• Language and Information
• /
• v.14 no.1
• /
• pp.49-65
• /
• 2010

It has been widely accepted that English object extraposition can be easily accounted for. However, recent research exhibits the fact that various cases of English object extraposition lead to many empirical and theoretical problems in generative grammar. To account for such cases, the previous lexical constraint-based analyses including Kim & Sag (2006, 2007) and Kim (2008) attempt to give an explanation on the phenomenon. They, however, seem to be unsuccessful in providing an appropriate analysis of object extraposition, mainly due to the mistaken data generalizations. Unlike the previous analyses, we claim that all verbs selecting CP objects allow object extraposition and propose a unified constraint-based analysis for the various cases of the construction. Further, it is shown that as a consequence, this analysis of object extraposition can be naturally extended to subject extraposition. Hence, this unified analysis enables us to further suggest that all verbs selecting CP allow subject and object extraposition in English.

• International Journal of Internet, Broadcasting and Communication
• /
• v.11 no.4
• /
• pp.76-85
• /
• 2019

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed forfull frame image. We reduced its computationalrequirementsignificantly without losing throughput and object detection accuracy with the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.23 no.9A
• /
• pp.2258-2270
• /
• 1998

Recently, the distributed processing environments provide various open multimedia serivces through telecommunication network and have been developing into information networking structure based on object oriented concepts and distributed systems which can apply new services with a few changes the existing networks. This paper proposes the object group model which is the collection of objects and can functionally and efficiently manage the individual object. this paper presents the analysis of the requirement and the function specifications to propose the object group model, and depicts the functional structure in details using its analysis. The goal of this paper is to decrease the complexity of the object's management and to voercome the limitations of among the components of object group for management and service functions based on our proposed the object group model and show interaction procedures to eTD (event tracing diagram)s and finally we design the object group model by TINA-ODL.

• Journal of Korea Multimedia Society
• /
• v.24 no.3
• /
• pp.345-354
• /
• 2021

Recently, deep learning-based image recognition systems have been adopted to various surveillance environments, but most of them are still picture-type object recognition methods, which are insufficient for the long term temporal analysis and high-dimensional situation management. Therefore, we propose a method recognizing the specific dangerous situation generated by human in real-time, and utilizing deep learning-based object analysis techniques. The proposed method uses deep learning-based object detection and tracking algorithms in order to recognize the situations such as 'trespassing', 'loitering', and so on. In addition, human's joint pose data are extracted and analyzed for the emergent awareness function such as 'falling down' to notify not only in the security but also in the emergency environmental utilizations.

• Journal of the Korea Computer Industry Society
• /
• v.5 no.4
• /
• pp.461-470
• /
• 2004

This paper is to design the user interface of the groundwater well based on an object oriented. In order to implementation geographic data base of the an complex geo-object of the real world, this paper is the study of analysis pattern at the level By specifying the pattern appropriate to the application domain and designing the analysis pattern using the UML based on the object oriented methodology, this paper shall contribute to enhance the reuse of components that can develop and distribute a large scale open system.

• Journal of Information Processing Systems
• /
• v.9 no.4
• /
• pp.592-601
• /
• 2013

Object segmentation is a challenging task in image processing and computer vision. In this paper, we present a visual attention based segmentation method to segment small sized interesting objects in natural images. Different from the traditional methods, we first search the region of interest by using our novel saliency-based method, which is mainly based on band-pass filtering, to obtain the appropriate frequency. Secondly, we applied the Gaussian Mixture Model (GMM) to locate the object region. By incorporating the visual attention analysis into object segmentation, our proposed approach is able to narrow the search region for object segmentation, so that the accuracy is increased and the computational complexity is reduced. The experimental results indicate that our proposed approach is efficient for object segmentation in natural images, especially for small objects. Our proposed method significantly outperforms traditional GMM based segmentation.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.1 no.1
• /
• pp.238-248
• /
• 1998

This paper presents an image analysis method for mearurement correction using the object contour based analysis, which measure the shape features of the imitation missile object. The image analysis is divided into object's tilting angle analysis and corner points detection. The tilting angle is calculated by edge extracting the region-of-interest image and by Radon transform it. The corner points are obtained by contour tracking of binary image and its curvature data processing and analysis. The ability of this presented method is simulated and evaluated by the results of accuracy testing.

• The Journal of Information Systems
• /
• v.10 no.1
• /
• pp.97-126
• /
• 2001

Object-orientation has been proposed as a promising software process innovation to improve software productivity and quality. It has not been understood clearly, however, what factors influences the diffusion of object-oriented technology in organizations. A research model was formulated and hypotheses were generated based on the literature of information technology implementation and software process innovation. To test the research hypotheses, a questionnaire survey was conducted. The results based on 121 responses from Korean companies revealed that project characteristics, use of external experts, and number of development projects are significantly related to the diffusion of object-oriented analysis and design and object-oriented programming. Innovation champion is positively related to the diffusion of object-oriented analysis and design, whereas it is not related to the diffusion of object-oriented programming language. Only project complexity was significantly related to the diffusion of visual programming language. On the other hand, organizational size was not significantly related to any object-oriented technology in this study.

• Aerospace Engineering and Technology
• /
• v.6 no.1
• /
• pp.213-221
• /
• 2007

This paper showed how to analysis the object-based classification for wildfire fuel type map using Hyperion hyperspectral remote sensing data acquired in April, 2002 and compared the results of the object-based classification with the results of the pixel-based classification. Our methodological approach for wildfire fuel type map firstly processed correcting abnormal pixels and atypical bands and also calibrating atmospheric noise for enhanced image quality. Fuel type map is characterized by the results of the spectral mixture analysis(SMA). Object-based approach was based on segment-based endmember selection, while pixel-based method used standard SMA. To validate and compare, we used true-color high resolution orthoimagery.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.3
• /
• pp.125-132
• /
• 2023

Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.