• Journal of Advanced Navigation Technology
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• v.27 no.2
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• pp.190-196
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• 2023

Recently, following the development of LIDAR technology which can detect distance from the object, the interest for LIDAR based 3D object detection network is getting higher. Previous networks generate inaccurate localization results due to spatial information loss during voxelization and downsampling. In this study, we propose an attention-based convergence method and a camera-LIDAR convergence system to acquire high-level features and high positional accuracy. First, by introducing the attention method into the Voxel-RCNN structure, which is a grid-based 3D object detection network, the multi-scale sparse 3D convolution feature is effectively fused to improve the performance of 3D object detection. Additionally, we propose the late-fusion mechanism for fusing outcomes in 3D object detection network and 2D object detection network to delete false positive. Comparative experiments with existing algorithms are performed using the KITTI data set, which is widely used in the field of autonomous driving. The proposed method showed performance improvement in both 2D object detection on BEV and 3D object detection. In particular, the precision was improved by about 0.54% for the car moderate class compared to Voxel-RCNN.

• Journal of Broadcast Engineering
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• v.20 no.5
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• pp.706-717
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• 2015

The computer generated hologram (CGH) method is the technology which can generate a hologram by using only a personal computer (PC) commonly used. However, the CGH method requires a huge amount of calculational time for the 3D object with a super multi-light source or a high-definition hologram. Hence, some solutions are obviously necessary for reducing the computational complexity of a CGH algorithm or increasing the computing performance of hardware. In this paper, we propose a method which can generate a digital hologram of the 3D object with a super multi-light source using parallel distributed computing. The traditional methods has the limitation of improving CGH performance by using a single PC. However, the proposed method where a server PC efficiently uses the computing power of client PCs can quickly calculate the CGH method for 3D object with super multi-light source. In the experimental result, we verified that the proposed method can generate the digital hologram with 1,5361,536 resolution size of 3D object with 157,771 light source in 121 ms. In addition, in the proposed method, we verify that the proposed method can reduce generation time of a digital hologram in proportion to the number of client PCs.

• Journal of Korean Society for Geospatial Information Science
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• v.21 no.3
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• pp.11-17
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• 2013

With high social demands for the diverse utilizations of forest lands, the illegal forest land use changes have increased. We studied change detection technique to detect changes in forest land use using an object-oriented segmentation of RED bands differencing in multi-temporal aerial photographs. The new object-oriented segmentation method consists of the 5 steps, "Image Composite - Segmentation - Reshaping - Noise Remover - Change Detection". The method enabled extraction of deforested objects by selecting a suitable threshold to determine whether the objects was divided or merged, based on the relations between the objects, spectral characteristics and contextual information from multi-temporal aerial photographs. The results found that the object-oriented segmentation method detected 12% of changes in forest land use, with 96% of the average detection accuracy compared by visual interpretation. Therefore this research showed that the spatial data by the object-oriented segmentation method can be complementary to the one by a visual interpretation method, and proved the possibility of automatically detecting and extracting changes in forest land use from multi-temporal aerial photographs.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.489-497
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• 2001

This paper describe design and implementation of a medical multi-agent system platform called MAMI (Multi-Agent system for Medical Image), which provides intelligent medical information services. The most important component of MAMI is a medical multi-agent system platform that supports a physical environment that medical agents can be deployed. MAMI follows FIPA (Foundation for Intelligent Physical Agent)\`s agent management reference model. In MAMI, COM(Common Object Model) and XML (eXtensibel Markup Language) for encoding ACL (Agent Communication Language) are used for multi-agent communications. In MAMI, a medical staff is conceptualized as an agent and integrated with multi-agent systems. MAMI agent platform provides an infrastructure applicable to share necessary knowledge between human agents and software agents. So MAMI makes intelligent medical information services easier.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.89-97
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• 2011

When examining current process of object tracking and search, objects were tracked by extracting them from image that was inputted through fixed single camera and objects were recognized through Zoom function to know detailed information on objects tracked. This study proposed system that expresses information on area that can seek and recognize object tracked as augmented reality by recognizing and seeking object by using multi camera. The result of experiment on proposed system showed that the number of pixels that was included in calculation was remarkably reduced and recognition rate of object was enhanced and time that took to identify information was shortened. Compared with existing methods, this system has advantage of better accuracy that can detect the motion of object and advantage of shortening time that took to detect motion.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.3
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• pp.69-81
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• 2006

In this paper, an adaptive algorithm is proposed in streaming MPEG-4 contents with fluctuating resource amount such as throughput of network conditions. In the area of adaptive streaming issue, a lot of researches have been made on how to represent encoded media(such as video) bitstream in scalable way. By contrast, MPEG-4 supports object-based multimedia content which is composed of various types of media streams such as audio, video, image and other graphical elements. Thus, it can be more effective to provide individual media streams in scalable way for streaming object-based content to heterogeneous environment. The proposed method provides the multiple media streams corresponding to an object with different qualities and bit rate in order to support object based scalability to the MPEG-4 content. In addition, an optimal selection of the multiple streams for each object to meet a given constraint is proposed. The selection process is adopted a multiple choice knapsack problem with multi-step selection for the MPEG-4 objects with different scalability levels. The proposed algorithm enforces the optimal selection process to maintain the perceptual qualities of more important objects at the best effort. The experimental results show that the set of selected media stream for presenting objects meets a current transmission condition with more high perceptual quality.

• Management Science and Financial Engineering
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• v.7 no.1
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• pp.27-39
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• 2001

The multi-projective model considers attributes and the relationships among attributes called projections. The critical features of the multi-projective model are the way of relating attributes in the description of the system, the way of reasoning incomplete projections, and the determination of connected patterns between projection. In order to get a full picture of the system, we build a set of projections. The multi-projective model can be thought of as projections of a multi-dimensional reality onto simplified “model space”. The multi-projective database modeling approach used in this paper unified the ideas and terminology of various database models. Most importantly, the multi-projective modeling is presented as a tool of database design in the relational and other database models.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.773-776
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• 1998

In this paper, we propose an efficient multi-view images coding algorithm which finds the optimal texture from the restricted number of multi-view images. The X-Y plane of the normalized object space is divided into triangular patches. The depth value of the node is determined by applying the block based disparity compensation method and then the texture of the each patch is extracted by applying the affine transformation patch is extracted by applying the affine transformation based disparity compensation method to the multi-view images. We restricted the number of images contributed to determining the texture comapred to traditional methods which use all the multi-view images in the texture extraction. Experimental results show that the SNR of images encoded by the proposed algorithm is better than that of imaes encoded by the traditional method by the amount about 0.2dB for the test sets of multi-view images called dragon, kid, city and santa. The recovered images from the encoded data by the proposed method show the better visual images than the recovered images from the encoded data by the traditional methods.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.45-52
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• 2006

This paper presents an image-space algorithm to real-time collision detection, which is run completely by GPU. For a single object or for multiple objects with no collision, the front and back faces appear alternately along the view direction. However, such alternation is violated when objects collide. Based on these observations, the algorithm propose the depth peeling method which renders the minimal surface of objects, not whole surface, to find colliding. The Depth peeling method utilizes the state-of-the-art functionalities of GPU such as framebuffer object, vertexbuffer object, and occlusion query. Combining these functions, multi-pass rendering and context switch can be done with low overhead. Therefore proposed approach has less rendering times and rendering overhead than previous image-space collision detection. The algorithm can handle deformable objects and complex objects, and its precision is governed by the resolution of the render-target-texture. The experimental results show the feasibility of GPU-based collision detection and its performance gain in real-time applications such as 3D games.