• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1169-1193
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• 2010

In this paper, we present and compare two different multiple camera collaboration strategies to reduce false association in finding the correspondence of objects. Collaboration matrices are defined with the required minimum separation for an effective collaboration because homographic lines for objects association are ineffective with the insufficient separation. The first strategy uses the collaboration matrices to select the best pair out of many cameras having the maximum separation to efficiently collaborate on the object association. The association information in selected cameras is propagated to unselected cameras by the global information constructed from the associated targets. While the first strategy requires the long operation time to achieve the high association rate due to the limited view by the best pair, it reduces the computational cost using homographic lines. The second strategy initiates the collaboration process of objects association for all the pairing cases of cameras regardless of the separation. In each collaboration process, only crossed targets by a transformed homographic line from the other collaborating camera generate homographic lines. While the repetitive association processes improve the association performance, the transformation processes of homographic lines increase exponentially. The proposed methods are evaluated with real video sequences and compared in terms of the computational cost and the association performance. The simulation results demonstrate that the proposed methods effectively reduce the false association rate as compared with basic pair-wise collaboration.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1987.10b
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• pp.83-86
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• 1987

A vision algorithm is presented for the separation of near objects from distant objects. In the algorithm, a difference field of a stereo pair of images is computed to obtain the range information and the median filter is used for the suppression of distant objects. The objects within a given distance is segmented by thresholding the gray scale cross-section of the median filtered difference field. The experiment is performed in a laboratory setting.

• Land and Housing Review
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• v.11 no.3
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• pp.93-101
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• 2020

Recently, as a BIM-based construction simulation system, 4D CAD tools using virtual reality (VR) objects are being applied in construction project. In such a system, since the expression of the object is based on VR image, it has a sense of separation from the real environment, thus limiting the use of field engineers. For this reason, there are increasing cases of applying augmented reality (AR) technology to reduce the sense of separation from the field and express realistic VR objects. This study attempts to develop a methodology and BIM module for the pre-construction verification system using AR technology to increase the practical utility of VR-based BIM objects. To this end, authors develop an AR-based drawing verification function and drawing object-based 4D model augmentation function that can increase the practical utility of 2D drawings, and verify the applicability of the system by performing case analysis. Since VR object-based image has a problem of low realism to field engineers, the linking technology between AR object and 4D model is expected to contribute to the expansion of the use of 4D CADsystem in the construction project.

• Journal of The Korean Astronomical Society
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• v.30 no.1
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• pp.83-94
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• 1997

We have identified the candidates for the primordial galaxies in the process of formation in the Hubble Deep Field (hereafter HDF). In order to select these objects we have removed objects brighter than 29-th magnitude in the HDF images and smoothed the maps with the Gaussian filters with the FWHM of 0.8' and 4' to obtain the difference maps. This has enabled us to find. very faint diffuse structures close to the sky level. Peaks are identified in the difference map for each of three HDF chips with three filters (F450W, F606W, and F814W). They have the apparent AB magnitudes typically between 29 and 31. The objects identified in different wavelengths filters have a strong cross-correlations. The correlation lengths are about 0.8'. This means that an object found in one filter can be also found as a peak within 0.8' separation in another filter, thus telling the reality of the identified objects. This angular scale is also the size of the primordial galaxies which have strong color fluctuations on their surfaces. Their large-scale distribution quite resembles that of nearby galaxies, supporting the idea that these objects are ancestors of the present bright galaxies forming at statistically high density regions. Inspections on individual objects show that these primordial galaxy candidates have tiny multiple glares embedded in diffuse backgrounds. Their radial light distributions are quite different from that of nearby bright galaxies. We may be now looking at the epoch of galaxy formation.

• 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 ...

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.9
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• pp.4152-4158
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• 2012

The Stereo X-ray inspection system is designed for effectively providing the additional information of objects than the conventional inspection system that offers only 2D cross-section of objects. We studied the geometric improvement of the stereo X-ray inspection system, the stereo matching algorithm of the single object using the edge and the volume reconstruction method for the inspected object. In this paper, we conduct a matching algorithm to find the correspondences between the images and reconstruct 3-D shapes of real objects using the stereo X-ray images. Also, we apply a new 3D reconstruction algorithm for the discrimination of two objects. For the separation of the overlapping objects, we calculate the vector of the object and divide inner and outer voxel of objects. And for the elimination of the overlapping area, we study the reconstruct 3D shapes using the threshold based Z-axis. The experimental results show that the proposed technique can enhance the accuracy of stereo matching and give more efficient visualization for overlap objects in the restricted environment.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.77-86
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• 2018

Although separation of touching pigs in real-time is an important issue for a 24-h pig monitoring system, it is challenging to separate accurately the touching pigs in a crowded pig room. In this study, we propose a separation method for touching pigs using the information generated from Convolutional Neural Network(CNN). Especially, we apply one of the CNN-based object detection methods(i.e., You Look Only Once, YOLO) to solve the touching objects separation problem in an active manner. First, we evaluate and select the bounding boxes generated from YOLO, and then separate touching pigs by analyzing the relations between the selected bounding boxes. Our experimental results show that the proposed method is more effective than widely-used methods for separating touching pigs, in terms of both accuracy and execution time.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.481-487
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• 2023

Recently, interest in UAM (Urban Air Mobility) has surged as a critical solution to urban traffic congestion and air pollution issues. However, efficient UAM operation requires accurate 3D Point Cloud data processing, particularly in separating the ground and objects. This paper proposes and validates a method for effectively separating ground and objects in a UAM environment, taking into account its dynamic and complex characteristics. Our approach combines attitude information from MEMS sensors with ground plane estimation using RANSAC, allowing for ground/object separation that isless affected by GPS errors. Simulation results demonstrate that this method effectively operates in UAM settings, marking a significant step toward enhancing safety and efficiency in urban air mobility. Future research will focus on improving the accuracy of this algorithm, evaluating its performance in various UAM scenarios, and proceeding with actual drone tests.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.311-320
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• 2003

The Optimal filter yielding optimal texture feature separation is a most effective technique for extracting the texture objects from multiple textures images. But, most optimal filter design approaches are restricted to the issue of supervised problems. No full-unsupervised method is based on the recognition of texture objects in image. We propose a novel approach that uses unsupervised learning schemes for efficient texture image analysis, and the band-pass feature of Gabor-filter is used for the optimal filter design. In our approach, the self-organizing neural network for multiple texture image identification is based on block-based clustering. The optimal frequency of Gabor-filter is turned to the optimal frequency of the distinct texture in frequency domain by analyzing the spatial frequency. In order to show the performance of the designed filters, after we have attempted to build a various texture images. The texture objects extraction is achieved by using the designed Gabor-filter. Our experimental results show that the performance of the system is very successful.