• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.252-253
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• 2023

Container terminal automation might offer many potential benefits, such as increased productivity, reduced cost, and improved safety. Autonomous trucks can lead to more efficient container transport. A robust lane detection method is proposed using score-based generative modeling through stochastic differential equations for image-to-image translation. Image processing techniques are combined with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Genetic Algorithm (GA) to ensure lane positioning robustness. The proposed method is validated by a dataset collected from the port terminals under different environmental conditions and tested the robustness of the lane detection method with stochastic noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.721-723
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• 2007

Recently, the size and location of the acquired container image while the container is loading and unloading in Harbors is not fixed. And it is difficult to get a good image for recognition because of the variation of external environment as those the size of container and where the yard-tractor stop is. In this paper, we estimate where the container ISO-code set is using Top-hat transform from realtime images and get an image to recognize container ISO-code using PAN/TILT/ZOOM camera. We extract the container ISO-code using Top-hat transform and Histogram projection. After binarization, we extract each character from complex background using labeling. We use BP(Backpropagation Network) to recognize extracted characters.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.88-95
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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.

• IE interfaces
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• v.10 no.2
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• pp.69-77
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• 1997

Material handling equipment such as container cranes and transtainer cranes have made larger and faster to improve the efficiency of container handling. As conditions of use in container terminal have become severe, and also the automation level required has become higher. For the high level automation for transtainer crane, the following characteristics have to be developed 1) Container Terminal Operation & Planning System with high efficiency. 2)Autosteering System of transtainer crane with precise position sensing system using image processing and feedback control system. 3)Automatic Position Identification System with transponder. We have developed an AGSS(Automatic Gantry Steering System) of transtainer crane with image processing technology preferentially. In this paper, the system will be introduced.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.549-557
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• 2013

This paper proposes a method using the multiple threshold regions in the image preprocessing procedure for container identifier recognition system. The multiple threshold regions are set by considering the container image characteristics and used as the candidates for the final one, The image is transformed to black and white images using these threshold regions, then labeling, panelling and panels merging are executed for each candidate, respectively. Finally the best threshold region is selected through this procedure and the character region can be extracted. Applying the similar method the noises are removed and the characters of identifier are segmented from the extracted region. In the experiments with 162 different images the success rates for extracting of the character region and segmenting the characters are 99.04% and 98.09%, respectively.

• Journal of the Korea Society of Computer and Information
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• v.15 no.9
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• pp.57-65
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• 2010

This paper describes the method of efficient container number recognition in colored container image with number plate at ATC(Automatic Transfer Crane) in container terminal yard. At the Sinseondae terminal gate in Busan, the container number recognition system is installed by "intelligent port-logistics system technology development", that is government research and development project. It is the method that it sets up the tunnel structure inside camera on the gate and it recognizes the container number in order to recognize the export container cargo automatically. However, as the automation equipment is introduced to the container terminal and the unmanned of a task is gradually accomplished, the container number recognition system for the confirmation of the object of work is required at ATC in container terminal yard. Therefore, the container number recognition system fitted for it is necessary for ATC in container terminal yard in which there are many intrusive of the character recognition through image including a sunlight, rain, snow, shadow, and etc. unlike the gate. In this paper, hardware components of the camera, illumination, and sensor lamp were altered and software elements of an algorithm were changed. that is, the difference of the brightness of the surrounding environment, and etc. were regulated for recognize a container number. Through this, a shadow problem, and etc. that it is thickly below hung with the sunlight or the cargo equipment were solved and the recognition time was shortened and the recognition rate was raised.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.100-104
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• 2008

Generally, it is difficult to find constant patterns on identifiers in a container image, since the identifiers are not normalized in color, size, and position, etc. and their shapes are damaged by external environmental factors. This paper distinguishes identifier areas from background noises and removes noises by using an ART2-based quantization method and general morphological information on the identifiers such as color, size, ratio of height to width, and a distance from other identifiers. Individual identifier is extracted by applying the 8-directional contour tracking method to each identifier area. This paper proposes a refined ART2-based RBF network and applies it to the recognition of identifiers. Through experiments with 300 container images, the proposed algorithm showed more improved accuracy of recognizing container identifiers than the others proposed previously, in spite of using shorter training time.

• Journal of the Korean Society of Food Culture
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• v.35 no.6
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• pp.538-552
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• 2020

Tis study was an attempt to systematically analyze the characteristics of modern Korean food styling using the menu image photos of the Michelin Guide Seoul 2020 restaurants. The first sampling was conducted on the 27th of March 2020 and the second on the 27th of October 2020. A collection of images on the web and 442 photographic cases obtained by a search through theoretical background literature and research papers were studied by a qualitative analysis method. First, the food styling contents were analyzed and based on that, the food styling characteristics of the menus of 11 restaurants in Korea including the contemporary restaurants were considered. The analysis revealed several aspects of Korean food styling. First, food styling appeared to have three major characteristics: color, shape, and container styling. Color styling was further subdivided into single color/similar color, color contrast, source type, and accent color type. The shape/formative styling was classified into figure type, shape type, accessory type, and garnish type, and container styling was categorized as container color type, container shape type, and container material type. Second, the modern Korean food color styling characteristics of Michelin restaurants were categorized in the order of monochromatic/similar type, sauce type, accent color type, and color contrast. In the formative styling category, it was categorized in the order of shape type, small piece type, garnish/garnish type, and figure type. In container styling, container material type and formative type accounted for the major portion of the category. The food styling characteristics of the modern Korean menu were systematized and image examples were presented visually. Please use it as food styling educational material or personal food styling skill.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 1998.10a
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• pp.137-141
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• 1998

Todays the efficient management of container has not been realized in container terminal, because of the excessive quantity of container transported and manual system. For the efficient and automated management of container in terminal, the automated container identifier recognition system in terminal is a significant problem. However, the identifier recognition rate is decreased owing to the difficulty of image preprocessing caused the refraction of container surface, the change of weather and the damaged identifier characters. Therefore, this paper proposes more accurate system for container identifier recognition as suggestion of Line-Scan Proper Region Detect for stronger preprocessing against external noisy element and Moment Back-Propagation Neural Network to recognize identifier.

• Journal of Korean Port Research
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• v.12 no.2
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• pp.225-232
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• 1998

Todays, the efficient management of container has not been realized in container terminal, because of the excessive quantity of container transported and manual system. For the efficient and automated management of container in terminal, the automated container identifier recognition system in terminal is a significant problem. However, the identifier recognition rate is decreased owing to the difficulty of image preprocessing caused the refraction of container surface, the change of weather and the damaged identifier characters. Therefore, this paper proposes more accurate system for container identifier recognition as suggestion of LSPRD(Line-Scan Proper Region Detection) for stronger preprocessing against external noisy element and MBP(Momentum Back-Propagation) neural network to recognize the identifier.