• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.173-179
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• 2012

In this paper, we presents a new method for vehicle number plate detection. Our method is basically the method extracting a rectangles from a car image because the shape of a vehicle number plate is a rectangle. For detecting the vehicle number plate, firstly, the contrast of the input image is enhanced. Then, the lines in the image are obtained by using LSD(line segment detector), and rectangles in the image are detected from the line data. These rectangles are the candidates of the car plate, from which the car plate is selected. In this procedure, the method of detecting rectangles is our proposed method, which consists of three stages: (1) extracting corners from the line segments by LSD; (2) extracting diagonal lines from the corner data; and (3) detecting rectangles from diagonal line information. And finally the vehicle number plate is selected from these rectangles by using the feature of the vehicle number plate and the inside information of rectangles. In the experiments with the 100 images captured by our digital camera, we have achieved a detection rate of 94%.

• Journal of Korea Multimedia Society
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• v.14 no.4
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• pp.538-545
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• 2011

Today, there is a wide variety of digital contents on the Internet. Especially, comic images are one of popular digital contents. Most of them are scanned from comic books by digital scanners, but they were not normalized in sense of their size, skew and boundary margin. The normalization is very important step in comic image analysis. It can be achieved by finding out-boundary rectangles in comic images. In this paper, we propose a method for detecting the out-boundary rectangle using the gradient Radon transform in comic images. We applied the Radon transform using image gradients to extract line segments which are the out-boundary rectangle sides' candidates in comic images. The final out-boundary rectangle can be detected by local histogram and the candidate line segments. Experimental results show that our proposed method effectively detect the out-boundary rectangle in comic images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.410-412
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• 2021

This paper proposes an improved pedestrian detection method using object and background histograms. Objects detected through the HOG & SVM algorithm are detected in a square shape. Inside the square area, the background and the object area are mixed. If only the area of the object excluding the background is detected, various object-related information may be easily obtained. The size of the detected rectangle is readjusted using an xy-axis projection algorithm to fit the size of the object. And then, the improved object is detected by dividing the background and the object based on the histogram of the object in the readjusted square. The average values of precision and recall, which are reliability evaluations comparing the detected object with the original object, are 97.9% and 90%, respectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.129-132
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• 2006

본 논문에서는 카메라 영상으로부터 사각형 형태의 마커를 검출하고 인식하는 방법을 제안한다. 본 논문에서는 사각형 형태의 마커 검출을 위하여 입력 영상을 이진 영상으로 변환하고 객체들의 윤곽선을 추출한 다음에 윤곽선을 선분으로 근사화 한다. 근사화된 선분으로부터 기하학적 특징을 이용하여 사각형을 찾는다. 마커의 사각형 영역을 찾은 다음에는 워핑 기법을 이용하여 사각형 마커 영상으로부터 특징 벡터를 추출하고 표준 마커에 대한 특징 벡터와의 최소 거래법에 의해 마커의 종류를 인식한다. 인식 실험 결과 마커의 종류가 50개일 때에 최대 98%의 인식률을 얻을 수 있었다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.97-107
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• 2006

In this paper, we present a method for the detection and recognition of rectangular markers from a camera image. It converts the camera image to a binary image and extracts contours of objects in the binary image. After that. it approximates the contours to a list of line segments. It finds rectangular markers by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted markers into exact squares by using the warping technique. It extracts feature vectors from marker image by using principal component analysis. It then calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the Proposed method achieves 98% recognition rate at maximum for 50 markers and execution speed of 11.1 frames/sec for images which contains eleven markers.

