• 항공우주기술
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• 제6권1호
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• pp.213-221
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• 2007

본 연구에서는 2002년 4월에 획득된 Hyperion 초분광 원격탐사 자료를 이용하여 산불연료지도 제작을 위한 객체기반 분류 기법을 제시하였으며, 또한 객체기반 분석결과와 화소기반 분석결과를 비교해 보았다. 이를 위해 우선적으로 Hyperion 위성영상에 있는 잡음 화소 보정과 잡음 밴드를 제거하였으며, 또한 정확한 자료 처리를 위해 대기보정을 수행하였다. 산불 연료 지도 제작을 위한 방법은 분광혼합분석(SMA) 처리 결과를 재구성하여 얻었다. 객체 기반 접근 방법은 세그먼트 기반의 endmember 선택방법을 활용하였으며, 화소기반 분석은 표준 분광혼합분석기법을 적용하였다. 검증 및 비교를 위해서는 고해상도 칼라 항공정사영상이 활용되었다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.363-365
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• 2003

This paper presents an object oriented approach(OOA) for classification of multi-channel and multi-polarization NASA/JPL POLSAR images. Some test results in Taiwan are also given and analyzed. It is concluded that this approach can utilize as more information of both low- and high-levels involved in all images as possible for image classification and thus provides a better classification accuracy. For instance, the OOA has a better overall classification accuracy(98.27%) than the nearest-neighbor classifier(91.31%) and minimum-distance classifier(80.52%).

• 한국측량학회지
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• 제31권6_2호
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• pp.521-527
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• 2013

Rule set is an important step which impacts significantly on accuracy of object-oriented classification result. Therefore, this paper proposes a rule set to extract land cover from Landsat Thematic Mapper (TM) imagery acquired in Donganh, Hanoi, Vietnam. The rules were generated to distinguish five classes, namely river, pond, residential areas, vegetation and paddy. These classes were classified not only based on spectral characteristics of features, but also indices of water, soil, vegetation, and urban. The study selected five indices, including largest difference index max.diff; length/width; hue, saturation and intensity (HSI); normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) based on membership functions of objects. Overall accuracy of classification result is 0.84% as the rule set is used in classification process.

• Asia pacific journal of information systems
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• 제5권2호
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• pp.143-176
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• 1995

We present an overview of the Extensional Object Model (ExOM) and describe in detail the learning and classification components which integrate concepts from machine learning and object-oriented databases. The ExOM emphasizes flexibility in information acquisition, learning, and classification which are useful to support tasks such as diagnosis, planning, design, and database mining. As a vehicle to integrate machine learning and databases, the ExOM supports a broad range of learning and classification methods and integrates the learning and classification components with traditional database functions. To ensure the integrity of ExOM databases, a subsumption testing rule is developed that encompasses categories defined by type expressions as well as concept definitions generated by machine learning algorithms. A prototype of the learning and classification components of the ExOM is implemented in Smalltalk/V Windows.

• 한국정보전자통신기술학회논문지
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• 제1권3호
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• pp.83-89
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• 2008

This paper introduces a visual object classification algorithm based on statistical information. Objects are characterized through the Histogram of Oriented Gradients (HOG) method and classification is performed using Multiclass AdaBoost. Salient features of an object's appearance are detected by HOG blocks Blocks of different sizes are tested to define the most suitable configuration. To select the most informative blocks for classification a multiclass AdaBoostSVM algorithm is applied. The proposed method has a high speed processing and classification rate. Results of the evaluation based on example of hand gesture recognition are presented.

• International Journal of Contents
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• 제2권3호
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• pp.11-17
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• 2006

The Clustering is representative method of components classification. The previous clustering methods that use cohesion and coupling cannot be effective because design pattern has focused on relation between classes. In this paper, we classified design patterns with features of object-oriented relationship. The result is that classification by clustering showed higher precision than classification by facet. It is effective that design patterns are classified by automatic clustering algorithm. When patterns are retrieved in classification of design patterns, we can use to compare them because similar pattern is saved to same category. Also we can manage repository efficiently because of storing patterns with link information.

• 한국BIM학회 논문집
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• 제13권4호
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• pp.96-105
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• 2023

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.917-922
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• 1990

Pattern classification is an essential step in automatic robotic assembly which joins together finite number of seperated industrial parts. In this paper, a fast and systematic algorithm for classifying occlusion-free objects is proposed, using the notion of incremental circle transform which describes the boundary contour of an object as a parametric vector function of incremental elements. With similarity transform and line integral, normalized determinant curve of an object classifies each object, independent of position, orientation, scaling of an object and cyclic shift of the stating point for the boundary description.

• 융합보안논문지
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• 제18권3호
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• pp.53-59
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• 2018

본 논문에서는 효율적인 사물 이미지 분류를 위한 계층적 이미지 분류 체계 방안에 대해 제안한다. 기존의 전체 이미지를 한 번에 분류하는 무 계층 이미지 분류에서는 상대적으로 유사한 모양을 가진 사물은 효율적으로 인식하지 못하는 모습을 보여줬다. 따라서 본 논문에서는 사물 이미지에 대해 계층적으로 분류를 시도하는 단계적 계층 구조에서의 이미지 분류 기법을 소개한다. 또한, 실제 시스템에 딥 러닝 이미지 분류가 적용되었을 때 발생할 수 있는 확장성에 대해서 고려하기 위해 확장성이 고려된 효율적인 클래스 구성 방식과 알고리즘도 소개한다. 이와 같은 방식은 상대적으로 유사한 형태를 보인 사물 이미지에 대해 더 높은 신뢰도로 이미지를 분류하는 것을 가능하게 한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.708-708
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• 2002

Recent developments in sensor technologies have provided remotely sensed data with very high spatial resolution. In order to fully utilize the potential of high resolution images, new image classification strategies are necessary. Unfortunately, the high resolution images increase the spectral within-field variability, and the classification accuracy of traditional methods based on pixel-based classification algorithms such as Maximum-Likelihood method may be decreased (Schiewe 2001). Recent development in Object Oriented Classification based on image segmentation algorithms can be used for the classification of forest patches on rugged terrain of Korea. The objectives of this paper are as follows. First, to compare the pros and cons of image classification methods based on pixel-based and object oriented classification algorithm for the forest patch classification. Landsat ETM＋ data and IKONOS data will be used for the classification. Second, to investigate ways to increase classification accuracy of forest patches. Supplemental data such as DTM and Forest Type Map of 1:25,000 scale are used for topographic correction and image segmentation. Third, to propose the best classification strategy for forest patch classification in terms of accuracy and data requirement. The research site for this paper is Namhansansung Provincial Park located at the eastern suburb of Seoul Metropolitan City for its diverse forest patch types and data availability. Both Landsat ETM＋ and IKONOS data are used for the classification. Preliminary results can be summarized as follows. First, topographic correction of reflectance is essential for the classification of forest patches on rugged terrain. Second, object oriented classification of IKONOS data enables higher classification accuracy compared to Landsat ETM＋ and pixel-based classification. Third, multi-stage segmentation is very useful to investigate landscape ecological aspect of forest communities of Korea.