• Proceedings of the Korea Multimedia Society Conference
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• 2002.11b
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• pp.149-154
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• 2002

운송 컨테이너의 식별자를 추출하고 인식하는 것은 컨테이너 식별자들의 크기나 위치가 정형화되어 있지 않고 외부의 잡음으로 인하여 식별자의 형태가 훼손되어 있기 때문에 어렵다. 본 논문에서는 이러한 특성을 고려하여 컨테이너 영상에 대해 Canny 에지 추출 기법을 이용하여 컨테이너의 식별자 영역을 추출하고 추출된 컨테이너 식별자 영역에서 히스토그램 방법과 윤곽선 추적 알고리즘을 결합하여 개별 식별자를 추출한다. 추출된 컨테이너 개별 식별자 인식은 ART1을 수정하여 지도 학습 방법과 결합한 개선된 자가생성 지도학습 알고리즘을 제안하여 적용한다. 실험결과에서는 제안된 컨테이너 식별자 추출 및 인식 방법이 다양한 컨테이너 영상에 대해 효율적인 것을 보인다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.195-202
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• 2004

일반적으로 운송 컨테이너의 식별자들은 크기나 위치가 정형화되어 있지 않고 외부 잡음으로 인하여 식별자의 형태가 변형될 수 있기 때문에 일정한 규칙으로 찾기는 힘들다. 본 논문에서는 이러한 특성을 고려하여 컨테이너 영상에 대해 Canny 마스크를 이용하여 에지를 검출하고, 검출된 에지 정보에서 영상획득 시 외부 광원에 의해 수직으로 길게 발생하는 잡음들을 퍼지추론 방법을 적용하여 제거한 후에 수직 블록과 수평 블록을 검출하여 컨테이너의 식별자 영역을 추출하고 이진화 한다. 이진화된 식별자 영역에 대해 검정색의 빈도수를 이용하여 흰바탕과 민바탕을 구분하고 윤곽선 추적 알고리즘을 적용하여 개별 식별자를 추출한다. 추출된 개별 식별자의 인식은 개선된 퍼지 RBF 네트워크를 제안하여 적용한다. 제안된 퍼지 RBF 네트워크는 퍼지 C-Means 알고리즘을 중간층으로 적용하고 중간층과 출력층 간의 학습에는 일반화된 델타 학습 방법과 Delta-bar-Delta 알고리즘을 적용하여 학습 성능을 개선한다. 실제 컨테이너 영상을 대상으로 실험한 결과, 기존의 식별자 추출 방법보다 제안된 식별자 추출 방법이 개선되었고 기존의 퍼지 RBF 네트워크 보다 제안된 퍼지 RBF 네트워크가 컨테이너 식별자의 학습 및 인식에 우수함을 확인하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.224-230
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• 2013

Lane boundary detection plays a key role in the driver assistance system. This study proposes a robust method for detecting lane boundary in severe environment. First, a horizontal line detects form the original image using improved Vertical Mean Distribution Method (iVMD) and the sub-region image which is under the horizontal line, is determined. Second, we extract the lane marking from the sub-region image using Canny edge detector. Finally, K-means clustering algorithm classifi left and right lane cluster under variant illumination, cracked road, complex lane marking and passing traffic. Experimental results show that the proposed method satisfie the real-time and efficient requirement of the intelligent transportation system.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1281-1286
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• 2018

The sea cucumber, which is distributed over 1,500 species worldwide, is a highly value-added variety that has been considered an important source of marine resources in many countries for a long period of time. Most of the research on sea cucumbers involves the effectiveness of food and its extractions; however, there was no research on the extraction of sea cucumbers. In response, this research suggested a boundary detection algorithm to extract the special spot of sea cucumbers Therefore, in order to capture a large quantity of high value-added in sea cucumbers and we believe that they will be a great help to the sea cucumber recognition program in the future.

• Journal of Korea Multimedia Society
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• v.11 no.3
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• pp.292-300
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• 2008

An algorithm for detecting a line segment in an image is presented using incremental pixel extension. We use a different approach from conventional algorithms, such as the Hough transform approach and the line segment grouping approach. The Canny edge is calculated and an arbitrary point is selected among the edge elements. After the arbitrary point is selected, a base line approximating the line segment is calculated and edge pixels within an arbitrary radius are selected. A weighted value is assigned to each edge pixel, which is selected by using the error of the distance and the direction between the pixel and the base line. A line segment is extracted by Jilting a line using the weighted least square method after determining whether selected pixels are linked or delinked using the sum comparison of the weights. The proposed algorithm is compared with two other methods and results show that our algorithm is faster and can detect the real line segment.

