• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1003-1006
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• 1999

Hand segmentation and tracking is essential to the development of a hand gesture recognition system. This research features segementation and tracking of hand regions based the hue component of color. We propose a method that employs HSI color model, and segments and tracks hand regions using the hue component of color alone. In order to track the segmented hand regions, we only apply Kalman filter to a region of interest represented by a rectangle region. Initial experimental results show that the system accurately segments and tracks hand regions although it only uses the hue compoent of color. The system yields near real time throghput of 8 frames per second on a Pentium II 233MHz PC.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.631-636
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• 2009

Foreground segmentation methods have steadily been researched in the field of computer vision. Especially, background subtraction which extracts a foreground image from the difference between the current frame and a reference image, called as "background image" have been widely used for a variety of real-time applications because of low computation and high-quality. However, if the background scene was dynamically changed, the background subtraction causes lots of errors. In this paper, we propose an efficient background subtraction method in dynamic environment with both static and dynamic scene. The proposed method is a hybrid method that uses the conventional background subtraction for static scene and depth information for dynamic scene. Its validity and efficiency are verified by demonstration in dynamic environment, where a video projector projects various images in the background.

• 전자공학회논문지 IE
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• v.43 no.2
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• pp.39-48
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• 2006

This paper is proposed the wavelet morphology algorithm with the spatial auto-object segmentation concept and the clustering concept. When it is segmented the color face by using the proposed algorithm, it is made to the simple image. Also, it is used the spatial quality in order to segment and detect the image as a real time without the user's manufacturing. This removed a small part that is regarded as a noise in image by HSV color model and applied the wavelet morphology to remove a part excepting for the face image. In this paper, it is made a comparison between the wavelet morphology algorithm and the morphology algorithm. And It is showed to accurately detect the face object parts in the image appled to HSV color space model.

• Journal of KIISE:Computer Systems and Theory
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• v.32 no.11_12
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• pp.705-716
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• 2005

There exist many feature point detection algorithms that developed in pattern recognition research . However, interactive applications for the pen-input displays such as Tablet PCs and LCD tablets have set different goals; reliable segmentation for different drawing styles and real-time on-the-fly fieature point defection. This paper presents a curvature estimation method crucial for segmenting freeHand pen input. It considers only local shape descriptors, thus, peforming a novel curvature estimation on-the-fly while drawing on a pen-input display This has been used for pen marking recognition to build a 3D sketch-based modeling application.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.780-791
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• 1996

A hybrid image processing system which automatically distinguished lean tissues in the image of a complex beef cut surface and generated the lean tissue contour has been developed. Because of the in homegeneous distribution and fuzzy pattern of fat and lean tissue on the beef cut, conventional image segmentation and contour generation algorithm suffer from a heavy computing requirement, algorithm complexity and poor robustness. The proposed system utilizes an artificial neural network enhance the robustness of processing. The system is composed of pre-network , network and post-network processing stages. At the pre-network stage, gray level images of beef cuts were segmented and resized to be adequate to the network input. Features such as fat and bone were enhanced and the enhanced input image was converted tot he grid pattern image, whose grid was formed as 4 X4 pixel size. at the network stage, the normalized gray value of each grid image was taken as the network input. Th pre-trained network generated the grid image output of the isolated lean tissue. A training scheme of the network and the separating performance were presented and analyzed. The developed hybrid system showed the feasibility of the human like robust object segmentation and contour generation for the complex , fuzzy and irregular image.

• Journal of Multimedia Information System
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• v.5 no.1
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• pp.9-14
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• 2018

A road recognition system or Lane departure warning system is an early stage technology that has been commercialized as early as 10 years but can be optional and used as an expensive premium vehicle, with a very small number of users. Since the system installed on a vehicle should not be error prone and operate reliably, the introduction of robust feature extraction and tracking techniques requires the development of algorithms that can provide reliable information. In this paper, we investigate and analyze various real-time road detection algorithms based on color information. Through these analyses, we would like to suggest the algorithms that are actually applicable.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.1
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• pp.9-18
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• 2007

There have been continuous efforts for automation of joint tracking system. This automation process is mainly used to do in root pass of gas metal arc welding in the field of heavy industry and shipbuilding etc. For automation, it is important using of vision sensor. Welding robot with vision sensor is used for weld seam tracking on welding fabrication. Recently, it is used to on post-weld inspection for weld quality evaluation. For real time seam tracking, it is very important role in vision process technique. Vision process is included in filtering and thinning, segmentation processing, feature extraction and recognition. In this paper, it has shown performance comparison results of seam tracking for real time root pass on gas metal arc welding. It can be concluded better segment splitting method than iterative averaging technique in the performance results of seam tracking.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.10
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• pp.1234-1243
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• 1988

In this paper, a target tracking algorithm using the centroid of moving edges is presented. It aims to avoid the difficulty of image segmentation in case of extracting the centroid from only one frame. The proposed algorithm can more easily segment the target than the conventional one in images with complex background. Moreover, it can track the target well when the target is occluded by an object. The result of applying it to a real-time target tracker is shown to be comparatively good.

• Journal of the Korean Institute of Telematics and Electronics D
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• v.34D no.7
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• pp.88-96
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• 1997

As the need of automatized system has been increased recently together with the development of industrial and military technologies, the adaptive real-time target detection technologies that can be embedded on vehicles, planes, ships, robots and so on, are hgihly demanded. Accordingly, this paper proposes a novel approach to detect and segment the moving targets using the binary phase extraction joint transform correlator (BPEJTC), the advanced image subtraction filter and convex hull processing. The BPEJTC which was used as a target detection unit mainly for target tracking compensating the camera movement. The target region has been detected by processing the successful three frames using the advanced image subtraction filter, and has become more accurate by applying the developed convex hull filter. As shown by some experimental results, it is expected that the proposed approaches for compensation of the camera movement and segmentationof of target region, can be used for th emissile guiddance, aero surveillance, automatic inspectin system as well as the target detection unit of automatic target recognition system that request adaptive real-time processing.