• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.257-262
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• 2004

This paper will develope an ocean environmental evaluation system. The system analysis by means of introducing the object oriented pattern analysis methodology. We will test water quality according to 40 sea water measurement points and evaluate the ocean environment by means of spatial statistical method. By analyzing the object oriented pattern ocean environmental system, we will contribute on enhancing the efficient development and maintenance other geographic information system.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.260-262
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• 2004

Spatial database system provides many query types and most of them are required frequent disk I/O and much CPU time. k-NN search is to find k-th closest object from the query point and up to now, several k-NN search methods have been proposed. Among these, MINMAX distance method has an aim not to visit unnecessary node by applying pruning technique. But this method access more disk than necessary while pruning unnecessary node. In this paper, we propose new k-NN search algorithm based on density of object. With this method, we predict the radius to be expected to contain k-NN object using density of data set and search those objects within this radius and then adjust radius if failed. Experimental results show that this method outperforms the previous MINMAX distance method. This algorithm visit fewer disks than MINMAX method by the factor of maximum $22\%\;and\;average\;6\%.$

• Journal of Welding and Joining
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• v.22 no.6
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• pp.43-49
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• 2004

In this study, a visual sensor system which can detect I-butt weld joint with height variation and includes a seam tracking algorithm was investigated. Three-dimensional position of an object can be acquired by using the method of distance measurement, i.e., an optical trigonometry which results from the spatial relations between the camera, the object and the structured light by a visible laser. Effects of laser intensity and iris number for the image quality as well as object material were investigated for the optical system design. For the image processing, a region of interest is defined from the whole image and a line image of laser is drew by using the gray level difference in the image. From the drew laser line, the weld joint can be recognized in searching the biggest point position calculated from the central difference method. Through a series of welding experiments, a good tracking performance was confirmed under GMA welding.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.8
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• pp.1275-1283
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• 1990

Perception of surfaces from range images plays a key role in 3-D object recognition. Recognition of 3-D objects from range images is performed by matching the perceived surface descriptions with stored object models. The first step of the 3-d object recognition from range images is image segmentation. In this paper, an approach for segmenting 3-D range images into symbolic surface descriptions using spatial Gabor filter is proposed. Since the phase of data has a lot of important information, the phase information with magnitude information can effectively segment the range imagery into regions satisfying a common homogeneity criterion. The phase and magnitude of Gabor filter can represent a unique featur vector at a point of range data. As a result, range images are trnasformed into feature vectors in 3-parameter representation. The methods not only to extract meaningful features but also to classify a patch information from range images is presented.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.1 s.6
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• pp.23-29
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• 1996

The purpose of this study is to develope an Object-Oriented Graphic database management system to handle geographic data of geographic information systems. As the result of this study, unstructured vector model was developed to handle geographic data and graphic database management was implemented by object-oriented programming. This study was focused on liking function between graphic data and attribute data, and not focused on network analysis function.

• IEMEK Journal of Embedded Systems and Applications
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• v.11 no.4
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• pp.219-225
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• 2016

Visual object tracking is one of the key tasks in computer vision. Robust trackers should address challenging issues such as fast motion, deformation, occlusion and so on. In this paper, we therefore propose a visual object tracking method that exploits inter-frame correlations of convolutional feature maps in Convolutional Neural Net (ConvNet). The proposed method predicts the location of a target by considering inter-frame spatial correlation between target location proposals in the present frame and its location in the previous frame. The experimental results show that the proposed algorithm outperforms the state-of-the-art work especially in hard-to-track sequences.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.688-695
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• 2000

We present a multi-view autostereoscopic display system based on volume holographic storage technique. In this proposed system, the interference pattern of spatial multiplexed plane reference and angular multiplexed plane object beams are recorded into a photorefractive crystal, which plays a role of guiding object beams of multi-view images into the desired persfective directions. For reconstruction, object beams containing the desired multi-view image information, which satisfy Bragg matching condition, are illuminated in the time-division multiplexed manner onto the crystal. Then multiple stereoscopic images are Projected to the display plane for autostereoscopic 3D viewing.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.11-15
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• 2020

In a typical initial setup of a PCB component inspection system, operators should manually input various information such as category, position, and inspection area for each component to be inspected, thus causing much inconvenience and longer setup time. Although there are many deep learning based object detectors, RetinaNet is regarded as one of best object detectors currently available. In this paper, a method using an extended RetinaNet is proposed that automatically detects its component category and position for each component mounted on PCBs from a high-resolution color input image. We extended the basic RetinaNet feature pyramid network by adding a feature pyramid layer having higher spatial resolution to the basic feature pyramid. It was demonstrated by experiments that the extended RetinaNet can detect successfully very small components that could be missed by the basic RetinaNet. Using the proposed method could enable automatic generation of inspection areas, thus considerably reducing the setup time of PCB component inspection systems.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.5 s.23
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• pp.17-28
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• 2002

Time is regarded as an essential feature of land information enabling to track historical landmarks of land uses, ownerships, and taxations based on cadastral maps. Object-oriented temporal modeling helps to simulate and imitate time-varying cadastral data in a chronological and persistent manner. The aim of study is to analyze the role of temporal cadastre tracing footprints of foregoing events in response to various needs and demands associated with historical information of cadastral transactions. In this paper, temporal cadastral object model (TCOM) is proposed to delineate object version history. As an evidence of a new approach and conceptual idea for the importance of temporal cadastre, a part of spatio-temporal processes is illustrated to explain major changes of cadastral map. The feasibility and application of the approach is confirmed by proof-of-concept of temporal cadastre in land information management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3493-3506
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• 2022

In the field of scene segmentation, the precise segmentation of object boundaries in sports movement scene images is a great challenge. The geometric information and spatial information of the image are very important, but in many models, they are usually easy to be lost, which has a big influence on the performance of the model. To alleviate this problem, a parallel dense dilated convolution merging Network (termed PDDCM-Net) was proposed. The proposed PDDCMNet consists of a feature extractor, parallel dilated convolutions, and dense dilated convolutions merged with different dilation rates. We utilize different combinations of dilated convolutions that expand the receptive field of the model with fewer parameters than other advanced methods. Importantly, PDDCM-Net fuses both low-level and high-level information, in effect alleviating the problem of accurately segmenting the edge of the object and positioning the object position accurately. Experimental results validate that the proposed PDDCM-Net achieves a great improvement compared to several representative models on the COCO-Stuff data set.