• Journal of Digital Convergence
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• v.12 no.7
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• pp.239-245
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• 2014

In this paper, a weighted value wrinkle detection method is suggested based on the analysis on the entire facial features such as face contour, face size, eyes and ears. Firstly, the main facial elements are detected with AAM method entirely from the input screen images. Such elements are mainly composed of shape-based and appearance methods. These are used for learning the facial model and for matching the face from new screen images based on the learned models. Secondly, the face and background are separated in the screen image. Four points with the biggest possibilities for wrinkling are selected from the face and high wrinkle weighted values are assigned to them. Finally, the wrinkles are detected by applying Canny edge algorithm for the interested points of weighted value. The suggested algorithm adopts various screen images for experiment. The experiments display the excellent results of face and wrinkle detection in the most of the screen images.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2982-2988
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• 2014

The edge detection is a pre-processing of such as image segmentation, image recognition, etc, and many related studies are being conducted both in domestic and abroad. Representative edge detection methods are Sobel, Prewitt, Laplacian, Roberts and Canny edge detectors. Such existing methods are possible for superb detections of edges if edges are detected from videos without noises. However, for video degraded by the salt-and-pepper noise, the edge detection characteristic is shown to be insufficient due to the noise influence. Therefore, in this study, the area is separated as the top, down, left and right from the mask's center pixel first to acquire a superb edge detection characteristic from the video damaged by the salt-and-pepper noise. And the algorithm that detects the final edge by applying the directional mask on the assumed factor of mask that is obtained according to the result of determination for the noise status of representative pixel value of each area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.1
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• pp.47-53
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• 2007

Satellite image mosaicking is essential for image interpretation and analysis especially for a large area such as the Korean Peninsula. This paper proposed the technique of automatic seam-line extraction and the method of creating image mosaic in automated fashion. The seam-line to minimize artificial discontinuity was extracted using Minimum Absolute Gray Difference Sum algorithm with constraint condition on search-area width and Canny Edge Detection algorithm. To maintain the radiometric balance among images acquired at different time epochs, we utilized Match Cumulative Frequency method. Experimental results showed that edge detection algorithm extracted the seam-lines significantly well along linear features such as roads and rivers.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.51-57
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• 2012

Hepatic vessel tree is the key structure for hepatic disease diagnosis and liver surgery planning. Especially, it is used to evaluate the donors' and recipients' liver for the LDLT(Living Donors Liver Transplantation) and estimate the volumes of left and right hepatic lobes for securing their life in the LDLT. In this study, we propose a method to apply canny edge detection that is not affected by noise to the liver images for automatic segmentation of hepatic vessels tree in contrast abdominal MDCT image. Using histograms and average pixel values of the various liver CT images, optimized parameters of the Canny algorithm are determined. It is more time-efficient to use the common parameters than to change parameters manually according to CT images. Candidates of hepatic vessels are extracted by threshold filtering around the detected the vessel edge. Finally, using a system which detects the true-negatives and the false-positives in horizontal and vertical direction, the true-negatives are added in candidate of hepatic vessels and the false-positives are removed. As a result of the process, the various hepatic vessel trees of patients are accurately reconstructed in 3D.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4D
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• pp.687-693
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• 2006

Image co-registration is the process of overlaying two images of the same scene, one of which represents a reference image, while the other is geometrically transformed to the one. In order to improve efficiency and effectiveness of the co-registration approach, the author proposed a pre-qualified area matching algorithm which is composed of feature extraction with canny operator and area matching algorithm with cross correlation coefficient. For refining matching points, outlier detection using studentized residual was used and iteratively removes outliers at the level of three standard deviation. Throughout the pre-qualification and the refining processes, the computation time was significantly improved and the registration accuracy is enhanced. A prototype of the proposed algorithm was implemented and the performance test of 3 Landsat images of Korea. showed: (1) average RMSE error of the approach was 0.435 pixel; (2) the average number of matching points was over 25,573; (3) the average processing time was 4.2 min per image with a regular workstation equipped with a 3 GHz Intel Pentium 4 CPU and 1 Gbytes Ram. The proposed approach achieved robustness, full automation, and time efficiency.