• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.5
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• pp.510-516
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• 2015

This paper describes an approach to detect interesting objects using satellite images. This paper focuses on the interesting objects that have common special patterns but do not have identical shapes and sizes. The previous technologies are still insufficient for automatic finding of the interesting objects based on operation of special pattern analysis. In order to overcome the circumstances, this paper proposes a methodology to obtain the special patterns of interesting objects considering their common features and their related characteristics. This paper applies MSER(Maximally Stable Extremal Regions) for the region detection and corner detector in order to extract the features of the interesting object. This paper conducts a case study and obtains the experimental results of the case study, which is efficient in reducing processing time and efforts comparing to the previous manual searching.

• Journal of Digital Contents Society
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• v.16 no.6
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• pp.951-959
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• 2015

Scene text extraction is important because it offers some important information on different image based applications pouring in current smart generation. Edge-Enhanced MSER(Maximally Stable Extremal Regions) which enhances the boundaries using the canny operator after extracting the basic MSER shows excellent performance in terms of text extraction. But according to setting the threshold of the canny operator, the result images using Edge-Enhanced MSER are different, so there needs a method figuring out the threshold. In this paper, we propose a AEMSER(Adaptive Edge-enhanced MSER) that applies the method extracting the boundary using the middle value of histogram to Edge-Enhanced MSER to get the canny operator's threshold. The proposed method can acquire better result images than the existing methods because it extracts the area only for the obvious boundaries.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1709-1712
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• 2013

교통 신호등은 운전자 혹은 보행자들의 뚜렷한 시인성 확보를 위해 가능한 주위 배경과 구분되는 색상, 모양, 질감 등으로 구성하여 설치되어 있는 특징을 가지고 있다. 결국 기존 교통 신호등 검출 연구들에서는 대부분 교통 신호등의 색상과 모양을 기반으로 한 검출 연구가 주류를 이루고 있는 것이 사실이다. 하지만, 외부 날씨, 복잡한 시내, 다른 물체와의 겹침 등의 문제로 인해 색상 및 모양 기반의 교통 신호등, motion blur, 검출 오류가 증가 되고 있다. 따라서 본 연구에서는 입력 영상에서 색상정보를 배제하고 motion blur나 밝기 변화에 덜 민감하고 먼 거리에서도 뛰어난 시인성을 가진 spot light 검출을 통해 입력 영상에서 가장 밝은 교통표지판 후보 영역들을 검출한다. 그리고 교통 신호등의 특징인 가능한 원형을 유지하고 있으며 원형 외부 색상과 내부 색상이 현저하게 두드러지는 영역을 maximally stable extremal regions (MSER) 알고리즘을 사용하여 입력 영상에서 후보 영역을 선택한다. 마지막으로, 검출된 영역들에서 교통 신호등 영역을 검출하기 위해 템플릿 매칭 방법을 적용한다. 제안한 방법을 도로 상에서 실험한 결과, 평균 94% 이상의 검출율을 제시하였고, 특히 야간 시간대에 검출율이 비교적 높게 제시되었다.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.123-129
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• 2015

This paper proposes an efficient method to detect direction indicators on road surfaces to support drivers in driving safely using the Simulink model. In the proposed method, the ROIs are detected using the detection method of maximally stable extremal regions (MSER), and the road indicator regions are detected using the speeded up robust features (SURF) matching method for the corresponding point matching of the detected ROIs and the road indicator templates. Experiments on various road satiations show that the processing time of about 0.32 sec per frame was required, and a detection rate of 91% was achieved.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.5
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• pp.951-959
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• 2017

In this paper, we design and implement an Android-based Hangul food menu recognition application that recognizes characters from images captured by a smart phone. Optical Character Recognition (OCR) technology is divided into preprocessing, recognition and post-processing. In the preprocessing process, the characters are extracted using Maximally Stable Extremal Regions (MSER). In recognition process, Tesseract-OCR, a free OCR engine, is used to recognize characters. In the post-processing process, the wrong result is corrected by using the dictionary DB for the food menu. In order to evaluate the performance of the proposed method, experiments were conducted to compare the recognition performance using the actual menu plate as the DB. The recognition rate measurement experiment with OCR Instantly Free, Text Scanner and Text Fairy, which is a character recognizing application in Google Play Store, was conducted. The experimental results show that the proposed method shows an average recognition rate of 14.1% higher than other techniques.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.142-149
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• 2016

