• The KIPS Transactions:PartB
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• v.16B no.2
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• pp.109-116
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• 2009

In this thesis, an object tracking method based on the active feature model and the optical flow in stereo images is proposed. We acquired the translation information of object of interest and the features of object by utilizing the geometric information and depth of stereo images. Tracking performance is improved for the occlude object with this information by predicting the movement information of features of the occlude object. Rigid and non-rigid objects are experimented. From the result of experiment, the OOI can be real-time tracked from complicate back ground. Besides, we got the improved result of object tracking in any occlusion state, no matter what it is rigid or non-rigid object.

• The Journal of Korea Robotics Society
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• v.12 no.1
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• pp.33-41
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• 2017

We present a region-based approach for accurate pose estimation of small mechanical components. Our algorithm consists of two key phases: Multi-view object co-segmentation and pose estimation. In the first phase, we explain an automatic method to extract binary masks of a target object captured from multiple viewpoints. For initialization, we assume the target object is bounded by the convex volume of interest defined by a few user inputs. The co-segmented target object shares the same geometric representation in space, and has distinctive color models from those of the backgrounds. In the second phase, we retrieve a 3D model instance with correct upright orientation, and estimate a relative pose of the object observed from images. Our energy function, combining region and boundary terms for the proposed measures, maximizes the overlapping regions and boundaries between the multi-view co-segmentations and projected masks of the reference model. Based on high-quality co-segmentations consistent across all different viewpoints, our final results are accurate model indices and pose parameters of the extracted object. We demonstrate the effectiveness of the proposed method using various examples.

• Journal of Korean Society for Geospatial Information Science
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• v.3 no.1 s.5
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• pp.45-54
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• 1995

The relational data model is based on mathematical concept of relations and is well formulated, and so there have been numerous practical applications and studies. However, it is not suitable for representing a complex hierarchical structure, which is the characteristic of most geographical objects. On the other hand, the object-oriented data model can naturally represent a complex hierarchical structure, but there is a difficulty in sharing data with the relational data model which is currently used by most commercial GIS users. Also it has no standard query language with standardized format. In this paper, we propose an Object Grouping based on RDBMS to use data from a traditional relational data model while supporting various concepts of the object-oriented data model, and we applied this data model to design a GIS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.5
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• pp.2346-2362
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• 2016

As one of the most important issues in computer vision and image processing, online object tracking plays a key role in numerous areas of research and in many real applications. In this study, we present a novel tracking method based on the proposed structured sparse representation model, in which the tracked object is assumed to be sparsely represented by a set of object and background templates. The contributions of this work are threefold. First, the structure information of all the candidate samples is utilized by a joint sparse representation model, where the representation coefficients of these candidates are promoted to share the same sparse patterns. This representation model can be effectively solved by the simultaneous orthogonal matching pursuit method. In addition, we develop a tracking algorithm based on the proposed representation model, a discriminative candidate selection scheme, and a simple model updating method. Finally, we conduct numerous experiments on several challenging video clips to evaluate the proposed tracker in comparison with various state-of-the-art tracking algorithms. Both qualitative and quantitative evaluations on a number of challenging video clips show that our tracker achieves better performance than the other state-of-the-art methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.1
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• pp.39-46
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• 2006

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and intelligent space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• Proceedings of the ESK Conference
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• 1993.04a
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• pp.10-17
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• 1993

An object-oriented database was developed as a framesork for integrating into ergonomic interface models data for workplace modelling and ergonomic evaluation functions as well as basic anthropometric data required to construct a man model. In order to develop an ergonomic man model representing operators that interact with his working evnironments, not only anthro- pometric data but also efficient handling of such data and accurate representation of the work- space are needed as a prerequistite to proper ergonomic evaluation. Most existing man models are not, however, capable of fully utilizing these data due to the lack of a generallized formalism of data handling, which results in system performance degradation or a potential difficulty when the system is upgraded. In this research, these three sets of data with distinct characteristics were incorporated into a comon integrated database required to manipulate an ergonomic interface model fully coupled with the man model itself. An object- oriented scheme was sued for the database design Specifically, UniSQL/X, an object-oriented database management system and the X-window system on SPARC workstation were used for implementation. The ergonomic man model generated from the object-oriented database is found to possess great flexibility and performance compared to existing ergonomic interface models or ergonomic CAD systems.

• Journal of the Korea Computer Industry Society
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• v.4 no.4
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• pp.607-612
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• 2003

This paper has been designed extend object-orientid database model that introducted new class basing the Heurilistic Classfication model. In order to implement this model, we have introducted heurilistic class to traditional object-orinted database. And we designed querry for search data that basis on the heurilistic classficasion model using stored data in extened object-oriend data model.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Spatial Information Research
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• v.5 no.1
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• pp.77-88
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• 1997

This paper presents the design and implementaion of an object-relational geographic information system. This system has been developed on top of a commercial object-relational database management system. It provides flexible spatial data model, spatial query language, visual user interface, and efficient spatial access methods(D0T) in which traditional primary-key access methods can be applied. We report on our design choices and describe the current status of Implementation. The conceptual model of the system is based on SDTS, and is mapped to the intemal obiect-oriented data model. Kevwords : object-oriented data model, GIS, spatial data model, spatial access method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2721-2726
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• 2014

In a MOG algorithm using the GMM to subtract background, the model parameter computation and the object classification to be performed at every pixel require a huge computation and are the chief obstacles to its uses. This paper proposes a fast MOG algorithm that partly adopts the simple model parameter computation and the object classification skip on the basis of the object prediction. The former is applied to the pixels that gives little effect on the model parameter and the latter is applied to the pixels whose object prediction is firmly trusted. In comparative experiment between the conventional and proposed algorithms using videos, the proposed algorithm carries out the simple model parameter computation and the object classification skip over 77.75% and 92.97%, respectively, nevertheless it retains more than 99.98% and 99.36% in terms of image and moving object-unit average classification accuracies, respectively.