• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1775-1778
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• 2003

본 논문에서는 컬러 NTGST (noise-tolerant generalized symmetry transform)를 기초로 하여 부분적인 조명 변화뿐 아니라 그림자 및 잡음이 있는 환경에서도 효과적으로 코너만을 검출할 수 있는 코너 검출자를 제안하였다. 제안한 코너 검출자는 잡음에 둔감한 NTGST를 기초로 하여 코너에 가까울수록, 두 직선 에지가 이루는 각이 작을수록 큰 값이 코너에 누적되도록 하여 코너의 정확한 위치를 검출할 수 있도록 하였다 특히 조명 변화에 둔감한 HSI 색 공간에서 색상 (hue) 성분을 강조하고 채도 (saturation) 및 휘도 (intensity) 성분을 보조적인 정보로 활용함으로써 부분적인 조명 및 그림자의 영향을 줄일 수 있도록 가중조합 벡터 미분 연산자 (weighted combination of vector gradient vector operator)를 제안 적용하여 그림자로 인한 거짓 경계선 및 거짓 코너를 제거할 수 있도록 하였다. 실험을 통하여 제안한 코너 검출 방법이 잡음 및 조명 변화에 둔감하게 효과적으로 코너를 검출함을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.580-585
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• 2009

This paper introduces GP(Genetic Programming) based corner detectors for an image processing. Various empirical algorithms have been studied to improve computational speed and accuracy including typical approaches, such as Harris and SUSAN. The these techniques are highly efficient, because properties of corner points are inspected and reflected into the algorithms. However these approaches are limited in discovering an innovative algorithm. In this study, we try to discover a more efficient technique by creating corner detector automatically using evolution of GP. The proposed method is compared to the existing corner detectors for test images.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.6
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• pp.239-245
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• 2004

A hue-based noise-tolerant corner detector is proposed for the exact detection of the real corners in spite of the shadows and random noise. Based on the fact that the hue gradient at the border of the opaque objects' shadow is smaller than the intensity gradient in HSI (hue-saturation-intensity) color space, the effects of shadow are eliminated by introducing the hue-weighted combination of vector gradient to the proposed corner detector. Furthermore, the proposed corner detector is robust to random noise by offsetting the contribution to the corner candidate when the polarities of the color gradients of the pixel pairs are out of phase each other. Results of the experiment show that the proposed corner detector can effectively detect the real corners.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1062-1068
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• 2004

In this paper, we introduce a new gray level comer detection method to recognize corner points accurately. The new corner detector divides the corner region into many homocentric circles according to the window size, and calculates the corner response and angle of corner area about each layer to get an accurate corner point. The new corner detector has a hierarchical structure so it can detect corner point more quickly than general gray level corner detector

• Journal of KIISE:Software and Applications
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• v.31 no.6
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• pp.792-798
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• 2004

In this paper, we will introduce a new corner detector for the wafer die position recognition. The die position recognition procedure is necessary for WSCSP(Wafer Scale Chip Scale Packaging) technology, decide the accuracy of post-procedure. We present a hierarchical gray level corner detection method for the recognition of the die position from a wafer image. The new corner detector divides the corner region into many homocentric circles, and calculates the comer response and the angle of direction about each circle to get an accurate toner point. The new corner detector has a hierarchical structure so it can detect comer point more quickly than general gray level corner detector.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.646-648
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• 2012

In this paper, an image detection technique is proposed to extract image features in a mobile terminal environment. To detect objects, the HSI color model of the image is used. The object's corner points are detected using the Harris corner detection method. Finally we detect the object of interest using region growing The experiment results show that the proposed method improves detection performance and reduces the amount of computation.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.3
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• pp.60-66
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• 2012

In this paper, we propose abandoned object detection algorithm. When abandoned object was occluded other object, the existing methods cannot detect abandoned object because those methods are not able to estimate the location of abandoned object. In order to overcome this problem, the proposed algorithm extracts the corners around abandoned object. The detected corners are linked to center of abandoned object called by supporters. We can then estimate the location of abandoned object by using supporters. Therefore, the proposed algorithm can detect and estimate the location of abandoned object, when abandoned object is occluded by other object. For this reason, the proposed algorithm can be applied to intelligent surveillance system to prevent bomb terror, which disguises as luggage or box.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.96-99
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• 2008

본 논문은 의료 영상을 효과적으로 분류하고 검색 하기 위한 새로운 알고리즘을 제안한다. 의료 영상 중 X-Ray 영상은 어두운 배경에 반해 밝은 전경을 갖고 있기 때문에, 전경의 두드러진 부분에서만 시각 기술자로 추출한다. 우선, 색 구조 기술자(H-CSD)에서 해리스 코너 검출기로 검출한 관심 포인트들에서 색상 특징을 추출하고, 경계선 히스토그램 기술자에서 영상의 전역 및 지역적 질감 특징을 추출한다. 추출된 특징 벡터는 멀티클래스 SVM 에 적용되어 각 영상을 위한 멤버십 스코어를 얻는다. 이후, H-CSD와 EHD 에 대한 SVM 의 멤버십 스코어를 앙상블 결합하여 하나의 특징 벡터로 생성하고, K-nearest Neighborhood 방법을 이용하여 상위-K 개의 영상을 검색을 하도록 하였다. imageCLEFmed2007 을 이용한 실험 결과에서 다른 전역적 속성 또는 분류 기반 검색 방법에 비교하여 보다 개선된 검색 성능을 나타냄을 확인하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.339-345
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• 2010

This paper introduces GP(Genetic Programming) based robust corner detectors for scaled and rotated images. Various empirical algorithms have been studied to improve computational speed and accuracy including approaches, such as the Harris and SUSAN, FAST corner detectors. These techniques are highly efficient for well-defined corners, but are limited to corner-like edges which are often generated in rotated images. It is very difficult to detect correctly edges which have characteristics similar to corners. In this paper, we have focused the above challenging problem and proposed Genetic Programming-based automated generation of corner detectors which is robust to scaled and rotated images. The proposed method is compared to the existing corner detectors on test images and shows superior results.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.11
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• pp.411-418
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• 2022

Incorrectly loaded containers can easily knock down by strong winds. Container collapse accidents can lead to material damage and paralysis of the port system. In this paper, We propose a deep learning-based container loading state and accident risk detection technique. Using Darknet-based YOLO, the container load status identifies in real-time through corner casting on the top and bottom of the container, and the risk of accidents notifies the manager. We present criteria for classifying container alignment states and select efficient learning algorithms based on inference speed, classification accuracy, detection accuracy, and FPS in real embedded devices in the same environment. The study found that YOLOv4 had a weaker inference speed and performance of FPS than YOLOv3, but showed strong performance in classification accuracy and detection accuracy.