• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.4
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• pp.494-499
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• 2012

In this paper, a new method for detecting various objects that can be risks to safety navigation in sea environment is proposed. By analysing Infrared(IR) images obtained from various sea environments, we could find out that object regions include both horizontal and vertical direction edges while background regions of sea surface mainly include vertical direction edges. Therefore, we present an approach to detecting object regions considering horizontal and vertical edges. To this end, in the first step, image enhancement is performed by suppressing noises such as sea glint and complex clutters using a statistical filter. In the second step, a horizontal edge map and a vertical edge map are generated by 1-D Discrete Cosine Transform technique. Then, a combined map integrating the horizontal and the vertical edge maps is generated. In the third step, candidate object regions are detected by a adaptive thresholding method. Finally, exact object regions are extracted by eliminating background and clutter regions based on morphological operation.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.35-47
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• 2010

To complement inaccurate edge information and detect correctly the boundary of a vehicle in an image, an extended edge analysis technique is presented in this paper. The vehicle is detected using the bottom boundary generated by a vehicle and the road surface and the left and right side boundaries of the vehicle. The proposed extended edge analysis method extracts the horizontal edge by merging or dividing the nearby edges inside the region of interest set beforehand because various noises deteriorates the horizontal edge which can be a bottom boundary. The horizontal edge is considered as the bottom boundary and the vertical edges as the side boundaries of a vehicle if the extracted horizontal edge intersects with two vertical edges which satisfy the vehicle width condition at the height of the horizontal edge. This proposed algorithm is more efficient than the other existing methods when the road surface is complex. It is proved by the experiments executed on the roads having various backgrounds.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.249-255
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• 2013

In this paper, we propose an algorithm to detect scale invariant object from IR image obtained in the sea environment. We create horizontal edge (HL), vertical edge (LH), diagonal edge (HH) of images through 2-D discrete Haar wavelet transform (DHWT) technique after noise reduction using morphology operations. Considering the sea environment, Gaussian blurring to the horizontal and vertical edge images at each level of wavelet is performed and then saliency map is generated by multiplying the blurred horizontal and vertical edges and combining into one image. Then we extract object candidate region by performing a binarization to saliency map. A small area in the object candidate region are removed to produce final result. Experiment results show the feasibility of the proposed algorithm.

• Journal of the Korea Society of Computer and Information
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• v.16 no.9
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• pp.69-76
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• 2011

According to the related reports, about 60 percents of ship collisions have resulted from operating mistake caused by human factor. Specially, the report said that negligence of observation caused 66.8 percents of the accidents due to a human factor. Hence automatic detection and tracking of an object from an IR images are crucial for safety navigation because it can relieve officer's burden and remedies imperfections of human visual system. In this paper, we present a method to detect an object such as ship, rock and buoy from a sea IR image. Most edge directions of the sea image are horizontal and most vertical edges come out from the object areas. The presented method uses them as a characteristic for the object detection. Vertical edges are extracted from the input image and isolated edges are eliminated. Then morphological closing operation is performed on the vertical edges. This caused vertical edges that actually compose an object be connected and become an object candidate region. Next, reference object regions are extracted using horizontal edges, which appear on the boundaries between surface of the sea and the objects. Finally, object regions are acquired by sequentially integrating reference region and object candidate regions.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.192-197
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• 2004

본 논문에서는 수평ㆍ수직 에지의 형태학적 정보를 이용한 차량 번호판 추출과 개선된 RBF 네트워크를 이용한 차량 번호판 인식 시스템을 제안한다. 번호판 영역은 수평ㆍ수직 에지의 형태학적 정보를 이용하여 추출하고 개별 문자는 히스토그램 방법과 위치 정보를 이용한 방법에 윤곽선 추적 알고리즘을 병합하여 추출한다. 개별 문자 인식은 ARTI 알고리즘을 개선하여 지도 학습 방법과 결합한 개선된 신경망을 제안하여 차량 번호판 인식에 적용한다. 제안된 방법의 성능을 확인하기 위하여 트루 컬러 차량 영상 155개와 그레이 컬러 차량 영상 100개를 대상으로 실험한 결과, 수평ㆍ수직 에지의 형태학적 정보를 이용한 차량 번호판 추출 방법이 임계화를 이용한 차량 번호판 추출 방법, RGB와 HSI 컬러 정보를 각각 이용한 차량 번호판 추출 방법보다 추출률이 개선되었으며, 인식 성능도 개선된 신경망의 학습 알고리즘이 기존의 학습 알고리즘들보다 우수한 성능이 있음을 확인하였다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1851_1852
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• 2009

