• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.741-745
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• 1991

This paper describes a feature extraction in digitized chest X-ray image and CT head Image. There are Extraction, Thresholding, Region G rowing, Split-Merge and Relaxation in feature extraction technique. In this study, Region Growing System was realized and Fuzzy Set Theory was applied in order to extract the vague region which the conventional method has difficulties in extracting. The performance of proposed algorithm was proved by being applied to chest X-ray image and CT head image.

• Proceedings of the IEEK Conference
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• 2002.06c
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• pp.173-176
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• 2002

This paper describes an implementation of fast face detection algorithm. This algorithm can robustly detect human faces with unknown sizes and positions in complex backgrounds. This paper provides a powerful face detection algorithm using skin color segmenting. Skin Color is modeled by a Gaussian distribution in the HSI color space among different persons within the same race, Oriental. The main feature of the Algorithm is achieved face detection robust to illumination changes and a simple adaptive thresholding technique for skin color segmentation is employed to achieve robust face detection.

• Journal of Biosystems Engineering
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• v.24 no.1
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• pp.51-58
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• 1999

A color image processing algorithm was developed to assist the robotic plug-seedling transplanter. The algorithm was designed to identify and locate empty cells in the seedling tray. The image of pepper seedling tray was segmented into regions of plant, frame and soil using thresholding technique which utilized HSI or RGB color characteristics of each region. The detection algorithm was able to successfully identify empty cells and locate their two-dimensional location. The overall success rate of the algorithm was about 88%.

• Journal of Ocean Engineering and Technology
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• v.8 no.2
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• pp.166-172
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• 1994

This paper presents an image processing method for two dimensional position measurement of a floating structure. This method is based on image processing technique using concept of window and threshold processing to track the target object. The experimental results for position measurement of the target object in two dimensional water tank demonstrate the validity of this method.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.3
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• pp.309-315
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• 1995

This paper describes a monitoring system for fish farm formation effect based on personal computer by using an image processing technique. This method is based on image processing technique incorporating concept of window and threshold processing to track the target object and to distinguish it from background. The image processing program runs in the veal time so that all program modules are able to process multi-task. The effectiveness is evaluated through the comparative study on the motion of lantern net for the scallop culturing by wave action in an experimental wave tank.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.125-130
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• 2016

Automatic quality inspection by computer vision can be applied and give a solution to the pharmaceutical industry field. Pharmaceutical capsule can be easily affected by flaws like dents, cracks, holes, etc. In order to solve the quality inspection problem, it is required computationally efficient image processing technique like thresholding, boundary edge detection and segmentation and some automated systems are available but they are very expensive to use. In this paper, we have developed a dented capsule image processing technique using edge-based image segmentation, TLS(Total Least Squares) curve fitting technique and adopted low cost camera module for capsule image capturing. We have tested and evaluated the accuracy, training and testing time of the classification recognition algorithms like PCA(Principal Component Analysis), ICA(Independent Component Analysis) and SVM(Support Vector Machine) to show the performance. With the result, PCA, ICA has low accuracy, but SVM has good accuracy to use for classifying the dented capsule.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.9
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• pp.1531-1540
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• 1997

Mordern industrial society pursues unmanned system and automation of manufacturing process. Abreast with this tendensy, production of goods which requires advanced accuracy is increasing as well. According to this, the work sensing time of dressing by monitoring and diagnosing the condition of grinding, which is th representative way in accurate manufacturing, is an important work to prevent serious damages which affect grinding process or products by wearing grinding wheel. Computer vision system was composed, so that grinding wheel surface was acquired by CCD camera and the change of cutting edge ratio was measured. Then we used automatic thresholding technique from histogram as a way of dividing grinding cutting edge from grinding surface. As a result, we are trying to approach unmanned system and automation by deciding more accurate time of dressing and by visualizing behavior of grinding wheel by making use of computer vision.

• ETRI Journal
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• v.28 no.6
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• pp.721-731
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• 2006

A fast intra skip detection algorithm based on the ratedistortion (RD) cost for an inter frame (P-slices) is proposed for H.264/AVC video encoding. In the H.264/AVC coding standard, a robust rate-distortion optimization technique is used to select the best coding mode and reference frame for each macroblock (MB). There are three types of intra predictions according to profiles. These are $16{\times}16$ and $4{\times}4$ intra predictions for luminance and an $8{\times}8$ intra prediction for chroma. For the high profile, an $8{\times}8$ intra prediction has been added for luminance. The $4{\times}4$ prediction mode has 9 prediction directions with 4 directions for $16{\times}16$ and $8{\times}8$ luma, and $8{\times}8$ chrominance. In addition to the inter mode search procedure, an intra mode search causes a significant increase in the complexity and computational load for an inter frame. To reduce the computational load of the intra mode search at the inter frame, the RD costs of the neighborhood MBs for the current MB are used and we propose an adaptive thresholding scheme for the intra skip extraction. We verified the performance of the proposed scheme through comparative analysis of experimental results using joint model reference software. The overall encoding time was reduced up to 32% for the IPPP sequence type and 35% for the IBBPBBP sequence type.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.5
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• pp.277-285
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• 2016

Moving object detection system has been an emerging research field in various advanced driver assistance systems (ADAS) and surveillance system. In this paper, we propose two optical flow based moving object detection methods at dynamic scenes. Both proposed methods consist of three successive steps; pre-processing, foreground segmentation, and post-processing steps. Two proposed methods have the same pre-processing and post-processing steps, but different foreground segmentation step. Pre-processing calculates mainly optical flow map of which each pixel has the amplitude of motion vector. Dense optical flows are estimated by using Farneback technique, and the amplitude of the motion normalized into the range from 0 to 255 is assigned to each pixel of optical flow map. In the foreground segmentation step, moving object and background are classified by using the optical flow map. Here, we proposed two algorithms. One is Gaussian mixture model (GMM) based background subtraction, which is applied on optical map. Another is adaptive thresholding based foreground segmentation, which classifies each pixel into object and background by updating threshold value column by column. Through the simulations, we show that both optical flow based methods can achieve good enough object detection performances in dynamic scenes.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.4
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• pp.212-222
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• 2003

The ability to efficiently and robustly recover accurate 3D terrain models from sets of stereoscopic images is important to many civilian and military applications. A stereo matching has been an important tool for reconstructing three dimensional terrain. However, there exist many factors causing stereo matching error, such as occlusion, no feature or repetitive pattern in the correlation window, intensity variation, etc. Among them, occlusion can be only resolved by true multi-image stereo. In this paper, we present multi-image stereo method using DEM fusion as one of efficient and reliable true multi-image methods. Elevations generated by all pairs of images are combined by the fusion process which accepts an accurate elevation and rejects an outlier. We propose three fusion schemes: THD(Thresholding), BPS(Best Pair Selection) and MS(Median Selection). THD averages elevations after rejecting outliers by thresholding, while BPS selects the most reliable elevation. To determine the reliability of a elevation or detect the outlier, we employ the measure of self-consistency. The last scheme, MS, selects the median value of elevations. We test the effectiveness of the proposed methods with a quantitative analysis using simulated images. Experimental results indicate that all three fusion schemes showed much better improvement over the conventional binocular stereo in natural terrain of 29 Palms and urban site of Avenches.