• 한국통신학회논문지
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• 제31권4C호
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• pp.407-416
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• 2006

카메라를 통한 객체의 움직임 검출은 불필요한 잡음이나 조명의 변화에 따라 정확한 움직임을 검출하는 것은 어렵다. 또한 객체의 유입 후 일정시간 동안 움직임이 없을 경우에는 배경으로 인식될 수도 있다. 따라서 실시간으로 입력되는 영상에서 정확한 객체를 추출하고 움직임을 검출할 수 있는 알고리즘이 필요하다. 본 논문에서는 객체의 정확한 움직임을 검출하기 위한 방법은 시간에 따라 변화하는 배경영상의 일부를 교체하고, 객체가 유입된 시점에서 객체의 영역을 추출하기 위하여 그물형 픽셀검사를 통하여 객체의 윤곽점을 추출한다. 추출된 윤곽점은 객체의 사각영역인 최소블록의 생성과 객체의 움직임을 빠르게 검출하기 위한 가변 탐색블록을 생성하여 정확한 객체의 움직임을 검출한다. 설계하고 구현한 시스템은 실험을 통한 성능평가에서 95% 이상의 높은 정확도를 보였다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 춘계학술발표대회
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• pp.405-408
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• 2010

This paper presents a new feature extraction technique, correlation coefficient and Manhattan distance (MD) based method for recognition of rotated object in an image. This paper also represented a new concept of intensity invariant. We extracted global features of an image and converts a large size image into a one-dimensional vector called circular feature vector's (CFVs). An especial advantage of the proposed technique is that the extracted features are same even if original image is rotated with rotation angles 1 to 360 or rotated. The proposed technique is based on fuzzy sets and finally we have recognized the object by using histogram matching, correlation coefficient and manhattan distance of the objects. The proposed approach is very easy in implementation and it has implemented in Matlab7 on Windows XP. The experimental results have demonstrated that the proposed approach performs successfully on a variety of small as well as large scale rotated images.

• International journal of advanced smart convergence
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• 제1권2호
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• pp.39-42
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• 2012

This paper proposed a method that detects emergency situations in a video stream using MHI (Motion History Image) and template matching for a video-based intelligent security system. The proposed method creates a MHI of each human object through image processing technique such as background removing based on GMM (Gaussian Mixture Model), labeling and accumulating the foreground images, then the obtained MHI is compared with the existing MHI templates for detecting an emergency situation. To evaluate the proposed emergency detection method, a set of experiments on the dataset of video clips captured from a security camera has been conducted. And we successfully detected emergency situations using the proposed method. In addition, the implemented system also provides MMS (Multimedia Message Service) so that a security manager can deal with the emergency situation appropriately.

• Journal of Computing Science and Engineering
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• 제8권2호
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• pp.65-77
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• 2014

To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.

• 대한기계학회논문집A
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• 제23권11호
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• pp.1929-1939
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• 1999

This paper presents a neural network approach, which was named PRONET, to 3D object recognition and pose calculation. 3D objects are represented using a set of centroidal profile patterns that describe the boundary of the 2D views taken from evenly distributed view points. PRONET consists of the training stage and the execution stage. In the training stage, a three-layer feed-forward neural network is trained with the centroidal profile patterns using an error back-propagation method. In the execution stage, by matching a centroidal profile pattern of the given image with the best fitting centroidal profile pattern using the neural network, the identity and approximate orientation of the real object, such as a workpiece in arbitrary pose, are obtained. In the matching procedure, line-to-line correspondence between image features and 3D CAD features are also obtained. An iterative model posing method then calculates the more exact pose of the object based on initial orientation and correspondence.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.601-605
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• 2009

We have proposed a measurement system for measuring a whole shape of an object easily. The proposed system consists of a camera and a cylinder whose inside is coated by a mirror layer. A target object is placed inside the cylinder and an image is captured by the camera from right above. The captured image includes sets of points that are observed from multiple viewpoints: one is observed directly, and others are observed via the mirror. Therefore, the whole shape of the object can be measured using stereo vision in a single shot. This paper shows that a prototype of the proposed system was implemented and an actual object was measured using the prototype. A method based on a pattern matching which uses a value of SSD (Sum of Squared Difference), and a method based on DP (Dynamic Programming) are employed to identify a set of corresponding points in warped captured images.

• Journal of Information Processing Systems
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• 제13권2호
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• pp.256-267
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• 2017

An active research area in computer vision, stereo matching is aimed at obtaining three-dimensional (3D) information from a stereo image pair captured by a stereo camera. To extract accurate 3D information, a number of studies have examined stereo matching algorithms that employ adaptive support weight. Among them, the adaptive census transform (ACT) algorithm has yielded a relatively strong matching capability. The drawbacks of the ACT, however, are that it produces low matching accuracy at the border of an object and is vulnerable to noise. To mitigate these drawbacks, this paper proposes and analyzes the features of an improved stereo matching algorithm that not only enhances matching accuracy but also is also robust to noise. The proposed algorithm, based on the ACT, adopts the truncated absolute difference and the multiple sparse windows method. The experimental results show that compared to the ACT, the proposed algorithm reduces the average error rate of depth maps on Middlebury dataset images by as much as 2% and that is has a strong robustness to noise.

• ETRI Journal
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• 제34권1호
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• pp.87-97
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• 2012

This study presents an automatic matching method for generating a dense, accurate, and discontinuity-preserved digital surface model (DSM) using multiple images acquired by an aerial digital frame camera. The proposed method consists of two main procedures: area-based multi-image matching (AMIM) and stereo-pair epipolar line matching (SELM). AMIM evaluates the sum of the normalized cross correlation of corresponding image points from multiple images to determine the optimal height of an object point. A novel method is introduced for determining the search height range and incremental height, which are necessary for the vertical line locus used in the AMIM. This procedure also includes the means to select the best reference and target images for each strip so that multi-image matching can resolve the common problem over occlusion areas. The SELM extracts densely positioned distinct points along epipolar lines from the multiple images and generates a discontinuity-preserved DSM using geometric and radiometric constraints. The matched points derived by the AMIM are used as anchor points between overlapped images to find conjugate distinct points using epipolar geometry. The performance of the proposed method was evaluated for several different test areas, including urban areas.

• 산업공학
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• 제4권2호
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• pp.43-51
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• 1991

A line search method is developed to locate all the conerpoints of 2-dimensional polygon images for inspection purposes. This optimization-based method is used to approximate a 2-D curved object by a polygon. This scheme is also developed for inspection of objects in industrial environment. The inspection includes dimensional verification and pattern matching which compares a 2-D image of an object to a pattern image. The method proves to be computationally efficient and accurate for real time application.

• 전자공학회논문지B
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• 제28B권11호
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• pp.925-933
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• 1991

Conventional stereo matching techniques are based on the assumption that the features representing an object in left and right images have fixed attribute values. But, in fact, such features may take different values due to the practical stereo image formation and the image acquisition error, and thus the conventional techniques tend to result in the in the incorrect matching of features. In this paper, we propose a stereo matching mathod with a possibilistic view which copes with the possible variability of feature values. As a result, this method decreases the number of incorrect matching features when the values of corresponding features are somewhat large. The effectiveness of the proposed method is shown via computer simulation.