• The Journal of the Korea Contents Association
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• v.10 no.1
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• pp.19-26
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• 2010

This paper proposes a classifier based on rectangular feature to detect face in real time. The goal is to realize a strong detection algorithm which satisfies both efficiency in calculation and detection performance. The proposed algorithm consists of the following three stages: Feature creation, classifier study and real time facial domain detection. Feature creation organizes a feature set with the proposed five rectangular features and calculates the feature values efficiently by using SAT (Summed-Area Tables). Classifier learning creates classifiers hierarchically by using the AdaBoost algorithm. In addition, it gets excellent detection performance by applying important face patterns repeatedly at the next level. Real time facial domain detection finds facial domains rapidly and efficiently through the classifier based on the rectangular feature that was created. Also, the recognition rate was improved by using the domain which detected a face domain as the input image and by using PCA and KNN algorithms and a Class to Class rather than the existing Point to Point technique.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.923-933
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• 2007

In this paper, we present a method for performance enhancement of the marker detection system by using SVM(Support Vector Machine) and LDA(Linear Discriminant Analysis). It converts the input image to a binary image and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds quadrangle by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted quadrangle into exact squares by using the warping technique and scale transformation. It extracts feature vectors from the square image by using principal component analysis. It then checks if the square image is a marker image or a non-marker image by using a SVM classifier. After that, it computes feature vectors by using LDA for the extracted marker images. And it calculates the distance between feature vector of input marker image and those of standard markers. Finally, it recognizes the marker by using minimum distance method. Experimental results show that the proposed method achieves enhancement of recognition rate with smaller feature vectors by using LDA and it can decrease false detection errors by using SVM.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2005.11a
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• pp.550-554
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• 2005

본 논문에서는 블럽을 사용해서 다수의 자동차 후면의 번호판을 추출하는 방법을 제안한다. 입력 영상에서 번호판의 문자와 배경사이의 명암도 차이를 이용하여, 입력 영상의 모든 블럽을 찾고, 찾아낸 블럽을 둘러싸는 최소의 사각형들을 구한다. 이 사각형들 중에서 일련의 경향성을 갖는 블럽 그룹을 찾는다. 찾아난 블럽 그룹이 자동차 번호판인지 아닌지를 SVM을 이용하여 확인한다. 적응적 이진화를 제외한 전처리작업을 하지 않았음에도 불구하고 번호판 검출률은 매우 높았으며, 번호판을 검출하는데 걸리는 시간도 길지 않았다.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.191-198
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• 2007

In this paper, we present a marker detection and recognition method from camera image for a disabled person to interact with a server system which can control appliance of surrounding environment. It converts the camera image to a binary image by using multi-threshold and extracts contours of objects in the binary image. After that, it approximates the contours to a list of line segments. It finds rectangular markers by using geometrical features which are extracted from the approximated line segments. It normalizes the shape of extracted markers into exact squares by using the warping technique. It extracts feature vectors from marker image by using principal component analysis and then recognizes the marker. The proposed marker recognition system is robust for light change by using multi-threshold. Also, it is robust for angular variation of camera by using warping technique and principal component analysis. Experimental results show that the proposed method achieves 100% recognition rate at maximum for 21 markers and execution speed of 12 frames/sec.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.367-370
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• 2009

증강현실에서 자연스럽고 인간 친화적인 인터페이스로는 비전 기반의 손동작을 이용한 인터페이스가 가장 각광받고 있다. 그러나 복잡한 배경에서 손을 찾고 손동작을 인식하는 것은 여전히 어려운 문제로 남아 있다. 특히, 배경에 살색을 가진 물체가 많이 있다면 이 문제는 더욱 해결하기 어려워진다. 이 논문은 손 영역을 정확하게 검출 하는 방법에 초점이 맞춰져 있으며, 효과적인 방법을 제안한다. 제안하는 방법은 기본적으로 손과 팔을 포함하는 영역이 다른 피부색 영역과 다른 밝기를 가지고 있다고 가정한다. 구체적으로 제안하는 방법은 밝기 차이를 이용하여 피부색 영역으로부터 손과 팔을 포함하는 영역을 검출한다. 본 논문에서는 밝기 차이를 구분하는 방법으로 �o지(edge) 영상을 이용한다. 그 다음 손과 팔의 기하학적 특징을 이용하여 손목을 찾고 손을 포함하는 사각형 영역을 검출한다. 마지막으로 사각형 영역으로부터 손을 찾아낸다. 손을 찾는 방법 또한 약간 다르지만 비슷한 밝기 기반의 추출 방법을 사용한다. 우리는 간단한 손동작 기반의 증강현실 인터페이스를 구현함으로써 제안한 방법의 효용성을 검증한다.