• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.54-61
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• 2008

This paper proposes a new algorithm for detecting and recognizing overlapped objects among a stack of arbitrarily located objects using a signature representation scheme. The proposed algorithm consists of two processes of detecting overlap of objects and of determining the boundary between overlapping objects. To determine overlap of objects, in the first step, the edge image of object region is extracted and those areas in the object region are considered as the object areas if an area is surrounded by a closed edge. For each object, its signature image is constructed by measuring the distances of those edge points from the center of the object, along the angle axis, which are located at every angle with reference to the center of the object. When an object is not overlapped, its features which consist of the positions and angles of outstanding points in the signature are searched in the database to find its corresponding model. When an object is overlapped, its features are partially matched with those object models among which the best matching model is selected as the corresponding model. The boundary among the overlapping objects is determined by projecting the signature to the original image. The performance of the proposed algorithm has been tested with the task of picking the top or non-overlapped object from a stack of arbitrarily located objects. In the experiment, a recognition rate of 98% has been achieved.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.19-27
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• 2019

This paper proposes a new method using multi-layer overlapped windows to improve the performance of YOLO network which is vulnerable to detect small dense objects. In particular, the proposed method uses the YOLO Network based on the multi-layer overlapped windows to track small dense vehicles that approach from long distances. The method improves the detection performance for location and size of small vehicles. It allows crossing area of two multi-layer overlapped windows to track moving vehicles from a long distance to a short distance. And the YOLO network is optimized so that GPU computation time due to multi-layer overlapped windows should be reduced. The superiority of the proposed algorithm has been proved through various experiments using captured images from road surveillance cameras.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.2
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• pp.11-22
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• 2004

Conventional algorithms of separating overlapped objects are mostly based on template matching methods and thus their application domain is restricted to 2D objects and the processing time increases when the number of templates (object models) does. To solve these problems, this paper proposes a new approach of separating the occluding object from the stack of 3D objects using the relationship between surfaces without any information on the objects. The proposed algorithm considers an object as a combination of surfaces which are consisted with a set of boundary edges. Overlap of 3D objects appears as overlap of surfaces and thus as crossings of edges in 2D image. Based on this observation, the types of edge crossings are classified from which the types of overlap of 3D objects can be identified. The relationships between surfaces are represented by an attributed graph where the types of overlaps are represented by relation values. Using the relation values, the surfaces pertained to the same object are discerned and the overlapping object on the top of the stack can be separated. The performance of the proposed algorithm has been proved by the experiments using the overlapped images of 3D objects selected among the standard industrial parts.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.1
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• pp.53-59
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• 2014

In multiple object tracking, accurate detection for each of objects that appear sequentially and effective tracking in complicated cases that they are overlapped with each other are very important. In this paper, we propose a multiple object tracking system that has a concrete detection and tracking characteristics by using multi-lateral histogram and SIFT feature extraction algorithm. Especially, by limiting the matching area to object's inside and by utilizing the location informations in the keypoint matching process of SIFT algorithm, we advanced the tracking performance for multiple objects. Based on the experimental results, we found that the proposed tracking system has a robust tracking operation in the complicated environments that multiple objects are frequently overlapped in various of directions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.1
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• pp.54-68
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• 2001

This paper proposes a new algorithm that detects and separates the occluding and occluded objects in a 2D image. An input image is represented by the attributed graph where a node corresponds to a surface and an arc connecting two nodes describes the adjacency of the nodes in the image. Each end of arc is weighted by relation value which tells the number of edges connected to the surface represented by the node in the opposite side of the arc. In attributed graph, homogeneous nodes pertained to a same object always construct one of three special patterns which can be simply classified by comparison of relation values of the arcs. The experimental results have shown that the proposed algorithm efficiently separates the objects overlapped arbitrarily, and that this approach of separating objects before matching operation reduces the matching time significantly by simplifying the matching problem of overlapped objects as the one of individual single object.

• Journal of the Korea Computer Graphics Society
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• v.8 no.3
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• pp.1-7
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• 2002

This paper presents a practical technique for approximating the boundary surface of the volume swept out by three-dimensional objects using the depth-buffer. Objects may change their geometries and orientations while sweeping. The sweep volume is approximated as a union of volume elements, which are just rendered inside appropriate viewing frusta of virtual cameras and mapped into screen viewports with depth-buffer. From the depth of each pixel in the screen space of each rendering, the corresponding point in the original world space can be computed. Appropriately connecting these points yields polygonal faces forming polygonal surface patches approximately covering some portion of the sweep volume. Each view frustum adds one or more surface patches in this way, and these presumably overlapped polygonal surface patches approximately enclose the whole sweep volume. These patches may further be processed to yield non-overlapped polygonal surfaces as an approximation to the boundary of the original 3D sweep volume.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.12
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• pp.1465-1474
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• 1992

This paper describes a new overlapped object recognition method using extended local features. At first, we extract the extended local features consisting of corners, arcs, parallel-lines, and corner-arcs from the images consisting of model objects. Based on the extended local features we construct a knowledge-base. In order to match objects, we also extract the extended local features from the input image, and then check the compatibility between the extracted features and the features in the knowledge-base. From the set of compatible features, we compute geometric transforms. If any geometric transforms are clustered, we generate the hypothesis of the objects as the centers of the clusters, and then verify the hypothesis by a reverse geometric transform. An experiment shows that the proposed method increases the recognition rate and the accuracy as compared with existing methods.

• Journal of Korea Spatial Information System Society
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• v.6 no.2 s.12
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• pp.3-13
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• 2004

The TPR-tree exploits bounding rectangles based on the function of time in order to index moving objects. As time passes on, each edge of a BR expands with the fastest velocity vector. Since the expansion of the BR results in a serious overlaps between neighboring nodes, the performance of range query is getting worse. In this paper, we propose schemes to reorganize bounding rectangles of nodes. When inserting a moving object, we exploit a forced merging scheme to merge two overlapped nodes and re-split it. When deleting a moving object, we used forced reinsertion schemes to reinsert other objects of a node into a tree. The forced reinsertion schemes are classified into a deleted node reinsertion scheme and an overlapped nodes reinsertion scheme. The overlapped nodes reinsertion scheme outperforms the forced merging scheme and the deleted node reinsertion scheme in all experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1524-1529
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• 2005

Particle filter is a kind of conditional density propagation model. Its similar characteristics to both selection and mutation operator of evolutionary strategy (ES) due to its Bayesian inference rule structure, shows better performance than any other tracking algorithms. When a new object is entering the region of interest, particle filter sets which have been swarming around the existing objects have to move and track the new one instantaneously. Moreover, there is another problem that it could not track multiple objects well if they were moving away from each other after having been overlapped. To resolve reinitialization problem, we use competitive-AVQ algorithm of neural network. And we regard interfarme difference (IFD) of background images as potential field and give priority to the particles according to this IFD to track multiple objects independently. In this paper, we showed that the possibility of real-time object tracking as intelligent interfaces by simulating the deformable contour particle filters.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.28.3-28
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• 2001

This paper proposes a new algorithm that detects and separates the occluding and occluded objects in a 2D image. An input image is represented by the attributed graph where a node corresponds to a surface and an arc connecting two nodes describes the adjacency of the nodes in the image. Each end of arc is weighted by relation value which tells the number of edges connected to the surface represented by the node in the opposite side of the arc. In attributed graph homogeneous nodes pertained to the same object always construct one of three special patterns which can be simply classified by comparison of relation values of the arcs. The experimental results have shown that the proposed algorithm efficiently separates the objects ...