This paper proposes a method to detect LP(License Plate) of vehicles in indoor and outdoor parking lots. In restricted environment, there are many conventional methods for detecting LP. But, it is difficult to detect LP in natural and complex scenes with background clutters because several patterns similar with text or LP always exist in complicated backgrounds. To verify the performance of LP text detection in natural images, we apply MB-LGP feature by combining with ensemble machine learning algorithm in purpose of selecting optimal features of small number in huge pool. The feature selection is performed by adaptive boosting algorithm that shows great performance in minimum false positive detection ratio and in computing time when combined with cascade approach. MSER is used to provide initial text regions of vehicle LP. Throughout the experiment using real images, the proposed method functions robustly extracting LP in natural scene as well as the controlled environment.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.219-226
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• 2016

In our general life, what we recognize information with our human eyes and use it is diverse and massive. But even the current technologies improved by artificial intelligence are exorbitantly deficient comparing to human visual processing ability. Nevertheless, many researchers are trying to get information in everyday life, especially concentrate effort on recognizing information consisted of text. In the fields of recognizing text, to extract the text from the general document is used in some information processing fields, but to extract and recognize the text from real image is deficient too much yet. It is because the real images have many properties like color, size, orientation and something in common. In this paper, we applies an adaptive edge enhanced MSER(Maximally Stable Extremal Regions) to extract the text area in those diverse environments and the scene text, and show that the proposed method is a comparatively nice method with experiments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.181-182
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• 2014

본 논문에서는 어파인(affine) 변환에 불변하는 지역 검출기에 대하여 다양한 기술자의 성능을 비교하였다. 지난 수 년간 다양한 특징 기술자들이 연구되어 왔고, 이러한 특징 기술자들은 각각의 목적에 따라 상이한 특성을 갖고 있기 때문에 동일한 조건에서 다양한 기술자들의 성능을 비교하는 연구가 필요하다. 그러나 어파인 변환에 불변하는 지역 검출기에 대해 최적의 특징 기술자를 찾는 연구는 미흡한 실정이다. 따라서 본 논문에서는 지역적인 패치 기반의 특징 기술자뿐만 아니라 바이너리 기술자와 최근에 제안된 기술자들의 성능을 비교하였다. 제안하는 실험에서는 MSER (maximally stable extremal regions) 검출기를 이용하여 어파인 변환에 불변하는 지역을 검출하였고, 영상 확대 및 축소, 회전, 시점 변환 및 변형 가능한 물체에 대하여 각각 기술자의 성능을 비교하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.11a
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• pp.194-196
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• 2016

본 논문에서는 안드로이드 환경에서 글자 인식을 위한 전처리 과정으로 입력 영상에서 글자 영역만을 추출하는 기법을 제안한다. 대부분의 글자 인식 어플리케이션에서 글자를 인식하는 방법은 RoI(Region of Interest)에 인식하려는 글자를 위치시켜 놓고 사용자가 촬영함으로써 진행된다. 하지만 촬영된 영상 그대로를 인식에 사용하기 때문에 잡음 및 글자가 아닌 영역들을 글자로 인식하는 문제 등으로 인하여 인식률이 현저히 떨어진다. 제안하는 기법에서는 MSER(Maximally Stable Extremal Regions) 기법을 통해 각각의 글자를 추출한 후, 글자의 특성을 이용하여 글자 영역만을 추출한다. 기법의 성능 평가는 무료 OCR(Optical Character Recognition) 엔진인 Tesseract-OCR을 통해 글자 인식률을 비교하였으며, 제안하는 기법을 적용한 글자 인식 시스템이 적용하지 않은 시스템보다 글자의 인식률이 향상되는 것을 확인하였다.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.1
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• pp.8-16
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• 2016

In the field of computer vision and robotics, bin picking is an important application area in which object pose estimation is necessary. Different approaches, such as 2D feature tracking and 3D surface reconstruction, have been introduced to estimate the object pose accurately. We propose a new approach where we can use both 2D image features and 3D surface information to identify the target object and estimate its pose accurately. First, we introduce a label detection technique using Maximally Stable Extremal Regions (MSERs) where the label detection results are used to identify the target objects separately. Then, the 2D image features on the detected label areas are utilized to generate 3D surface information. Finally, we calculate the 3D position and the orientation of the target objects using the information of the 3D surface.