• Journal of Intelligence and Information Systems
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• v.9 no.2
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• pp.1-18
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• 2003

The automatic recognition of transport containers using image processing is very hard because of the irregular size and position of identifiers, diverse colors of background and identifiers, and the impaired shapes of identifiers caused by container damages and the bent surface of container, etc. We proposed and evaluated the novel recognition algorithm of container identifiers that overcomes effectively the hardness and recognizes identifiers from container images captured in the various environments. The proposed algorithm, first, extracts the area including only all identifiers from container images by using CANNY masking and bi-directional histogram method. The extracted identifier area is binarized by the fuzzy binarization method newly proposed in this paper and by applying contour tracking method to the binarized area, container identifiers which are targets of recognition are extracted. We proposed and applied the ART2-based RBF network for recognition of container identifiers. The results of experiment for performance evaluation on the real container images showed that the proposed algorithm has more improved performance in the extraction and recognition of container identifiers than the previous algorithms.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.231-233
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• 2010

Many algorithms for processing LIDAR data are being developed for diverse applications not limited to patch segmentation, bare-earth filtering and building extraction. However, since we cannot exactly know the true locations of LIDAR points, it is difficult to assess the performance of a LIDAR data processing algorithm. In this paper, we thus attempted the performance assessment of the segmentation algorithm developed by Lee (2006) using the LIDAR data generated through simulation based on sensor modelling. Consequently, based on simulation, we can perform the performance assessment of a LIDAR processing algorithm more objectively and quantitatively with an automatic procedure.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.65-79
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• 2003

In general, the extraction and recognition of identifier is very hard work, because the scale or location of identifier is not fixed-form. And, because the provided image is contained by camera, it has some noises. In this paper, we propose methods for automatic detecting edge using canny edge mask. After detecting edges, we extract regions of identifier by detected edge information's. In regions of identifier, we extract each identifier using contour tracking algorithm. The self-generation supervised learning algorithm is proposed for recognizing them, which has the algorithm of combining the enhanced ART1 and the supervised teaming method. The proposed method has applied to the container images. The extraction rate of identifier obtained by using contour tracking algorithm showed better results than that from the histogram method. Furthermore, the recognition rate of the self-generation supervised teaming method based on enhanced ART1 was improved much more than that of the self-generation supervised learning method based conventional ART1.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.93-96
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• 2007

There is increasingly interested in the measurement of antenna's characteristics for one's manufacture according to one's various application. Due to this, the antenna measurement system need be made with more and more great precision. On measuring of the antenna's characteristic, the conventional system handled by human generates the error due to controlling the position of the system by user. Therefore there need be introduced the automatic measurement system of antenna's characteristic. In this paper, we propose image processing algorithm for the automatic measurement system of antenna's characteristic. The proposed algorithm gets image of the standard gain horn antenna in the chamber room, extracts an antenna object from the image, and extracts the parameters for automatic alignment of AUT(Antenna Under Test) and Probe. Also the proposed algorithm gets the error for corrects of slope and distortion of AUT junction due to weight of fitted antenna.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.915-918
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• 2000

In this paper, we propose an efficient feature vector extraction and decision fusion methods for the automatic classification of power system disturbances. Here, FFT and WPT(wavelet packet transform) are und to extract an appropriate feature for classifying power quality disturbances with variable properties. In particular, the WPT can be utilized to develop an adaptable feature extraction algorithm using best basis selection. Furthermore. the extracted feature vectors are applied as input to the decision fusion system which combines the decisions of several classifiers having complementary performances, leading to improvement of the classification performance. Finally, the applicability of the proposed approach is demonstrated using some simulations results obtained by analyzing power quality disturbances data generated by using Matlab.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.25 no.3
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• pp.51-60
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• 2017

Estimation of the taxi-out and taxi-in times of aircraft on a airport surface is one of the essential features of Departure Manager (DMAN). Especially for an airport with multiple runways and large ramp areas, estimation of the taxi-out and taxi-in times are mainly dependent on the taxi routes on airport surface. This paper described the method of automatic extraction of the the taxi routes using the ASDE track data and the Dijkstra algorithm based on the node-link model of a airport surface movements. In addition, we analyzed the ground operation status of Incheon International Airport using the extracted taxi routes.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.3
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• pp.472-478
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• 2012

The inspection machine in PCB (printed circuit board) assembly line checks assembly errors by inspecting the images inside of the component inspection region. The component inspection region consists of region of component package and region of soldering. It is necessary to extract the regions automatically for auto-teaching system of the inspection machine. We propose an image segmentation method to extract the component inspection regions automatically from images of PCB. The acquired image is transformed to HSI color model, and then segmented by several regions by clustering method. We develop a modified K-means algorithm to increase the accuracy of extraction. The heuristics generating the initial clusters and merging the final clusters are newly proposed. The vertical and horizontal projection is also developed to distinguish the region of component package and region of soldering. The experimental results are presented to verify the usefulness of the proposed method.

• Journal of Information Processing Systems
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• v.9 no.4
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• pp.621-632
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• 2013

The most important things for a forest fire detection system are the exact extraction of the smoke from image and being able to clearly distinguish the smoke from those with similar qualities, such as clouds and fog. This research presents an intelligent forest fire detection algorithm via image processing by using the Gaussian Mixture model (GMM), which can be applied to detect smoke at the earliest time possible in a forest. GMMs are usually addressed by making the model adaptive so that its parameters can track changing illuminations and by making the model more complex so that it can represent multimodal backgrounds more accurately for smoke plume segmentation in the forest. Also, in this paper, we suggest a way to classify the smoke plumes via a feature extraction using HSL(Hue, Saturation and Lightness or Luminanace) color space analysis.

• Korean Journal of Remote Sensing
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• v.24 no.3
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• pp.235-243
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• 2008

Object tracking is a common vision task to detect and trace objects between consecutive frames. It is also important for a variety of applications such as surveillance, video based traffic monitoring system, and so on. An efficient moving vehicle clustering and tracking algorithm suitable for traffic monitoring system is proposed in this paper. First, automatic background extraction method is used to get a reliable background as a reference. The moving blob(object) is then separated from the background by mean shift method. Second, the scale invariant feature based method extracts the salient features from the clustered foreground blob. It is robust to change the illumination, scale, and affine shape. The simulation results on various road situations demonstrate good performance achieved by proposed method.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.55-67
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• 1998

This paper presents an automatic facial feature point extraction algorithm in sequential color images. To extract facial region in the video sequence, a moving color detection technique is proposed that emphasize moving skin color region by applying motion detection algorithm on the skin-color transformed images. The threshold value for the pixel difference detection is also decided according to the transformed pixel value that represents the probability of the desired color information. Eye candidate regions are selected using both of the black/white color information inside the skin-color region and the valley information of the moving skin region detected using morphological operators. Eye region is finally decided by the geometrical relationship of the eyes and color histogram. To decide the exact feature points, the PCA(Principal Component Analysis) is used on each eye and mouth regions. Experimental results show that the feature points of eye and mouth can be obtained correctly irrespective of background, direction and size of face.