in which the data of length and girth can be calculated through changing a modeling by comparing the data with circular 3-dimensional physical configuration data. This information was computed from the process (such as the conversion to a standardize image) which utilizes the image capture of 2-dimensional three sides (front, side, and rear), contour tracing, and key-node selection and by realizing it in the real world.

• Smart Structures and Systems
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• v.30 no.5
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• pp.521-535
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• 2022

Efficient management of deteriorating civil infrastructure is one of the most important research topics in many developed countries. In particular, the remote displacement measurement of bridges using linear variable differential transformers, global positioning systems, laser Doppler vibrometers, and computer vision technologies has been attempted extensively. This paper proposes a remote displacement measurement system using closed-circuit televisions (CCTVs) and a computer-vision-based method for in-situ bridge bearings having relatively large displacement due to temperature change in long term. The hardware of the system is composed of a reference target for displacement measurement, a CCTV to capture target images, a gateway to transmit images via a mobile network, and a central server to store and process transmitted images. The usage of CCTV capable of night vision capture and wireless data communication enable long-term 24-hour monitoring on wide range of bridge area. The computer vision algorithm to estimate displacement from the images involves image preprocessing for enhancing the circular features of the target, circular Hough transformation for detecting circles on the target in the whole field-of-view (FOV), and homography transformation for converting the movement of the target in the images into an actual expansion displacement. The simple target design and robust circle detection algorithm help to measure displacement using target images where the targets are far apart from each other. The proposed system is installed at the Tancheon Overpass located in Seoul, and field experiments are performed to evaluate the accuracy of circle detection and displacement measurements. The circle detection accuracy is evaluated using 28,542 images captured from 71 CCTVs installed at the testbed, and only 48 images (0.168%) fail to detect the circles on the target because of subpar imaging conditions. The accuracy of displacement measurement is evaluated using images captured for 17 days from three CCTVs; the average and root-mean-square errors are 0.10 and 0.131 mm, respectively, compared with a similar displacement measurement. The long-term operation of the system, as evaluated using 8-month data, shows high accuracy and stability of the proposed system.