본 논문은 차량 영역에 나타나는 수평, 수직 에지 특성만을 이용하여 빠르고 효율적으로 차량을 검출하는 방법을 제안한다. 차량을 포함하는 입력영상의 긴 수직 에지 성분을 찾아 차량의 후보 영역을 결정한다. 영상의 에지 성분의 누적 대신 연속적으로 나타나는 긴 수직 에지 성분을 찾음으로써 차량의 후보 영역의 검출과 동시에 중요한 정보를 담고 있는 도로와 접하는 차량의 하단부를 함께 검출한다. 후보 영역 내에서 차량과 비 차량을 구분하는 검증 단계에서는 차량의 후면의 대칭성(Symmetry)을 이용하여 후보 영역 내에서 차량이 있을 가능성이 있는 바닥 점 위에서 좌측과 우측의 유사도(Matching rate)를 이용하여 차량과 비 차량을 판별한다. 기존의 템플릿 기반 방법이나 외관 기반 방법이 아닌 에지 성분만을 이용하여 후보 영역을 결정하고 검증하기 때문에 다른 검출 기법들에 비해 비교적 검출 시간이 짧고 실시간 차량 검출에 적합하다.

• Journal of Broadcast Engineering
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• v.8 no.4
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• pp.492-500
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• 2003

In this paper, we propose a new deinterlacing algorithm which is an edge dependent Interpolation (EDI) algorithm based on a horizontal edge pattern. Generally, a conventional EDI algorithm has a visually better performance than any other deinterlacing algorithm using one field. However, it produces unpleasant results due to the failure of estimating edge direction. In order to exactly detect edge direction, we use not only simple difference but also edge patterns. Experimental results indicate that the proposed algorithm outperforms conventional approaches with respect to both objective and subjective criteria. Index Terms I-to-P conversion, deinterlacing, edge dependent interpolation, edge pattern.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.5
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• pp.610-616
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• 2012

In this paper, we present a method to detect an object such as ship, rock and buoy from sea IR image for the safety navigation. To this end, we do the image smoothing first and the apply watershed algorithm to segment image into subregions. Since watershed algorithm almost always produces over-segmented regions, it requires posterior merging process to get meaningful segmented regions. We propose an efficient merger algorithm that requires only two times of direct access to the pixels regardless of the number of regions. Also by analyzing IR image obtained from sea environments, we could find out that most horizontal edge come out from object regions. For the given input IR image we extract horizontal edge and eliminate isolated edges produced from background and noises by adopting morphological operator. Among the segmented regions, the regions that have horizontal edges are extracted as final results. Experimental results show the adequacy of the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.11 no.6 s.44
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• pp.151-156
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• 2006

This paper presents an background edge generation based automatic algorithm for detection of moving objects under moving camera. Background image is generated by rotating the fixed the camera on the tripod horizontally, aligning and reorganizing this images. We develop an efficient approach for robust panoramic background edge generation as well as method of edge matching between input image and background image. We applied the proposed algorithm to real image sequences. The proposed method can be successfully realized in various monitoring systems like intrusion detection as well as video surveillance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.3
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• pp.684-689
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• 2005

In this paper, we propose car license plate recognition using morphological information and an enhanced neural network. Morphological information on horizontal and vertical edges was used to extract the license plate from a car image. We used a contour tracking algorithm combined with the method of histogram and location information to extract individual characters in the extracted plate. The enhanced neural network is proposed for recognizing them, which has the method of combining the ART-1 and the supervised teaming method. The proposed method has applied to real world car images. The experimental results show that the proposed method has better the extraction rates than the methods with information of the thresholding, the RGB and the HSI, respectively. And the proposed neural network has better recognition performance than the conventional neural networks.