• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.5
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• pp.136-146
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• 2015

The active control of a vehicle based on vehicle recognition is one of key technologies for the intelligent vehicle, and the part-based image representation is necessary to recognize vehicles with only partial shapes of vehicles especially in urban scene where occlusions frequently occur. In this paper, we implemented a part-based image representation scheme using non-negative tensor factorization(NTF) and realized a robust vehicle recognition system using the NTF feature. The result shows that the proposed method gives more intuitive part-based representation and more robust recognition in urban scene.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.6
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• pp.682-689
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• 2019

In part-based image representation, the partial shapes of an object are represented as basis vectors, and an image is decomposed as a linear combination of basis vectors where the coefficients of those basis vectors represent the partial (or local) feature of an object. In this paper, a face recognition for occluded faces is proposed in which face images are represented using non-negative matrix factorization(NMF), one of part-based representation techniques, and recognized using an artificial neural network technique. Standard NMF, projected gradient NMF and orthogonal NMF were used in part-based representation of face images, and their performances were compared. Learning vector quantizer were used in the recognizer where Euclidean distance was used as the distance measure. Experimental results show that proposed recognition is more robust than the conventional face recognition for the occluded faces.

• Journal of KIISE:Software and Applications
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• v.33 no.5
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• pp.481-488
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• 2006

The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of 'recognition by parts.' It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization) and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architectureII, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.4-6
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• 2003

A holistic representation, such as sparse ceding or independent component analysis (ICA), was successfully applied to explain early auditory processing and sound classification. In contrast, Part-based representation is an alternative way of understanding object recognition in brain. In this paper. we employ the non-negative matrix factorization (NMF)［1］which learns parts-based representation for sound classification. Feature extraction methods from spectrogram using NMF are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.193-200
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• 2001

In this study, the methodology to develop a data model for steel box girder bridge based on STEP part 104 is presented. The concept of STEP and the schema of part 104 are briefly reviewed, and then the procedure of data model standardization is described. A new data model for steel box girder bridge is developed by incorporating with not only the geometric and topological representation schema of the part 42 but also the representation structure information of the part 43 and the detailed finite element analysis information of the part 104. The prototype of integrated finite element analysis(FEA) system by interfacing STEP physical file is also presented. The applicability of developed data model for FEA is verified by preprocessor system of FEA.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.4
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• pp.307-314
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• 2012

IFC-based representation method of part library for superstructure module of modular steel bridge is proposed. The library is capable of efficiently offering and exchanging part information in process of manufacture, assembly, design, and construction of modular steel bridge. Entities, representing physical part information in IFC model, are matched semantically with parts of the superstructure module for representation of part information with IFC model. Either types of matched entities are applied in order to verify the role of each part, or new types are defined as a user-defined types. In addition, assembly system has been classified and defined into 4 levels of LoD(Level of Detail) to provide appropriate part information efficiently from the part library in each step of the process. Then, new property is defined for representing the LoD information with IFC Model. Finally, IFC-based test library of modular steel bridge is generated by applying the matched entities and entity types to the actual the superstructure module of modular steel bridge.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• Korean Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.122-132
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• 1996

Features are generic shapes with which engineers associate certain attributes and knowledge useful in reasoning about the product. Feature-based modeling systems support additional levels of information beyond those available in geometric modelers. The objective of this study is to develop a PC level feature-based modeling system which explicitly represents dimensions of the part. The feature-based modeler retains all the benefits of traditional B-rep. solid models, and represents the dimensions at a high level of a abstraction so that dimension driven geometry can be achieved.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.251-257
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• 2006

The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of 'recognition by parts.' It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (Localized Non-negative Matrix Factorization)and LFA (Local Feature Analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architecture II, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortion

• Journal of the Korean Society for Precision Engineering
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• v.23 no.9 s.186
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• pp.119-126
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• 2006

This paper addresses the methodology development far the automated machining setup planning system using case-based reasoning(CBR). The case-based reasoning is used to develop a setup planning system. which consists of part input and representation module, case retrieval module, and case adaptation module. We present new approaches in the part input and representation module and the case retrieval module focusing on the similarity index determination. An illustrative example is included to demonstrate the proposed method.